diff --git a/.gitignore b/.gitignore
index 342e5f928bc0fa6730aa9e4d8ebc27a36b15f980..81d8b95ffab6bd1919c5d7b813985d4d5ae3dc12 100644
--- a/.gitignore
+++ b/.gitignore
@@ -168,4 +168,5 @@ data/PROTAC-Pedia.csv
 data/cellosaurus.txt
 logs/
 notebooks/per-protein*
-*slurm*.out
\ No newline at end of file
+*slurm*.out
+.DS_Store
\ No newline at end of file
diff --git a/README.md b/README.md
index 47918a6faa1cbf43b4c15d3ce001ca682e24a5c7..1a555c48923d40a6e0e9de77dd6bf78c3c0832ad 100644
--- a/README.md
+++ b/README.md
@@ -98,6 +98,10 @@ If you use this tool in your research, please cite the following paper:
 }
 ```
 
+The directories [logs](logs/) and [reports](reports/) contain the logs and reports generated during the experiments reported in the paper. Additionally, in [reports](reports/), one can find the pickled Optuna studies for the reported experiments.
+
+The directory [models](models/) contains the trained models for the experiments reported in the paper.
+
 ## 📜 License
 
 This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
diff --git a/data/README.md b/data/README.md
index 863f7d37455db391019c90f698e84920b418f14e..3f1c3d3d1763790190aa6bc6aff9094b687257b7 100644
--- a/data/README.md
+++ b/data/README.md
@@ -1,4 +1,9 @@
 # Data
 
-* [PROTAC-DB](http://cadd.zju.edu.cn/protacdb/about)
-* [PROTAC-Pedia](https://protacpedia.weizmann.ac.il/ptcb/main)
+- [PROTAC-DB](http://cadd.zju.edu.cn/protacdb/about)
+- [PROTAC-Pedia](https://protacpedia.weizmann.ac.il/ptcb/main)
+- [Curated dataset](PROTAC-Degradation-DB.csv)
+
+The folder [studies](studies/) contains the data used in each study reported in our paper.
+
+The folder [processed](processed/) contains pickled dictionary files used for standardize cell names and to map Uniprot IDs to their amino acid sequences.
\ No newline at end of file
diff --git a/data/best_model_n0_random-epoch=6-val_acc=0.74-val_roc_auc=0.796.ckpt b/data/best_model_n0_random-epoch=6-val_acc=0.74-val_roc_auc=0.796.ckpt
deleted file mode 100644
index 7705ea48efb9aded319790e1cd4be256f7871a4e..0000000000000000000000000000000000000000
--- a/data/best_model_n0_random-epoch=6-val_acc=0.74-val_roc_auc=0.796.ckpt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52e12060ef0d21f4eb4d84570c708e8c8502a1fbe6ebcbae1d86044e23b77708
-size 101362856
diff --git a/data/processed/multiple_cell_types.csv b/data/processed/multiple_cell_types.csv
deleted file mode 100644
index 7dcd3cdf1738c716ce27ddb2a56276616137d9d9..0000000000000000000000000000000000000000
--- a/data/processed/multiple_cell_types.csv
+++ /dev/null
@@ -1,121 +0,0 @@
-Compound ID,Cell Type,Comments,DC50 (nM),Dmax (%)
-106,"RS4;11, MOLM-13","Direct degration studies in this paper only conducted for first and final compounds; during the med chem campaign cell viability was used as the read out. Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD. DC50 is between 3nM-10nM.",< 10 nM,100 %
-107,"RS4;11, MOLM-13","Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD",< 1 nM,100 %
-108,"RS4;11, MOLM-13","Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD",< 1 nM,100 %
-109,"RS4;11, MOLM-13","Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD",~ 3 nM,
-110,"RS4;11, MOLM-13","Complex structure with the ligand was modeled based on 4Z93. IC50 of the ligand is between 2nM and 7nM, depending on which BRD",~ 10 nM,
-111,"MV4-11, MOLM-13, KG1","They show ""dual"" degradation but don't do any test that the dual degradation actually made any difference. They make it sound novel but of course we can call our PROTACs ""dual"" degraders if you can the off-targets ""targets"". EC50 of ligand is in measured in MV4-11 cell line. EC50 of PROTAC is between 4.5nM-6.94nM (depended on the cell line).",< 5 nM,~ 100 %
-149,"MOLT-4, MOLM14","General kinase PROTAC, DCmax is for the most degraded kinase. IC50 of the ligand is for 193 kinases in the panel. IC50 of the PROTAC is by FLT3 kinase activity.",< 100 nM,> 85 %
-150,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-151,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-152,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-153,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-154,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-155,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-156,"MOLM14, MV4-11",IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines,,
-157,"MOLM14, MV4-11",Dmax is difficult to quantify because AC220 raises FLT3 expression levels. IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines. EC50 of PROTAC was measured on MOLM-14 cell lines.,< 10 nM,~ 100 %
-158,"MOLM14, MV4-11",Dmax is difficult to quantify because AC220 raises FLT3 expression levels. IC50 of ligand is 1.6 nM (binding); 1.1-4 (activity). EC50 of ligand is measured on MOLM-14 cell lines. EC50 of PROTAC was measured on MOLM-14 cell lines.,< 10 nM,~ 100 %
-159,"MOLM14, MV4-11",IC50 of ligand is inhibitory - data from Selleckchem. EC50 of the lidand is Src-dependent cell proliferation - data from Selleckchem.,~ 100 nM,~ 50 %
-202,"NAMALWA, XLA","DC50 is 9.1 nM (WT BTK, NAMALWA cells), 14.6 nM (WT BTK, XLA cells), 14.9 nM (C481S, XLA cells). IC50 of PROTAC is 46.9 (WT BTK), 20.9 (C481S). IC50 of ligand is 51.0 nM (WT BTK), 30.7 (C481S)",< 14.9 nM,> 99 %
-258,"MV-4-11 SK-MEL-5, NCI-H1568","EC50 of PROTAC is 28nM in MV-4-11, 68nM in NCI-H1568. DC50 is (SMARCA2 6nM, SMARCA4 11nM, PBRM1 32nM in MV-4-11; SMARCA2 3.3nM, PBRM1 15.6nM in NCI-H1568)",< 32 nM,
-283,"HeLa, DLBCL",DC50 value is for HeLa cells. DC50 is 0.61 uM for DLBCL cells. DMAX value is for HeLa cells. DMAX is 96 % for DLBCL cells.,0.79 uM,92 %
-290,Jurkat and Panc Tu‐I,"proteomics of PDEdelta degradation show upregulation of enzymes involved in lipid metabolism - deltasonamide 1 also causes this. Dmax was measured in Panc-Tu-1 cell line. DC50 is 83.4% (24 h, Panc-Tu-1), 85% (24 h, 1 uM, Jurkat). IC50 Deltasonamide 1 is 203 pM, and of the Bn derivative is 8 nM.",48 nM,> 83.4 %
-291,Jurkat and Panc Tu‐I,"proteomics of PDEdelta degradation show upregulation of enzymes involved in lipid metabolism - deltasonamide 1 also causes this. IC50 Deltasonamide 1 is 203 pM, and of the Bn derivative is 8 nM.",,
-323,"HeLa, MV4;11, A549",DC50 is 1-3nM,< 3 nM,100 %
-324,"HeLa, MV4;11, A549",DC50 is 10-30nM,< 30 nM,99 %
-327,SU-DHL-1 and NCI-H2228,"competition with ligand only slightly rescued protein degradation. EC50 of ligand and PROTAC is measured on SUDHL-1 cell line. DC50 is SU-DHL-1: 3 ± 1 nM, NCI-H2228: 34 ± 9 nM.",< 34 nM,> 90 %
-328,SU-DHL-1 and NCI-H2228,"competition with ligand only slightly rescued protein degradation. EC50 of ligand and PROTAC is measured on SUDHL-1 cell line. DC50 is SU-DHL-1: 11 ± 2 nM, NCI-H2228: 59 ± 16 nM.",< 59 nM,> 90 %
-331,"MCF-7, MM.1S",DC50 and Dmax are for MCF-7 cells,34 nM,70.5 %
-343,DU145/Cy,"Tested reversal of docetaxel resistance by degradation of CYP, followed by lysis and WB, model with the ligand is available in previous publication",,
-344,DU145/Cy,"Tested reversal of docetaxel resistance by degradation of CYP, followed by lysis and WB, model with the ligand is available in previous publication",,
-345,DU145/Cy,"Tested reversal of docetaxel resistance by degradation of CYP, followed by lysis and WB, model with the ligand is available in previous publication",,
-346,DU145/Cy,"Tested reversal of docetaxel resistance by degradation of CYP, followed by lysis and WB, model with the ligand is available in previous publication",,
-375,"Namalwa, CA-46, Ramos","tested also biological effects - proliferation and caspase activity on BL cells. IC50 of ligand is 14 nM (BD1), 3.5 nM (BD2). IC50 of PROTAC is 90 nM (BD1), 28 nM (BD2).",< 1 nM,~ 100 %
-376,"HBL1, Ramos, Mino, IgE MM","also degrades ibrutinib-resistant C481S BTK. DC50 is 6.3 nM (HBL1), 8.5 nM (Ramos), 9.2 nM (Mino), 11.4 nM (IgE MM)",< 11.4 nM,94 %
-388,"Ramos, THP-1",Ligand name is from PMID 24068666,,
-389,"Ramos, THP-1",Ligand name is from PMID 24068666,,
-390,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 18-33%,,< 33 %
-391,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 15-34%,,< 34 %
-392,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 36-64%. DC50 is 1487 - 1994.5 nM.,< 1994.5 nM,< 64 %
-393,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 68-85%. DC50 is 36.9 - 398.5 nM.,< 398.5 nM,> 68 %
-394,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 63-84%. DC50 is 21.8 - 469.9 nM.,< 469.9 nM,> 63 %
-395,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 75-87%. DC50 is 4.5 - 90.5 nM.,< 90.5 nM,> 75 %
-396,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 71-85%. DC50 is 5.9 - 217.7 nM.,< 217.7 nM,> 71 %
-397,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 80-87%. DC50 is 1.1 - 37.4 nM.,< 37.4 nM,> 80 %
-398,"Ramos, THP-1",Ligand name is from PMID 24068666. DCmax is 70-85%. DC50 is 9.7 - 184.1 nM.,< 184.1 nM,> 70 %
-399,"Ramos, THP-1",Ligand name is from PMID 24068666,,
-400,"Ramos, THP-1",Ligand name is from PMID 24068666,,
-467,"MV4-11, MOLM-14","IC50 of ligand is FLT-3 ITD 10.3± 0.3nM, FLT-3 WT 7.4± 0.3nM. IC50 of PROTAC is FLT-3 ITD 43± 3.2nM, FLT-3 WT 36± 3nM.",,
-472,"MDA-MB-231, HeLa",DC50 error ± 1.0 nM. DMAX error ± 1.1 %.,9.5 nM,99.6 %
-473,"MDA-MB-231, HeLa",DC50 error ± 81.3 nM. DMAX error ± 10.1 %.,45.9 nM,34.5 %
-561,"MCF-7, MDA-MB-231, HepG2, LO2, B16",Dmax is measured in 10uM. EC50 of PROTAC is MCF-7: 2.70 ± 0.19 MDA-MB-231: 21.21 ± 1.95 HepG2: 18.70 ± 1.65 LO2: 41.11 ± 3.70 B16: 22.68 ± 2.03 uM. EC50 of ligand is MCF-7: 4.17 ± 0.31 MDA-MB-231: 21.33 ± 1.96 HepG2: 10.59 ± 0.94 LO2: 35.57 ± 2.81 B16: 14.49 ± 1.28 uM.,~ 0.5 uM,81 %
-562,"Karpas 422,ULA, SUDHL4, OCI-Ly1, Ramos","half the paper develops the ligand and checks its selectivity. Dmax is 76% Karpas 422, 59% ULA, 84% SUDHL4, 82% OCI-Ly1, 82% Ramos.",~ 1 uM,> 76 %
-572,"SR, H2228, NCI–H69, NCI–H1688, NCI–H446","EC50 of ligand is 2.7 ± 0.4nM (SF cells), 58.2 ± 20.7 nM (H2228 cells). EC50 of PROTAC is 1.7 ± 1 nM (SF cells), 46nM ± 16 nM (H2228 cells).",7.0 nM,
-573,"OVCAR8 (WT EGFR), HeLa (EGFR Exon 20 Ins), SKBr3 (HER2)",DC50 is for WT EGFR. DC50 for EGFR Exon 20 Ins is 736.2 nM. DMAX is for WT EGFR. DMAX for EGFR Exon 20 Ins is 68.8 %.,39.2 nM,97.6 %
-574,"HCC827 (Exon 19 del), H3255 (L858R)",DC50 is for EGFR (Exon 19 del). DC50 for EGFR (L858R) 22.3 nM. DMAX is for EGFR (Exon 19 del). DMAX for EGFR (L858R) 96.6 %.,11.7 nM,98.9 %
-578,"RS4;11, MV4;11, MOLM-13,",EC50 of ligand is 55.8-207 nM.,,
-579,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 19.7nM-311nM. EC50 of ligand is 55.8-207 nM.,,
-580,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 0.379nM-2.23nM. EC50 of ligand is 55.8-207 nM.,,
-581,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 0.038nM-0.56nM. EC50 of ligand is 55.8-207 nM.,,
-582,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 0.02nM-0.19nM. EC50 of ligand is 55.8-207 nM.,,
-583,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 0.02nM-0.19nM. EC50 of ligand is 55.8-207 nM.,,
-584,"RS4;11, MV4;11, MOLM-13,",EC50 of PROTAC is 0.0083nM-0.062nM. EC50 of ligand is 55.8-207 nM.,< 0.5 nM,100 %
-589,"MCF-7, U2OS, MDA-MB-231, MDA-MB-435, Flag-Cdc20, MDA-MB-231, 22Rv1, LNCaP",EC50 of PROTAC is 2.6 uM for MDA-MB-231 and 1.99 uM for MDA-MB-435,1.6 uM,
-607,"Hella, RI-1, EOL-1, A-204",DC50 is 1.76 nM and 4.5 nM,< 4.5 nM,90 %
-630,"Molm-16, SU-DHL-1",EC50 of PROTAC is 10nM-35nM,0.06 uM,
-661,"RS4;11, MV4;11",EC50 of ligand and PROTAC was measured in RS4;11 cell line.,,98 %
-662,"RS4;11, MV4;11",EC50 of ligand and PROTAC was measured in RS4;11 cell line.,,99 %
-692,"LNCaP, VCaP, and 22Rv","DC50 is 0.86nM in LNCaP, 0.76 in VCaP and 10.4 nM at 1uM in 22Rv1. EC50 in cells is 0.25nM for LNCaP, 0.34 nM for VCaP, 183nM for 22Rv1.",< 10.4 nM,> 95 %
-727,"MV4-11, Molm-13","EC50 of ligand is 0.370 uM for MV4- 11, 3.369 uM for Molm-13. EC50 of PROTAC is 1.648uM for MV4-11, >10uM for Molm-13",,
-728,"MV4-11, Molm-13","EC50 of ligand is 0.370 uM for MV4- 11, 3.369 uM for Molm-13. EC50 of PROTAC is 0.025uM for MV4-11, 0.18uM for Molm-13",,
-730,"MV4-11, Molm-13","EC50 of ligand is 0.370 uM for MV4- 11, 3.369 uM for Molm-13. EC50 of PROTAC is 0.032uM for MV4-11, 0.177uM for Molm-13.",,
-731,"MV4-11, Molm-13","EC50 of ligand is 0.370 uM for MV4- 11, 3.369 uM for Molm-13. EC50 of PROTAC is 3.429uM for MV4-11, >10uM for Molm-13.",,
-779,HOP62/INC-H23,Inactive PROTAC for KRAS based on AMG-510.,,
-814,E14 mouse embryonic stem cells; Human colon cancer cell line HCT116,"For HDAC1/HDAC2/HDAC3, the Dmax of this PROTAC is: >85%, >76%, >63%, respectively.",~ 1 uM,
-815,Pancreatic cancer cell lines BxPC-3; MIA PaCa-2,"XD2-149 was initially designed to degrade STAT3. However, experiments showed that XD2-149 down-regulate STAT3 level in a proteasome-independent manner. Proteomics data revealed that an E3 ligase, ZFP91, was the true substrate for this PROTAC. This paper reported a total of 22 PROTACs, which differed from each other in linker design and E3 binder choices (pomalidomide/thalidomide/lenalidomide). However, the authors didn't mention whether the remaining 21 molecules could degrade ZFP91 or not. It is also interesting that pomalidomide itself can induce the degradation of CRBN neo-substrates like ZFP91 (DC50: 0.42uM, 5-fold less potent than XD2-149), since pomalidomide can remodel CRBN surface for binding proteins like ZFP91 (Nat Med., 2019, doi: 10.1038/s41591-019-0668-z).",~ 70 nM,> 90 %
-830,KYSE520 esophageal cancer cell line; MV4;11 cell line,Degradation rate for KYSE520 cell: 87% (1uM compound); Degradation rate for MV4;11 cell: 85% (0.1uM compound),,
-832,KYSE520 esophageal cancer cell line; MV4;11 cell line,Dmax for KYSE520 cell: >95%; Degradation rate in MV4;11 cell: 47% (0.1uM compound),,
-833,KYSE520 esophageal cancer cell line; MV4;11 cell line,Dmax for KYSE520 cell: >95%; Degradation rate in MV4;11 cell: 6% (0.1uM compound),,
-834,KYSE520 esophageal cancer cell line; MV4;11 cell line,Degradation rate in KYSE520 cell: 90% (1uM compound); Degradation rate in MV4;11 cell: 8% (0.1uM compound),,
-835,KYSE520 esophageal cancer cell line; MV4;11 cell line,DC50 for KYSE520 cell: 6.0nM;DC50 for MV4;11 cell: 2.6nM; EC50 for KYSE520 cell: 0.66uM; EC50 for MV4;11 cell: 9.9nM;,6.0 nM,100 %
-837,KYSE520 esophageal cancer cell line; MV4;11 cell line,Dmax for KYSE520 cell: >95%; Dmax for MV4;11 cell: >90%,,> 95 %
-838,KYSE520 esophageal cancer cell line; MV4;11 cell line,Dmax for KYSE520 cell: >80%; Degradation rate for MV4;11 cell: 9% (0.1uM compound),,
-839,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 6.75±0.03 and 5.84±0.06, respectively.
-pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 7.0/7.0/6.5/6.2, respectively (24h, HeLa cells).
-Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 96%/97%/97%/93%, respectively (HeLa cells).",< 0.1 uM,> 93 %
-840,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 7.57±0.03 and 6.66±0.05, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/8.6/7.0/7.4, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 98%/100%/100%/98%, respectively (HeLa cells).",< 2.5 nM,> 98 %
-841,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 6.91±0.04 and 5.90±0.05, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.4/8.0/6.5/6.7, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 99%/100%/99%/97%, respectively (HeLa cells).",< 4 nM,> 97 %
-842,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 7.77±0.06 and 7.46±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 9.2/9.0/9.1/8.2, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 97%/100%/98%/83%, respectively (HeLa cells).",< 1 nM,> 83 %
-843,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 6.24±0.05 and 6.17±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 6.9/6.7/6.8/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 94%/78%/74%/37%, respectively (HeLa cells).",~ 0.1 uM,
-844,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 7.31±0.03 and 6.57±0.02, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/7.6/7.3/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 98%/95%/91%/43%, respectively (HeLa cells).",~ 7.9 nM,> 90 %
-845,"HeLa, HL60, MV4;11","pEC50 for MV4;11 and HL60 cells: 7.08±0.05 and 6.37±0.03, respectively. pDC50 for Brd4 short/Brd4 long/Brd3/Brd2: 8.1/7.5/7.7/NA, respectively (24h, HeLa cells). Dmax for Brd4 short/Brd4 long/Brd3/Brd2: 95%/93%/92%/26%, respectively (HeLa cells).",~ 7.9 nM,> 90 %
-848,"HeLa, HEK293, U2OS",ITC/SEC/AlphaLISA assays were carried out for CM09/10/11 to confirm the formation of ternary complex in vitro.,< 100 nM,100 %
-853,"MDA-MB-231, A549, A549/DDP, HUVEC","EC50 of PROTAC1: 22.56±1.89uM/25.75±0.91uM/79.51±3.10uM/29.92±1.81uM for MDA-MB-231, A549, A549/DDP, HUVEC cells, respectively (MTT assay, 72h).",,
-854,"MDA-MB-231, A549, A549/DDP, HUVEC","EC50 of PROTAC2: 17.36±1.43uM/31.19±2.95uM/57.55±4.20uM/34.39±2.99uM for MDA-MB-231, A549, A549/DDP, HUVEC cells, respectively (MTT assay, 72h).",,> 80 %
-855,"MDA-MB-231, A549, A549/DDP, HUVEC","EC50 of PROTAC3: 21.32±1.85uM/29.77±0.84uM/30.68±2.45uM/33.92±2.84uM for MDA-MB-231, A549, A549/DDP, HUVEC cells, respectively (MTT assay, 72h).",,
-856,"MDA-MB-231, A549, A549/DDP, HUVEC","EC50 of PROTAC4: 31.67±2.97uM/36.46±0.52uM/>200uM/37.13±2.52uM for MDA-MB-231, A549, A549/DDP, HUVEC cells, respectively (MTT assay, 72h).",,
-859,"HeLa, HEK293",Reported DC50 and Dmax above are in HeLa cells. DC50 for HEK293 cells: 230nM; Dmax for HEK293 cells: 98%. 14a can degrade VHL at higher concentration. See Target UniprotID P40337 for details.,200 nM,88 %
-927,PC9; HCC827; H1975,"EC50: 1.425±0.052uM, 1.874±0.098uM, 1.388±0.556uM for PC9/HCC827/H1975 cells, respectively.",,
-928,PC9; HCC827; H1975,"EC50: 1.117±0.105uM, 1.611±0.139uM, 1.698±0.614uM for PC9/HCC827/H1975 cells, respectively.",,
-929,PC9; HCC827; H1975,"EC50: 0.413±0.087uM, 1.344±0.112uM, 0.657±0.008uM for PC9/HCC827/H1975 cells, respectively.",0.161 uM,68 %
-936,5W1573; NCI-H2030; NCI-H358; HCT116; MIA-PaCa-2,"EC50/DC50/Dmax reported above were obtained using NCI-H2030 cells. DC50: 0.25~0.76uM; Dmax: ~75%-90%, specific value depends on the cell line.",0.59 uM,~ 80 %
-943,SU-DHL-1; H3122,"EC50: 0.058uM/0.18uM/>10uM for SU-DHL-1/H3122/A549 cells, respectively.",,> 90 %
-960,Jurkat; Molt4; Granta-519; Mino; Jeko; Rec1; Maver,"IC50: 25.7nM/7.57nM for CDK4/CDK6, respectively. BSJ-02-162 is capable of degrading both CDK4 and CDK6.",,
-969,Jurkat; Molt4; Granta-519; Mino; Jeko; Rec1; Maver,"IC50: 26.9nM/10.4nM for CDK4/CDK6, respectively. Can degrade both CDK4 and CDK6.",,
-970,Mice primary Sertoli cells and primary Germ cells,"This is an article that use a previously reported PROTAC (PMID: 33062164) as chemical biology tool to investigate the non-enzymatic FAK function in mice. Therefore, the relevant data were not given in this article.",~ 1 nM,~ 100 %
-993,Ba/F3; H1975,EC50: 0.096uM for L858R/T790M mutant EGFR Ba/F3 cells; >10uM for wildtype EGFR Ba/F3 cells,,> 70 %
-997,Ba/F3,Targeting EGFR L858R/T790M mutant,,
-1027,"BxPC3, DLD-1, HCT-116, MDA-MB-231, A549, PC-9","Above EC50 was obtained in BxPC3 cells. EC50 for DLD-1/HCT-116/MDA-MB-231/A549/PC-9 cells: 4.09uM/6.47uM/>10uM/2.42uM/>10uM, respectively.",,
-1028,"BxPC3, DLD-1, HCT-116, MDA-MB-231, A549, PC-9","Above EC50 was obtained in BxPC3 cells. EC50 for DLD-1/HCT-116/MDA-MB-231/A549/PC-9 cells: 5.96uM/2.22uM/>10uM/0.39uM/>10uM, respectively.",,
-1029,"BxPC3, DLD-1, HCT-116, MDA-MB-231, A549, PC-9","Above EC50 was obtained in BxPC3 cells. EC50 for DLD-1/HCT-116/MDA-MB-231/A549/PC-9 cells: 2.04uM/4.39uM/>10uM/0.30uM/>10uM, respectively.",,
-1030,"BxPC3, DLD-1, HCT-116, MDA-MB-231, A549, PC-9","Above EC50 was obtained in BxPC3 cells. EC50 for DLD-1/HCT-116/MDA-MB-231/A549/PC-9 cells: >10uM/>10uM/>10uM/2.14uM/>10uM, respectively.",,
-1073,MOLT4; BT549; HCC1806; COV362; Kuramochi; OVCAR8,IC50 for AZD1175: 1.52±0.17 nM; IC50 for ZNL-02-096: 3.58±0.36 nM,~ 10 nM,
-1155,"BBL358, T47D","Dmax in BBL358/T47D: 8% and 0, respectively.",,8 %
-1156,"BBL358, T47D","Dmax in BBL358/T47D: 40%±27% and 0, respectively.",,40 %
-1163,"BBL358, T47D","Dmax in BBL358/T47D: 3%±3% and 20%±20%, respectively.",,3 %
-1164,"BBL358, T47D","Dmax in BBL358/T47D: 0.5% and 76%, respectively.",,76 %
-1165,"BBL358, T47D","Dmax in BBL358/T47D: 67%±17% and 47%±32%, respectively.",,67 %
-1166,"BBL358, T47D","Dmax in BBL358/T47D: 77%±3% and 82%±10%, respectively.",,77 %
-1167,"BBL358, T47D","Dmax in BBL358/T47D: 93%±5% and 87%±3%, respectively.",,93 %
-1168,"BBL358, T47D","Dmax in BBL358/T47D: 74%±3% and 16%±13%, respectively.",,74 %
-1176,"BBL358, T47D","Dmax in BBL358/T47D: 84%±2% and 89%±2%, respectively.",24 nM,84 %
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diff --git a/models/xgboost_best_model_random_n1.json b/models/xgboost_best_model_random_n1.json
deleted file mode 100644
index 7e851aa31e2f1b3a9f2b4e6f7b853b2ca637efb3..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_random_n1.json
+++ /dev/null
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diff --git a/models/xgboost_best_model_random_n2.json b/models/xgboost_best_model_random_n2.json
deleted file mode 100644
index aed47133f69acabceb20b5e754605f81ac9d3faa..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_best_model_tanimoto_n0.json b/models/xgboost_best_model_tanimoto_n0.json
deleted file mode 100644
index d19968398645a43057e53d9ca5cb53fa7a52b960..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_tanimoto_n0.json
+++ /dev/null
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diff --git a/models/xgboost_best_model_tanimoto_n1.json b/models/xgboost_best_model_tanimoto_n1.json
deleted file mode 100644
index 93cbb643f059fd693d450061a88885262677e2f9..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_tanimoto_n1.json
+++ /dev/null
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diff --git a/models/xgboost_best_model_tanimoto_n2.json b/models/xgboost_best_model_tanimoto_n2.json
deleted file mode 100644
index 4c6550ba655a5007baf4be542d6314938490e98c..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_tanimoto_n2.json
+++ /dev/null
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diff --git a/models/xgboost_best_model_uniprot_n0.json b/models/xgboost_best_model_uniprot_n0.json
deleted file mode 100644
index 8fea71b3e5680b2a9dc244804688ecc553416210..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_uniprot_n0.json
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diff --git a/models/xgboost_best_model_uniprot_n1.json b/models/xgboost_best_model_uniprot_n1.json
deleted file mode 100644
index 22e36d3bc8de3ff0aec4cff66e4379acba37529f..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_uniprot_n1.json
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diff --git a/models/xgboost_best_model_uniprot_n2.json b/models/xgboost_best_model_uniprot_n2.json
deleted file mode 100644
index b56e9c8d156b7d490779c9daaafadb580573f434..0000000000000000000000000000000000000000
--- a/models/xgboost_best_model_uniprot_n2.json
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diff --git a/models/xgboost_cv_model_random_fold0.json b/models/xgboost_cv_model_random_fold0.json
deleted file mode 100644
index 61a4f991304c1e7abcbd61ee825cc8ea6694a00c..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_random_fold1.json b/models/xgboost_cv_model_random_fold1.json
deleted file mode 100644
index 693373147382f0cced5bdb2ea1d42a0f4b660897..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_random_fold2.json b/models/xgboost_cv_model_random_fold2.json
deleted file mode 100644
index bc5e5e65321338ee417387ea601819b2fcebc289..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_random_fold2.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/models/xgboost_cv_model_random_fold3.json b/models/xgboost_cv_model_random_fold3.json
deleted file mode 100644
index 2a4b5e3f35f3038f4b0912ce35d4b8aa18959a6d..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_random_fold3.json
+++ /dev/null
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diff --git a/models/xgboost_cv_model_random_fold4.json b/models/xgboost_cv_model_random_fold4.json
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index 0cb952aecc1d47281b12c15b0957baaf17ed9c2e..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_tanimoto_fold0.json b/models/xgboost_cv_model_tanimoto_fold0.json
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index 989a6c806b4b13bd7c9646535095d6bfb1de845e..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_tanimoto_fold1.json b/models/xgboost_cv_model_tanimoto_fold1.json
deleted file mode 100644
index 4ef21fd209171812f40e5e93e11ba11f8a8895c2..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_tanimoto_fold2.json b/models/xgboost_cv_model_tanimoto_fold2.json
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index 63064f2034f3e0742bbdc8821b72c99475e08c7f..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_tanimoto_fold3.json b/models/xgboost_cv_model_tanimoto_fold3.json
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index 0c2947dc3d313b360a436b6fab2aff3219e03330..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_tanimoto_fold4.json b/models/xgboost_cv_model_tanimoto_fold4.json
deleted file mode 100644
index 1a4b0544a2a09ca4c08b431707a708865f152da0..0000000000000000000000000000000000000000
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diff --git a/models/xgboost_cv_model_uniprot_fold0.json b/models/xgboost_cv_model_uniprot_fold0.json
deleted file mode 100644
index eb95c02cdc4d36ee98278255f56fcd7fc83244c9..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_uniprot_fold0.json
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diff --git a/models/xgboost_cv_model_uniprot_fold1.json b/models/xgboost_cv_model_uniprot_fold1.json
deleted file mode 100644
index 8052e22be6c8d93d33c3c94b8b1991f5172a5a87..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_uniprot_fold1.json
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diff --git a/models/xgboost_cv_model_uniprot_fold2.json b/models/xgboost_cv_model_uniprot_fold2.json
deleted file mode 100644
index f19b57da7b73ebaccc5ce30a5b60d549a514ab5a..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_uniprot_fold2.json
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diff --git a/models/xgboost_cv_model_uniprot_fold3.json b/models/xgboost_cv_model_uniprot_fold3.json
deleted file mode 100644
index 76b31b6bb073e1a8e3920da4d6898dc07e83d1dc..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_uniprot_fold3.json
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diff --git a/models/xgboost_cv_model_uniprot_fold4.json b/models/xgboost_cv_model_uniprot_fold4.json
deleted file mode 100644
index 1207487d350a8928cc5e7053b71baeb42046a5d2..0000000000000000000000000000000000000000
--- a/models/xgboost_cv_model_uniprot_fold4.json
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\ No newline at end of file
diff --git a/notebooks/README.md b/notebooks/README.md
index 52e02be70ff006d92838c60fd9be1ffff8b01d50..caafb96937174f334967352befa0da39f50f7649 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -1,6 +1,10 @@
 # Notebooks
 
-* [data_curation.ipynb](data_curation.ipynb)
-* [protac_degradation_predictor.ipynb](protac_degradation_predictor.ipynb)
+The following is a short description of the Jupyter notebooks that are available in this repository:
 
-Currently working on moving code into a python package.
\ No newline at end of file
+- [Data curation](data_curation.ipynb)
+- [Generating cell line embeddings](cell_line_embeddings.ipynb)
+- [Finding the best Morgan fingerprint parameters via Optuna](best_fingerprint_search.ipynb)
+- [Comparing deep learning model (DNN) and XGBoost (XGB) performance](compare_dnn_xgb_distributions.ipynb)
+- Plotting of [paper results](plot_experimental_results.ipynb) and [dataset information](plot_dataset_info.ipynb)
+- A [tutorial](protac_degradation_predictor_tutorial.ipynb) on how to use the pretrained models for predicting degradation activity
\ No newline at end of file
diff --git a/notebooks/ablation_study_e3_ligase.pdf b/notebooks/ablation_study_e3_ligase.pdf
deleted file mode 100644
index c945b75cf08a1885cdba266a472ce2cb59c1d962..0000000000000000000000000000000000000000
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diff --git a/notebooks/ablation_study_random.pdf b/notebooks/ablation_study_random.pdf
deleted file mode 100644
index a0b4b8aea1f01981d3b778c63151fd4ad1152b88..0000000000000000000000000000000000000000
Binary files a/notebooks/ablation_study_random.pdf and /dev/null differ
diff --git a/notebooks/ablation_study_tanimoto.pdf b/notebooks/ablation_study_tanimoto.pdf
deleted file mode 100644
index 7ba02bacb1b3bd242b0f6a0871885e2b669093a3..0000000000000000000000000000000000000000
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diff --git a/notebooks/ablation_study_uniprot.pdf b/notebooks/ablation_study_uniprot.pdf
deleted file mode 100644
index 0dfc13ddf8bcfb1de1198d35d71ac54962bdef12..0000000000000000000000000000000000000000
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diff --git a/notebooks/article_scraping.ipynb b/notebooks/article_scraping.ipynb
index 8239c69f39e6f2bbf5724342d90d3d1425d3060c..71f661f222cd2288551fa77a458cf9d3c4832bbf 100644
--- a/notebooks/article_scraping.ipynb
+++ b/notebooks/article_scraping.ipynb
@@ -1,252 +1,14 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Lunedì (Giorno 1)</th>\n",
-       "      <th>Martedì (Giorno 2)</th>\n",
-       "      <th>Mercoledì (Giorno 3)</th>\n",
-       "      <th>Giovedì (Giorno 4)</th>\n",
-       "      <th>Venerdì (Giorno 5)</th>\n",
-       "      <th>Sabato (Giorno 6)</th>\n",
-       "      <th>Domenica (Giorno 7)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Spesa: quinoa, broccoli, carote, olive, pomodo...</td>\n",
-       "      <td>-</td>\n",
-       "      <td>Spesa: salmone, patate dolci, lenticchie, brod...</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-</td>\n",
-       "      <td>Pranzo: Insalata di Quinoa Avanzata con Tonno</td>\n",
-       "      <td>-</td>\n",
-       "      <td>Pranzo: Insalata di Spinaci con Frittata Avanzata</td>\n",
-       "      <td>-</td>\n",
-       "      <td>Pranzo: Zuppa di Lenticchie Avanzata</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Cena: Insalata di Quinoa con Verdure a Vapore</td>\n",
-       "      <td>Cena: Pollo al Limone con Patate al Forno</td>\n",
-       "      <td>Cena: Frittata di Spinaci e Feta</td>\n",
-       "      <td>Cena: Salmone al Vapore con Patate Dolci</td>\n",
-       "      <td>Cena: Zuppa di Lenticchie e Verdure</td>\n",
-       "      <td>Cena: Insalata di Patate con Verdure e Fagioli</td>\n",
-       "      <td>-</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   Lunedì (Giorno 1)  \\\n",
-       "0  Spesa: quinoa, broccoli, carote, olive, pomodo...   \n",
-       "1                                                  -   \n",
-       "2      Cena: Insalata di Quinoa con Verdure a Vapore   \n",
-       "\n",
-       "                              Martedì (Giorno 2)  \\\n",
-       "0                                              -   \n",
-       "1  Pranzo: Insalata di Quinoa Avanzata con Tonno   \n",
-       "2      Cena: Pollo al Limone con Patate al Forno   \n",
-       "\n",
-       "                                Mercoledì (Giorno 3)  \\\n",
-       "0  Spesa: salmone, patate dolci, lenticchie, brod...   \n",
-       "1                                                  -   \n",
-       "2                   Cena: Frittata di Spinaci e Feta   \n",
-       "\n",
-       "                                  Giovedì (Giorno 4)  \\\n",
-       "0                                                  -   \n",
-       "1  Pranzo: Insalata di Spinaci con Frittata Avanzata   \n",
-       "2           Cena: Salmone al Vapore con Patate Dolci   \n",
-       "\n",
-       "                    Venerdì (Giorno 5)  \\\n",
-       "0                                    -   \n",
-       "1                                    -   \n",
-       "2  Cena: Zuppa di Lenticchie e Verdure   \n",
-       "\n",
-       "                                Sabato (Giorno 6) Domenica (Giorno 7)  \n",
-       "0                                               -                   -  \n",
-       "1            Pranzo: Zuppa di Lenticchie Avanzata                   -  \n",
-       "2  Cena: Insalata di Patate con Verdure e Fagioli                   -  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "# Creazione della tabella con le informazioni richieste\n",
-    "data = {\n",
-    "    'Lunedì (Giorno 1)': [\"Spesa: quinoa, broccoli, carote, olive, pomodorini, petto di pollo, limoni, patate, uova, spinaci freschi, feta\",\n",
-    "                          \"-\",\n",
-    "                          \"Cena: Insalata di Quinoa con Verdure a Vapore\"],\n",
-    "    'Martedì (Giorno 2)': [\"-\",\n",
-    "                           \"Pranzo: Insalata di Quinoa Avanzata con Tonno\",\n",
-    "                           \"Cena: Pollo al Limone con Patate al Forno\"],\n",
-    "    'Mercoledì (Giorno 3)': [\"Spesa: salmone, patate dolci, lenticchie, brodo vegetale, fagioli cannellini, cipolle, aglio, erbe aromatiche\",\n",
-    "                             \"-\",\n",
-    "                             \"Cena: Frittata di Spinaci e Feta\"],\n",
-    "    'Giovedì (Giorno 4)': [\"-\",\n",
-    "                           \"Pranzo: Insalata di Spinaci con Frittata Avanzata\",\n",
-    "                           \"Cena: Salmone al Vapore con Patate Dolci\"],\n",
-    "    'Venerdì (Giorno 5)': [\"-\",\n",
-    "                           \"-\",\n",
-    "                           \"Cena: Zuppa di Lenticchie e Verdure\"],\n",
-    "    'Sabato (Giorno 6)': [\"-\",\n",
-    "                          \"Pranzo: Zuppa di Lenticchie Avanzata\",\n",
-    "                          \"Cena: Insalata di Patate con Verdure e Fagioli\"],\n",
-    "    'Domenica (Giorno 7)': [\"-\",\n",
-    "                            \"-\",\n",
-    "                            \"-\"]\n",
-    "}\n",
-    "\n",
-    "# Conversione del dizionario in DataFrame\n",
-    "menu_settimanale = pd.DataFrame(data)\n",
-    "\n",
-    "# save table to excel file\n",
-    "menu_settimanale.to_excel('~/Downloads/menu_settimanale.xlsx', index=False)\n",
-    "menu_settimanale"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'2024-03-29': 'Workday',\n",
-       " '2024-03-30': 'Workday',\n",
-       " '2024-03-31': 'Workday',\n",
-       " '2024-04-01': 'Rest day'}"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "from datetime import datetime, timedelta\n",
+    "# PROTAC-DB Article Scraping\n",
     "\n",
-    "# Correct the function with the right logic considering a cycle that includes the rest days 17th, 18th, 19th of April.\n",
-    "def is_working_day(input_date_str):\n",
-    "    # The date format expected\n",
-    "    date_format = \"%Y-%m-%d\"\n",
-    "    \n",
-    "    # Parse the input date\n",
-    "    input_date = datetime.strptime(input_date_str, date_format)\n",
-    "    \n",
-    "    # Known first rest day in the cycle\n",
-    "    first_rest_day = datetime(2024, 4, 17)\n",
-    "    \n",
-    "    # Calculate the difference in days from the known first rest day\n",
-    "    delta_days = (input_date - first_rest_day).days\n",
-    "    \n",
-    "    # Adjust the difference to be on the work cycle,\n",
-    "    # considering that the first rest day is 5 days after the cycle start\n",
-    "    cycle_day = (delta_days + 5) % 8\n",
-    "\n",
-    "    # Days 0-4 after the cycle start are workdays, 5-7 are rest days\n",
-    "    return \"Workday\" if cycle_day < 5 else \"Rest day\"\n",
-    "\n",
-    "# Check the dates 13th, 14th, 15th, and 16th of April 2024\n",
-    "# 24/02 == work day\n",
-    "dates_to_check = [\n",
-    "    \"2024-03-29\",\n",
-    "    \"2024-03-30\",\n",
-    "    \"2024-03-31\",\n",
-    "    \"2024-04-01\",\n",
-    "]\n",
-    "workday_status = {date: is_working_day(date) for date in dates_to_check}\n",
-    "workday_status"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('2024-03-09', '2024-03-10'),\n",
-       " ('2024-03-16', '2024-03-17'),\n",
-       " ('2024-05-04', '2024-05-05')]"
-      ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Define the new function to find the next 3 weekends with both rest days\n",
-    "def find_rest_weekends(start_date_str):\n",
-    "    # Parse the start date\n",
-    "    start_date = datetime.strptime(start_date_str, \"%Y-%m-%d\")\n",
-    "    \n",
-    "    # List to store the rest weekends\n",
-    "    rest_weekends = []\n",
-    "    \n",
-    "    # Counter for the number of weekends found\n",
-    "    weekends_found = 0\n",
-    "    \n",
-    "    # While we don't have 3 weekends\n",
-    "    while weekends_found < 3:\n",
-    "        # If the current day is a Saturday\n",
-    "        if start_date.weekday() == 5:  # Monday is 0 and Sunday is 6\n",
-    "            # Check if both Saturday and Sunday are rest days\n",
-    "            if is_working_day(start_date.strftime(\"%Y-%m-%d\")) == \"Rest day\" and \\\n",
-    "               is_working_day((start_date + timedelta(days=1)).strftime(\"%Y-%m-%d\")) == \"Rest day\":\n",
-    "                # Add the weekend to the list\n",
-    "                rest_weekends.append((start_date, start_date + timedelta(days=1)))\n",
-    "                weekends_found += 1\n",
-    "        \n",
-    "        # Go to the next day\n",
-    "        start_date += timedelta(days=1)\n",
-    "    \n",
-    "    # Return the weekends as strings in the format YYYY-MM-DD\n",
-    "    return [(day[0].strftime(\"%Y-%m-%d\"), day[1].strftime(\"%Y-%m-%d\")) for day in rest_weekends]\n",
+    "This notebook is used to scrape the article data from the PROTAC-DB website. The data is then saved in a CSV file for further analysis.\n",
     "\n",
-    "# Example usage: find the next 3 rest weekends starting from April 20, 2024\n",
-    "find_rest_weekends(\"2024-03-01\")"
+    "This code has been developed to double check that the reported information in the PROTAC-DB website is the same as the one reported in their CSV downloadeable file."
    ]
   },
   {
diff --git a/notebooks/best_fingerprint_search.ipynb b/notebooks/best_fingerprint_search.ipynb
index 6232984830ed88a0c593c7480ee977c7e1309ffc..b2e5bc5b870bfd594b5a871a2f2550a7ee55b393 100644
--- a/notebooks/best_fingerprint_search.ipynb
+++ b/notebooks/best_fingerprint_search.ipynb
@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Best Fingerprint Parameters\n",
+    "\n",
+    "This notebook is used to identify the best parameters for encoding PROTAC SMILES as Morgan fingerprints.\n",
+    "\n",
+    "It uses Optuna to optimize the parameters."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -103,13 +114,6 @@
     "mean_pDC50"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -2148,13 +2152,6 @@
     "#         print()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 29,
diff --git a/notebooks/cell_type_embedding.ipynb b/notebooks/cell_line_embedding.ipynb
similarity index 100%
rename from notebooks/cell_type_embedding.ipynb
rename to notebooks/cell_line_embedding.ipynb
diff --git a/notebooks/data_visualization.ipynb b/notebooks/data_visualization.ipynb
deleted file mode 100644
index 66010937845c3ed91b982426f28b20f5439462c9..0000000000000000000000000000000000000000
--- a/notebooks/data_visualization.ipynb
+++ /dev/null
@@ -1,587 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "\n",
-    "sys.path.append('~/PROTAC-Degradation-Predictor/protac_degradation_predictor')\n",
-    "sys.path.append('~/PROTAC-Degradation-Predictor')\n",
-    "\n",
-    "import protac_degradation_predictor as pdp\n",
-    "\n",
-    "import os\n",
-    "from collections import defaultdict\n",
-    "\n",
-    "from rdkit import DataStructs\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Compound ID</th>\n",
-       "      <th>Uniprot</th>\n",
-       "      <th>Smiles</th>\n",
-       "      <th>E3 Ligase</th>\n",
-       "      <th>InChI</th>\n",
-       "      <th>InChI Key</th>\n",
-       "      <th>Molecular Weight</th>\n",
-       "      <th>Heavy Atom Count</th>\n",
-       "      <th>Ring Count</th>\n",
-       "      <th>Rotatable Bond Count</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Assay (DC50/Dmax)</th>\n",
-       "      <th>Exact Mass</th>\n",
-       "      <th>XLogP3</th>\n",
-       "      <th>Target (Parsed)</th>\n",
-       "      <th>POI Sequence</th>\n",
-       "      <th>E3 Ligase Uniprot</th>\n",
-       "      <th>E3 Ligase Sequence</th>\n",
-       "      <th>Cell Line Identifier</th>\n",
-       "      <th>Active - OR</th>\n",
-       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
-       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
-       "      <td>1486.282</td>\n",
-       "      <td>101</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
-       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
-       "      <td>1500.309</td>\n",
-       "      <td>102</td>\n",
-       "      <td>10</td>\n",
-       "      <td>25</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
-       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
-       "      <td>1514.336</td>\n",
-       "      <td>103</td>\n",
-       "      <td>10</td>\n",
-       "      <td>26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
-       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
-       "      <td>1528.363</td>\n",
-       "      <td>104</td>\n",
-       "      <td>10</td>\n",
-       "      <td>27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
-       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
-       "      <td>1542.390</td>\n",
-       "      <td>105</td>\n",
-       "      <td>10</td>\n",
-       "      <td>28</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Compound ID Uniprot                                             Smiles  \\\n",
-       "0            1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "1            2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "2            3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "3            4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "4            5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "\n",
-       "  E3 Ligase                                              InChI  \\\n",
-       "0       VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
-       "1       VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
-       "2       VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
-       "3       VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
-       "4       VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
-       "\n",
-       "                     InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
-       "0  SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
-       "1  HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
-       "2  ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
-       "3  FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
-       "4  PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
-       "\n",
-       "   Ring Count  Rotatable Bond Count  ...  Assay (DC50/Dmax)  Exact Mass  \\\n",
-       "0          10                    24  ...                NaN         NaN   \n",
-       "1          10                    25  ...                NaN         NaN   \n",
-       "2          10                    26  ...                NaN         NaN   \n",
-       "3          10                    27  ...                NaN         NaN   \n",
-       "4          10                    28  ...                NaN         NaN   \n",
-       "\n",
-       "   XLogP3 Target (Parsed)                                       POI Sequence  \\\n",
-       "0     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "1     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "2     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "3     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "4     NaN             NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "\n",
-       "   E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
-       "0             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "1             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "2             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "3             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "4             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "\n",
-       "  Cell Line Identifier Active - OR Active (Dmax 0.6, pDC50 6.0)  \n",
-       "0               MOLT-4         NaN                          NaN  \n",
-       "1               MOLT-4         NaN                          NaN  \n",
-       "2               MOLT-4         NaN                          NaN  \n",
-       "3               MOLT-4         NaN                          NaN  \n",
-       "4               MOLT-4        True                         True  \n",
-       "\n",
-       "[5 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the PROTAC dataset\n",
-    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
-    "\n",
-    "# Get Dmax_threshold from the active_col\n",
-    "Dmax_threshold = float(active_col.split('Dmax')[1].split(',')[0].strip('(').strip(')').strip())\n",
-    "pDC50_threshold = float(active_col.split('pDC50')[1].strip('(').strip(')').strip())\n",
-    "\n",
-    "protac_df = pd.read_csv('~/PROTAC-Degradation-Predictor/data/PROTAC-Degradation-DB.csv')\n",
-    "# Map E3 Ligase Iap to IAP\n",
-    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')\n",
-    "protac_df[active_col] = protac_df.apply(\n",
-    "    lambda x: pdp.is_active(\n",
-    "        x['DC50 (nM)'],\n",
-    "        x['Dmax (%)'],\n",
-    "        pDC50_threshold=pDC50_threshold,\n",
-    "        Dmax_threshold=Dmax_threshold,\n",
-    "    ),\n",
-    "    axis=1\n",
-    ")\n",
-    "protac_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Precomputing fingerprints: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1467/1467 [00:02<00:00, 669.56it/s]\n",
-      "Computing Tanimoto similarity: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1467/1467 [00:00<00:00, 3748.38it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_smiles2fp_and_avg_tanimoto(protac_df: pd.DataFrame) -> tuple:\n",
-    "    \"\"\" Get the SMILES to fingerprint dictionary and the average Tanimoto similarity.\n",
-    "    \n",
-    "    Args:\n",
-    "        protac_df (pd.DataFrame): The DataFrame containing the PROTACs.\n",
-    "    \n",
-    "    Returns:\n",
-    "        tuple: The SMILES to fingerprint dictionary and the average Tanimoto similarity.\n",
-    "    \"\"\"\n",
-    "    unique_smiles = protac_df['Smiles'].unique().tolist()\n",
-    "\n",
-    "    smiles2fp = {}\n",
-    "    for smiles in tqdm(unique_smiles, desc='Precomputing fingerprints'):\n",
-    "        smiles2fp[smiles] = pdp.get_fingerprint(smiles)\n",
-    "\n",
-    "    # # Get the pair-wise tanimoto similarity between the PROTAC fingerprints\n",
-    "    # tanimoto_matrix = defaultdict(list)\n",
-    "    # for i, smiles1 in enumerate(tqdm(protac_df['Smiles'].unique(), desc='Computing Tanimoto similarity')):\n",
-    "    #     fp1 = smiles2fp[smiles1]\n",
-    "    #     # TODO: Use BulkTanimotoSimilarity for better performance\n",
-    "    #     for j, smiles2 in enumerate(protac_df['Smiles'].unique()[i:]):\n",
-    "    #         fp2 = smiles2fp[smiles2]\n",
-    "    #         tanimoto_dist = 1 - DataStructs.TanimotoSimilarity(fp1, fp2)\n",
-    "    #         tanimoto_matrix[smiles1].append(tanimoto_dist)\n",
-    "    # avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}\n",
-    "    # protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)\n",
-    "\n",
-    "\n",
-    "    tanimoto_matrix = defaultdict(list)\n",
-    "    fps = list(smiles2fp.values())\n",
-    "\n",
-    "    # Compute all-against-all Tanimoto similarity using BulkTanimotoSimilarity\n",
-    "    for i, (smiles1, fp1) in enumerate(tqdm(zip(unique_smiles, fps), desc='Computing Tanimoto similarity', total=len(fps))):\n",
-    "        similarities = DataStructs.BulkTanimotoSimilarity(fp1, fps[i:])  # Only compute for i to end, avoiding duplicates\n",
-    "        for j, similarity in enumerate(similarities):\n",
-    "            distance = 1 - similarity\n",
-    "            tanimoto_matrix[smiles1].append(distance)  # Store as distance\n",
-    "            if i != i + j:\n",
-    "                tanimoto_matrix[unique_smiles[i + j]].append(distance)  # Symmetric filling\n",
-    "\n",
-    "    # Calculate average Tanimoto distance for each unique SMILES\n",
-    "    avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}\n",
-    "    protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)\n",
-    "\n",
-    "    smiles2fp = {s: np.array(fp) for s, fp in smiles2fp.items()}\n",
-    "\n",
-    "    return smiles2fp, protac_df\n",
-    "\n",
-    "smiles2fp, protac_df = get_smiles2fp_and_avg_tanimoto(protac_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load embedding dictionaries\n",
-    "protein2embedding = pdp.load_protein2embedding('../data/uniprot2embedding.h5')\n",
-    "cell2embedding = pdp.load_cell2embedding('../data/cell2embedding.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Smiles': StandardScaler(),\n",
-       " 'Uniprot': StandardScaler(),\n",
-       " 'E3 Ligase Uniprot': StandardScaler(),\n",
-       " 'Cell Line Identifier': StandardScaler()}"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "protac_ds = pdp.protac_dataset.PROTAC_Dataset(\n",
-    "    protac_df[protac_df[active_col].notna()].copy(),\n",
-    "    protein2embedding,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    active_label=active_col,\n",
-    ")\n",
-    "scalers = protac_ds.fit_scaling()\n",
-    "# protac_ds.apply_scaling(scalers)\n",
-    "protac_ds.data.head()\n",
-    "scalers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(857, 256)"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all, _ = protac_ds.get_numpy_arrays()\n",
-    "smiles, _ = protac_ds.get_numpy_arrays('Smiles')\n",
-    "smiles.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import minmax scaler\n",
-    "from sklearn.preprocessing import MinMaxScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 3000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot whisker plot of the smiles array in seaborn\n",
-    "plt.figure(figsize=(30, 10))\n",
-    "sns.boxplot(smiles)\n",
-    "plt.title('Boxplot of the SMILES fingerprints')\n",
-    "plt.xlabel('Fingerprint bit')\n",
-    "plt.ylabel('Fingerprint value')\n",
-    "# Rotate x-axis labels\n",
-    "plt.xticks(rotation=90)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scaled_smiles = scalers['Smiles'].transform(smiles)\n",
-    "\n",
-    "plt.figure(figsize=(30, 10))\n",
-    "sns.boxplot(scaled_smiles)\n",
-    "plt.title('Boxplot of the SMILES fingerprints')\n",
-    "plt.xlabel('Fingerprint bit')\n",
-    "plt.ylabel('Fingerprint value')\n",
-    "# Rotate x-axis labels\n",
-    "plt.xticks(rotation=90)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot a scatter plot the smiles features (the smiles array is (samples, features))\n",
-    "num_samples = smiles.shape[0]\n",
-    "\n",
-    "for i in range(smiles.shape[1]):\n",
-    "    plt.scatter([i] * num_samples, smiles[:, i], alpha=0.1, c=smiles[:, i], cmap='viridis')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Value')\n",
-    "# Plot the colorbar in the same color map (viridis)\n",
-    "plt.colorbar()\n",
-    "plt.title('Scatter plot of the SMILES features')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scaled_smiles = scalers['Smiles'].transform(smiles)\n",
-    "# scaled_smiles = MinMaxScaler().fit_transform(scaled_smiles)\n",
-    "for i in range(scaled_smiles.shape[1]):\n",
-    "    # Viridis palette\n",
-    "    plt.scatter([i] * num_samples, scaled_smiles[:, i], alpha=0.1, c=scaled_smiles[:, i], cmap='viridis')\n",
-    "plt.xlabel('Features')\n",
-    "plt.ylabel('Value')\n",
-    "plt.colorbar()\n",
-    "plt.title('Scatter plot of the SMILES features')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the distribution of te smiles array\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.hist(scalers['Smiles'].transform(smiles).flatten(), bins=100)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/plotting_dragradation_activity_performance.ipynb b/notebooks/plot_dataset_info.ipynb
similarity index 58%
rename from notebooks/plotting_dragradation_activity_performance.ipynb
rename to notebooks/plot_dataset_info.ipynb
index 79edc00e0327069ee2aad9adfdf66ef222a0c3a2..8174044651a87deffb7277130152123c705b951a 100644
--- a/notebooks/plotting_dragradation_activity_performance.ipynb
+++ b/notebooks/plot_dataset_info.ipynb
@@ -3345,1503 +3345,6 @@
     "plt.savefig('train_val_test_split.pdf', bbox_inches='tight')\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['fold', 'group_type', 'train_len', 'val_len', 'train_perc', 'val_perc',\n",
-       "       'train_active_perc', 'train_inactive_perc', 'val_active_perc',\n",
-       "       'val_inactive_perc', 'test_active_perc', 'test_inactive_perc',\n",
-       "       'num_leaking_uniprot', 'num_leaking_smiles',\n",
-       "       'train_leaking_uniprot_perc', 'train_leaking_smiles_perc', 'val_loss',\n",
-       "       'val_acc', 'val_f1_score', 'val_hp_metric', 'val_opt_score',\n",
-       "       'val_precision', 'val_recall', 'val_roc_auc', 'test_loss', 'test_acc',\n",
-       "       'test_f1_score', 'test_hp_metric', 'test_opt_score', 'test_precision',\n",
-       "       'test_recall', 'test_roc_auc', 'hparam_hidden_dim', 'hparam_batch_size',\n",
-       "       'hparam_learning_rate', 'hparam_join_embeddings',\n",
-       "       'hparam_smote_k_neighbors', 'hparam_use_smote', 'hparam_apply_scaling',\n",
-       "       'hparam_dropout', 'disabled_embeddings', 'train_unique_groups',\n",
-       "       'val_unique_groups', 'dummy_val_acc', 'dummy_test_acc', 'test_len',\n",
-       "       'test_perc'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "add dummy score bar, but the description of it can go in the text only, not in the legend\n",
-    "\n",
-    "replace uniprot-wise to target-wise\n",
-    "\n",
-    "remove the title, it's redundant because it goes into the caption"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{llr}\n",
-      "\\toprule\n",
-      "       CV-Groups &        Metric &  Score \\\\\n",
-      "\\midrule\n",
-      "     Dummy model & Test Accuracy &  0.535 \\\\\n",
-      "     Dummy model &  Test ROC AUC &  0.500 \\\\\n",
-      "     Dummy model &  Val Accuracy &  0.516 \\\\\n",
-      "     Dummy model &   Val ROC AUC &  0.500 \\\\\n",
-      "Similarity split & Test Accuracy &  0.715 \\\\\n",
-      "Similarity split &  Test ROC AUC &  0.840 \\\\\n",
-      "Similarity split &  Val Accuracy &  0.775 \\\\\n",
-      "Similarity split &   Val ROC AUC &  0.852 \\\\\n",
-      "  Standard split & Test Accuracy &  0.714 \\\\\n",
-      "  Standard split &  Test ROC AUC &  0.779 \\\\\n",
-      "  Standard split &  Val Accuracy &  0.846 \\\\\n",
-      "  Standard split &   Val ROC AUC &  0.905 \\\\\n",
-      "    Target split & Test Accuracy &  0.584 \\\\\n",
-      "    Target split &  Test ROC AUC &  0.592 \\\\\n",
-      "    Target split &  Val Accuracy &  0.628 \\\\\n",
-      "    Target split &   Val ROC AUC &  0.654 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CV-Groups</th>\n",
-       "      <th>Metric</th>\n",
-       "      <th>Score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Dummy model</td>\n",
-       "      <td>Test Accuracy</td>\n",
-       "      <td>0.535</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Dummy model</td>\n",
-       "      <td>Test ROC AUC</td>\n",
-       "      <td>0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Dummy model</td>\n",
-       "      <td>Val Accuracy</td>\n",
-       "      <td>0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Dummy model</td>\n",
-       "      <td>Val ROC AUC</td>\n",
-       "      <td>0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Similarity split</td>\n",
-       "      <td>Test Accuracy</td>\n",
-       "      <td>0.715</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Similarity split</td>\n",
-       "      <td>Test ROC AUC</td>\n",
-       "      <td>0.840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Similarity split</td>\n",
-       "      <td>Val Accuracy</td>\n",
-       "      <td>0.775</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Similarity split</td>\n",
-       "      <td>Val ROC AUC</td>\n",
-       "      <td>0.852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>Standard split</td>\n",
-       "      <td>Test Accuracy</td>\n",
-       "      <td>0.714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>Standard split</td>\n",
-       "      <td>Test ROC AUC</td>\n",
-       "      <td>0.779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Standard split</td>\n",
-       "      <td>Val Accuracy</td>\n",
-       "      <td>0.846</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Standard split</td>\n",
-       "      <td>Val ROC AUC</td>\n",
-       "      <td>0.905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Target split</td>\n",
-       "      <td>Test Accuracy</td>\n",
-       "      <td>0.584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Target split</td>\n",
-       "      <td>Test ROC AUC</td>\n",
-       "      <td>0.592</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>Target split</td>\n",
-       "      <td>Val Accuracy</td>\n",
-       "      <td>0.628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Target split</td>\n",
-       "      <td>Val ROC AUC</td>\n",
-       "      <td>0.654</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           CV-Groups         Metric  Score\n",
-       "0        Dummy model  Test Accuracy  0.535\n",
-       "1        Dummy model   Test ROC AUC  0.500\n",
-       "2        Dummy model   Val Accuracy  0.516\n",
-       "3        Dummy model    Val ROC AUC  0.500\n",
-       "4   Similarity split  Test Accuracy  0.715\n",
-       "5   Similarity split   Test ROC AUC  0.840\n",
-       "6   Similarity split   Val Accuracy  0.775\n",
-       "7   Similarity split    Val ROC AUC  0.852\n",
-       "8     Standard split  Test Accuracy  0.714\n",
-       "9     Standard split   Test ROC AUC  0.779\n",
-       "10    Standard split   Val Accuracy  0.846\n",
-       "11    Standard split    Val ROC AUC  0.905\n",
-       "12      Target split  Test Accuracy  0.584\n",
-       "13      Target split   Test ROC AUC  0.592\n",
-       "14      Target split   Val Accuracy  0.628\n",
-       "15      Target split    Val ROC AUC  0.654"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{rllr}\n",
-      "\\toprule\n",
-      " Fold &        CV-Groups &        Metric &    Score \\\\\n",
-      "\\midrule\n",
-      "  0.0 &   Standard split &  Val Accuracy & 0.838710 \\\\\n",
-      "  1.0 &   Standard split &  Val Accuracy & 0.824675 \\\\\n",
-      "  2.0 &   Standard split &  Val Accuracy & 0.850649 \\\\\n",
-      "  3.0 &   Standard split &  Val Accuracy & 0.857143 \\\\\n",
-      "  4.0 &   Standard split &  Val Accuracy & 0.857143 \\\\\n",
-      "  0.0 &     Target split &  Val Accuracy & 0.646226 \\\\\n",
-      "  1.0 &     Target split &  Val Accuracy & 0.882759 \\\\\n",
-      "  2.0 &     Target split &  Val Accuracy & 0.554545 \\\\\n",
-      "  3.0 &     Target split &  Val Accuracy & 0.561947 \\\\\n",
-      "  4.0 &     Target split &  Val Accuracy & 0.493671 \\\\\n",
-      "  0.0 & Similarity split &  Val Accuracy & 0.760000 \\\\\n",
-      "  1.0 & Similarity split &  Val Accuracy & 0.767857 \\\\\n",
-      "  2.0 & Similarity split &  Val Accuracy & 0.765306 \\\\\n",
-      "  3.0 & Similarity split &  Val Accuracy & 0.732955 \\\\\n",
-      "  4.0 & Similarity split &  Val Accuracy & 0.846847 \\\\\n",
-      "  0.0 &   Standard split &   Val ROC AUC & 0.903500 \\\\\n",
-      "  1.0 &   Standard split &   Val ROC AUC & 0.909797 \\\\\n",
-      "  2.0 &   Standard split &   Val ROC AUC & 0.900253 \\\\\n",
-      "  3.0 &   Standard split &   Val ROC AUC & 0.902785 \\\\\n",
-      "  4.0 &   Standard split &   Val ROC AUC & 0.906667 \\\\\n",
-      "  0.0 &     Target split &   Val ROC AUC & 0.740495 \\\\\n",
-      "  1.0 &     Target split &   Val ROC AUC & 0.914657 \\\\\n",
-      "  2.0 &     Target split &   Val ROC AUC & 0.613922 \\\\\n",
-      "  3.0 &     Target split &   Val ROC AUC & 0.570508 \\\\\n",
-      "  4.0 &     Target split &   Val ROC AUC & 0.431061 \\\\\n",
-      "  0.0 & Similarity split &   Val ROC AUC & 0.837271 \\\\\n",
-      "  1.0 & Similarity split &   Val ROC AUC & 0.856459 \\\\\n",
-      "  2.0 & Similarity split &   Val ROC AUC & 0.825875 \\\\\n",
-      "  3.0 & Similarity split &   Val ROC AUC & 0.824699 \\\\\n",
-      "  4.0 & Similarity split &   Val ROC AUC & 0.918188 \\\\\n",
-      "  0.0 &   Standard split & Test Accuracy & 0.732558 \\\\\n",
-      "  1.0 &   Standard split & Test Accuracy & 0.697674 \\\\\n",
-      "  2.0 &   Standard split & Test Accuracy & 0.709302 \\\\\n",
-      "  3.0 &   Standard split & Test Accuracy & 0.720930 \\\\\n",
-      "  4.0 &   Standard split & Test Accuracy & 0.709302 \\\\\n",
-      "  0.0 &     Target split & Test Accuracy & 0.611765 \\\\\n",
-      "  1.0 &     Target split & Test Accuracy & 0.647059 \\\\\n",
-      "  2.0 &     Target split & Test Accuracy & 0.611765 \\\\\n",
-      "  3.0 &     Target split & Test Accuracy & 0.505882 \\\\\n",
-      "  4.0 &     Target split & Test Accuracy & 0.541176 \\\\\n",
-      "  0.0 & Similarity split & Test Accuracy & 0.705882 \\\\\n",
-      "  1.0 & Similarity split & Test Accuracy & 0.717647 \\\\\n",
-      "  2.0 & Similarity split & Test Accuracy & 0.729412 \\\\\n",
-      "  3.0 & Similarity split & Test Accuracy & 0.705882 \\\\\n",
-      "  4.0 & Similarity split & Test Accuracy & 0.717647 \\\\\n",
-      "  0.0 &   Standard split &  Test ROC AUC & 0.791304 \\\\\n",
-      "  1.0 &   Standard split &  Test ROC AUC & 0.764130 \\\\\n",
-      "  2.0 &   Standard split &  Test ROC AUC & 0.772826 \\\\\n",
-      "  3.0 &   Standard split &  Test ROC AUC & 0.764674 \\\\\n",
-      "  4.0 &   Standard split &  Test ROC AUC & 0.801087 \\\\\n",
-      "  0.0 &     Target split &  Test ROC AUC & 0.571349 \\\\\n",
-      "  1.0 &     Target split &  Test ROC AUC & 0.743590 \\\\\n",
-      "  2.0 &     Target split &  Test ROC AUC & 0.652174 \\\\\n",
-      "  3.0 &     Target split &  Test ROC AUC & 0.448718 \\\\\n",
-      "  4.0 &     Target split &  Test ROC AUC & 0.542921 \\\\\n",
-      "  0.0 & Similarity split &  Test ROC AUC & 0.803333 \\\\\n",
-      "  1.0 & Similarity split &  Test ROC AUC & 0.861111 \\\\\n",
-      "  2.0 & Similarity split &  Test ROC AUC & 0.855556 \\\\\n",
-      "  3.0 & Similarity split &  Test ROC AUC & 0.842778 \\\\\n",
-      "  4.0 & Similarity split &  Test ROC AUC & 0.838333 \\\\\n",
-      "  NaN &      Dummy model &  Val Accuracy & 0.516094 \\\\\n",
-      "  NaN &      Dummy model &   Val ROC AUC & 0.500000 \\\\\n",
-      "  NaN &      Dummy model & Test Accuracy & 0.535157 \\\\\n",
-      "  NaN &      Dummy model &  Test ROC AUC & 0.500000 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n",
-      "Plotting value: 0.8456640124320984 -> 84.6%\n",
-      "Plotting value: 0.9046003818511963 -> 0.90\n",
-      "Plotting value: 0.7139534711837768 -> 71.4%\n",
-      "Plotting value: 0.778804337978363 -> 0.78\n",
-      "Plotting value: 0.627829658985138 -> 62.8%\n",
-      "Plotting value: 0.6541285753250122 -> 0.65\n",
-      "Plotting value: 0.5835294365882874 -> 58.4%\n",
-      "Plotting value: 0.5917502701282501 -> 0.59\n",
-      "Plotting value: 0.774592924118042 -> 77.5%\n",
-      "Plotting value: 0.8524984359741211 -> 0.85\n",
-      "Plotting value: 0.7152941346168518 -> 71.5%\n",
-      "Plotting value: 0.8402222156524658 -> 0.84\n",
-      "Plotting value: 0.516094026565312 -> 51.6%\n",
-      "Plotting value: 0.5 -> 0.50\n",
-      "Plotting value: 0.53515731874145 -> 53.5%\n",
-      "Plotting value: 0.5 -> 0.50\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# sns.set_style(\"whitegrid\")\n",
-    "\n",
-    "\n",
-    "def plot_report(report_df, title=None):\n",
-    "    n_splits = 5\n",
-    "    tmp = report_df.copy()[[\n",
-    "        'fold', 'group_type',\n",
-    "        'val_acc', 'val_roc_auc', # 'val_precision', 'val_recall', 'val_f1_score',\n",
-    "        'test_acc', 'test_roc_auc', # 'test_precision', 'test_recall', 'test_f1_score',\n",
-    "    ]]\n",
-    "    # Rename columns\n",
-    "    tmp.columns = [\n",
-    "        'Fold', 'CV-Groups',\n",
-    "        'Val Accuracy', 'Val ROC AUC', # 'Val Precision', 'Val Recall', 'Val F1 score',\n",
-    "        'Test Accuracy', 'Test ROC AUC', # 'Test Precision', 'Test Recall', 'Test F1 score',\n",
-    "    ]\n",
-    "    # Remove all entries with 'CV-Groups' == 'e3_ligase'\n",
-    "    tmp = tmp[tmp['CV-Groups'] != 'e3_ligase']\n",
-    "    # Plot aggregated mean value of all metrics in one bar plot\n",
-    "    tmp = tmp.melt(id_vars=['Fold', 'CV-Groups'], var_name='Metric', value_name='Score')\n",
-    "    # Rename elements in 'CV-Groups' column\n",
-    "    group2name = {\n",
-    "        'random': 'Standard split',\n",
-    "        'uniprot': 'Target split',\n",
-    "        'tanimoto': 'Similarity split',\n",
-    "    }\n",
-    "\n",
-    "    tmp['CV-Groups'] = tmp['CV-Groups'].map(group2name)\n",
-    "    # Draw one horizontal line for each CV-group representing the max value of inactive PROTACs percentage across the folds\n",
-    "    dummy_val_acc = 0\n",
-    "    dummy_test_acc = 0\n",
-    "    for i, group in enumerate(group2name.keys()):\n",
-    "        # Get the majority class in group_df\n",
-    "        group_df = report_df[report_df['group_type'] == group]\n",
-    "        \n",
-    "        major_col = 'inactive' if group_df['val_inactive_perc'].mean() > 0.5 else 'active'\n",
-    "        dummy_val_acc += group_df[f'val_{major_col}_perc'].mean()\n",
-    "\n",
-    "        major_col = 'inactive' if group_df['test_inactive_perc'].mean() > 0.5 else 'active'\n",
-    "        dummy_test_acc += group_df[f'test_{major_col}_perc'].mean()\n",
-    "\n",
-    "    #     # plt.axhline(group_df[f'val_{major_col}_perc'].max(), color=f'C{i}', linestyle='-.', label=f'Max val {major_col} (%) for {group2name[group]}')\n",
-    "    #     plt.axhline(group_df[f'val_{major_col}_perc'].mean(), color=f'C{i}', linestyle='--', label=f'Mean val {major_col} (%) for {group2name[group].split(\" (\")[0]}')\n",
-    "    # plt.axhline(report_df[f'test_{major_col}_perc'].max(), color='black', linestyle=':', label=f'Max test {major_col} (%)')\n",
-    "    \n",
-    "    dummy_val_acc /= len(group2name)\n",
-    "    dummy_test_acc /= len(group2name)\n",
-    "\n",
-    "    # create a dummy model dataframe:\n",
-    "    # The \"Metric\" column shall have: 'Val Accuracy', 'Val ROC AUC', 'Test Accuracy', 'Test ROC AUC'\n",
-    "    # The \"CV-Groups\" column shall have: 'Dummy model'\n",
-    "    # The \"Score\" column shall have the following values:\n",
-    "    # its val and test ROC AUC is 0.5,\n",
-    "    # its val accuracy is the max value of val_active_perc and val_inactive_perc per group,\n",
-    "    # and its test accuracy is the max value of test_active_perc and test_inactive_perc per group.\n",
-    "    # The \"Fold\" column shall have the same value for all rows, e.g. 0\n",
-    "    dummy_model = pd.DataFrame({\n",
-    "        'Metric': ['Val Accuracy', 'Val ROC AUC', 'Test Accuracy', 'Test ROC AUC'],\n",
-    "        'CV-Groups': 'Dummy model',\n",
-    "        'Score': [\n",
-    "            dummy_val_acc,\n",
-    "            0.5,\n",
-    "            dummy_test_acc,\n",
-    "            0.5,\n",
-    "        ],\n",
-    "    })\n",
-    "\n",
-    "    # Append the dummy model dataframe to the tmp dataframe\n",
-    "    tmp = pd.concat([tmp, dummy_model])\n",
-    "\n",
-    "    summary_df = tmp.groupby(['CV-Groups', 'Metric']).mean().round(3).reset_index().drop('Fold', axis=1)\n",
-    "    # Print summary_df to Latex\n",
-    "    print(summary_df.to_latex(index=False))\n",
-    "    display(summary_df)\n",
-    "\n",
-    "    # Setup the plot size\n",
-    "    # plt.figure(figsize=(8, 4)) # Original\n",
-    "    plt.figure(figsize=(8, 4))\n",
-    "    \n",
-    "    # Plot the bar plot\n",
-    "    sns.barplot(data=tmp,\n",
-    "        x='Metric',\n",
-    "        y='Score',\n",
-    "        hue='CV-Groups',\n",
-    "        errorbar=('sd', 1),\n",
-    "        # Lighten the color of the error bars\n",
-    "        errcolor='0.5',\n",
-    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
-    "    )\n",
-    "\n",
-    "    print(tmp.to_latex(index=False))\n",
-    "\n",
-    "    if title is not None:\n",
-    "        plt.title(title)\n",
-    "\n",
-    "    plt.grid(axis='y', alpha=0.4)\n",
-    "    # Set y-axis to end at 1.0\n",
-    "    plt.ylim(0, 1.0)\n",
-    "    # Make the y-axis as percentage\n",
-    "    plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    # Plot the legend below the x-axis, outside the plot, and divided in two columns\n",
-    "    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=4)\n",
-    "\n",
-    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
-    "    for i, p in enumerate(plt.gca().patches):\n",
-    "        # TODO: For some reasons, there are 4 additional rectangles being\n",
-    "        # plotted... I suspect it's because the dummy_df doesn't have the same\n",
-    "        # shape as the df containing all the evaluation data...\n",
-    "        if p.get_height() < 0.01:\n",
-    "            continue\n",
-    "        if i % 2 == 0:\n",
-    "            value = '{:.1f}%'.format(100 * p.get_height())\n",
-    "        else:\n",
-    "            value = '{:.2f}'.format(p.get_height())\n",
-    "        \n",
-    "        print(f'Plotting value: {p.get_height()} -> {value}')\n",
-    "        x = p.get_x() + p.get_width() / 2\n",
-    "        y = 0.4 # p.get_height() - p.get_height() / 2\n",
-    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    # Remove axis labels\n",
-    "    plt.xlabel('')\n",
-    "    plt.ylabel('')\n",
-    "    plt.savefig('genlife_poster_performance.pdf', bbox_inches='tight')\n",
-    "    plt.savefig('stefano_performance_plot.pdf', bbox_inches='tight')\n",
-    "    plt.show()\n",
-    "\n",
-    "tmp = report[report['disabled_embeddings'].isna()]\n",
-    "plot_report(tmp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([nan, 'disabled poi', 'disabled cell', 'disabled smiles',\n",
-       "       'disabled e3 cell', 'disabled poi e3 cell', 'disabled e3'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report['disabled_embeddings'].unique()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ablation study:\n",
-    "\n",
-    "- one single embedding/branch active at the time\n",
-    "- just the cell and no other embeddings -> hopefully the performance is bad\n",
-    "- just the SMILES branch and no other embeddings\n",
-    "\n",
-    "**TODO**: Ordinal encoding for the cell line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([nan, 'disabled poi', 'disabled cell', 'disabled smiles',\n",
-       "       'disabled e3 cell', 'disabled poi e3 cell', 'disabled e3'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report['disabled_embeddings'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{lllr}\n",
-      "\\toprule\n",
-      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
-      "\\midrule\n",
-      "0  &                             All embeddings enabled &        Test Accuracy &  0.714 \\\\\n",
-      "1  &                             All embeddings enabled &  Validation Accuracy &  0.846 \\\\\n",
-      "2  &                            Disabled E3 information &        Test Accuracy &  0.700 \\\\\n",
-      "3  &                            Disabled E3 information &  Validation Accuracy &  0.786 \\\\\n",
-      "4  &                          Disabled cell information &        Test Accuracy &  0.670 \\\\\n",
-      "5  &                          Disabled cell information &  Validation Accuracy &  0.776 \\\\\n",
-      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.647 \\\\\n",
-      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.730 \\\\\n",
-      "8  &                      Disabled compound information &        Test Accuracy &  0.663 \\\\\n",
-      "9  &                      Disabled compound information &  Validation Accuracy &  0.799 \\\\\n",
-      "10 &                        Disabled target information &        Test Accuracy &  0.686 \\\\\n",
-      "11 &                        Disabled target information &  Validation Accuracy &  0.763 \\\\\n",
-      "12 &                                        Dummy model &        Test Accuracy &  0.535 \\\\\n",
-      "13 &                                        Dummy model &  Validation Accuracy &  0.515 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{lllr}\n",
-      "\\toprule\n",
-      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
-      "\\midrule\n",
-      "0  &                             All embeddings enabled &        Test Accuracy &  0.478 \\\\\n",
-      "1  &                             All embeddings enabled &  Validation Accuracy &  0.806 \\\\\n",
-      "2  &                            Disabled E3 information &        Test Accuracy &  0.506 \\\\\n",
-      "3  &                            Disabled E3 information &  Validation Accuracy &  0.784 \\\\\n",
-      "4  &                          Disabled cell information &        Test Accuracy &  0.396 \\\\\n",
-      "5  &                          Disabled cell information &  Validation Accuracy &  0.753 \\\\\n",
-      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.439 \\\\\n",
-      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.700 \\\\\n",
-      "8  &                      Disabled compound information &        Test Accuracy &  0.580 \\\\\n",
-      "9  &                      Disabled compound information &  Validation Accuracy &  0.774 \\\\\n",
-      "10 &                        Disabled target information &        Test Accuracy &  0.443 \\\\\n",
-      "11 &                        Disabled target information &  Validation Accuracy &  0.764 \\\\\n",
-      "12 &                                        Dummy model &        Test Accuracy &  0.824 \\\\\n",
-      "13 &                                        Dummy model &  Validation Accuracy &  0.531 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{lllr}\n",
-      "\\toprule\n",
-      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
-      "\\midrule\n",
-      "0  &                             All embeddings enabled &        Test Accuracy &  0.584 \\\\\n",
-      "1  &                             All embeddings enabled &  Validation Accuracy &  0.628 \\\\\n",
-      "2  &                            Disabled E3 information &        Test Accuracy &  0.508 \\\\\n",
-      "3  &                            Disabled E3 information &  Validation Accuracy &  0.598 \\\\\n",
-      "4  &                          Disabled cell information &        Test Accuracy &  0.489 \\\\\n",
-      "5  &                          Disabled cell information &  Validation Accuracy &  0.580 \\\\\n",
-      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.501 \\\\\n",
-      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.507 \\\\\n",
-      "8  &                      Disabled compound information &        Test Accuracy &  0.600 \\\\\n",
-      "9  &                      Disabled compound information &  Validation Accuracy &  0.641 \\\\\n",
-      "10 &                        Disabled target information &        Test Accuracy &  0.471 \\\\\n",
-      "11 &                        Disabled target information &  Validation Accuracy &  0.541 \\\\\n",
-      "12 &                                        Dummy model &        Test Accuracy &  0.541 \\\\\n",
-      "13 &                                        Dummy model &  Validation Accuracy &  0.580 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{lllr}\n",
-      "\\toprule\n",
-      "{} &                                disabled\\_embeddings &               metric &  score \\\\\n",
-      "\\midrule\n",
-      "0  &                             All embeddings enabled &        Test Accuracy &  0.715 \\\\\n",
-      "1  &                             All embeddings enabled &  Validation Accuracy &  0.775 \\\\\n",
-      "2  &                            Disabled E3 information &        Test Accuracy &  0.666 \\\\\n",
-      "3  &                            Disabled E3 information &  Validation Accuracy &  0.750 \\\\\n",
-      "4  &                          Disabled cell information &        Test Accuracy &  0.687 \\\\\n",
-      "5  &                          Disabled cell information &  Validation Accuracy &  0.704 \\\\\n",
-      "6  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &        Test Accuracy &  0.614 \\\\\n",
-      "7  &  Disabled cell, E3, and target info\\textbackslash n(only comp... &  Validation Accuracy &  0.618 \\\\\n",
-      "8  &                      Disabled compound information &        Test Accuracy &  0.706 \\\\\n",
-      "9  &                      Disabled compound information &  Validation Accuracy &  0.773 \\\\\n",
-      "10 &                        Disabled target information &        Test Accuracy &  0.718 \\\\\n",
-      "11 &                        Disabled target information &  Validation Accuracy &  0.719 \\\\\n",
-      "12 &                                        Dummy model &        Test Accuracy &  0.529 \\\\\n",
-      "13 &                                        Dummy model &  Validation Accuracy &  0.555 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_2670585/1353845451.py:85: UserWarning: The palette list has more values (4) than needed (2), which may not be intended.\n",
-      "  sns.barplot(data=final_df,\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "\n",
-    "ablation_study_combinations = [\n",
-    "    'disabled smiles',\n",
-    "    'disabled poi',\n",
-    "    'disabled e3',\n",
-    "    'disabled cell',\n",
-    "    'disabled poi e3 smiles',\n",
-    "    'disabled poi e3 cell',\n",
-    "]\n",
-    "\n",
-    "for group in report['group_type'].unique():    \n",
-    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
-    "    baseline = baseline[baseline['group_type'] == group]\n",
-    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
-    "    metrics_to_show = ['val_acc', 'test_acc']\n",
-    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "\n",
-    "    ablation_dfs = []\n",
-    "    for disabled_embeddings in ablation_study_combinations:\n",
-    "        if pd.isnull(disabled_embeddings):\n",
-    "            continue\n",
-    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
-    "        tmp = tmp[tmp['group_type'] == group]\n",
-    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "        ablation_dfs.append(tmp)\n",
-    "    ablation_df = pd.concat(ablation_dfs)\n",
-    "\n",
-    "    dummy_val_df = pd.DataFrame()\n",
-    "    tmp = report[report['group_type'] == group]\n",
-    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
-    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
-    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_test_df = pd.DataFrame()\n",
-    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
-    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
-    "\n",
-    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
-    "\n",
-    "    final_df['metric'] = final_df['metric'].map({\n",
-    "        'val_acc': 'Validation Accuracy',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'val_roc_auc': 'Val ROC-AUC',\n",
-    "        'test_roc_auc': 'Test ROC-AUC',\n",
-    "    })\n",
-    "\n",
-    "    \n",
-    "\n",
-    "    # final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "    #     'all embeddings enabled': 'All embeddings',\n",
-    "    #     'dummy': 'Dummy model',\n",
-    "    #     'disabled smiles': 'E3, Cell, Target',\n",
-    "    #     'disabled poi e3 smiles': 'Cell only',\n",
-    "    #     'disabled poi e3 cell': 'SMILES only',\n",
-    "    #     'disabled poi': 'SMILES, E3, Cell',\n",
-    "    #     'disabled e3': 'SMILES, Cell, Target',\n",
-    "    #     'disabled cell': 'SMILES, E3, Target',\n",
-    "    # })\n",
-    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "        'all embeddings enabled': 'All embeddings enabled',\n",
-    "        'dummy': 'Dummy model',\n",
-    "        'disabled smiles': 'Disabled compound information',\n",
-    "        'disabled e3': 'Disabled E3 information',\n",
-    "        'disabled poi': 'Disabled target information',\n",
-    "        'disabled cell': 'Disabled cell information',\n",
-    "        'disabled poi e3 smiles': 'Disabled compound, E3, and target info\\n(only cell information left)',\n",
-    "        'disabled poi e3 cell': 'Disabled cell, E3, and target info\\n(only compound information left)',\n",
-    "    })\n",
-    "\n",
-    "    # Print final_df to latex\n",
-    "    tmp  = final_df.groupby(['disabled_embeddings', 'metric']).mean().round(3)\n",
-    "    # Remove fold column to tmp\n",
-    "    tmp = tmp.reset_index().drop('fold', axis=1)\n",
-    "    print(tmp.to_latex())\n",
-    "\n",
-    "    # fig, ax = plt.subplots(figsize=(5, 5))\n",
-    "    fig, ax = plt.subplots()\n",
-    "\n",
-    "    sns.barplot(data=final_df,\n",
-    "        y='disabled_embeddings',\n",
-    "        x='score',\n",
-    "        hue='metric',\n",
-    "        ax=ax,\n",
-    "        errorbar=('sd', 1),\n",
-    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
-    "        saturation=1,\n",
-    "    )\n",
-    "\n",
-    "    # ax.set_title(f'{group.replace(\"random\", \"standard\")} CV split')\n",
-    "    ax.grid(axis='x', alpha=0.5)\n",
-    "    ax.tick_params(axis='y', rotation=0)\n",
-    "    ax.set_xlim(0, 1.0)\n",
-    "    ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    ax.set_ylabel('')\n",
-    "    ax.set_xlabel('')\n",
-    "    # Set the legend outside the plot and below\n",
-    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)\n",
-    "\n",
-    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
-    "    for i, p in enumerate(plt.gca().patches):\n",
-    "        value = '{:.1f}%'.format(100 * p.get_width())\n",
-    "        y = p.get_y() + p.get_height() / 2\n",
-    "        x = 0.4 # p.get_height() - p.get_height() / 2\n",
-    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
-    "\n",
-    "    plt.savefig(f'ablation_study_{group}.pdf', bbox_inches='tight')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of times the scaling was applied:     95.0%\n",
-      "Number of times the scaling was not applied: 5.0%\n",
-      "hparam_hidden_dim:\n",
-      "512    9\n",
-      "256    6\n",
-      "768    5\n",
-      "Name: hparam_hidden_dim, dtype: int64\n",
-      "\n",
-      "hparam_batch_size:\n",
-      "32    10\n",
-      "16     6\n",
-      "8      4\n",
-      "Name: hparam_batch_size, dtype: int64\n",
-      "\n",
-      "hparam_learning_rate:\n",
-      "0.000056    1\n",
-      "0.000037    1\n",
-      "0.000014    1\n",
-      "0.000025    1\n",
-      "0.000010    1\n",
-      "0.000015    1\n",
-      "0.000014    1\n",
-      "0.000012    1\n",
-      "0.000012    1\n",
-      "0.000029    1\n",
-      "0.000454    1\n",
-      "0.000049    1\n",
-      "0.000013    1\n",
-      "0.000014    1\n",
-      "0.000013    1\n",
-      "0.000020    1\n",
-      "0.000029    1\n",
-      "0.000019    1\n",
-      "0.000024    1\n",
-      "0.000014    1\n",
-      "Name: hparam_learning_rate, dtype: int64\n",
-      "\n",
-      "hparam_join_embeddings:\n",
-      "sum          9\n",
-      "beginning    7\n",
-      "concat       4\n",
-      "Name: hparam_join_embeddings, dtype: int64\n",
-      "\n",
-      "hparam_smote_k_neighbors:\n",
-      "6     4\n",
-      "3     4\n",
-      "14    4\n",
-      "5     3\n",
-      "10    2\n",
-      "9     1\n",
-      "7     1\n",
-      "8     1\n",
-      "Name: hparam_smote_k_neighbors, dtype: int64\n",
-      "\n",
-      "hparam_use_smote:\n",
-      "True     10\n",
-      "False    10\n",
-      "Name: hparam_use_smote, dtype: int64\n",
-      "\n",
-      "hparam_apply_scaling:\n",
-      "True     19\n",
-      "False     1\n",
-      "Name: hparam_apply_scaling, dtype: int64\n",
-      "\n",
-      "hparam_dropout:\n",
-      "0.119461    1\n",
-      "0.277674    1\n",
-      "0.459840    1\n",
-      "0.124534    1\n",
-      "0.445686    1\n",
-      "0.134516    1\n",
-      "0.109033    1\n",
-      "0.194294    1\n",
-      "0.277930    1\n",
-      "0.269190    1\n",
-      "0.125423    1\n",
-      "0.169210    1\n",
-      "0.212074    1\n",
-      "0.176164    1\n",
-      "0.219126    1\n",
-      "0.226225    1\n",
-      "0.152720    1\n",
-      "0.326619    1\n",
-      "0.128565    1\n",
-      "0.446376    1\n",
-      "Name: hparam_dropout, dtype: int64\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# [c for c in report.columns if 'hparam' in c]\n",
-    "# display(report[report['hparam_apply_scaling']].groupby(['fold', 'group_type'])[['val_acc', 'test_acc']].mean().round(3))\n",
-    "# display(report[~report['hparam_apply_scaling']].groupby(['fold', 'group_type'])[['val_acc', 'test_acc']].mean().round(3))\n",
-    "\n",
-    "# Count the number of times the scaling was applied and its percentage\n",
-    "scaling_counts = report['hparam_apply_scaling'].value_counts()\n",
-    "scaling_counts = scaling_counts / scaling_counts.sum()\n",
-    "print(f'Number of times the scaling was applied:     {scaling_counts[True]:.1%}')\n",
-    "print(f'Number of times the scaling was not applied: {scaling_counts[False]:.1%}')\n",
-    "\n",
-    "# Count the number and percentage of occurance of the join_embeddings column\n",
-    "join_embeddings_counts = report['hparam_join_embeddings'].value_counts()\n",
-    "join_embeddings_counts = join_embeddings_counts / join_embeddings_counts.sum()\n",
-    "join_embeddings_counts\n",
-    "# print(f'Number of times the embeddings were joined: {join_embeddings_counts[True]:.1%}')\n",
-    "# print(f'Number of times the embeddings were not joined: {join_embeddings_counts[False]:.1%}')\n",
-    "\n",
-    "\n",
-    "# For each hparam, print some relevant statistics\n",
-    "hparam_cols = [c for c in report.columns if 'hparam' in c]\n",
-    "for hparam in hparam_cols:\n",
-    "    print(f'{hparam}:')\n",
-    "    print(report[hparam].value_counts() // 6)\n",
-    "    print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "../reports/study_Active_Dmax_0.6_pDC50_6.0_random_fold_0_test_split_0.2.pkl\n",
-      "{'hidden_dim': 512, 'batch_size': 16, 'learning_rate': 1.7221700002340547e-05, 'join_embeddings': 'sum', 'smote_k_neighbors': 14, 'use_smote': False, 'apply_scaling': True, 'dropout': 0.15240823317125454}\n",
-      "OrderedDict([('learning_rate', 0.42893372832121324), ('smote_k_neighbors', 0.30085702206051634), ('join_embeddings', 0.0975632989422997), ('batch_size', 0.05369329248321026), ('dropout', 0.05078700222041663), ('hidden_dim', 0.03849912063219203), ('use_smote', 0.01543507014670634), ('apply_scaling', 0.014231465193445552)])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import joblib\n",
-    "import optuna\n",
-    "\n",
-    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
-    "active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')\n",
-    "test_split = 0.2\n",
-    "cv_n_splits = 5\n",
-    "\n",
-    "for split_type in report.group_type.unique():\n",
-    "    for k in range(cv_n_splits):\n",
-    "        study_filename = f'../reports/study_{active_name}_{split_type}_fold_{k}_test_split_{test_split}.pkl'\n",
-    "        study = joblib.load(study_filename)\n",
-    "        print(f'{study_filename}')\n",
-    "        print(f'{study.best_params}')\n",
-    "        print(optuna.importance.get_param_importances(study))\n",
-    "        ax = optuna.visualization.matplotlib.plot_param_importances(study)\n",
-    "        plt.show()\n",
-    "        break\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Older Code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'active'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'active'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[50], line 20\u001b[0m\n\u001b[0;32m     18\u001b[0m baseline \u001b[38;5;241m=\u001b[39m report[report[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m     19\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgroup_type\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m group]\n\u001b[1;32m---> 20\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[\u001b[43mbaseline\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mactive\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m active_def]\n\u001b[0;32m     21\u001b[0m baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall embeddings enabled\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Melt accross folds and get acc and roc_auc\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\frame.py:3804\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3804\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3806\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'active'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Make two plots side by side, one for active - or, one for active - and.\n",
-    "# On the y-axis put the val and test accuracy (different hue).\n",
-    "# On the x-axis put the baseline (all embeddings on) with all the disabled embeddings combinations next.\n",
-    "\n",
-    "ablation_study_combinations = [\n",
-    "    'disabled smiles',\n",
-    "    'disabled poi',\n",
-    "    'disabled e3',\n",
-    "    'disabled cell',\n",
-    "    'disabled poi e3 smiles',\n",
-    "    'disabled poi e3 cell',\n",
-    "]\n",
-    "\n",
-    "for group in report['group_type'].unique():\n",
-    "    # Set the two plots side by side\n",
-    "    fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
-    "    for i, active_def in enumerate(['and', 'or']):\n",
-    "        baseline = report[report['disabled_embeddings'].isna()].copy()\n",
-    "        baseline = baseline[baseline['group_type'] == group]\n",
-    "        baseline = baseline[baseline['active'] == active_def]\n",
-    "        baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
-    "        # Melt accross folds and get acc and roc_auc\n",
-    "        metrics_to_show = ['val_acc', 'test_acc']\n",
-    "        # metrics_to_show = ['val_roc_auc', 'test_roc_auc']\n",
-    "\n",
-    "        baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "\n",
-    "        ablation_dfs = []\n",
-    "        for disabled_embeddings in ablation_study_combinations:\n",
-    "            if pd.isnull(disabled_embeddings):\n",
-    "                continue\n",
-    "            tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
-    "            tmp = tmp[tmp['group_type'] == group]\n",
-    "            tmp = tmp[tmp['active'] == active_def]\n",
-    "            # Melt accross folds and get acc and roc_auc\n",
-    "            tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "            ablation_dfs.append(tmp)\n",
-    "        ablation_df = pd.concat(ablation_dfs)\n",
-    "\n",
-    "        # Create a dummy df with the same structure as the baseline df\n",
-    "        # Score is the max between the val_active_perc and val_inactive_perc\n",
-    "        dummy_val_df = pd.DataFrame()\n",
-    "        tmp = report[report['group_type'] == group]\n",
-    "        tmp = tmp[tmp['active'] == active_def]\n",
-    "        dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
-    "        # dummy_val_df['score'] = 0.5\n",
-    "        dummy_val_df['metric'] = metrics_to_show[0]\n",
-    "        dummy_val_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "        dummy_test_df = pd.DataFrame()\n",
-    "        dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "        # dummy_test_df['score'] = 0.5\n",
-    "        dummy_test_df['metric'] = metrics_to_show[1]\n",
-    "        dummy_test_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "        dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
-    "\n",
-    "        final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
-    "\n",
-    "        # Rename 'val_acc' to 'Val Accuracy' and 'test_acc' to 'Test Accuracy'\n",
-    "        final_df['metric'] = final_df['metric'].map({\n",
-    "            'val_acc': 'Val Accuracy',\n",
-    "            'test_acc': 'Test Accuracy',\n",
-    "            'val_roc_auc': 'Val ROC-AUC',\n",
-    "            'test_roc_auc': 'Test ROC-AUC',\n",
-    "        })\n",
-    "        # Map 'all embeddings enabled' to 'Baseline', then turn disabled into the remaining embeddings\n",
-    "        final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "            'all embeddings enabled': 'All embeddings',\n",
-    "            'dummy': 'Dummy model',\n",
-    "            'disabled smiles': 'E3, Cell, Target',\n",
-    "            'disabled poi e3 smiles': 'Cell only',\n",
-    "            'disabled poi e3 cell': 'SMILES only',\n",
-    "            'disabled poi': 'SMILES, E3, Cell',\n",
-    "            'disabled e3': 'SMILES, Cell, Target',\n",
-    "            'disabled cell': 'SMILES, E3, Target',\n",
-    "        })\n",
-    "\n",
-    "        # display(dummy_df)\n",
-    "        # display(baseline)\n",
-    "        # display(ablation_df)\n",
-    "        # display(final_df)\n",
-    "\n",
-    "        # Plot\n",
-    "        sns.barplot(data=final_df,\n",
-    "            x='disabled_embeddings',\n",
-    "            y='score',\n",
-    "            hue='metric',\n",
-    "            ax=ax[i],\n",
-    "            errorbar=('sd', 1),\n",
-    "            # palette=sns.color_palette(\"tab10\", 8),\n",
-    "            palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
-    "            # Set brightness of the colors\n",
-    "            saturation=1,\n",
-    "        )\n",
-    "        # Add a bar for the dummy accuracy for the val and test set\n",
-    "\n",
-    "\n",
-    "        ax[i].set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
-    "        # Set legend outside the plot on the right, just on the second plot, the first one will be hidden\n",
-    "        if i > 0:\n",
-    "            ax[i].legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Metric')\n",
-    "        else:\n",
-    "            # Disable the legend for the first plot\n",
-    "            ax[i].legend().set_visible(False)\n",
-    "\n",
-    "        ax[i].grid(axis='y', alpha=0.5)\n",
-    "        # Rotate x-axis labels to 90 degrees\n",
-    "        ax[i].tick_params(axis='x', rotation=90)\n",
-    "        # Set y-axis to end at 1.0 and make the y-axis as percentage\n",
-    "        ax[i].set_ylim(0, 1.0)\n",
-    "        ax[i].yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "        # Remove axis labels\n",
-    "        ax[i].set_xlabel('')\n",
-    "        ax[i].set_ylabel('')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'active'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'active'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[41], line 12\u001b[0m\n\u001b[0;32m     10\u001b[0m baseline \u001b[38;5;241m=\u001b[39m report[report[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misna()]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m     11\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgroup_type\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m group]\n\u001b[1;32m---> 12\u001b[0m baseline \u001b[38;5;241m=\u001b[39m baseline[\u001b[43mbaseline\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mactive\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m active_def]\n\u001b[0;32m     13\u001b[0m baseline[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdisabled_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall embeddings enabled\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     14\u001b[0m metrics_to_show \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_acc\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest_acc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\frame.py:3804\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3804\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3806\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'active'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "\n",
-    "active_def = 'or'\n",
-    "\n",
-    "for group in report['group_type'].unique():\n",
-    "    fig, ax = plt.subplots()\n",
-    "    \n",
-    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
-    "    baseline = baseline[baseline['group_type'] == group]\n",
-    "    baseline = baseline[baseline['active'] == active_def]\n",
-    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
-    "    metrics_to_show = ['val_acc', 'test_acc']\n",
-    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "\n",
-    "    ablation_dfs = []\n",
-    "    for disabled_embeddings in ablation_study_combinations:\n",
-    "        if pd.isnull(disabled_embeddings):\n",
-    "            continue\n",
-    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
-    "        tmp = tmp[tmp['group_type'] == group]\n",
-    "        tmp = tmp[tmp['active'] == active_def]\n",
-    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "        ablation_dfs.append(tmp)\n",
-    "    ablation_df = pd.concat(ablation_dfs)\n",
-    "\n",
-    "    dummy_val_df = pd.DataFrame()\n",
-    "    tmp = report[report['group_type'] == group]\n",
-    "    tmp = tmp[tmp['active'] == active_def]\n",
-    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
-    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
-    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_test_df = pd.DataFrame()\n",
-    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
-    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
-    "\n",
-    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
-    "\n",
-    "    final_df['metric'] = final_df['metric'].map({\n",
-    "        'val_acc': 'Val Accuracy',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'val_roc_auc': 'Val ROC-AUC',\n",
-    "        'test_roc_auc': 'Test ROC-AUC',\n",
-    "    })\n",
-    "\n",
-    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "        'all embeddings enabled': 'All embeddings',\n",
-    "        'dummy': 'Dummy model',\n",
-    "        'disabled smiles': 'E3, Cell, Target',\n",
-    "        'disabled poi e3 smiles': 'Cell only',\n",
-    "        'disabled poi e3 cell': 'SMILES only',\n",
-    "        'disabled poi': 'SMILES, E3, Cell',\n",
-    "        'disabled e3': 'SMILES, Cell, Target',\n",
-    "        'disabled cell': 'SMILES, E3, Target',\n",
-    "    })\n",
-    "\n",
-    "    sns.barplot(data=final_df,\n",
-    "        x='disabled_embeddings',\n",
-    "        y='score',\n",
-    "        hue='metric',\n",
-    "        ax=ax,\n",
-    "        errorbar=('sd', 1),\n",
-    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
-    "        saturation=1,\n",
-    "    )\n",
-    "\n",
-    "    ax.set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
-    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Metric')\n",
-    "    ax.grid(axis='y', alpha=0.5)\n",
-    "    ax.tick_params(axis='x', rotation=90)\n",
-    "    ax.set_ylim(0, 1.0)\n",
-    "    ax.yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    ax.set_xlabel('')\n",
-    "    ax.set_ylabel('')\n",
-    "\n",
-    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
-    "    for i, p in enumerate(plt.gca().patches):\n",
-    "        value = '{:.1f}%'.format(100 * p.get_height())\n",
-    "        x = p.get_x() + p.get_width() / 2\n",
-    "        y = 0.4 # p.get_height() - p.get_height() / 2\n",
-    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, rotation=90, alpha=0.8)\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "\n",
-    "active_def = 'or'\n",
-    "\n",
-    "for group in report['group_type'].unique():\n",
-    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
-    "    \n",
-    "    baseline = report[report['disabled_embeddings'].isna()].copy()\n",
-    "    baseline = baseline[baseline['group_type'] == group]\n",
-    "    baseline = baseline[baseline['active'] == active_def]\n",
-    "    baseline['disabled_embeddings'] = 'all embeddings enabled'\n",
-    "    metrics_to_show = ['val_acc', 'test_acc']\n",
-    "    baseline = baseline.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "\n",
-    "    ablation_dfs = []\n",
-    "    for disabled_embeddings in ablation_study_combinations:\n",
-    "        if pd.isnull(disabled_embeddings):\n",
-    "            continue\n",
-    "        tmp = report[report['disabled_embeddings'] == disabled_embeddings].copy()\n",
-    "        tmp = tmp[tmp['group_type'] == group]\n",
-    "        tmp = tmp[tmp['active'] == active_def]\n",
-    "        tmp = tmp.melt(id_vars=['fold', 'disabled_embeddings'], value_vars=metrics_to_show, var_name='metric', value_name='score')\n",
-    "        ablation_dfs.append(tmp)\n",
-    "    ablation_df = pd.concat(ablation_dfs)\n",
-    "\n",
-    "    dummy_val_df = pd.DataFrame()\n",
-    "    tmp = report[report['group_type'] == group]\n",
-    "    tmp = tmp[tmp['active'] == active_def]\n",
-    "    dummy_val_df['score'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
-    "    dummy_val_df['metric'] = metrics_to_show[0]\n",
-    "    dummy_val_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_test_df = pd.DataFrame()\n",
-    "    dummy_test_df['score'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "    dummy_test_df['metric'] = metrics_to_show[1]\n",
-    "    dummy_test_df['disabled_embeddings'] = 'dummy'\n",
-    "\n",
-    "    dummy_df = pd.concat([dummy_val_df, dummy_test_df])\n",
-    "\n",
-    "    final_df = pd.concat([dummy_df, baseline, ablation_df])\n",
-    "\n",
-    "    final_df['metric'] = final_df['metric'].map({\n",
-    "        'val_acc': 'Val Accuracy',\n",
-    "        'test_acc': 'Test Accuracy',\n",
-    "        'val_roc_auc': 'Val ROC-AUC',\n",
-    "        'test_roc_auc': 'Test ROC-AUC',\n",
-    "    })\n",
-    "\n",
-    "    final_df['disabled_embeddings'] = final_df['disabled_embeddings'].map({\n",
-    "        'all embeddings enabled': 'All embeddings',\n",
-    "        'dummy': 'Dummy model',\n",
-    "        'disabled smiles': 'E3, Cell, Target',\n",
-    "        'disabled poi e3 smiles': 'Cell only',\n",
-    "        'disabled poi e3 cell': 'SMILES only',\n",
-    "        'disabled poi': 'SMILES, E3, Cell',\n",
-    "        'disabled e3': 'SMILES, Cell, Target',\n",
-    "        'disabled cell': 'SMILES, E3, Target',\n",
-    "    })\n",
-    "\n",
-    "    sns.barplot(data=final_df,\n",
-    "        y='disabled_embeddings',\n",
-    "        x='score',\n",
-    "        hue='metric',\n",
-    "        ax=ax,\n",
-    "        errorbar=('sd', 1),\n",
-    "        palette=sns.color_palette(adjusted_palette, len(adjusted_palette)),\n",
-    "        saturation=1,\n",
-    "    )\n",
-    "\n",
-    "    ax.set_title(f'Active - {active_def.upper()} definition, {group.replace(\"random\", \"standard\")} CV split')\n",
-    "    ax.grid(axis='x', alpha=0.5)\n",
-    "    ax.tick_params(axis='y', rotation=0)\n",
-    "    ax.set_xlim(0, 1.0)\n",
-    "    ax.xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    ax.set_ylabel('')\n",
-    "    ax.set_xlabel('')\n",
-    "    # Set the legend outside the plot and below\n",
-    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)\n",
-    "\n",
-    "    # For each bar, add the rotated value (as percentage), inside the bar\n",
-    "    for i, p in enumerate(plt.gca().patches):\n",
-    "        value = '{:.1f}%'.format(100 * p.get_width())\n",
-    "        y = p.get_y() + p.get_height() / 2\n",
-    "        x = 0.4 # p.get_height() - p.get_height() / 2\n",
-    "        plt.annotate(value, (x, y), ha='center', va='center', color='black', fontsize=10, alpha=0.8)\n",
-    "\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tmp = report[report['disabled_embeddings'].isna()]\n",
-    "# Barplot the dropout of the different groups\n",
-    "sns.barplot(data=tmp, x='group_type', y='hparam_dropout', hue='active', errorbar=('sd', 1))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['fold', 'group_type', 'train_len', 'val_len', 'train_perc', 'val_perc',\n",
-       "       'train_active_perc', 'train_inactive_perc', 'val_active_perc',\n",
-       "       'val_inactive_perc', 'test_active_perc', 'test_inactive_perc',\n",
-       "       'num_leaking_uniprot', 'num_leaking_smiles', 'disabled_embeddings',\n",
-       "       'val_loss', 'val_acc', 'val_f1_score', 'val_hp_metric', 'val_opt_score',\n",
-       "       'val_precision', 'val_recall', 'val_roc_auc', 'test_loss', 'test_acc',\n",
-       "       'test_f1_score', 'test_hp_metric', 'test_opt_score', 'test_precision',\n",
-       "       'test_recall', 'test_roc_auc', 'hparam_hidden_dim', 'hparam_batch_size',\n",
-       "       'hparam_learning_rate', 'hparam_dropout', 'hparam_join_embeddings',\n",
-       "       'hparam_smote_k_neighbors', 'train_unique_groups', 'val_unique_groups',\n",
-       "       'active', 'dummy_val_acc', 'dummy_test_acc'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tmp.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tmp = report[report['disabled_embeddings'].isna()]\n",
-    "# Barplot the dropout of the different groups\n",
-    "sns.barplot(data=tmp, x='group_type', y='hparam_learning_rate', hue='active', errorbar=('sd', 1))\n",
-    "# Set y-axis to log scale\n",
-    "plt.yscale('log')\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/protac_degradation_predictor.ipynb b/notebooks/protac_degradation_predictor.ipynb
deleted file mode 100644
index e38de2b9445b69fe5931d305ce213c1e5eea58e7..0000000000000000000000000000000000000000
--- a/notebooks/protac_degradation_predictor.ipynb
+++ /dev/null
@@ -1,13190 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PROTAC-Degradation-Predictor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Compound ID</th>\n",
-       "      <th>Uniprot</th>\n",
-       "      <th>Smiles</th>\n",
-       "      <th>E3 Ligase</th>\n",
-       "      <th>InChI</th>\n",
-       "      <th>InChI Key</th>\n",
-       "      <th>Molecular Weight</th>\n",
-       "      <th>Heavy Atom Count</th>\n",
-       "      <th>Ring Count</th>\n",
-       "      <th>Rotatable Bond Count</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Assay (DC50/Dmax)</th>\n",
-       "      <th>Exact Mass</th>\n",
-       "      <th>XLogP3</th>\n",
-       "      <th>Target (Parsed)</th>\n",
-       "      <th>POI Sequence</th>\n",
-       "      <th>E3 Ligase Uniprot</th>\n",
-       "      <th>E3 Ligase Sequence</th>\n",
-       "      <th>Cell Line Identifier</th>\n",
-       "      <th>Active - OR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...</td>\n",
-       "      <td>SXPDUCVNMGMWBJ-FMZBIETASA-N</td>\n",
-       "      <td>1486.282</td>\n",
-       "      <td>101</td>\n",
-       "      <td>10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...</td>\n",
-       "      <td>HQKUMELJMUNTTF-NMKDNUEVSA-N</td>\n",
-       "      <td>1500.309</td>\n",
-       "      <td>102</td>\n",
-       "      <td>10</td>\n",
-       "      <td>25</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...</td>\n",
-       "      <td>ATQCEJKUPSBDMA-QARNUTPLSA-N</td>\n",
-       "      <td>1514.336</td>\n",
-       "      <td>103</td>\n",
-       "      <td>10</td>\n",
-       "      <td>26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...</td>\n",
-       "      <td>FNKQAGMHNFFSEI-DTTPTBRMSA-N</td>\n",
-       "      <td>1528.363</td>\n",
-       "      <td>104</td>\n",
-       "      <td>10</td>\n",
-       "      <td>27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Q07817</td>\n",
-       "      <td>Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...</td>\n",
-       "      <td>PXVFFBGSTYQHRO-REQIQPEASA-N</td>\n",
-       "      <td>1542.390</td>\n",
-       "      <td>105</td>\n",
-       "      <td>10</td>\n",
-       "      <td>28</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...</td>\n",
-       "      <td>P40337</td>\n",
-       "      <td>MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...</td>\n",
-       "      <td>MOLT-4</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 35 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Compound ID Uniprot                                             Smiles  \\\n",
-       "0            1  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "1            2  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "2            3  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "3            4  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "4            5  Q07817  Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)...   \n",
-       "\n",
-       "  E3 Ligase                                              InChI  \\\n",
-       "0       VHL  InChI=1S/C73H88ClF3N10O10S4/c1-47(49-13-15-51(...   \n",
-       "1       VHL  InChI=1S/C74H90ClF3N10O10S4/c1-48(50-13-15-52(...   \n",
-       "2       VHL  InChI=1S/C75H92ClF3N10O10S4/c1-49(51-16-18-53(...   \n",
-       "3       VHL  InChI=1S/C76H94ClF3N10O10S4/c1-50(52-17-19-54(...   \n",
-       "4       VHL  InChI=1S/C77H96ClF3N10O10S4/c1-51(53-18-20-55(...   \n",
-       "\n",
-       "                     InChI Key  Molecular Weight  Heavy Atom Count  \\\n",
-       "0  SXPDUCVNMGMWBJ-FMZBIETASA-N          1486.282               101   \n",
-       "1  HQKUMELJMUNTTF-NMKDNUEVSA-N          1500.309               102   \n",
-       "2  ATQCEJKUPSBDMA-QARNUTPLSA-N          1514.336               103   \n",
-       "3  FNKQAGMHNFFSEI-DTTPTBRMSA-N          1528.363               104   \n",
-       "4  PXVFFBGSTYQHRO-REQIQPEASA-N          1542.390               105   \n",
-       "\n",
-       "   Ring Count  Rotatable Bond Count  ...  Name  Assay (DC50/Dmax)  Exact Mass  \\\n",
-       "0          10                    24  ...   NaN                NaN         NaN   \n",
-       "1          10                    25  ...   NaN                NaN         NaN   \n",
-       "2          10                    26  ...   NaN                NaN         NaN   \n",
-       "3          10                    27  ...   NaN                NaN         NaN   \n",
-       "4          10                    28  ...   NaN                NaN         NaN   \n",
-       "\n",
-       "  XLogP3  Target (Parsed)                                       POI Sequence  \\\n",
-       "0    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "1    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "2    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "3    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "4    NaN              NaN  MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEME...   \n",
-       "\n",
-       "   E3 Ligase Uniprot                                 E3 Ligase Sequence  \\\n",
-       "0             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "1             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "2             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "3             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "4             P40337  MPRRAENWDEAEVGAEEAGVEEYGPEEDGGEESGAEESGPEESGPE...   \n",
-       "\n",
-       "  Cell Line Identifier Active - OR  \n",
-       "0               MOLT-4         NaN  \n",
-       "1               MOLT-4         NaN  \n",
-       "2               MOLT-4         NaN  \n",
-       "3               MOLT-4         NaN  \n",
-       "4               MOLT-4        True  \n",
-       "\n",
-       "[5 rows x 35 columns]"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "protac_df = pd.read_csv('../data/PROTAC-Degradation-DB.csv')\n",
-    "protac_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of non-nan Dmax/DC50 values: Dmax (%)      812\n",
-      "DC50 (nM)    1350\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:\n",
-    "    \"\"\" Check if a PROTAC is active based on DC50 and Dmax.\t\n",
-    "    Args:\n",
-    "        DC50(float): DC50 in nM\n",
-    "        Dmax(float): Dmax in %\n",
-    "    Returns:\n",
-    "        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN\n",
-    "    \"\"\"\n",
-    "    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan\n",
-    "    Dmax = Dmax / 100\n",
-    "    if pd.notnull(pDC50):\n",
-    "        if pDC50 < pDC50_threshold:\n",
-    "            return False\n",
-    "    if pd.notnull(Dmax):\n",
-    "        if Dmax < Dmax_threshold:\n",
-    "            return False\n",
-    "    if oring:\n",
-    "        if pd.notnull(pDC50):\n",
-    "            return True if pDC50 >= pDC50_threshold else False\n",
-    "        elif pd.notnull(Dmax):\n",
-    "            return True if Dmax >= Dmax_threshold else False\n",
-    "        else:\n",
-    "            return np.nan\n",
-    "    else:\n",
-    "        if pd.notnull(pDC50) and pd.notnull(Dmax):\n",
-    "            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False\n",
-    "        else:\n",
-    "            return np.nan\n",
-    "\n",
-    "\n",
-    "# # Add a column for a definition of activivity in which \n",
-    "# for Dmax_threshold in range(10):\n",
-    "#     for pDC50_threshold in [5 + 0.5 * i for i in range(10)]:\n",
-    "#         protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'] = protac_df.apply(\n",
-    "#             lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold * 0.1), axis=1\n",
-    "#         )\n",
-    "#         num_active = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].value_counts()\n",
-    "#         num_nans = protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].isnull().sum()\n",
-    "#         total = len(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'])\n",
-    "#         # If the number of active, i.e. num_active[True], is close to the number of inactive, i.e. num_active[False],\n",
-    "#         # then plot the histogram of the active values with different Dmax and pDC50 definitions\n",
-    "#         if abs(num_active[True] - num_active[False]) < 50:\n",
-    "#             sns.histplot(protac_df[f'Active (Dmax {round(Dmax_threshold * 0.1, 1)}, pDC50 {round(pDC50_threshold, 1)})'].dropna())\n",
-    "#             plt.title(f'Dmax threshold: {Dmax_threshold * 0.1:.0%}, pDC50 threshold: {round(pDC50_threshold, 1)}, Active: {num_active[True]}, Inactive: {num_active[False]}, NaN: {num_nans}, Total: {num_active[True] + num_active[False]}')\n",
-    "#             plt.tight_layout()\n",
-    "#             plt.show()\n",
-    "\n",
-    "print(f'Number of non-nan Dmax/DC50 values: {protac_df[[\"Dmax (%)\", \"DC50 (nM)\"]].count()}')\n",
-    "\n",
-    "active_col = 'Active (Dmax 0.6, pDC50 6.0)'\n",
-    "protac_df[active_col] = protac_df.apply(\n",
-    "    lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=6.0, Dmax_threshold=0.6), axis=1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "       '10.1016/j.bmc.2020.115326', '10.1016/j.ejmech.2020.112061',\n",
-       "       '10.1021/acs.jmedchem.7b00635', '10.1039/c7cc03879h',\n",
-       "       '10.1021/acs.biochem.8b00391', '10.1002/anie.201507634',\n",
-       "       '10.1002/anie.201611281', '10.1038/nchembio.2538',\n",
-       "       '10.1021/acs.jmedchem.9b00455', '10.1038/s41589-019-0294-6',\n",
-       "       '10.1016/j.chembiol.2019.11.014', '10.1016/j.ejmech.2019.111952',\n",
-       "       '10.1002/anie.201913904', '10.1021/acs.jmedchem.9b01393',\n",
-       "       '10.1007/s10549-020-05564-y', '10.1016/j.ejmech.2019.01.023',\n",
-       "       '10.1016/j.bmcl.2008.07.114', '10.1016/j.chembiol.2018.11.006',\n",
-       "       '10.1016/j.bmcl.2018.05.057', '10.1021/acs.jmedchem.6b01781',\n",
-       "       '10.1038/nchembio.2329', '10.1021/acschembio.8b00705',\n",
-       "       '10.1016/j.chembiol.2019.11.004', '10.1021/acs.jmedchem.9b00919',\n",
-       "       '10.1039/c9cc08238g', '10.1021/acs.jmedchem.9b00871',\n",
-       "       '10.1039/c8cc09541h', '10.1039/c8cc08716d', '10.1039/c8cc07813k',\n",
-       "       '10.1016/j.neo.2019.12.003', '10.1016/j.bioorg.2018.08.028',\n",
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-       "       '10.1021/acs.jmedchem.8b01572', '10.1016/j.bmcl.2019.03.035',\n",
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-       "       '10.1016/j.apsb.2020.02.010', '10.1021/acs.jmedchem.0c00471',\n",
-       "       '10.1038/s41467-017-00954-1', '10.1016/j.bmc.2019.02.048',\n",
-       "       '10.3791/59472', '10.1038/s41589-019-0304-8',\n",
-       "       '10.1126/sciadv.aay5064', '10.1021/acs.jmedchem.9b02058',\n",
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-       "       '10.1016/j.ejmech.2018.03.071', '10.1021/jacs.8b05807',\n",
-       "       '10.1038/s41422-018-0055-1', '10.1073/pnas.1521738113',\n",
-       "       '10.1073/pnas.1521738113; 10.1021/acs.jmedchem.1c01496',\n",
-       "       '10.1016/j.chembiol.2015.05.009', '10.1126/science.aab1433',\n",
-       "       '10.1038/s41589-018-0055-y', '10.1002/anie.202108848',\n",
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-       "       '10.1039/d0sc00167h', '10.1016/j.bmcl.2019.126877',\n",
-       "       '10.1016/j.bbrc.2019.11.007', '10.1038/s41421-018-0079-1',\n",
-       "       '10.1021/acs.jmedchem.1c01118',\n",
-       "       '10.1021/acs.molpharmaceut.9b00673', '10.1111/cas.13284',\n",
-       "       '10.1074/jbc.RA117.001091', '10.1074/jbc.M116.768853',\n",
-       "       '10.1021/acs.jmedchem.9b01528', '10.1038/s41589-019-0379-2',\n",
-       "       '10.1016/j.chembiol.2020.04.008', '10.7554/eLife.45457',\n",
-       "       '10.1038/s41375-019-0467-z', '10.1038/s41467-019-11429-w',\n",
-       "       '10.1021/acsmedchemlett.9b00397', '10.1007/s13238-020-00732-8',\n",
-       "       '10.1002/anie.201914396', '10.1016/j.ejmech.2020.112451',\n",
-       "       '10.1039/d0cc02201b', '10.1073/pnas.1803662115',\n",
-       "       '10.1021/acs.jmedchem.0c01342', '10.1021/acs.jmedchem.0c01609',\n",
-       "       '10.1016/j.ejmech.2020.112800', '10.1039/d0cc03243c',\n",
-       "       '10.1038/s41589-020-00652-y', '10.1038/s41589-021-00742-5',\n",
-       "       '10.1038/s41467-020-17997-6', '10.1158/1535-7163.MCT-20-0417',\n",
-       "       '10.1021/acsmedchemlett.0c00589', '10.1021/acs.jmedchem.1c00270',\n",
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-       "       '10.1021/acs.jmedchem.1c02179', '10.1021/acs.jmedchem.1c01206',\n",
-       "       '10.1021/acs.jmedchem.1c01476', '10.1021/acs.jmedchem.1c01774',\n",
-       "       '10.1016/j.ejmech.2021.113849', '10.1016/j.ejmech.2021.113645',\n",
-       "       '10.1016/j.ejmech.2021.114012', '10.1016/j.ejmech.2021.113889',\n",
-       "       '10.1016/j.ejmech.2021.113425', '10.1021/acsmedchemlett.1c00650',\n",
-       "       '10.1021/acsmedchemlett.1c00645', '10.1021/acsmedchemlett.1c00155',\n",
-       "       '10.1002/anie.202109237', '10.1016/j.bioorg.2021.105508',\n",
-       "       '10.1021/acsmedchemlett.1c00244', '10.1021/acsmedchemlett.1c00178',\n",
-       "       '10.1021/acsmedchemlett.1c00580', '10.1021/acsmedchemlett.1c00657',\n",
-       "       '10.1021/acsmedchemlett.2c00039', '10.1016/j.apsb.2020.11.009',\n",
-       "       '10.1016/j.chembiol.2021.04.018', '10.1038/s41375-019-0440-x',\n",
-       "       '10.1038/s41422-021-00533-6', '10.1039/d2cc00272h',\n",
-       "       '10.1038/s41467-021-27210-x', '10.7150/thno.55680',\n",
-       "       '10.1021/jacs.1c03980', '10.1158/1538-7445.SABCS18-P5-04-18',\n",
-       "       '10.1200/JCO.2019.37.7_suppl.259'], dtype=object)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the unique Article IDs of the entries with NaN values in the Active column\n",
-    "nan_active = protac_df[protac_df[active_col].isna()]['Article DOI'].unique()\n",
-    "nan_active"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Map E3 Ligase Iap to IAP\n",
-    "protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Compound ID', 'Uniprot', 'Smiles', 'E3 Ligase', 'InChI', 'InChI Key',\n",
-       "       'Molecular Weight', 'Heavy Atom Count', 'Ring Count',\n",
-       "       'Rotatable Bond Count', 'Topological Polar Surface Area',\n",
-       "       'Hydrogen Bond Acceptor Count', 'Hydrogen Bond Donor Count',\n",
-       "       'Cell Type', 'Treatment Time (h)', 'DC50 (nM)', 'Dmax (%)', 'Active',\n",
-       "       'Article DOI', 'Comments', 'Database', 'Molecular Formula', 'cLogP',\n",
-       "       'Target', 'PDB', 'Name', 'Assay (DC50/Dmax)', 'Exact Mass', 'XLogP3',\n",
-       "       'Target (Parsed)', 'POI Sequence', 'E3 Ligase Uniprot',\n",
-       "       'E3 Ligase Sequence', 'Cell Line Identifier', 'Active - OR',\n",
-       "       'Active (Dmax 0.6, pDC50 6.0)'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "protac_df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of non-cleaned cell lines: 180\n"
-     ]
-    }
-   ],
-   "source": [
-    "cells = sorted(protac_df['Cell Type'].dropna().unique().tolist())\n",
-    "print(f'Number of non-cleaned cell lines: {len(cells)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of cleaned cell lines: 138\n"
-     ]
-    }
-   ],
-   "source": [
-    "cells = sorted(protac_df['Cell Line Identifier'].dropna().unique().tolist())\n",
-    "print(f'Number of cleaned cell lines: {len(cells)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of compounds in test set: 1284\n"
-     ]
-    }
-   ],
-   "source": [
-    "unlabeled_df = protac_df[protac_df[active_col].isna()]\n",
-    "print(f'Number of compounds in test set: {len(unlabeled_df)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Protein Embeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Protein embeddings downloaded from [Uniprot](https://www.uniprot.org/help/embeddings).\n",
-    "\n",
-    "Please note that running the following cell the first time might take a while."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading embeddings from https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/embeddings/UP000005640_9606/per-protein.h5\n"
-     ]
-    },
-    {
-     "ename": "URLError",
-     "evalue": "<urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:997)>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mSSLCertVerificationError\u001b[0m                  Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:1348\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1347\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1348\u001b[0m     \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1349\u001b[0m \u001b[43m              \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:1282\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1281\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:1328\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1327\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1328\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:1277\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1277\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:1037\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1036\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1037\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1040\u001b[0m \n\u001b[1;32m   1041\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:975\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    974\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[0;32m--> 975\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/http/client.py:1454\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1452\u001b[0m     server_hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n\u001b[0;32m-> 1454\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrap_socket\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1455\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/ssl.py:513\u001b[0m, in \u001b[0;36mSSLContext.wrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrap_socket\u001b[39m(\u001b[38;5;28mself\u001b[39m, sock, server_side\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    508\u001b[0m                 do_handshake_on_connect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    509\u001b[0m                 suppress_ragged_eofs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    510\u001b[0m                 server_hostname\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, session\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    511\u001b[0m     \u001b[38;5;66;03m# SSLSocket class handles server_hostname encoding before it calls\u001b[39;00m\n\u001b[1;32m    512\u001b[0m     \u001b[38;5;66;03m# ctx._wrap_socket()\u001b[39;00m\n\u001b[0;32m--> 513\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msslsocket_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m        \u001b[49m\u001b[43msock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    515\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_side\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_side\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdo_handshake_on_connect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuppress_ragged_eofs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[43m        \u001b[49m\u001b[43mserver_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserver_hostname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m        \u001b[49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msession\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/ssl.py:1071\u001b[0m, in \u001b[0;36mSSLSocket._create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1070\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdo_handshake_on_connect should not be specified for non-blocking sockets\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1071\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mOSError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/ssl.py:1342\u001b[0m, in \u001b[0;36mSSLSocket.do_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1341\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msettimeout(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 1342\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_handshake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1343\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
-      "\u001b[0;31mSSLCertVerificationError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:997)",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[9], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(embeddings_path):\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;66;03m# Download the file\u001b[39;00m\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDownloading embeddings from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdownload_link\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m     \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlretrieve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdownload_link\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings_path\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:241\u001b[0m, in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;124;03mRetrieve a URL into a temporary location on disk.\u001b[39;00m\n\u001b[1;32m    226\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[38;5;124;03mdata file as well as the resulting HTTPMessage object.\u001b[39;00m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    239\u001b[0m url_type, path \u001b[38;5;241m=\u001b[39m _splittype(url)\n\u001b[0;32m--> 241\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m contextlib\u001b[38;5;241m.\u001b[39mclosing(\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;28;01mas\u001b[39;00m fp:\n\u001b[1;32m    242\u001b[0m     headers \u001b[38;5;241m=\u001b[39m fp\u001b[38;5;241m.\u001b[39minfo()\n\u001b[1;32m    244\u001b[0m     \u001b[38;5;66;03m# Just return the local path and the \"headers\" for file://\u001b[39;00m\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;66;03m# URLs. No sense in performing a copy unless requested.\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:216\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m     opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[0;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:519\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    516\u001b[0m     req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[1;32m    518\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[0;32m--> 519\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[1;32m    522\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:536\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m    535\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[0;32m--> 536\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[1;32m    537\u001b[0m \u001b[43m                          \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[1;32m    539\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:496\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[1;32m    495\u001b[0m     func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 496\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    497\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    498\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:1391\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttps_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[0;32m-> 1391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPSConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1392\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontext\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_hostname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_hostname\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/conda/lib/python3.10/urllib/request.py:1351\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1348\u001b[0m         h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[1;32m   1349\u001b[0m                   encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m   1350\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[0;32m-> 1351\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[1;32m   1352\u001b[0m     r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m   1353\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
-      "\u001b[0;31mURLError\u001b[0m: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:997)>"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import urllib.request\n",
-    "\n",
-    "download_link = \"https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/embeddings/UP000005640_9606/per-protein.h5\"\n",
-    "embeddings_path = \"../data/uniprot2embedding.h5\"\n",
-    "if not os.path.exists(embeddings_path):\n",
-    "    # Download the file\n",
-    "    print(f'Downloading embeddings from {download_link}')\n",
-    "    urllib.request.urlretrieve(download_link, embeddings_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "number of entries: 20,594\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "caecdff147b64f319e5f295a8f92dddc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading protein embeddings: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\tid: Q07817, \tembeddings shape: (1024,), \tembeddings mean: -0.0005679130554199219\n",
-      "\tid: P00533, \tembeddings shape: (1024,), \tembeddings mean: 0.001171112060546875\n",
-      "\tid: Q9NWZ3, \tembeddings shape: (1024,), \tembeddings mean: 0.00041985511779785156\n",
-      "\tid: P00519, \tembeddings shape: (1024,), \tembeddings mean: 0.0009603500366210938\n",
-      "\tid: P11474, \tembeddings shape: (1024,), \tembeddings mean: -0.0018215179443359375\n",
-      "\tid: Q16288, \tembeddings shape: (1024,), \tembeddings mean: 0.0010194778442382812\n",
-      "\tid: O60674, \tembeddings shape: (1024,), \tembeddings mean: 0.0015687942504882812\n",
-      "\tid: Q06187, \tembeddings shape: (1024,), \tembeddings mean: 0.0006914138793945312\n",
-      "\tid: Q9UHD2, \tembeddings shape: (1024,), \tembeddings mean: 0.0012235641479492188\n",
-      "\tid: Q8IXJ6, \tembeddings shape: (1024,), \tembeddings mean: -0.00042366981506347656\n",
-      "KeyError for P31750\n",
-      "KeyError for P00520\n",
-      "KeyError for A8DG50\n"
-     ]
-    }
-   ],
-   "source": [
-    "import h5py\n",
-    "import numpy as np\n",
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "protein_embeddings = {}\n",
-    "with h5py.File(\"../data/uniprot2embedding.h5\", \"r\") as file:\n",
-    "    print(f\"number of entries: {len(file.items()):,}\")\n",
-    "    uniprots = protac_df['Uniprot'].unique().tolist()\n",
-    "    uniprots += protac_df['E3 Ligase Uniprot'].unique().tolist()\n",
-    "    for i, sequence_id in tqdm(enumerate(uniprots), desc='Loading protein embeddings'):\n",
-    "        try:\n",
-    "            embedding = file[sequence_id][:]\n",
-    "            protein_embeddings[sequence_id] = np.array(embedding)\n",
-    "            if i < 10:\n",
-    "                print(\n",
-    "                    f\"\\tid: {sequence_id}, \"\n",
-    "                    f\"\\tembeddings shape: {embedding.shape}, \"\n",
-    "                    f\"\\tembeddings mean: {np.array(embedding).mean()}\"\n",
-    "                )\n",
-    "        except KeyError:\n",
-    "            print(f'KeyError for {sequence_id}')\n",
-    "            protein_embeddings[sequence_id] = np.zeros((1024,))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Cell Embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 1138 cell lines\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "cell2embedding_filepath = '../data/cell2embedding.pkl'\n",
-    "with open(cell2embedding_filepath, 'rb') as f:\n",
-    "    cell2embedding = pickle.load(f)\n",
-    "print(f'Loaded {len(cell2embedding)} cell lines')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "emb_shape = cell2embedding[list(cell2embedding.keys())[0]].shape\n",
-    "# Assign all-zero vectors to cell lines that are not in the embedding file\n",
-    "for cell_line in protac_df['Cell Line Identifier'].unique():\n",
-    "    if cell_line not in cell2embedding:\n",
-    "        cell2embedding[cell_line] = np.zeros(emb_shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Precompute Molecular Fingerprints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "321da9b6e6d746ee868684c5bae33426",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Precomputing fingerprints:   0%|          | 0/1467 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique SMILES: 1467\n",
-      "Number of unique fingerprints: 1467\n",
-      "Number of SMILES with overlapping fingerprints: 0\n",
-      "Number of overlapping SMILES in protac_df: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "from rdkit import Chem\n",
-    "from rdkit.Chem import AllChem\n",
-    "from rdkit.Chem import Draw\n",
-    "from rdkit.Chem import MACCSkeys\n",
-    "from rdkit.Chem import AllChem, Descriptors3D\n",
-    "\n",
-    "fingerprint_size = 128 + 64 + 32\n",
-    "morgan_fpgen = AllChem.GetMorganGenerator(\n",
-    "    radius=15,\n",
-    "    fpSize=fingerprint_size,\n",
-    "    includeChirality=True,\n",
-    ")\n",
-    "\n",
-    "smiles2fp = {}\n",
-    "for smiles in tqdm(protac_df['Smiles'].unique().tolist(), desc='Precomputing fingerprints'):\n",
-    "    mol = Chem.MolFromSmiles(smiles)\n",
-    "    # Get the fingerprint as a bit vector\n",
-    "    morgan_fp = morgan_fpgen.GetFingerprint(mol)\n",
-    "    smiles2fp[smiles] = morgan_fp\n",
-    "\n",
-    "# Count the number of unique SMILES and the number of unique Morgan fingerprints\n",
-    "unique_fps = set([tuple(fp) for fp in smiles2fp.values()])\n",
-    "print(f'Number of unique SMILES: {len(smiles2fp)}')\n",
-    "print(f'Number of unique fingerprints: {len(unique_fps)}')\n",
-    "# Get the list of SMILES with overlapping fingerprints\n",
-    "overlapping_smiles = []\n",
-    "unique_fps = set()\n",
-    "for smiles, fp in smiles2fp.items():\n",
-    "    if tuple(fp) in unique_fps:\n",
-    "        overlapping_smiles.append(smiles)\n",
-    "    else:\n",
-    "        unique_fps.add(tuple(fp))\n",
-    "print(f'Number of SMILES with overlapping fingerprints: {len(overlapping_smiles)}')\n",
-    "print(f'Number of overlapping SMILES in protac_df: {len(protac_df[protac_df[\"Smiles\"].isin(overlapping_smiles)])}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e89c67067474404798507a1d6eb92295",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Computing Tanimoto similarity:   0%|          | 0/1467 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "count    2141.000000\n",
-       "mean        0.736825\n",
-       "std         0.049720\n",
-       "min         0.295575\n",
-       "25%         0.712771\n",
-       "50%         0.742824\n",
-       "75%         0.768864\n",
-       "max         1.000000\n",
-       "Name: Avg Tanimoto, dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the pair-wise tanimoto similarity between the PROTAC fingerprints\n",
-    "from rdkit import DataStructs\n",
-    "from collections import defaultdict\n",
-    "\n",
-    "tanimoto_matrix = defaultdict(list)\n",
-    "for i, smiles1 in enumerate(tqdm(protac_df['Smiles'].unique(), desc='Computing Tanimoto similarity')):\n",
-    "    fp1 = smiles2fp[smiles1]\n",
-    "    # TODO: Use BulkTanimotoSimilarity\n",
-    "    for j, smiles2 in enumerate(protac_df['Smiles'].unique()):\n",
-    "        if j < i:\n",
-    "            continue\n",
-    "        fp2 = smiles2fp[smiles2]\n",
-    "        tanimoto_dist = DataStructs.TanimotoSimilarity(fp1, fp2)\n",
-    "        tanimoto_matrix[smiles1].append(tanimoto_dist)\n",
-    "avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}\n",
-    "protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)\n",
-    "protac_df['Avg Tanimoto'].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # Plot the distribution of the average Tanimoto similarity\n",
-    "# import seaborn as sns\n",
-    "# import matplotlib.pyplot as plt\n",
-    "\n",
-    "# sns.histplot(protac_df['Avg Tanimoto'], bins=50)\n",
-    "# plt.xlabel('Average Tanimoto similarity')\n",
-    "# plt.ylabel('Count')\n",
-    "# plt.title('Distribution of average Tanimoto similarity')\n",
-    "# plt.grid(axis='y', alpha=0.5)\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "smiles2fp = {s: np.array(fp) for s, fp in smiles2fp.items()}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set the Column to Predict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# active_col = active_col\n",
-    "# active_col = 'Active - OR'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dimensionality Reduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "RUN_DIMENSIONALITY_REDUCTION = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "def get_data_for_dim_reduction(\n",
-    "        protac_df,\n",
-    "        lookup_embeddings=True,\n",
-    "        protein_embeddings=protein_embeddings,\n",
-    "        cell2embedding=cell2embedding,\n",
-    "        smiles2fp=smiles2fp,\n",
-    "):\n",
-    "    \"\"\" Get the data and labels for dimensionality reduction.\n",
-    "\n",
-    "    Args:\n",
-    "        protac_df (pd.DataFrame): The PROTAC dataframe.\n",
-    "        lookup_embeddings (bool): Whether to look up the embeddings or use the provided ones.\n",
-    "        protein_embeddings (dict): The protein embeddings.\n",
-    "        cell2embedding (dict): The cell line embeddings.\n",
-    "        smiles2fp (dict): The SMILES to fingerprint dictionary.\n",
-    "\n",
-    "    Returns:\n",
-    "        np.ndarray: The data for dimensionality reduction.\n",
-    "        dict: The labels for dimensionality reduction.\n",
-    "    \"\"\"\n",
-    "    protac_data = []\n",
-    "    protac_labels = defaultdict(list)\n",
-    "    for _, row in protac_df.iterrows():\n",
-    "        if lookup_embeddings:\n",
-    "            protac_data.append(\n",
-    "                np.concatenate([\n",
-    "                    protein_embeddings[row['Uniprot']],\n",
-    "                    protein_embeddings[row['E3 Ligase Uniprot']],\n",
-    "                    cell2embedding[row['Cell Line Identifier']],\n",
-    "                    smiles2fp[row['Smiles']],\n",
-    "                    # [row['Treatment Time (h)']] if not pd.isna(row['Treatment Time (h)']) else [0],\n",
-    "                ])\n",
-    "            )\n",
-    "            for col in row.keys():\n",
-    "                protac_labels[col].append(row[col])\n",
-    "        else:\n",
-    "            protac_data.append(\n",
-    "                np.concatenate([\n",
-    "                    row['Uniprot'],\n",
-    "                    row['E3 Ligase Uniprot'],\n",
-    "                    row['Cell Line Identifier'],\n",
-    "                    row['Smiles'],\n",
-    "                ])\n",
-    "            )\n",
-    "            for col in [active_col, 'Active - OR']:\n",
-    "                if col in row:\n",
-    "                    protac_labels[col].append(row[col])\n",
-    "    protac_data = np.array(protac_data)\n",
-    "    return protac_data, protac_labels\n",
-    "\n",
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    protac_data, protac_labels = get_data_for_dim_reduction(protac_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UMAP-ing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    import umap\n",
-    "    from sklearn.preprocessing import StandardScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    # Define UMAP and Scaler\n",
-    "    umap_reducer = umap.UMAP(\n",
-    "        n_neighbors=50,  # Good value: 50\n",
-    "        min_dist=0.5,  # Good value: 0.5\n",
-    "        # spread=1.0, # Good value: (not set, default)\n",
-    "        metric='euclidean',\n",
-    "        random_state=42,\n",
-    "        unique=True,\n",
-    "        # n_epochs=100,\n",
-    "        init='spectral',  # Default: 'spectral'\n",
-    "        learning_rate=0.8,  # Default: 1.0\n",
-    "        verbose=False,\n",
-    "    )\n",
-    "    scaler = StandardScaler()\n",
-    "\n",
-    "    # Get the embeddings as a numpy array\n",
-    "    umap_data = umap_reducer.fit_transform(scaler.fit_transform(protac_data))\n",
-    "\n",
-    "    umap_data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    import seaborn as sns\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import warnings\n",
-    "\n",
-    "    labels_to_plot = [\n",
-    "        active_col,\n",
-    "        'Active - OR',\n",
-    "        'Cell Line Identifier',\n",
-    "        'E3 Ligase',\n",
-    "        'Uniprot',\n",
-    "        # 'Smiles',\n",
-    "        'Treatment Time (h)',\n",
-    "        'DC50 (nM)',\n",
-    "        'Dmax (%)',\n",
-    "    ]\n",
-    "    for col in labels_to_plot:\n",
-    "        # print(f'Plotting UMAP for {col}')\n",
-    "        umap_embeddings = {\n",
-    "            'UMAP 1': umap_data[:, 0],\n",
-    "            'UMAP 2': umap_data[:, 1],\n",
-    "            col: protac_labels[col],\n",
-    "        }\n",
-    "        umap_embeddings = pd.DataFrame(umap_embeddings).drop_duplicates()\n",
-    "\n",
-    "        with warnings.catch_warnings():\n",
-    "            warnings.simplefilter(\"ignore\")\n",
-    "            sns.scatterplot(data=umap_embeddings, x='UMAP 1', y='UMAP 2',\n",
-    "                            hue=col) #, palette=sns.color_palette('tab10'))\n",
-    "            # Make the legend external and plot it for active and e3 ligase only\n",
-    "            if col in [active_col, 'E3 Ligase', 'Active - OR']:\n",
-    "                plt.legend(title=f'{col}:')\n",
-    "            else:\n",
-    "                plt.legend().remove()\n",
-    "            plt.title(f'UMAP embedding coloring for \"{col}\"')\n",
-    "            plt.grid(axis='both', alpha=0.5)\n",
-    "            plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### PCA-ing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run PCA analysis on `protac_data` and `protac_labels`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    import warnings\n",
-    "    import seaborn as sns\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    from sklearn.decomposition import PCA\n",
-    "    from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "    def plot_pca(protac_data, protac_labels):\n",
-    "        \"\"\" Plot the PCA embeddings.\n",
-    "\n",
-    "        Args:\n",
-    "            protac_data (np.ndarray): The PROTAC data.\n",
-    "            protac_labels (dict): The labels for the PROTAC data.\n",
-    "        \"\"\"\n",
-    "        pca = PCA(n_components=2, random_state=42)\n",
-    "        scaler = StandardScaler()\n",
-    "        pca_data = pca.fit_transform(scaler.fit_transform(protac_data))\n",
-    "\n",
-    "        # Plot PCA accordingly\n",
-    "        labels_to_plot = [\n",
-    "            active_col,\n",
-    "            'Active - OR',\n",
-    "            'Cell Line Identifier',\n",
-    "            'E3 Ligase',\n",
-    "            'Uniprot',\n",
-    "            # 'Smiles',\n",
-    "            'Treatment Time (h)',\n",
-    "            'DC50 (nM)',\n",
-    "            'Dmax (%)',\n",
-    "        ]\n",
-    "        for label in labels_to_plot:\n",
-    "            if label not in protac_labels:\n",
-    "                continue\n",
-    "            pca_embeddings = {\n",
-    "                'PCA 1': pca_data[:, 0],\n",
-    "                'PCA 2': pca_data[:, 1],\n",
-    "                label: protac_labels[label],\n",
-    "            }\n",
-    "            pca_embeddings = pd.DataFrame(pca_embeddings).drop_duplicates()\n",
-    "            # Scatter plot\n",
-    "            with warnings.catch_warnings():\n",
-    "                warnings.simplefilter(\"ignore\")\n",
-    "                sns.scatterplot(data=pca_embeddings, x='PCA 1', y='PCA 2',\n",
-    "                                hue=label) #, palette=sns.color_palette('tab10'))\n",
-    "                # Plot legend for active and e3 ligase only\n",
-    "                if label in [active_col, 'E3 Ligase']:\n",
-    "                    plt.legend(title=f'{label}:')\n",
-    "                else:\n",
-    "                    plt.legend().remove()\n",
-    "                plt.title(f'PCA embedding coloring for \"{label}\"')\n",
-    "                plt.grid(axis='both', alpha=0.5)\n",
-    "                plt.show()\n",
-    "\n",
-    "    plot_pca(protac_data, protac_labels)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also run PCA and \"collapse\" the data into a 3D space:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    # Run PCA analysis on protac_data and protac_labels\n",
-    "    import warnings\n",
-    "    import seaborn as sns\n",
-    "    import matplotlib.pyplot as plt\n",
-    "    from mpl_toolkits.mplot3d import Axes3D\n",
-    "    from sklearn.decomposition import PCA\n",
-    "\n",
-    "\n",
-    "    def plot_pca_3d(protac_data, protac_labels):\n",
-    "        \"\"\" Plot the PCA embeddings.\n",
-    "\n",
-    "        Args:\n",
-    "            protac_data (np.ndarray): The PROTAC data.\n",
-    "            protac_labels (dict): The labels for the PROTAC data.\n",
-    "        \"\"\"\n",
-    "        pca = PCA(n_components=3, random_state=42)\n",
-    "        scaler = StandardScaler()\n",
-    "        pca_data = pca.fit_transform(scaler.fit_transform(protac_data))\n",
-    "\n",
-    "        # Plot PCA accordingly\n",
-    "        labels_to_plot = [\n",
-    "            active_col,\n",
-    "            'Cell Line Identifier',\n",
-    "            'E3 Ligase',\n",
-    "            'Uniprot',\n",
-    "            # 'Smiles',\n",
-    "            'Treatment Time (h)',\n",
-    "        ]\n",
-    "        for label in labels_to_plot:\n",
-    "            pca_embeddings = {\n",
-    "                'PCA 1': pca_data[:, 0],\n",
-    "                'PCA 2': pca_data[:, 1],\n",
-    "                'PCA 3': pca_data[:, 2],\n",
-    "                label: protac_labels[label],\n",
-    "            }\n",
-    "            pca_embeddings = pd.DataFrame(pca_embeddings).drop_duplicates()\n",
-    "            # Scatter plot\n",
-    "            with warnings.catch_warnings():\n",
-    "                warnings.simplefilter(\"ignore\")\n",
-    "                # Plot 3D scatter\n",
-    "                fig = plt.figure()\n",
-    "                ax = fig.add_subplot(111, projection='3d')\n",
-    "                for l in pd.Series(protac_labels[label]).unique():\n",
-    "                    ax.scatter(\n",
-    "                        pca_embeddings[pca_embeddings[label] == l]['PCA 1'],\n",
-    "                        pca_embeddings[pca_embeddings[label] == l]['PCA 2'],\n",
-    "                        pca_embeddings[pca_embeddings[label] == l]['PCA 3'],\n",
-    "                        label=l,\n",
-    "                    )\n",
-    "\n",
-    "                # sns.scatterplot(data=pca_embeddings, x='PCA 1', y='PCA 2',\n",
-    "                #                 hue=label) #, palette=sns.color_palette('tab10'))\n",
-    "                # Plot legend for active and e3 ligase only\n",
-    "                if label in [active_col, 'E3 Ligase']:\n",
-    "                    plt.legend(title=f'{label}:')\n",
-    "                else:\n",
-    "                    plt.legend().remove()\n",
-    "                plt.title(f'PCA embedding coloring for \"{label}\"')\n",
-    "                plt.grid(axis='both', alpha=0.5)\n",
-    "                plt.show()\n",
-    "\n",
-    "    plot_pca_3d(protac_data, protac_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    import seaborn as sns\n",
-    "    import matplotlib.pyplot as plt\n",
-    "\n",
-    "    # Plot the DC50 and Dmax\n",
-    "    tmp = protac_df.dropna(subset=['DC50 (nM)', 'Dmax (%)']).copy()\n",
-    "    # Bin the DC50 and Dmax\n",
-    "    tmp['pDC50'] = pd.cut(-np.log10(tmp['DC50 (nM)'] * 1e-9), bins=10).copy()\n",
-    "    tmp['Dmax (%)'] = pd.cut(tmp['Dmax (%)'], bins=10).copy()\n",
-    "    # Plot tmp\n",
-    "    fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "    sns.countplot(data=tmp, x='pDC50', ax=ax[0], color='C0')\n",
-    "    sns.countplot(data=tmp, x='Dmax (%)', ax=ax[1], color='C0')\n",
-    "    # Rotate x-axis labels\n",
-    "    for a in ax:\n",
-    "        a.set_xticklabels(a.get_xticklabels(), rotation=90)\n",
-    "    ax[0].grid(axis='y', alpha=0.5)\n",
-    "    ax[1].grid(axis='y', alpha=0.5)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define Torch Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from imblearn.over_sampling import SMOTE, SMOTENC, ADASYN\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch_geometric\n",
-    "import torch_geometric.nn as geom_nn\n",
-    "from torch_geometric.utils.smiles import from_smiles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 857 PROTACs\n",
-      "Smile embeddings dimensions: 224\n",
-      "Protein embeddings dimensions: 1024\n",
-      "Cell embeddings dimensions: 768\n",
-      "Sum of the embeddings dimensions: 3040\n",
-      "Sum of the embeddings dimensions: 3040\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "from torch_geometric.data import Data, Batch\n",
-    "from torch.utils.data._utils.collate import collate\n",
-    "from torch.utils.data._utils.collate import default_collate_fn_map\n",
-    "# import sklearn MinMaxScaler\n",
-    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
-    "\n",
-    "def graph_collate(batch, *, collate_fn_map=None):\n",
-    "    # Handle graph data separately: graph representation and computation can be\n",
-    "    # greatly optimized due to their sparse nature. In fact, multiple graphs in\n",
-    "    # a batch can be seen as a 'big' graph of unconnected sub-graphs. Hence,\n",
-    "    # their adjecency matrices can be combined together to form a single one.\n",
-    "    return Batch.from_data_list(batch)\n",
-    "\n",
-    "\n",
-    "def custom_collate(batch):\n",
-    "    collate_map = default_collate_fn_map.copy()\n",
-    "    collate_map.update({Data: graph_collate})\n",
-    "    return collate(batch, collate_fn_map=collate_map)\n",
-    "\n",
-    "\n",
-    "class PROTAC_Dataset(Dataset):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        protac_df,\n",
-    "        protein_embeddings=protein_embeddings,\n",
-    "        cell2embedding=cell2embedding,\n",
-    "        smiles2fp=smiles2fp,\n",
-    "        use_smote=False,\n",
-    "        oversampler=None,\n",
-    "        active_label=active_col,\n",
-    "        include_mol_graphs=False,\n",
-    "    ):\n",
-    "        \"\"\" Initialize the PROTAC dataset\n",
-    "\n",
-    "        Args:\n",
-    "            protac_df (pd.DataFrame): The PROTAC dataframe\n",
-    "            protein_embeddings (dict): Dictionary of protein embeddings\n",
-    "            cell2embedding (dict): Dictionary of cell line embeddings\n",
-    "            smiles2fp (dict): Dictionary of SMILES to fingerprint\n",
-    "            use_smote (bool): Whether to use SMOTE for oversampling\n",
-    "            use_ored_activity (bool): Whether to use the 'Active - OR' column\n",
-    "        \"\"\"\n",
-    "        # Filter out examples with NaN in active_col column\n",
-    "        self.data = protac_df  # [~protac_df[active_col].isna()]\n",
-    "        self.protein_embeddings = protein_embeddings\n",
-    "        self.cell2embedding = cell2embedding\n",
-    "        self.smiles2fp = smiles2fp\n",
-    "        self.active_label = active_label\n",
-    "        self.include_mol_graphs = include_mol_graphs\n",
-    "\n",
-    "        self.smiles_emb_dim = smiles2fp[list(smiles2fp.keys())[0]].shape[0]\n",
-    "        self.protein_emb_dim = protein_embeddings[list(\n",
-    "            protein_embeddings.keys())[0]].shape[0]\n",
-    "        self.cell_emb_dim = cell2embedding[list(\n",
-    "            cell2embedding.keys())[0]].shape[0]\n",
-    "\n",
-    "        if not use_smote:\n",
-    "            self.mol_graphs = [from_smiles(s) for s in self.data['Smiles']]\n",
-    "\n",
-    "        # Look up the embeddings\n",
-    "        self.data = pd.DataFrame({\n",
-    "            'Smiles': self.data['Smiles'].apply(lambda x: smiles2fp[x].astype(np.float32)).tolist(),\n",
-    "            'Uniprot': self.data['Uniprot'].apply(lambda x: protein_embeddings[x].astype(np.float32)).tolist(),\n",
-    "            'E3 Ligase Uniprot': self.data['E3 Ligase Uniprot'].apply(lambda x: protein_embeddings[x].astype(np.float32)).tolist(),\n",
-    "            'Cell Line Identifier': self.data['Cell Line Identifier'].apply(lambda x: cell2embedding[x].astype(np.float32)).tolist(),\n",
-    "            self.active_label: self.data[self.active_label].astype(np.float32).tolist(),\n",
-    "            'pDC50': (-np.log10(self.data['DC50 (nM)'] * 1e-9)).astype(np.float32).tolist(),\n",
-    "            'Dmax': self.data['Dmax (%)'].astype(np.float32).tolist(),\n",
-    "        })\n",
-    "\n",
-    "        # Apply SMOTE\n",
-    "        self.use_smote = use_smote\n",
-    "        self.oversampler = oversampler\n",
-    "        self.smote_applied = False\n",
-    "        if self.use_smote:\n",
-    "            self.apply_smote()\n",
-    "\n",
-    "    def apply_smote(self):\n",
-    "        # Prepare the dataset for SMOTE\n",
-    "        features = []\n",
-    "        labels = []\n",
-    "        for _, row in self.data.iterrows():\n",
-    "            features.append(np.hstack([\n",
-    "                row['Smiles'],\n",
-    "                row['Uniprot'],\n",
-    "                row['E3 Ligase Uniprot'],\n",
-    "                row['Cell Line Identifier'],\n",
-    "            ]))\n",
-    "            labels.append(row[self.active_label])\n",
-    "\n",
-    "        # Convert to numpy array\n",
-    "        features = np.array(features).astype(np.float32)\n",
-    "        labels = np.array(labels).astype(np.float32)\n",
-    "\n",
-    "        # Initialize SMOTE and fit\n",
-    "        if self.oversampler is None:\n",
-    "            oversampler = SMOTE(random_state=42)\n",
-    "        else:\n",
-    "            oversampler = self.oversampler\n",
-    "        features_smote, labels_smote = oversampler.fit_resample(features, labels)\n",
-    "\n",
-    "        # Separate the features back into their respective embeddings\n",
-    "        smiles_embs = features_smote[:, :self.smiles_emb_dim]\n",
-    "        poi_embs = features_smote[:,\n",
-    "                                  self.smiles_emb_dim:self.smiles_emb_dim+self.protein_emb_dim]\n",
-    "        e3_embs = features_smote[:, self.smiles_emb_dim +\n",
-    "                                 self.protein_emb_dim:self.smiles_emb_dim+2*self.protein_emb_dim]\n",
-    "        cell_embs = features_smote[:, -self.cell_emb_dim:]\n",
-    "\n",
-    "        # Reconstruct the dataframe with oversampled data\n",
-    "        df_smote = pd.DataFrame({\n",
-    "            'Smiles': list(smiles_embs),\n",
-    "            'Uniprot': list(poi_embs),\n",
-    "            'E3 Ligase Uniprot': list(e3_embs),\n",
-    "            'Cell Line Identifier': list(cell_embs),\n",
-    "            # self.active_label: labels_smote,\n",
-    "            self.active_label: labels_smote,\n",
-    "        })\n",
-    "        self.data = df_smote\n",
-    "        self.smote_applied = True\n",
-    "\n",
-    "    def fit_scaling(self, use_single_scaler=False, **scaler_kwargs) -> dict:\n",
-    "        \"\"\" Fit the scalers for the data.\n",
-    "\n",
-    "        Returns:\n",
-    "            dict: The fitted scalers.\n",
-    "        \"\"\"\n",
-    "        if use_single_scaler:\n",
-    "            scaler = StandardScaler(**scaler_kwargs)\n",
-    "            embeddings = np.hstack([\n",
-    "                np.array(self.data['Smiles'].tolist()),\n",
-    "                np.array(self.data['Uniprot'].tolist()),\n",
-    "                np.array(self.data['E3 Ligase Uniprot'].tolist()),\n",
-    "                np.array(self.data['Cell Line Identifier'].tolist()),\n",
-    "            ])\n",
-    "            scaler.fit(embeddings)\n",
-    "            return scaler\n",
-    "        else:\n",
-    "            scalers = {}\n",
-    "            scalers['Smiles'] = StandardScaler(**scaler_kwargs)\n",
-    "            scalers['Uniprot'] = StandardScaler(**scaler_kwargs)\n",
-    "            scalers['E3 Ligase Uniprot'] = StandardScaler(**scaler_kwargs)\n",
-    "            scalers['Cell Line Identifier'] = StandardScaler(**scaler_kwargs)\n",
-    "\n",
-    "            scalers['Smiles'].fit(np.stack(self.data['Smiles'].to_numpy()))\n",
-    "            scalers['Uniprot'].fit(np.stack(self.data['Uniprot'].to_numpy()))\n",
-    "            scalers['E3 Ligase Uniprot'].fit(np.stack(self.data['E3 Ligase Uniprot'].to_numpy()))\n",
-    "            scalers['Cell Line Identifier'].fit(np.stack(self.data['Cell Line Identifier'].to_numpy()))\n",
-    "\n",
-    "            return scalers\n",
-    "\n",
-    "    def apply_scaling(self, scalers: dict, use_single_scaler=False):\n",
-    "        \"\"\" Apply scaling to the data.\n",
-    "\n",
-    "        Args:\n",
-    "            scalers (dict): The scalers for each feature.\n",
-    "        \"\"\"\n",
-    "        if use_single_scaler:\n",
-    "            embeddings = np.hstack([\n",
-    "                np.array(self.data['Smiles'].tolist()),\n",
-    "                np.array(self.data['Uniprot'].tolist()),\n",
-    "                np.array(self.data['E3 Ligase Uniprot'].tolist()),\n",
-    "                np.array(self.data['Cell Line Identifier'].tolist()),\n",
-    "            ])\n",
-    "            scaled_embeddings = scalers.transform(embeddings)\n",
-    "            self.data = pd.DataFrame({\n",
-    "                'Smiles': list(scaled_embeddings[:, :self.smiles_emb_dim]),\n",
-    "                'Uniprot': list(scaled_embeddings[:, self.smiles_emb_dim:self.smiles_emb_dim+self.protein_emb_dim]),\n",
-    "                'E3 Ligase Uniprot': list(scaled_embeddings[:, self.smiles_emb_dim+self.protein_emb_dim:self.smiles_emb_dim+2*self.protein_emb_dim]),\n",
-    "                'Cell Line Identifier': list(scaled_embeddings[:, -self.cell_emb_dim:]),\n",
-    "                self.active_label: self.data[self.active_label],\n",
-    "                'pDC50': self.data['pDC50'],\n",
-    "                'Dmax': self.data['Dmax'],\n",
-    "            })\n",
-    "        else:\n",
-    "            self.data['Smiles'] = self.data['Smiles'].apply(lambda x: scalers['Smiles'].transform(x[np.newaxis, :])[0])\n",
-    "            self.data['Uniprot'] = self.data['Uniprot'].apply(lambda x: scalers['Uniprot'].transform(x[np.newaxis, :])[0])\n",
-    "            self.data['E3 Ligase Uniprot'] = self.data['E3 Ligase Uniprot'].apply(lambda x: scalers['E3 Ligase Uniprot'].transform(x[np.newaxis, :])[0])\n",
-    "            self.data['Cell Line Identifier'] = self.data['Cell Line Identifier'].apply(lambda x: scalers['Cell Line Identifier'].transform(x[np.newaxis, :])[0])\n",
-    "    \n",
-    "    def get_numpy_arrays(self):\n",
-    "        X = np.hstack([\n",
-    "            np.array(self.data['Smiles'].tolist()),\n",
-    "            np.array(self.data['Uniprot'].tolist()),\n",
-    "            np.array(self.data['E3 Ligase Uniprot'].tolist()),\n",
-    "            np.array(self.data['Cell Line Identifier'].tolist()),\n",
-    "        ]).copy()\n",
-    "        y = self.data[self.active_label].values.copy()\n",
-    "        return X, y\n",
-    "\n",
-    "    def normalize_pDC50(self):\n",
-    "        \"\"\" Normalize the pDC50 values using a MinMax scaler. \"\"\"\n",
-    "        self.pDC50_scaler = StandardScaler()\n",
-    "        self.data['pDC50'] = self.pDC50_scaler.fit_transform(self.data['pDC50'].values.reshape(-1, 1))\n",
-    "        return self.pDC50_scaler\n",
-    "    \n",
-    "    def denormalize_pDC50(self, scaler):\n",
-    "        \"\"\" Denormalize the pDC50 values using a MinMax scaler. \"\"\"\n",
-    "        self.data['pDC50'] = scaler.inverse_transform(self.data['pDC50'].values.reshape(-1, 1))\n",
-    "\n",
-    "    def normalize_Dmax(self):\n",
-    "        \"\"\" Normalize the Dmax values using a MinMax scaler. \"\"\"\n",
-    "        self.Dmax_scaler = StandardScaler()\n",
-    "        self.data['Dmax'] = self.Dmax_scaler.fit_transform(self.data['Dmax'].values.reshape(-1, 1))\n",
-    "        return self.Dmax_scaler\n",
-    "    \n",
-    "    def denormalize_Dmax(self, scaler):\n",
-    "        \"\"\" Denormalize the Dmax values using a MinMax scaler. \"\"\"\n",
-    "        self.data['Dmax'] = scaler.inverse_transform(self.data['Dmax'].values.reshape(-1, 1))\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        elem = {\n",
-    "            'smiles_emb': self.data['Smiles'].iloc[idx],\n",
-    "            'poi_emb': self.data['Uniprot'].iloc[idx],\n",
-    "            'e3_emb': self.data['E3 Ligase Uniprot'].iloc[idx],\n",
-    "            'cell_emb': self.data['Cell Line Identifier'].iloc[idx],\n",
-    "            'active': self.data[self.active_label].iloc[idx],\n",
-    "        }\n",
-    "        if not self.smote_applied:\n",
-    "            elem['pDC50'] = self.data['pDC50'].iloc[idx]\n",
-    "            elem['Dmax'] = self.data['Dmax'].iloc[idx]\n",
-    "        if not self.use_smote and self.include_mol_graphs:\n",
-    "            elem['mol_graph'] = self.mol_graphs[idx]\n",
-    "        return elem\n",
-    "\n",
-    "protac_ds = PROTAC_Dataset(\n",
-    "    protac_df[protac_df[active_col].notna()],\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "print(f'Loaded {len(protac_ds)} PROTACs')\n",
-    "\n",
-    "embeddings_unscaled = np.hstack([\n",
-    "    np.array(protac_ds.data['Smiles'].tolist()),\n",
-    "    np.array(protac_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(protac_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(protac_ds.data['Cell Line Identifier'].tolist()),\n",
-    "]).copy()\n",
-    "\n",
-    "# protac_ds.apply_smote()\n",
-    "\n",
-    "scalers = protac_ds.fit_scaling(use_single_scaler=True, with_mean=False)\n",
-    "protac_ds.apply_scaling(scalers, use_single_scaler=True)\n",
-    "\n",
-    "# print(scalers)\n",
-    "# display(protac_ds.data)\n",
-    "# display(protac_ds.data['Smiles'].iloc[0].shape)\n",
-    "# Get the sum of the embeddings dimensions\n",
-    "sum_emb_dim = protac_ds.smiles_emb_dim + protac_ds.protein_emb_dim * 2 + protac_ds.cell_emb_dim\n",
-    "print(f'Smile embeddings dimensions: {protac_ds.smiles_emb_dim}')\n",
-    "print(f'Protein embeddings dimensions: {protac_ds.protein_emb_dim}')\n",
-    "print(f'Cell embeddings dimensions: {protac_ds.cell_emb_dim}')\n",
-    "print(f'Sum of the embeddings dimensions: {sum_emb_dim}')\n",
-    "\n",
-    "sum_emb_dim = (\n",
-    "    protac_ds.data['Smiles'].iloc[0].shape[-1] +\n",
-    "    protac_ds.data['Uniprot'].iloc[0].shape[-1] +\n",
-    "    protac_ds.data['E3 Ligase Uniprot'].iloc[0].shape[-1] +\n",
-    "    protac_ds.data['Cell Line Identifier'].iloc[0].shape[-1]\n",
-    ")\n",
-    "print(f'Sum of the embeddings dimensions: {sum_emb_dim}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(857, 3040) (857,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "X, y = protac_ds.get_numpy_arrays()\n",
-    "print(X.shape, y.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predict Using SKlearn Models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import train_val_split\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "train_df, test_df = train_test_split(\n",
-    "    protac_df[protac_df[active_col].notna()],\n",
-    "    test_size=0.2,\n",
-    "    random_state=42,\n",
-    ")\n",
-    "train_ds = PROTAC_Dataset(\n",
-    "    train_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    active_label=active_col,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "scaler = train_ds.fit_scaling(use_single_scaler=True)\n",
-    "train_ds.apply_scaling(scaler, use_single_scaler=True)\n",
-    "test_ds = PROTAC_Dataset(\n",
-    "    test_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    active_label=active_col,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "test_ds.apply_scaling(scaler, use_single_scaler=True)\n",
-    "\n",
-    "X_train, y_train = train_ds.get_numpy_arrays()\n",
-    "X_test, y_test = test_ds.get_numpy_arrays()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 74.4%\n",
-      "ROC-AUC score: 0.768\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "\n",
-    "# Train the logistic regression model\n",
-    "logreg = LogisticRegression(\n",
-    "    penalty=None, #'l2',\n",
-    "    max_iter=2000,\n",
-    "    solver='lbfgs',\n",
-    "    # dual=True, # True when n_features > n_samples\n",
-    "    # C=0.05,\n",
-    "    # tol=1e-5,\n",
-    "    random_state=42,\n",
-    "    # n_jobs=-1,\n",
-    ")\n",
-    "logreg.fit(X_train, y_train)\n",
-    "accuracy = logreg.score(X_test, y_test)\n",
-    "print(f'Accuracy: {accuracy:.1%}')\n",
-    "# Get the ROC-AUC score\n",
-    "y_pred = logreg.predict_proba(X_test)[:, 1]\n",
-    "roc_auc = roc_auc_score(y_test, y_pred)\n",
-    "print(f'ROC-AUC score: {roc_auc:.3f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test_acc: 0.744\n",
-      "test_f1_score: 0.758\n",
-      "test_hp_metric: 0.744\n",
-      "test_opt_score: 1.502\n",
-      "test_precision: 0.726\n",
-      "test_recall: 0.793\n",
-      "test_roc_auc: 0.768\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torchmetrics import (\n",
-    "    Accuracy,\n",
-    "    AUROC,\n",
-    "    Precision,\n",
-    "    Recall,\n",
-    "    F1Score,\n",
-    "    MetricCollection,\n",
-    ")\n",
-    "\n",
-    "stages = ['train_metrics', 'val_metrics', 'test_metrics']\n",
-    "metrics = nn.ModuleDict({s: MetricCollection({\n",
-    "    'acc': Accuracy(task='binary'),\n",
-    "    'roc_auc': AUROC(task='binary'),\n",
-    "    'precision': Precision(task='binary'),\n",
-    "    'recall': Recall(task='binary'),\n",
-    "    'f1_score': F1Score(task='binary'),\n",
-    "    'opt_score': Accuracy(task='binary') + F1Score(task='binary'),\n",
-    "    'hp_metric': Accuracy(task='binary'),\n",
-    "}, prefix=s.replace('metrics', '')) for s in stages})\n",
-    "\n",
-    "y_pred = torch.tensor(logreg.predict_proba(X_test)[:, 1])\n",
-    "y_true = torch.tensor(y_test)\n",
-    "metrics['test_metrics'].update(y_pred, y_true)\n",
-    "# Print the metrics\n",
-    "for k, v in metrics['test_metrics'].compute().items():\n",
-    "    print(f'{k}: {v:.3f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train_acc: 1.000\n",
-      "train_f1_score: 1.000\n",
-      "train_hp_metric: 1.000\n",
-      "train_opt_score: 2.000\n",
-      "train_precision: 1.000\n",
-      "train_recall: 1.000\n",
-      "train_roc_auc: 1.000\n",
-      "val_acc: 0.744\n",
-      "val_f1_score: 0.758\n",
-      "val_hp_metric: 0.744\n",
-      "val_opt_score: 1.502\n",
-      "val_precision: 0.726\n",
-      "val_recall: 0.793\n",
-      "val_roc_auc: 0.768\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torchmetrics import (\n",
-    "    Accuracy,\n",
-    "    AUROC,\n",
-    "    Precision,\n",
-    "    Recall,\n",
-    "    F1Score,\n",
-    "    MetricCollection,\n",
-    ")\n",
-    "\n",
-    "# Generic function to fit and evaluate a classifier model (given as argument),\n",
-    "# on train and val sets (and optionally a test set) given as dataframes\n",
-    "def train_sklearn_model(\n",
-    "    clf: ClassifierMixin,\n",
-    "    train_df: pd.DataFrame,\n",
-    "    val_df: pd.DataFrame,\n",
-    "    test_df: Optional[pd.DataFrame] = None,\n",
-    "    active_label: str = 'Active',\n",
-    "    use_single_scaler: bool = True,\n",
-    ") -> Tuple[ClassifierMixin, nn.ModuleDict]:\n",
-    "    \"\"\" Train a classifier model on train and val sets and evaluate it on a test set.\n",
-    "\n",
-    "    Args:\n",
-    "        clf: The classifier model to train and evaluate.\n",
-    "        train_df (pd.DataFrame): The training set.\n",
-    "        val_df (pd.DataFrame): The validation set.\n",
-    "        test_df (Optional[pd.DataFrame]): The test set.\n",
-    "\n",
-    "    Returns:\n",
-    "        Tuple[ClassifierMixin, nn.ModuleDict]: The trained model and the metrics.\n",
-    "    \"\"\"\n",
-    "    # Initialize the datasets\n",
-    "    train_ds = PROTAC_Dataset(\n",
-    "        train_df,\n",
-    "        protein_embeddings,\n",
-    "        cell2embedding,\n",
-    "        smiles2fp,\n",
-    "        active_label=active_label,\n",
-    "        use_smote=False,\n",
-    "    )\n",
-    "    scaler = train_ds.fit_scaling(use_single_scaler=use_single_scaler)\n",
-    "    train_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)\n",
-    "    val_ds = PROTAC_Dataset(\n",
-    "        val_df,\n",
-    "        protein_embeddings,\n",
-    "        cell2embedding,\n",
-    "        smiles2fp,\n",
-    "        active_label=active_label,\n",
-    "        use_smote=False,\n",
-    "    )\n",
-    "    val_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)\n",
-    "    if test_df is not None:\n",
-    "        test_ds = PROTAC_Dataset(\n",
-    "            test_df,\n",
-    "            protein_embeddings,\n",
-    "            cell2embedding,\n",
-    "            smiles2fp,\n",
-    "            active_label=active_label,\n",
-    "            use_smote=False,\n",
-    "        )\n",
-    "        test_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)\n",
-    "\n",
-    "    # Get the numpy arrays\n",
-    "    X_train, y_train = train_ds.get_numpy_arrays()\n",
-    "    X_val, y_val = val_ds.get_numpy_arrays()\n",
-    "    if test_df is not None:\n",
-    "        X_test, y_test = test_ds.get_numpy_arrays()\n",
-    "\n",
-    "    # Train the model\n",
-    "    clf.fit(X_train, y_train)\n",
-    "    # Define the metrics as a module dict\n",
-    "    stages = ['train_metrics', 'val_metrics', 'test_metrics']\n",
-    "    metrics = nn.ModuleDict({s: MetricCollection({\n",
-    "        'acc': Accuracy(task='binary'),\n",
-    "        'roc_auc': AUROC(task='binary'),\n",
-    "        'precision': Precision(task='binary'),\n",
-    "        'recall': Recall(task='binary'),\n",
-    "        'f1_score': F1Score(task='binary'),\n",
-    "        'opt_score': Accuracy(task='binary') + F1Score(task='binary'),\n",
-    "        'hp_metric': Accuracy(task='binary'),\n",
-    "    }, prefix=s.replace('metrics', '')) for s in stages})\n",
-    "\n",
-    "    # Get the predictions\n",
-    "    metrics_out = {}\n",
-    "\n",
-    "    y_pred = torch.tensor(clf.predict_proba(X_train)[:, 1])\n",
-    "    y_true = torch.tensor(y_train)\n",
-    "    metrics['train_metrics'].update(y_pred, y_true)\n",
-    "    metrics_out.update(metrics['train_metrics'].compute())\n",
-    "\n",
-    "    y_pred = torch.tensor(clf.predict_proba(X_val)[:, 1])\n",
-    "    y_true = torch.tensor(y_val)\n",
-    "    metrics['val_metrics'].update(y_pred, y_true)\n",
-    "    metrics_out.update(metrics['val_metrics'].compute())\n",
-    "\n",
-    "    if test_df is not None:\n",
-    "        y_pred = torch.tensor(clf.predict_proba(X_test)[:, 1])\n",
-    "        y_true = torch.tensor(y_test)\n",
-    "        metrics['test_metrics'].update(y_pred, y_true)\n",
-    "        metrics_out.update(metrics['test_metrics'].compute())\n",
-    "\n",
-    "    return clf, metrics_out\n",
-    "\n",
-    "# Train the logistic regression model\n",
-    "logreg = LogisticRegression(\n",
-    "    penalty=None, #'l2',\n",
-    "    max_iter=2000,\n",
-    "    solver='lbfgs',\n",
-    "    # dual=True, # True when n_features > n_samples\n",
-    "    # C=0.05,\n",
-    "    # tol=1e-5,\n",
-    "    random_state=42,\n",
-    "    # n_jobs=-1,\n",
-    ")\n",
-    "logreg, metrics = train_sklearn_model(logreg, train_df, test_df, active_label=active_col)\n",
-    "\n",
-    "for k, v in metrics.items():\n",
-    "    print(f'{k}: {v:.3f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 75.0%\n",
-      "ROC-AUC score: 0.808\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "# Train the SVM model\n",
-    "svc = SVC(\n",
-    "    kernel='linear',\n",
-    "    gamma='scale',\n",
-    "    max_iter=-1,\n",
-    "    C=0.2,\n",
-    "    # tol=1e-5,\n",
-    "    probability=True,\n",
-    "    random_state=42,\n",
-    "    # n_jobs=-1,\n",
-    ")\n",
-    "svc.fit(X_train, y_train)\n",
-    "accuracy = svc.score(X_test, y_test)\n",
-    "print(f'Accuracy: {accuracy:.1%}')\n",
-    "# Get the ROC-AUC score\n",
-    "y_pred = svc.predict_proba(X_test)[:, 1]\n",
-    "roc_auc = roc_auc_score(y_test, y_pred)\n",
-    "print(f'ROC-AUC score: {roc_auc:.3f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get all data in protac_df not in test_df\n",
-    "dmax_dc50_df = protac_df[~protac_df.index.isin(test_df.index)]\n",
-    "# Get all the data with Dmax not nan\n",
-    "dmax_df = dmax_dc50_df[dmax_dc50_df['Dmax (%)'].notna()]\n",
-    "dc50_df = dmax_dc50_df[dmax_dc50_df['DC50 (nM)'].notna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import Ridge, RidgeCV, LinearRegression, ElasticNet\n",
-    "from sklearn.neural_network import MLPRegressor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4       100.0\n",
-       "7        90.0\n",
-       "60       90.0\n",
-       "69      100.0\n",
-       "72       50.0\n",
-       "        ...  \n",
-       "2121     81.0\n",
-       "2122     94.0\n",
-       "2125     85.0\n",
-       "2126     60.0\n",
-       "2127     80.0\n",
-       "Name: Dmax (%), Length: 812, dtype: float64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "protac_df[protac_df['Dmax (%)'].notna()]['Dmax (%)']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train contains NaNs: False\n",
-      "y_train contains NaNs: False\n",
-      "Number of training points: 752\n",
-      "Number of training points: 1452 (after resampling)\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'sns' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[47], line 88\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of training points: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(y_train)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (after resampling)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     87\u001b[0m \u001b[38;5;66;03m# Plot y_train\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241m.\u001b[39mhistplot(y_train, kde\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     89\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDmax (\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m) distribution\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     90\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDmax (\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import QuantileTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.utils import resample\n",
-    "\n",
-    "dmax_ds = PROTAC_Dataset(\n",
-    "    dmax_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "dmax_scaler = dmax_ds.fit_scaling(use_single_scaler=True)\n",
-    "dmax_ds.apply_scaling(dmax_scaler, use_single_scaler=True)\n",
-    "\n",
-    "X_train = np.hstack([\n",
-    "    np.array(dmax_ds.data['Smiles'].tolist()),\n",
-    "    np.array(dmax_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(dmax_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(dmax_ds.data['Cell Line Identifier'].tolist()),\n",
-    "])\n",
-    "y_train = dmax_df['Dmax (%)'].values / 100\n",
-    "# Check if X_train contains Nans\n",
-    "print(f'X_train contains NaNs: {np.isnan(X_train).any()}')\n",
-    "print(f'y_train contains NaNs: {np.isnan(y_train).any()}')\n",
-    "print(f'Number of training points: {len(y_train)}')\n",
-    "\n",
-    "# Normalize y_train values\n",
-    "scale_dmax = True\n",
-    "dmax_y_scaler = StandardScaler()\n",
-    "# dmax_y_scaler = QuantileTransformer(output_distribution='normal', random_state=42)\n",
-    "# dmax_y_scaler = Pipeline([\n",
-    "#     ('scaler', StandardScaler()),\n",
-    "#     ('transformer', QuantileTransformer(output_distribution='normal', random_state=42)),\n",
-    "# ])\n",
-    "if scale_dmax:\n",
-    "    y_train = dmax_y_scaler.fit_transform(y_train.reshape(-1, 1)).flatten()\n",
-    "\n",
-    "# Function to add random Gaussian noise\n",
-    "def add_noise(data, noise_level=0.01):\n",
-    "    noise = np.random.randn(*data.shape) * noise_level\n",
-    "    return data + noise\n",
-    "\n",
-    "# Function to interpolate between data points\n",
-    "def interpolate_data(points, n_samples=100):\n",
-    "    n_points = points.shape[0]\n",
-    "    new_points = np.zeros((n_samples, points.shape[1]))\n",
-    "    for i in range(n_samples):\n",
-    "        # Randomly choose two points to interpolate\n",
-    "        idx1, idx2 = np.random.randint(0, n_points, 2)\n",
-    "        alpha = np.random.random()\n",
-    "        new_points[i] = points[idx1] * (1 - alpha) + points[idx2] * alpha\n",
-    "    return new_points\n",
-    "\n",
-    "# Define a function to oversample the lower range\n",
-    "def oversample_data(X, y, quantile=0.25, n_samples=500, noise_level=0.01):\n",
-    "    # Determine the threshold for oversampling\n",
-    "    threshold = np.quantile(y, quantile)\n",
-    "    \n",
-    "    # Identify the underrepresented samples\n",
-    "    undersampled_mask = y < threshold\n",
-    "    X_undersampled = X[undersampled_mask]\n",
-    "    y_undersampled = y[undersampled_mask]\n",
-    "    \n",
-    "    # Interpolating data\n",
-    "    X_interpolated = interpolate_data(X_undersampled, n_samples=int(n_samples * 0.25))\n",
-    "    y_interpolated = interpolate_data(y_undersampled.reshape(-1, 1), n_samples=int(n_samples * 0.25)).flatten()\n",
-    "\n",
-    "    # Resample the undersampled data\n",
-    "    X_oversampled, y_oversampled = resample(X_undersampled, y_undersampled,\n",
-    "                                            replace=True,\n",
-    "                                            n_samples=int(n_samples * 0.75),\n",
-    "                                            random_state=42)\n",
-    "\n",
-    "    X_oversampled = add_noise(X_oversampled, noise_level)\n",
-    "    y_oversampled = add_noise(y_oversampled.reshape(-1, 1), noise_level).flatten()\n",
-    "    \n",
-    "    # Combine original, noisy, and interpolated data\n",
-    "    X_combined = np.vstack([X, X_oversampled, X_interpolated])\n",
-    "    y_combined = np.hstack([y, y_oversampled, y_interpolated])\n",
-    "    \n",
-    "    return X_combined, y_combined\n",
-    "\n",
-    "X_train, y_train = oversample_data(X_train, y_train, quantile=0.2, n_samples=700, noise_level=0.1)\n",
-    "\n",
-    "print(f'Number of training points: {len(y_train)} (after resampling)')\n",
-    "\n",
-    "# Plot y_train\n",
-    "sns.histplot(y_train, kde=True)\n",
-    "plt.title('Dmax (%) distribution')\n",
-    "plt.xlabel('Dmax (%)')\n",
-    "plt.ylabel('Count')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R^2 score: 0.953\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# dmax_regr = RidgeCV(alpha_per_target=True)\n",
-    "# dmax_regr = ElasticNet(random_state=42)\n",
-    "# dmax_regr = LinearRegression()\n",
-    "\n",
-    "solver = 'adam' # default='adam', other options={lbfgs, sgd}\n",
-    "dmax_regr = MLPRegressor(\n",
-    "    hidden_layer_sizes=(512, 512, 512,),\n",
-    "    learning_rate_init=1e-4, # default=0.001\n",
-    "    learning_rate='adaptive',\n",
-    "    max_iter=200,\n",
-    "    batch_size=128,\n",
-    "    alpha=0.01, # default=0.0001\n",
-    "    solver=solver, # default='adam'\n",
-    "    early_stopping=True,\n",
-    "    tol=1e-5, # default=0.0001\n",
-    "    verbose=True if solver == 'lbfgs' else False,\n",
-    "    random_state=42,\n",
-    ")\n",
-    "\n",
-    "# Train the Dmax model\n",
-    "dmax_regr.fit(X_train, y_train)\n",
-    "# Get the R^2 score\n",
-    "r2_score = dmax_regr.score(X_train, y_train)\n",
-    "print(f'R^2 score: {r2_score:.3f}')\n",
-    "\n",
-    "# Plot the loss curve\n",
-    "if solver != 'lbfgs':\n",
-    "    plt.plot(dmax_regr.loss_curve_, label='loss')\n",
-    "    plt.plot(dmax_regr.validation_scores_, label='R^2')\n",
-    "    plt.grid(axis='both', alpha=0.5)\n",
-    "    plt.legend()\n",
-    "    plt.ylim(-2, 2)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R^2 score: 0.920\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'plt' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[46], line 57\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# Plot the loss curve\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m solver \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlbfgs\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 57\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mplot(dc50_regr\u001b[38;5;241m.\u001b[39mloss_curve_, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     58\u001b[0m     plt\u001b[38;5;241m.\u001b[39mplot(dc50_regr\u001b[38;5;241m.\u001b[39mvalidation_scores_, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mR^2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     59\u001b[0m     plt\u001b[38;5;241m.\u001b[39mgrid(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "dc50_ds = PROTAC_Dataset(\n",
-    "    dc50_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "dc50_scaler = dc50_ds.fit_scaling(use_single_scaler=True)\n",
-    "dc50_ds.apply_scaling(dc50_scaler, use_single_scaler=True)\n",
-    "\n",
-    "X_train = np.hstack([\n",
-    "    np.array(dc50_ds.data['Smiles'].tolist()),\n",
-    "    np.array(dc50_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(dc50_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(dc50_ds.data['Cell Line Identifier'].tolist()),\n",
-    "])\n",
-    "y_train = dc50_df['DC50 (nM)'].values\n",
-    "# Convert to pDC50\n",
-    "y_train = -np.log10(y_train * 1e-9)\n",
-    "\n",
-    "scale_dc50 = True\n",
-    "dc50_y_scaler = StandardScaler()\n",
-    "# dc50_y_scaler = Pipeline([\n",
-    "#     ('scaler', StandardScaler()),\n",
-    "#     ('transformer', QuantileTransformer(output_distribution='normal', random_state=42)),\n",
-    "# ])\n",
-    "if scale_dc50:\n",
-    "    y_train = dc50_y_scaler.fit_transform(y_train.reshape(-1, 1)).flatten()\n",
-    "\n",
-    "# dc50_regr = Ridge(alpha=0.9)\n",
-    "# dc50_regr = RidgeCV(alpha_per_target=True)\n",
-    "# dc50_regr = ElasticNet(random_state=42, max_iter=1000)\n",
-    "# dc50_regr = LinearRegression()\n",
-    "solver = 'adam' # default='adam', other options={lbfgs, sgd}\n",
-    "\n",
-    "dc50_regr = MLPRegressor(\n",
-    "    hidden_layer_sizes=(64, 64, 64,),\n",
-    "    learning_rate_init=1e-4, # default=0.001\n",
-    "    alpha=0.0001, # default=0.0001\n",
-    "    max_iter=400,\n",
-    "    batch_size=128,\n",
-    "    solver=solver, # default='adam'\n",
-    "    early_stopping=True,\n",
-    "    tol=1e-5, # default=0.0001\n",
-    "    verbose=True if solver == 'lbfgs' else False,\n",
-    "    random_state=42,\n",
-    ")\n",
-    "\n",
-    "# Train the Dmax model\n",
-    "dc50_regr.fit(X_train, y_train)\n",
-    "# Get the R^2 score\n",
-    "r2_score = dc50_regr.score(X_train, y_train)\n",
-    "print(f'R^2 score: {r2_score:.3f}')\n",
-    "\n",
-    "# Plot the loss curve\n",
-    "if solver != 'lbfgs':\n",
-    "    plt.plot(dc50_regr.loss_curve_, label='loss')\n",
-    "    plt.plot(dc50_regr.validation_scores_, label='R^2')\n",
-    "    plt.grid(axis='both', alpha=0.5)\n",
-    "    plt.legend()\n",
-    "    plt.ylim(-2, 2)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 77.3%\n",
-      "ROC-AUC score: 0.773\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dmax R^2:  0.477\n",
-      "Dmax RMSD: 0.241\n",
-      "DC50 R^2:  0.658\n",
-      "DC50 RMSD: 0.725\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Predict the Dmax and DC50 of the X_test data\n",
-    "if scale_dmax:\n",
-    "    dmax_pred = dmax_y_scaler.inverse_transform(dmax_regr.predict(X_test).reshape(-1, 1)).flatten()\n",
-    "else:\n",
-    "    dmax_pred = dmax_regr.predict(X_test)\n",
-    "if scale_dc50:\n",
-    "    dc50_pred = dc50_y_scaler.inverse_transform(dc50_regr.predict(X_test).reshape(-1, 1)).flatten()\n",
-    "else:\n",
-    "    pdc50_pred = dc50_regr.predict(X_test)\n",
-    "\n",
-    "# Apply the active definition\n",
-    "y_pred = (dmax_pred >= 0.6) & (pdc50_pred >= 6)\n",
-    "# Get the accuracy and ROC-AUC\n",
-    "accuracy = np.mean(y_pred == y_test)\n",
-    "print(f'Accuracy: {accuracy:.1%}')\n",
-    "roc_auc = roc_auc_score(y_test, y_pred)\n",
-    "print(f'ROC-AUC score: {roc_auc:.3f}')\n",
-    "\n",
-    "# Plot confusion matrix\n",
-    "from sklearn.metrics import confusion_matrix\n",
-    "\n",
-    "cm = confusion_matrix(y_test, y_pred)\n",
-    "sns.heatmap(cm, annot=True, fmt='d', cmap=plt.cm.Blues)\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('True')\n",
-    "plt.title('Confusion matrix')\n",
-    "plt.show()\n",
-    "\n",
-    "# Get the y_test values that have Dmax not nan\n",
-    "test_dmax_df = protac_df[protac_df.index.isin(test_df.index)]\n",
-    "test_dmax_df = test_dmax_df[test_dmax_df['Dmax (%)'].notna()]\n",
-    "test_dmax_ds = PROTAC_Dataset(\n",
-    "    test_dmax_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "test_dmax_ds.apply_scaling(dmax_scaler, use_single_scaler=True)\n",
-    "X_test_dmax = np.hstack([\n",
-    "    np.array(test_dmax_ds.data['Smiles'].tolist()),\n",
-    "    np.array(test_dmax_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(test_dmax_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(test_dmax_ds.data['Cell Line Identifier'].tolist()),\n",
-    "])\n",
-    "y_test_dmax = test_dmax_df['Dmax (%)'].values / 100\n",
-    "\n",
-    "# Get the y_test values that have DC50 not nan\n",
-    "test_dc50_df = protac_df[protac_df.index.isin(test_df.index)]\n",
-    "test_dc50_df = test_dc50_df[test_dc50_df['DC50 (nM)'].notna()]\n",
-    "test_dc50_ds = PROTAC_Dataset(\n",
-    "    test_dc50_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=False,\n",
-    ")\n",
-    "test_dc50_ds.apply_scaling(dc50_scaler, use_single_scaler=True)\n",
-    "X_test_dc50 = np.hstack([\n",
-    "    np.array(test_dc50_ds.data['Smiles'].tolist()),\n",
-    "    np.array(test_dc50_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(test_dc50_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(test_dc50_ds.data['Cell Line Identifier'].tolist()),\n",
-    "])\n",
-    "y_test_dc50 = test_dc50_df['DC50 (nM)'].values\n",
-    "y_test_dc50 = -np.log10(y_test_dc50 * 1e-9)\n",
-    "# Get the predictions\n",
-    "if scale_dmax:\n",
-    "    dmax_pred = dmax_y_scaler.inverse_transform(dmax_regr.predict(X_test_dmax).reshape(-1, 1)).flatten()\n",
-    "else:\n",
-    "    dmax_pred = dmax_regr.predict(X_test_dmax)\n",
-    "if scale_dc50:\n",
-    "    dc50_pred = dc50_y_scaler.inverse_transform(dc50_regr.predict(X_test_dc50).reshape(-1, 1)).flatten()\n",
-    "else:\n",
-    "    pdc50_pred = dc50_regr.predict(X_test_dc50)\n",
-    "\n",
-    "# Get and print the R^2 and RMSD scores\n",
-    "from sklearn.metrics import r2_score, mean_squared_error\n",
-    "\n",
-    "r2_dmax = r2_score(y_test_dmax, dmax_pred)\n",
-    "r2_dc50 = r2_score(y_test_dc50, dc50_pred)\n",
-    "rmsd_dmax = np.sqrt(mean_squared_error(y_test_dmax, dmax_pred))\n",
-    "rmsd_dc50 = np.sqrt(mean_squared_error(y_test_dc50, dc50_pred))\n",
-    "print(f'Dmax R^2:  {r2_dmax:.3f}')\n",
-    "print(f'Dmax RMSD: {rmsd_dmax:.3f}')\n",
-    "print(f'DC50 R^2:  {r2_dc50:.3f}')\n",
-    "print(f'DC50 RMSD: {rmsd_dc50:.3f}')\n",
-    "\n",
-    "# Plot sorted predicted Dmax values\n",
-    "idx = np.argsort(y_test_dmax)\n",
-    "plt.scatter(range(len(y_test_dmax[idx])), y_test_dmax[idx], label='True')\n",
-    "plt.scatter(range(len(dmax_pred[idx])), dmax_pred[idx], label='Predicted', marker='x')\n",
-    "plt.title('Dmax (%) prediction')\n",
-    "plt.xlabel('Index')\n",
-    "plt.ylabel('Dmax (%)')\n",
-    "plt.legend()\n",
-    "plt.grid(axis='both', alpha=0.5)\n",
-    "plt.show()\n",
-    "\n",
-    "# Plot the true and predicted Dmax distributions together (faded)\n",
-    "sns.histplot(y_test_dmax, kde=True, color='C0', label='True', alpha=0.4, bins=20)\n",
-    "sns.histplot(dmax_pred, kde=True, color='C1', label='Predicted', alpha=0.4, bins=20)\n",
-    "plt.title('Dmax (%) distribution')\n",
-    "plt.xlabel('Dmax (%)')\n",
-    "plt.legend()\n",
-    "plt.grid(axis='both', alpha=0.5)\n",
-    "plt.show() \n",
-    "\n",
-    "# Plot sorted predicted DC50 values\n",
-    "idx = np.argsort(y_test_dc50)\n",
-    "plt.scatter(range(len(y_test_dc50[idx])), y_test_dc50[idx], label='True')\n",
-    "plt.scatter(range(len(dc50_pred[idx])), dc50_pred[idx], label='Predicted', marker='x')\n",
-    "plt.title('pDC50 prediction')\n",
-    "plt.xlabel('Index')\n",
-    "plt.ylabel('pDC50')\n",
-    "plt.legend()\n",
-    "plt.grid(axis='both', alpha=0.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other Tests on PROTAC Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 493,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(857, 224)\n",
-      "(857, 1024)\n",
-      "(857, 768)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.array(protac_ds.data['Smiles'].tolist()).shape)\n",
-    "print(np.array(protac_ds.data['Uniprot'].tolist()).shape)\n",
-    "print(np.array(protac_ds.data['Cell Line Identifier'].tolist()).shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 494,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "embedding shape: (857, 3040)\n",
-      "974,848\n"
-     ]
-    }
-   ],
-   "source": [
-    "embeddings = np.hstack([\n",
-    "    np.array(protac_ds.data['Smiles'].tolist()),\n",
-    "    np.array(protac_ds.data['Uniprot'].tolist()),\n",
-    "    np.array(protac_ds.data['E3 Ligase Uniprot'].tolist()),\n",
-    "    np.array(protac_ds.data['Cell Line Identifier'].tolist()),\n",
-    "])\n",
-    "print(f'embedding shape: {embeddings.shape}')\n",
-    "hidden_size = 256\n",
-    "print(f'{embeddings.shape[-1] * hidden_size + hidden_size * hidden_size * 3:,}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 495,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 2)\n",
-    "sns.kdeplot(embeddings_unscaled[:, :1], ax=ax[0], warn_singular=True)\n",
-    "sns.kdeplot(embeddings[:, :1], ax=ax[1], warn_singular=True)\n",
-    "ax[0].set_title('unscaled')\n",
-    "ax[1].set_title('scaled')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 496,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if RUN_DIMENSIONALITY_REDUCTION:\n",
-    "    # oversampler = ADASYN(random_state=42, sampling_strategy='minority', n_neighbors=10)\n",
-    "    oversampler = SMOTE(random_state=42, sampling_strategy='minority', k_neighbors=3)\n",
-    "\n",
-    "    protac_ds = PROTAC_Dataset(\n",
-    "        protac_df[protac_df[active_col].notna()], use_smote=True, oversampler=oversampler, use_ored_activity=False)\n",
-    "    print(f'Loaded {len(protac_ds)} PROTACs (SMOTE)')\n",
-    "\n",
-    "    # Plot PCA for the SMOTE dataset\n",
-    "    protac_data, protac_labels = get_data_for_dim_reduction(protac_ds.data, lookup_embeddings=False)\n",
-    "    plot_pca(protac_data, protac_labels)\n",
-    "\n",
-    "    protac_ds = PROTAC_Dataset(\n",
-    "        protac_df[protac_df[active_col].notna()], use_smote=False, oversampler=oversampler, use_ored_activity=False)\n",
-    "    print(f'Loaded {len(protac_ds)} PROTACs')\n",
-    "\n",
-    "    # Plot PCA for the non-SMOTE dataset\n",
-    "    protac_data, protac_labels = get_data_for_dim_reduction(protac_ds.data, lookup_embeddings=True)\n",
-    "    plot_pca(protac_data, protac_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 500,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded 857 PROTACs\n"
-     ]
-    }
-   ],
-   "source": [
-    "protac_ds = PROTAC_Dataset(\n",
-    "    protac_df[~protac_df[active_col].isna()], protein_embeddings, cell2embedding, smiles2fp, use_smote=False)\n",
-    "print(f'Loaded {len(protac_ds)} PROTACs')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 505,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "StandardScaler()"
-      ]
-     },
-     "execution_count": 505,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pDC50_scaler = protac_ds.normalize_pDC50()\n",
-    "Dmax_scaler = protac_ds.normalize_Dmax()\n",
-    "pDC50_scaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 506,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for elem in protac_ds:\n",
-    "    if elem['active'] == 1 or elem['active'] == 0:\n",
-    "        continue\n",
-    "    print(elem['active'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 507,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Example batch: {'smiles_emb': tensor([[2.4451, 9.8098, 2.3250,  ..., 2.2506, 2.2026, 2.8619],\n",
-      "        [2.4451, 9.8098, 2.3250,  ..., 2.2506, 2.2026, 2.8619],\n",
-      "        [2.4451, 9.8098, 2.3250,  ..., 2.2506, 2.2026, 0.0000],\n",
-      "        ...,\n",
-      "        [2.4451, 9.8098, 2.3250,  ..., 0.0000, 0.0000, 2.8619],\n",
-      "        [0.0000, 9.8098, 2.3250,  ..., 2.2506, 2.2026, 2.8619],\n",
-      "        [0.0000, 9.8098, 0.0000,  ..., 0.0000, 0.0000, 2.8619]]), 'poi_emb': tensor([[ 0.5457,  1.4438,  2.4114,  ..., -0.2300, -1.1045, -0.2744],\n",
-      "        [ 1.0732, -0.1348,  0.2196,  ...,  0.3468,  0.8279, -0.3303],\n",
-      "        [ 1.0397, -0.9426, -1.2832,  ...,  1.6156,  2.2682,  1.3624],\n",
-      "        ...,\n",
-      "        [ 0.4258,  0.8322,  0.8811,  ..., -1.8986, -1.1157,  2.0212],\n",
-      "        [ 3.3970,  2.7638,  2.2043,  ..., -0.6239, -0.7646,  1.8625],\n",
-      "        [ 0.9004,  0.0395,  1.2161,  ...,  0.5625,  1.4495,  1.9269]]), 'e3_emb': tensor([[ 1.8769,  5.2533,  1.7060,  ..., -1.9247, -0.5723,  4.4539],\n",
-      "        [ 1.8769,  5.2533,  1.7060,  ..., -1.9247, -0.5723,  4.4539],\n",
-      "        [ 1.8769,  5.2533,  1.7060,  ..., -1.9247, -0.5723,  4.4539],\n",
-      "        ...,\n",
-      "        [ 1.8769,  5.2533,  1.7060,  ..., -1.9247, -0.5723,  4.4539],\n",
-      "        [ 0.2346,  4.8804,  3.5026,  ..., -3.4816,  1.4724,  5.2759],\n",
-      "        [ 1.8769,  5.2533,  1.7060,  ..., -1.9247, -0.5723,  4.4539]]), 'cell_emb': tensor([[ 0.7540, -0.9740, -1.3841,  ..., -3.9310, -0.8280, -2.0885],\n",
-      "        [ 0.6465, -1.2065, -0.3017,  ..., -2.6993, -1.0373, -0.7301],\n",
-      "        [ 1.8356, -1.9312, -0.8351,  ..., -1.4646, -1.7357, -1.5997],\n",
-      "        ...,\n",
-      "        [ 2.5618, -2.6668,  1.0389,  ...,  0.7632, -2.5504, -0.9493],\n",
-      "        [-0.0644, -2.6478,  0.2194,  ..., -1.7372, -1.6838, -1.2652],\n",
-      "        [ 0.2249, -1.0855,  0.5576,  ..., -2.2042, -1.2294, -1.2731]]), 'active': tensor([0., 0., 1., 1., 0., 1., 0., 0., 0., 1., 1., 1., 0., 1., 0., 0., 0., 1.,\n",
-      "        1., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 0.],\n",
-      "       dtype=torch.float64), 'pDC50': tensor([-1.4368,     nan,  0.5401,  0.9547,     nan,  1.8408, -2.1790, -1.0487,\n",
-      "            nan,  1.5323,  0.6615, -0.2208, -0.6977,  0.7805,     nan,     nan,\n",
-      "        -1.2133,  0.4051,  0.1405, -0.9180,  0.5187,  0.4337,     nan, -0.7368,\n",
-      "        -1.0487, -0.8252,  0.1706,  0.3124,  1.1220,  0.7900, -1.1056, -1.8837],\n",
-      "       dtype=torch.float64), 'Dmax': tensor([    nan, -1.6527,  0.6587,  0.9646, -0.6329,  1.0326,     nan,     nan,\n",
-      "        -1.9926,  0.5568,  1.0326,  0.7301,  0.8967,  0.9986, -1.7546, -0.7349,\n",
-      "            nan,  0.8967,  0.2168,     nan,  1.0326,  0.4888, -1.1088, -1.1938,\n",
-      "            nan,     nan,  0.8967,  0.6587,  0.7607, -1.2108, -0.0211,     nan],\n",
-      "       dtype=torch.float64)}\n"
-     ]
-    }
-   ],
-   "source": [
-    "protac_dl = DataLoader(protac_ds, batch_size=32, shuffle=True, collate_fn=custom_collate)\n",
-    "print(f'Example batch: {next(iter(protac_dl))}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[1.0000, 1.0000, 1.0000,  ..., 1.0000, 0.0231, 0.0231],\n",
-      "        [1.0000, 1.0000, 0.0000,  ..., 1.0000, 1.0000, 1.0000],\n",
-      "        [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],\n",
-      "        ...,\n",
-      "        [0.0000, 1.0000, 1.0000,  ..., 0.0000, 1.0000, 1.0000],\n",
-      "        [1.0000, 1.0000, 1.0000,  ..., 0.0000, 1.0000, 1.0000],\n",
-      "        [1.0000, 1.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]])\n",
-      "torch.Size([32, 224])\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch = next(iter(protac_dl))\n",
-    "print(batch['smiles_emb'])\n",
-    "print(batch['smiles_emb'].size())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define PyTorch Lightning Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### GNN Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pytorch_lightning as pl\n",
-    "import torch_geometric.nn as geom_nn\n",
-    "from torch_geometric.nn.models import GIN, GAT, GCN, AttentiveFP\n",
-    "import torch as torch\n",
-    "from typing import Literal\n",
-    "\n",
-    "class GnnSubModel(pl.LightningModule):\n",
-    "\n",
-    "    def __init__(\n",
-    "            self,\n",
-    "            num_node_features: int = 9,\n",
-    "            node_edge_dim: int = 3,\n",
-    "            model_type: Literal['gin', 'gat', 'gcn', 'attentivefp'] = 'gin',\n",
-    "            hidden_channels: int = 32,\n",
-    "            num_layers: int = 8,\n",
-    "            out_channels: int = 8,\n",
-    "            dropout: float = 0.2,\n",
-    "            act: Literal['relu', 'elu'] = 'relu',\n",
-    "            jk: Literal['max', 'last', 'cat', 'lstm'] = 'max',\n",
-    "            norm: Literal['batch', 'layer'] = 'batch',\n",
-    "            num_timesteps: int = 16,\n",
-    "        ):\n",
-    "        \"\"\"Initialize a GNN submodel for encoding SMILES strings into a fixed-length vector representation.\n",
-    "        The SMILES embedding size will be `out_channels` unless the `model_type` is set to `gin`. If so, the SMILES embedding size will be `hidden_channels`.\n",
-    "        \n",
-    "        Args:\n",
-    "            num_node_features (int, optional): Number of node features. Defaults to 9. See `from_smiles` [implementation](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/utils/smiles.html#from_smiles).\n",
-    "            node_edge_dim (int, optional): Number of edge features. Defaults to 3. See `from_smiles` [implementation](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/utils/smiles.html#from_smiles).\n",
-    "            model_type (Literal['gin', 'gat', 'gcn', 'attentivefp'], optional): Type of GNN to use. Defaults to 'gin'.\n",
-    "            hidden_channels (int, optional): Number of hidden channels. Defaults to 32.\n",
-    "            num_layers (int, optional): Number of GNN layers. Defaults to 8.\n",
-    "            out_channels (int, optional): Number of output channels. Defaults to 8.\n",
-    "            dropout (float, optional): Dropout probability. Defaults to 0.2.\n",
-    "            act (Literal['relu', 'elu'], optional): Activation function. Defaults to 'relu'.\n",
-    "            jk (Literal['max', 'last', 'cat', 'lstm'], optional): JK aggregation type. Defaults to 'max'.\n",
-    "            norm (Literal['batch', 'layer'], optional): Normalization type. Defaults to 'batch'.\n",
-    "            num_timesteps (int, optional): Number of timesteps for AttentiveFP. Defaults to 16.\n",
-    "        \"\"\"\n",
-    "        super().__init__()\n",
-    "        # Set our init args as class attributes\n",
-    "        self.__dict__.update(locals()) # Add arguments as attributes\n",
-    "        self.save_hyperparameters()\n",
-    "        self.smiles_embedding_size = out_channels\n",
-    "        if model_type == 'gin':\n",
-    "            self.smiles_embedding_size = hidden_channels\n",
-    "            self.gnn = GIN(\n",
-    "                in_channels=num_node_features,\n",
-    "                hidden_channels=hidden_channels,\n",
-    "                num_layers=num_layers,\n",
-    "                dropout=dropout,\n",
-    "                act=act,\n",
-    "                norm=norm,\n",
-    "                jk=jk,\n",
-    "            )\n",
-    "        elif model_type == 'gat':\n",
-    "            self.gnn = GAT(\n",
-    "                in_channels=num_node_features,\n",
-    "                hidden_channels=hidden_channels,\n",
-    "                num_layers=num_layers,\n",
-    "                out_channels=out_channels,\n",
-    "                dropout=dropout,\n",
-    "                act=act,\n",
-    "                norm=norm,\n",
-    "                jk=jk,\n",
-    "            )\n",
-    "        elif model_type == 'gcn':\n",
-    "            self.gnn = GCN(\n",
-    "                in_channels=num_node_features,\n",
-    "                hidden_channels=hidden_channels,\n",
-    "                num_layers=num_layers,\n",
-    "                out_channels=out_channels,\n",
-    "                dropout=dropout,\n",
-    "                act=act,\n",
-    "                norm=norm,\n",
-    "                jk=jk,\n",
-    "            )\n",
-    "        elif model_type == 'attentivefp':\n",
-    "            self.gnn = AttentiveFP(\n",
-    "                in_channels=num_node_features,\n",
-    "                hidden_channels=hidden_channels,\n",
-    "                out_channels=out_channels,\n",
-    "                edge_dim=node_edge_dim,\n",
-    "                num_layers=num_layers,\n",
-    "                num_timesteps=num_timesteps,\n",
-    "                dropout=dropout,\n",
-    "            )\n",
-    "        else:\n",
-    "            raise ValueError(f'Unknown model type: {model_type}. Available: gin, gat, gcn, attentivefp')\n",
-    "        \n",
-    "        \n",
-    "    def forward(self, mol_graph):\n",
-    "        if self.model_type == 'gin':\n",
-    "            x = self.gnn(mol_graph.x, mol_graph.edge_index)\n",
-    "            smiles_emb = geom_nn.global_add_pool(x, mol_graph.batch)\n",
-    "        elif self.model_type == 'gat':\n",
-    "            x = self.gnn(\n",
-    "                x=mol_graph.x.to(torch.float),\n",
-    "                edge_index=mol_graph.edge_index,\n",
-    "                edge_attr=mol_graph.edge_attr,\n",
-    "            )\n",
-    "            smiles_emb = geom_nn.global_add_pool(x, mol_graph.batch)\n",
-    "        elif self.model_type == 'gcn':\n",
-    "            x = self.gnn(\n",
-    "                x=mol_graph.x.to(torch.float),\n",
-    "                edge_index=mol_graph.edge_index,\n",
-    "                edge_attr=mol_graph.edge_attr,\n",
-    "            )\n",
-    "            smiles_emb = geom_nn.global_add_pool(x, mol_graph.batch)\n",
-    "        elif self.model_type == 'attentivefp':\n",
-    "            smiles_emb = self.gnn(\n",
-    "                mol_graph.x.to(torch.float),\n",
-    "                mol_graph.edge_index,\n",
-    "                mol_graph.edge_attr,\n",
-    "                mol_graph.batch,\n",
-    "            )\n",
-    "        return smiles_emb\n",
-    "\n",
-    "\n",
-    "    def get_embedding_size(self):\n",
-    "        return self.smiles_embedding_size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 416,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'mol_graph'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[416], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m protac_dl \u001b[38;5;241m=\u001b[39m DataLoader(protac_ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, collate_fn\u001b[38;5;241m=\u001b[39mcustom_collate)\n\u001b[0;32m      6\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(protac_dl))\n\u001b[1;32m----> 7\u001b[0m smiles_emb \u001b[38;5;241m=\u001b[39m gnn_model(\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmol_graph\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSMILES embedding shape: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msmiles_emb\u001b[38;5;241m.\u001b[39msize()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'mol_graph'"
-     ]
-    }
-   ],
-   "source": [
-    "gnn_model = GnnSubModel()\n",
-    "# Test it on a batch from the DataLoader\n",
-    "protac_ds = PROTAC_Dataset(\n",
-    "    protac_df[~protac_df[active_col].isna()], protein_embeddings, cell2embedding, smiles2fp, use_smote=False)\n",
-    "protac_dl = DataLoader(protac_ds, batch_size=8, shuffle=True, collate_fn=custom_collate)\n",
-    "batch = next(iter(protac_dl))\n",
-    "smiles_emb = gnn_model(batch['mol_graph'])\n",
-    "print(f'SMILES embedding shape: {smiles_emb.size()}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Full Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 460,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "import pytorch_lightning as pl\n",
-    "from torchmetrics import (\n",
-    "    Accuracy,\n",
-    "    AUROC,\n",
-    "    Precision,\n",
-    "    Recall,\n",
-    "    F1Score,\n",
-    ")\n",
-    "from torchmetrics import MetricCollection\n",
-    "\n",
-    "# Ignore UserWarning from PyTorch Lightning\n",
-    "warnings.filterwarnings(\"ignore\", \".*does not have many workers.*\")\n",
-    "\n",
-    "\n",
-    "class PROTAC_Model(pl.LightningModule):\n",
-    "\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        hidden_dim: int,\n",
-    "        smiles_emb_dim: int = fingerprint_size,\n",
-    "        poi_emb_dim: int = 1024,\n",
-    "        e3_emb_dim: int = 1024,\n",
-    "        cell_emb_dim: int = 768,\n",
-    "        batch_size: int = 32,\n",
-    "        learning_rate: float = 1e-3,\n",
-    "        dropout: float = 0.2,\n",
-    "        join_embeddings: Literal['beginning', 'concat', 'sum'] = 'concat',\n",
-    "        train_dataset: PROTAC_Dataset = None,\n",
-    "        val_dataset: PROTAC_Dataset = None,\n",
-    "        test_dataset: PROTAC_Dataset = None,\n",
-    "        disabled_embeddings: list = [],\n",
-    "        apply_scaling: bool = False,\n",
-    "    ):\n",
-    "        super().__init__()\n",
-    "        self.poi_emb_dim = poi_emb_dim\n",
-    "        self.e3_emb_dim = e3_emb_dim\n",
-    "        self.cell_emb_dim = cell_emb_dim\n",
-    "        self.smiles_emb_dim = smiles_emb_dim\n",
-    "        self.hidden_dim = hidden_dim\n",
-    "        self.batch_size = batch_size\n",
-    "        self.learning_rate = learning_rate\n",
-    "        self.join_embeddings = join_embeddings\n",
-    "        self.train_dataset = train_dataset\n",
-    "        self.val_dataset = val_dataset\n",
-    "        self.test_dataset = test_dataset\n",
-    "        self.disabled_embeddings = disabled_embeddings\n",
-    "        self.apply_scaling = apply_scaling\n",
-    "        # Set our init args as class attributes\n",
-    "        self.__dict__.update(locals())  # Add arguments as attributes\n",
-    "        # Save the arguments passed to init\n",
-    "        ignore_args_as_hyperparams = [\n",
-    "            'train_dataset',\n",
-    "            'test_dataset',\n",
-    "            'val_dataset',\n",
-    "        ]\n",
-    "        self.save_hyperparameters(ignore=ignore_args_as_hyperparams)\n",
-    "\n",
-    "        # Define \"surrogate models\" branches\n",
-    "        if self.join_embeddings != 'beginning':\n",
-    "            if 'poi' not in self.disabled_embeddings:\n",
-    "                self.poi_emb = nn.Linear(poi_emb_dim, hidden_dim)\n",
-    "            if 'e3' not in self.disabled_embeddings:\n",
-    "                self.e3_emb = nn.Linear(e3_emb_dim, hidden_dim)\n",
-    "            if 'cell' not in self.disabled_embeddings:\n",
-    "                self.cell_emb = nn.Linear(cell_emb_dim, hidden_dim)\n",
-    "            if 'smiles' not in self.disabled_embeddings:\n",
-    "                self.smiles_emb = nn.Linear(smiles_emb_dim, hidden_dim)\n",
-    "\n",
-    "        # Define hidden dimension for joining layer\n",
-    "        if self.join_embeddings == 'beginning':\n",
-    "            joint_dim = smiles_emb_dim if 'smiles' not in self.disabled_embeddings else 0\n",
-    "            joint_dim += poi_emb_dim if 'poi' not in self.disabled_embeddings else 0\n",
-    "            joint_dim += e3_emb_dim if 'e3' not in self.disabled_embeddings else 0\n",
-    "            joint_dim += cell_emb_dim if 'cell' not in self.disabled_embeddings else 0\n",
-    "        elif self.join_embeddings == 'concat':\n",
-    "            joint_dim = hidden_dim * (4 - len(self.disabled_embeddings))\n",
-    "        elif self.join_embeddings == 'sum':\n",
-    "            joint_dim = hidden_dim\n",
-    "\n",
-    "        self.fc0 = nn.Linear(joint_dim, joint_dim)\n",
-    "        self.fc1 = nn.Linear(joint_dim, hidden_dim)\n",
-    "        self.fc2 = nn.Linear(hidden_dim, hidden_dim)\n",
-    "        self.fc3 = nn.Linear(hidden_dim, 1)\n",
-    "\n",
-    "        self.dropout = nn.Dropout(p=dropout)\n",
-    "\n",
-    "        stages = ['train_metrics', 'val_metrics', 'test_metrics']\n",
-    "        self.metrics = nn.ModuleDict({s: MetricCollection({\n",
-    "            'acc': Accuracy(task='binary'),\n",
-    "            'roc_auc': AUROC(task='binary'),\n",
-    "            'precision': Precision(task='binary'),\n",
-    "            'recall': Recall(task='binary'),\n",
-    "            'f1_score': F1Score(task='binary'),\n",
-    "            'opt_score': Accuracy(task='binary') + F1Score(task='binary'),\n",
-    "            'hp_metric': Accuracy(task='binary'),\n",
-    "        }, prefix=s.replace('metrics', '')) for s in stages})\n",
-    "\n",
-    "        # Misc settings\n",
-    "        self.missing_dataset_error = \\\n",
-    "            '''Class variable `{0}` is None. If the model was loaded from a checkpoint, the dataset must be set manually:\n",
-    "            \n",
-    "            model = {1}.load_from_checkpoint('checkpoint.ckpt')\n",
-    "            model.{0} = my_{0}\n",
-    "            '''\n",
-    "        \n",
-    "        # Apply scaling in datasets\n",
-    "        if self.apply_scaling:\n",
-    "            use_single_scaler = True if self.join_embeddings == 'beginning' else False\n",
-    "            self.scalers = self.train_dataset.fit_scaling(use_single_scaler)\n",
-    "            self.train_dataset.apply_scaling(self.scalers, use_single_scaler)\n",
-    "            self.val_dataset.apply_scaling(self.scalers, use_single_scaler)\n",
-    "            self.test_dataset.apply_scaling(self.scalers, use_single_scaler)\n",
-    "\n",
-    "    def forward(self, poi_emb, e3_emb, cell_emb, smiles_emb):\n",
-    "        embeddings = []\n",
-    "        if self.join_embeddings == 'beginning':\n",
-    "            if 'poi' not in self.disabled_embeddings:\n",
-    "                embeddings.append(poi_emb)\n",
-    "            if 'e3' not in self.disabled_embeddings:\n",
-    "                embeddings.append(e3_emb)\n",
-    "            if 'cell' not in self.disabled_embeddings:\n",
-    "                embeddings.append(cell_emb)\n",
-    "            if 'smiles' not in self.disabled_embeddings:\n",
-    "                embeddings.append(smiles_emb)\n",
-    "            x = torch.cat(embeddings, dim=1)\n",
-    "            x = self.dropout(F.relu(self.fc0(x)))\n",
-    "        else:\n",
-    "            if 'poi' not in self.disabled_embeddings:\n",
-    "                embeddings.append(self.poi_emb(poi_emb))\n",
-    "            if 'e3' not in self.disabled_embeddings:\n",
-    "                embeddings.append(self.e3_emb(e3_emb))\n",
-    "            if 'cell' not in self.disabled_embeddings:\n",
-    "                embeddings.append(self.cell_emb(cell_emb))\n",
-    "            if 'smiles' not in self.disabled_embeddings:\n",
-    "                embeddings.append(self.smiles_emb(smiles_emb))\n",
-    "            if self.join_embeddings == 'concat':\n",
-    "                x = torch.cat(embeddings, dim=1)\n",
-    "            elif self.join_embeddings == 'sum':\n",
-    "                if len(embeddings) > 1:\n",
-    "                    embeddings = torch.stack(embeddings, dim=1)\n",
-    "                    x = torch.sum(embeddings, dim=1)\n",
-    "                else:\n",
-    "                    x = embeddings[0]\n",
-    "        x = self.dropout(F.relu(self.fc1(x)))\n",
-    "        x = self.dropout(F.relu(self.fc2(x)))\n",
-    "        x = self.fc3(x)\n",
-    "        return x\n",
-    "\n",
-    "    def step(self, batch, batch_idx, stage):\n",
-    "        poi_emb = batch['poi_emb']\n",
-    "        e3_emb = batch['e3_emb']\n",
-    "        cell_emb = batch['cell_emb']\n",
-    "        smiles_emb = batch['smiles_emb']\n",
-    "        y = batch['active'].float().unsqueeze(1)\n",
-    "\n",
-    "        y_hat = self.forward(poi_emb, e3_emb, cell_emb, smiles_emb)\n",
-    "        loss = F.binary_cross_entropy_with_logits(y_hat, y)\n",
-    "\n",
-    "        self.metrics[f'{stage}_metrics'].update(y_hat, y)\n",
-    "        self.log(f'{stage}_loss', loss, on_epoch=True, prog_bar=True)\n",
-    "        self.log_dict(self.metrics[f'{stage}_metrics'], on_epoch=True)\n",
-    "\n",
-    "        return loss\n",
-    "\n",
-    "    def training_step(self, batch, batch_idx):\n",
-    "        return self.step(batch, batch_idx, 'train')\n",
-    "\n",
-    "    def validation_step(self, batch, batch_idx):\n",
-    "        return self.step(batch, batch_idx, 'val')\n",
-    "\n",
-    "    def test_step(self, batch, batch_idx):\n",
-    "        return self.step(batch, batch_idx, 'test')\n",
-    "\n",
-    "    def configure_optimizers(self):\n",
-    "        return optim.Adam(self.parameters(), lr=self.learning_rate)\n",
-    "\n",
-    "    def predict_step(self, batch, batch_idx):\n",
-    "        poi_emb = batch['poi_emb']\n",
-    "        e3_emb = batch['e3_emb']\n",
-    "        cell_emb = batch['cell_emb']\n",
-    "        smiles_emb = batch['smiles_emb']\n",
-    "\n",
-    "        if self.apply_scaling:\n",
-    "            if self.join_embeddings == 'beginning':\n",
-    "                embeddings = np.hstack([\n",
-    "                    np.array(smiles_emb.tolist()),\n",
-    "                    np.array(poi_emb.tolist()),\n",
-    "                    np.array(e3_emb.tolist()),\n",
-    "                    np.array(cell_emb.tolist()),\n",
-    "                ])\n",
-    "                embeddings = self.scalers.transform(embeddings)\n",
-    "                smiles_emb = embeddings[:, :self.smiles_emb_dim]\n",
-    "                poi_emb = embeddings[:, self.smiles_emb_dim:self.smiles_emb_dim+self.poi_emb_dim]\n",
-    "                e3_emb = embeddings[:, self.smiles_emb_dim+self.poi_emb_dim:self.smiles_emb_dim+2*self.poi_emb_dim]\n",
-    "                cell_emb = embeddings[:, -self.cell_emb_dim:]\n",
-    "            else:\n",
-    "                poi_emb = self.scalers['Uniprot'].transform(poi_emb)\n",
-    "                e3_emb = self.scalers['E3 Ligase Uniprot'].transform(e3_emb)\n",
-    "                cell_emb = self.scalers['Cell Line Identifier'].transform(cell_emb)\n",
-    "                smiles_emb = self.scalers['Smiles'].transform(smiles_emb)\n",
-    "\n",
-    "        y_hat = self.forward(poi_emb, e3_emb, cell_emb, smiles_emb)\n",
-    "        return torch.sigmoid(y_hat)\n",
-    "\n",
-    "    def train_dataloader(self):\n",
-    "        if self.train_dataset is None:\n",
-    "            format = 'train_dataset', self.__class__.__name__\n",
-    "            raise ValueError(self.missing_dataset_error.format(*format))\n",
-    "        \n",
-    "        return DataLoader(\n",
-    "            self.train_dataset,\n",
-    "            batch_size=self.batch_size,\n",
-    "            shuffle=True,\n",
-    "            # drop_last=True,\n",
-    "        )\n",
-    "\n",
-    "    def val_dataloader(self):\n",
-    "        if self.val_dataset is None:\n",
-    "            format = 'val_dataset', self.__class__.__name__\n",
-    "            raise ValueError(self.missing_dataset_error.format(*format))\n",
-    "        return DataLoader(\n",
-    "            self.val_dataset,\n",
-    "            batch_size=self.batch_size,\n",
-    "            shuffle=False,\n",
-    "        )\n",
-    "\n",
-    "    def test_dataloader(self):\n",
-    "        if self.test_dataset is None:\n",
-    "            format = 'test_dataset', self.__class__.__name__\n",
-    "            raise ValueError(self.missing_dataset_error.format(*format))\n",
-    "        return DataLoader(\n",
-    "            self.test_dataset,\n",
-    "            batch_size=self.batch_size,\n",
-    "            shuffle=False,\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 418,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of PROTACs with Dmax NaN: 1329\n",
-      "Number of PROTACs with DC50 NaN: 791\n",
-      "Percentage of PROTACs with Dmax NaN: 62.07%\n",
-      "Percentage of PROTACs with DC50 NaN: 36.95%\n",
-      "Number of PROTACs with Dmax not-NaN: 812\n",
-      "Number of PROTACs with DC50 not-NaN: 1350\n",
-      "Number of PROTACs with Dmax and DC50 not-NaN: 525\n",
-      "Percentage of PROTACs with Dmax not-NaN: 37.93%\n",
-      "Percentage of PROTACs with DC50 not-NaN: 63.05%\n",
-      "Percentage of PROTACs with Dmax and DC50 not-NaN: 24.52%\n",
-      "Percentage of PROTACs with Dmax or DC50 not-NaN: 100.98%\n"
-     ]
-    }
-   ],
-   "source": [
-    "dmax_isna = protac_df[protac_df['Dmax (%)'].isna()]\n",
-    "dc50_isna = protac_df[protac_df['DC50 (nM)'].isna()]\n",
-    "print(f'Number of PROTACs with Dmax NaN: {len(dmax_isna)}')\n",
-    "print(f'Number of PROTACs with DC50 NaN: {len(dc50_isna)}')\n",
-    "\n",
-    "# Print the percentages\n",
-    "print(f'Percentage of PROTACs with Dmax NaN: {len(dmax_isna) / len(protac_df) * 100:.2f}%')\n",
-    "print(f'Percentage of PROTACs with DC50 NaN: {len(dc50_isna) / len(protac_df) * 100:.2f}%')\n",
-    "\n",
-    "dmax_notna = protac_df[protac_df['Dmax (%)'].notna()]\n",
-    "dc50_notna = protac_df[protac_df['DC50 (nM)'].notna()]\n",
-    "print(f'Number of PROTACs with Dmax not-NaN: {len(dmax_notna)}')\n",
-    "print(f'Number of PROTACs with DC50 not-NaN: {len(dc50_notna)}')\n",
-    "print(f'Number of PROTACs with Dmax and DC50 not-NaN: {len(protac_df[protac_df[\"Dmax (%)\"].notna() & protac_df[\"DC50 (nM)\"].notna()])}')\n",
-    "\n",
-    "# Print the percentages\n",
-    "print(f'Percentage of PROTACs with Dmax not-NaN: {len(dmax_notna) / len(protac_df) * 100:.2f}%')\n",
-    "print(f'Percentage of PROTACs with DC50 not-NaN: {len(dc50_notna) / len(protac_df) * 100:.2f}%')\n",
-    "\n",
-    "notna_idividual = len(dmax_notna) / len(protac_df) * 100 + len(dc50_notna) / len(protac_df) * 100\n",
-    "notna_combined = len(protac_df[protac_df['Dmax (%)'].notna() & protac_df['DC50 (nM)'].notna()]) / len(protac_df) * 100\n",
-    "\n",
-    "print(f'Percentage of PROTACs with Dmax and DC50 not-NaN: {notna_combined:.2f}%')\n",
-    "print(f'Percentage of PROTACs with Dmax or DC50 not-NaN: {notna_idividual:.2f}%')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test Sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Old Approach"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want a different test set per Cross-Validation (CV) experiment (see further down). We are interested in three scenarios:\n",
-    "* Randomly splitting the data into training and test sets. Hence, the test st shall contain unique SMILES and Uniprots\n",
-    "* Splitting the data according to their Uniprot. Hence, the test set shall contain unique Uniprots\n",
-    "* Splitting the data according to their SMILES, _i.e._, the test set shall contain unique SMILES"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Isolating the unique SMILES and Uniprots:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 419,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique SMILES: 606\n",
-      "Number of unique Uniprot: 24\n",
-      "Number of unique indices: 40 (2.4%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "active_df = protac_df.dropna(subset=['Dmax (%)', 'DC50 (nM)'], how='all').copy()\n",
-    "\n",
-    "# Get the unique SMILES and Uniprot\n",
-    "unique_smiles = active_df['Smiles'].value_counts() == 1\n",
-    "unique_uniprot = active_df['Uniprot'].value_counts() == 1\n",
-    "print(f'Number of unique SMILES: {unique_smiles.sum()}')\n",
-    "print(f'Number of unique Uniprot: {unique_uniprot.sum()}')\n",
-    "# Sample 1% of the len(active_df) from unique SMILES and Uniprot and get the\n",
-    "# indices for a test set\n",
-    "n = int(0.05 * len(active_df)) // 2\n",
-    "unique_smiles = unique_smiles[unique_smiles].sample(n=n, random_state=42)\n",
-    "# unique_uniprot = unique_uniprot[unique_uniprot].sample(n=, random_state=42)\n",
-    "unique_indices = active_df[\n",
-    "    active_df['Smiles'].isin(unique_smiles.index) &\n",
-    "    active_df['Uniprot'].isin(unique_uniprot.index)\n",
-    "].index\n",
-    "print(f'Number of unique indices: {len(unique_indices)} ({len(unique_indices) / len(active_df):.1%})')\n",
-    "\n",
-    "# # Get the test set\n",
-    "# test_df = active_df.loc[unique_indices]\n",
-    "# # Bar plot of the test Active distribution as percentage\n",
-    "# test_df[active_col].value_counts(normalize=True).plot(kind='bar')\n",
-    "# plt.title('Test set Active distribution')\n",
-    "# plt.show()\n",
-    "# # Bar plot of the test Active - OR distribution as percentage\n",
-    "# test_df['Active - OR'].value_counts(normalize=True).plot(kind='bar')\n",
-    "# plt.title('Test set Active - OR distribution')\n",
-    "# plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Isolating the unique Uniprots:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 420,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique Uniprot: 24\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Index(['P10275', 'O60885', 'Q06187', 'Q05397', 'P04035', 'P00533', 'P03372',\n",
-       "       'P00520', 'P40763', 'Q00534',\n",
-       "       ...\n",
-       "       'P62937', 'Q460N5', 'O14965', 'Q92831', 'P53350', 'O14744', 'P51451',\n",
-       "       'P62942', 'Q16186', 'P16591'],\n",
-       "      dtype='object', length=102)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique indices: 1637 (100.0%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "active_df = protac_df.dropna(subset=['Dmax (%)', 'DC50 (nM)'], how='all').copy()\n",
-    "\n",
-    "unique_uniprot = active_df['Uniprot'].value_counts() == 1\n",
-    "print(f'Number of unique Uniprot: {unique_uniprot.sum()}')\n",
-    "display(unique_uniprot.index)\n",
-    "\n",
-    "# NOTE: Since they are very few, all unique Uniprot will be used as test set.\n",
-    "# Get the indices for a test set\n",
-    "unique_indices = active_df[active_df['Uniprot'].isin(unique_uniprot.index)].index\n",
-    "\n",
-    "print(f'Number of unique indices: {len(unique_indices)} ({len(unique_indices) / len(active_df):.1%})')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "DEPRECATED: The following results in a too Before starting any training, we isolate a small group of test data. Each element in the test set is selected so that all the following conditions are met:\n",
-    "* its SMILES is unique\n",
-    "* its POI is unique\n",
-    "* its (SMILES, POI) pair is unique"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 421,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique samples: 10\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
-       "      <th>Active</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>321</th>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>793</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>924</th>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1249</th>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1606</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1624</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1956</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1980</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2084</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Active (Dmax 0.6, pDC50 6.0) Active\n",
-       "104                          True  False\n",
-       "321                          True   True\n",
-       "793                         False  False\n",
-       "924                          True   True\n",
-       "1249                         True   True\n",
-       "1606                        False  False\n",
-       "1624                        False  False\n",
-       "1956                        False  False\n",
-       "1980                         True  False\n",
-       "2084                         True  False"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Percentage of active/inactive PROTACs in test set:\n",
-      "True     0.6\n",
-      "False    0.4\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "from rdkit import Chem\n",
-    "from rdkit.Chem import Draw\n",
-    "\n",
-    "# active_df = protac_df[protac_df[active_col].notna()]\n",
-    "active_df = protac_df.dropna(subset=['Dmax (%)', 'DC50 (nM)']).copy()\n",
-    "\n",
-    "# Find the samples that:\n",
-    "# * have their SMILES appearing only once in the dataframe\n",
-    "# * have their Uniprot appearing only once in the dataframe\n",
-    "# * have their (Smiles, Uniprot) pair appearing only once in the dataframe\n",
-    "unique_smiles = active_df['Smiles'].value_counts() == 1\n",
-    "unique_uniprot = active_df['Uniprot'].value_counts() == 1\n",
-    "unique_smiles_uniprot = active_df.groupby(['Smiles', 'Uniprot']).size() == 1\n",
-    "\n",
-    "# Get the indices of the unique samples\n",
-    "unique_smiles_idx = active_df['Smiles'].map(unique_smiles)\n",
-    "unique_uniprot_idx = active_df['Uniprot'].map(unique_uniprot)\n",
-    "unique_smiles_uniprot_idx = active_df.set_index(['Smiles', 'Uniprot']).index.map(unique_smiles_uniprot)\n",
-    "\n",
-    "# Cross the indices to get the unique samples\n",
-    "unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx & unique_smiles_uniprot_idx].index\n",
-    "# unique_samples = active_df[unique_smiles_idx & unique_uniprot_idx].index\n",
-    "test_df = active_df.loc[unique_samples]\n",
-    "\n",
-    "# Reporting\n",
-    "print(f'Number of unique samples: {len(unique_samples)}')\n",
-    "img = Draw.MolsToGridImage(\n",
-    "    [Chem.MolFromSmiles(s) for s in test_df['Smiles']],\n",
-    "    molsPerRow=5,\n",
-    "    subImgSize=(400, 200),\n",
-    "    legends=[f'{u}\\n({s})' for u, s in zip(test_df['Article DOI'], test_df['Database'])],\n",
-    ")\n",
-    "display(img)\n",
-    "display(test_df[[active_col, 'Active']])\n",
-    "print(f'Percentage of active/inactive PROTACs in test set:\\n{test_df[active_col].value_counts(normalize=True)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following is a way to determine additional sample to include in the test set:\n",
-    "\n",
-    "1. For each target Uniprot, count the number of different E3 ligases associated to it\n",
-    "2. For each target Uniprot, get the SMILES associated to it\n",
-    "3. Based on those SMILES, count all entries that have the same SMILES, but different target Uniprot\n",
-    "\n",
-    "Ideally, we want all entries associated to a target Uniprot IDs that have a balanced amount of active/inactive samples, and well distributed E3 ligase classes.\n",
-    "\n",
-    "The following code helps isolating those entries, but empirically, we already found Uniprot P00533 to be a good candidate for a test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 422,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Uniprot                                               P00533\n",
-       "Number of entries                                         45\n",
-       "Number of entries per E3 ligase                            4\n",
-       "Number of entries with same SMILES                        45\n",
-       "Number of entries with same SMILES and not Uniprot         0\n",
-       "Number of active entries                                  22\n",
-       "Number of inactive entries                                23\n",
-       "Name: 3, dtype: object"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Uniprot                                               P01116\n",
-       "Number of entries                                         31\n",
-       "Number of entries per E3 ligase                            2\n",
-       "Number of entries with same SMILES                        31\n",
-       "Number of entries with same SMILES and not Uniprot         0\n",
-       "Number of active entries                                   7\n",
-       "Number of inactive entries                                24\n",
-       "Name: 7, dtype: object"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Uniprot                                               P33981\n",
-       "Number of entries                                         32\n",
-       "Number of entries per E3 ligase                            1\n",
-       "Number of entries with same SMILES                        32\n",
-       "Number of entries with same SMILES and not Uniprot         0\n",
-       "Number of active entries                                  21\n",
-       "Number of inactive entries                                11\n",
-       "Name: 6, dtype: object"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# In active_df, for each uniprot ID, count the corresponding number of entries,\n",
-    "# then count its corresponding number of entries per E3 ligase. The final dataframe\n",
-    "# should have the following columns:\n",
-    "# * Uniprot\n",
-    "# * Number of entries\n",
-    "# * Number of entries per E3 ligase type (e.g. CRL, VHL, etc.)\n",
-    "\n",
-    "active_df = protac_df[protac_df[active_col].notna()]\n",
-    "# active_df = protac_df.dropna(subset=['Dmax (%)', 'DC50 (nM)']).copy()\n",
-    "\n",
-    "test_candidate_df = active_df['Uniprot'].value_counts().reset_index()\n",
-    "test_candidate_df.columns = ['Uniprot', 'Number of entries']\n",
-    "test_candidate_df['Number of entries per E3 ligase'] = test_candidate_df['Uniprot'].map(active_df.groupby('Uniprot')['E3 Ligase'].nunique())\n",
-    "# Sort by the number of entries per E3 ligase\n",
-    "test_candidate_df = test_candidate_df.sort_values('Number of entries per E3 ligase', ascending=False)\n",
-    "# Take the first row, then get all the SMILES associated to that Uniprot ID\n",
-    "for row_idx in range(len(test_candidate_df)):\n",
-    "    uniprot_id = test_candidate_df['Uniprot'].iloc[row_idx]\n",
-    "    smiles = active_df[active_df['Uniprot'] == uniprot_id]['Smiles']\n",
-    "    # Get the entries in active_df that have the same SMILES but NOT the same Uniprot ID\n",
-    "    uniprot_count = test_candidate_df.iloc[row_idx].copy()\n",
-    "    # uniprot_count['SMILES'] = smiles\n",
-    "    uniprot_count['Number of entries with same SMILES'] = active_df[active_df['Smiles'].isin(smiles)].shape[0]\n",
-    "    uniprot_count['Number of entries with same SMILES and not Uniprot'] = active_df[active_df['Smiles'].isin(smiles) & (active_df['Uniprot'] != uniprot_id)].shape[0]\n",
-    "    # Get the number of active and inactive entries with the same UniProt ID\n",
-    "    uniprot_count['Number of active entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == True)].shape[0]\n",
-    "    uniprot_count['Number of inactive entries'] = active_df[(active_df['Uniprot'] == uniprot_id) & (active_df[active_col] == False)].shape[0]\n",
-    "    \n",
-    "    if uniprot_count['Number of entries'] > 30 and abs(uniprot_count['Number of active entries'] - uniprot_count['Number of inactive entries']) < 20:\n",
-    "        display(uniprot_count)\n",
-    "        # Plot the distribution of E3 in active_df[active_df['Uniprot'] == uniprot_id]\n",
-    "        sns.countplot(data=active_df[active_df['Uniprot'] == uniprot_id], x='E3 Ligase', hue=active_col)\n",
-    "        plt.title(f'Distribution of E3 ligase for UniProt ID {uniprot_id}')\n",
-    "        # plt.xticks(rotation=45)\n",
-    "        plt.grid(axis='y', alpha=0.5)\n",
-    "        plt.tight_layout()\n",
-    "        plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 423,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of entries with UniProt ID P00533: 45\n",
-      "Number of entries in the test set: 55\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:261: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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l6eVLevyYHjxQ53EKhcLHx4eI3r59a2VlJfLpKixZsqRSpUoJCQn6+vp//PHH7NmzPzVyKKgWLerj7LxqwoRYMzO1xjs6OorFQ0NDw1GjRgUEBHh7e3t6en7zzTctW7asXr26jY3NlClT0tLSKleu3LZt27yNHj5n4eHhpLJjaiZmIqIKFcjIiCIiqFIlqliRDAzo1SvFyZOB+/bt++23386dO0dEZmZm6hxQkEjIze3C2bM7Fy9e+PEvA/LdxIkZP/zwAy1YQLNmEVFGC+hsBQQErFq1ysDAYMeOHWa5zXTm5uaiXZJYpQbIP40bk0RCI0cqt29XMSo9PT04ODg1NVWjYzNTeHhzK6sqrq6uuQ6eOJGmTaMVK2jMGFIqaft2OnmS1q6lgQOpenVq1cq1a9euwcHBRMTMhoaGpdXpZw06cvUqeXvThg1qP+CPP6h0aZo6la5cobt38y4w+LxhvoI8gPkK8hszjR9PXbtSxYoUFaXraD5KlSp05QoR0blzVKuWrqPRJYlE4ufn17Bhw0ePHrm6ulpYWFhYWOjr62cOSEhIiIuLS01NVSqVa9eujY+P12G0BYWuM/4FzMuXPGgQh4fz7NkcGMgNG7K5eS430TEz844dO4hIbDpWs7rxhQsX6tat26BBA319/a+//vpTI4cC6e7du1Kp1MDAICoqSv1HKZXKL774Iuvk9R5DQ8Ny5crlVD8LQBAdLPv37696WHg4N2zIv/3GcXGcnMxZK3U+faoICQnZs2dPhw4dxBuPiDR64yUkJIj7fjZs2JCYuXEaCrahQ/noUT56lN3deeZMnjqVb95kT08eMYI9PHjBAt66lU+duvDo0aPU1NQnT55YWVkR0fLly9U8/r1798TEqKI1NECe8PVNs7XtVLnye10E4uPjg4OD582bJ64fzM3N7e3tw8PD1T/wpUtMxOXKsfoNgaOjuUsX9vPjJUu4e/eM+4qIuF27qa1bt54+ffrOnTtFlbk6derEov10QaXxjtGpUzkmhpn5r7941668Cww+e5ivQNswX0F+e/qUZ8zI+HnoUB0GkpaWdurUqRjxflZfaiovWsSbN/PMmezry6mpeRPd5yQqKqpEiRJZU1WWlpbOzs7u7u4+Pj6BgYFhYWG1atUioq+++krNOrSFmDwPc/mfIysrmjGDjh6lTp1o3Tq6eZOSkui77xSnT6ctWPBh/zciioqK2rt377x584jI0NCQmUeOHKnOUzVs2PDs2bMVK1ZMTU3NdUc8FEyHDpGVFTVuTDt2UK9e//4+ISHh6dOnz58/9/b2ViqV/fr1s7GxUf+wEolk6dKl48aNS0pKqlSpko2NTenSpa2trW1tba2trW1sbIoVK6b9FwOFzrVr1wwMDHbu3Fm+fHlPT09LS8sPx8TEUJs2dOcO2dnR8OFERJ6e//61dGlp6dJOTk5OTZo0KVeunNhC5evr6+bmpmYMR48effv2rVQqHTBgwIABA6ysrOzs7MqWLWtvb5/5g5WVVb169T799YIWGRkREUkkRESjR9O0aWRoSOvXU1KS+D0bGrokJSVJJBIjI6PExMRu3bqJ+9PVUbly5U6dOu3bt8/Pz2/q1Kl59BIAsuHhsSw29sCMGcd69Vq3bl1qauqZM2dOnz4dGhqqVCrFEIlEoq+vHxER0a5du5MnT5YoUUKdA+/ZQ0TUqRNJxMdGPU2a0O3bZGpKLi6kp0fNmpGzM9WuPUv+7vTcxcWlRYsW165d69Sp05EjR0QpQihQypcnIjI1pdzfKYGBZGdHDg508SJ16EAhIdSjR57HB58vzFegbZivIL8VK0YxMUREiYmU827CvBAVFXXr1q3Q0NBLly7dunXr9u3biYmJlStX/u233zQoEnDwII0bR+bm1Lw5+fqSnl5ehvx5sLGxCQoKWrRoUdOmTWvUqFGzZs3ixYu/N2bv3r2NGjX666+/pkyZIjKlRZeuM/4FVUoKt2zJRCyTsZ7ebhcXBweHmzdvZv49LCzMx8fH2dlZlKsjIplMNmPGjHPnzqn/JH5+fkRUu3Zt9fcXQIHi7c29e3NKCo8Zwz16cPPmXKUKW1qyRJLxrpDL5aampu/tTAHIHzExMZUqVRJvRQsLi4kTJ763ASo+Pr5Ll1tEXK8ef9As4H39+/c3MzD4pWLFpKpVWb3q2xEREeLaTyKRSCQS0U3rQ/r6+pMnT/7olwlaN3Lkvz/MnMmxsRwYyO3b886dvHgxjx3LffvGN2vWrEyZMnK53NDQUCaTde7cuUmTJup/lx09epSIbGxsUlJS8uplAORg2LBh9O6uREEqlZqbm8vf5Y1WrFhRv3590mSrZp06TMR//aVBGNHRPHcuR0dzpUp8716Ow548eVK2bFkicnV1TcXWqoKnWzeOj+cLF/j4cZXjdu5kfX0uWZLPn+clS3jWLN62LZ9ChM8Z5ivQIsxXoAObN/OMGTx+PD94wHnc4yEqKqp9+/b16tX7cKesRCIRexYtLS3v3r2r7hG7dmUilkrZ0JBfvcrL2Aub4OBgsQt+zZo1uo5Fl5Bwz1l6Ont5sVT61NHRxNiYiMzMzLy8vH744YeqVatmfnRNTEy6des2ceJEiUQil8vVT7grlUoHBwci2vKZto8AZm9vPnaMFyzgMWO4ZMl/76+0ta1VoUKFZs2alStXjojq16+PpBLoSnBwsKurq9jEpKen17Nnz/PnzzNzSkpKu3btLCyKffXVGXUKe0Reu6YoWZINDJiIBw3K/QHp6aEDB1oaGoqrxHHjxjFzdHT0lStX9u7du3z58smTJ/ft21dMg+XLl8cdZwVHRAQzc3w8167NS5dmNE19+DCbkenp6aNGjcrMBQQFBalz/OTk5KtXrzo6OhJRu3bt5s6du2nTplPnz4cnJ6e8S9mviIz0i4o6+PKldl4SQBZv3rzR09MzNDS0tbXNusFTKpXWrFlz6NChZ86ciY6Orl69OhE1a9bs7du3Ko728CHfu8e3b/OYMZycrEEYoaE8bx6/fcuBgblcxN25c6dUqVJE1Lt3b4VC8d5f09LSQkJCfH19u3fvXrFixVmzZmkQBHyyb7/l6dP56FFVPQWT169nmYyJWF+fK1fm6Oh8DBA+b5ivQIswX4EuLV/OcjkvXpx3z+Dm5pbZTcrGxqZNmzYeHh6rVq0KDg6Oj49XKpVff/01ETk61oyLU+PCMyaG9fVZKmUi/uabvAu7sFq7di0RGRoanj17Vtex6AwS7rlQHDnSuEoVele8OFPJkiUHDRq0Z8+ezKrEnp6eRFS5cuX4+Hh1jrx7924iKlOmDNb/P1/e3vziBU+bxn368F9/8cmTfPv2vzuFz58/b2trK/JQP/00RaeRQlF36dKl7777LnM/VJs2bVq0aCGmsjt37qh7lC++yFjkNzDgXJP0Xl5MFF26dKfy5Rs0aJDTmpNSqRRXiQEBAZq8IMhz//sfi7sf0tJUDYuIiNDT05PJZGJHmzpHHjVqlL6+frly5cQ1udCoVy+nkBCnkJC2164N+uefBU+eaOdlAHwgICBAZKY8PT2NjY2dnZ09PDx27Njx4sWLrMPCw8PFqrnqrZobNnDHjpyWxmPGaBbGzz8zEQ8bptbgK1euiJ1ZI0aM4P8Wcc5aZU4ul8vlcrR4yU/jx/Ovv7KPD8+ZwxUrsocHBwb+p8qrr6/vl+XKpRcrxnI5E3HfvrnMqgBZYL4CLcJ8BboUEMASCctkfOBAXhz+2bNnYlY5dOjQmzdvsh0THx/fvLlL48aH27XL/a29xNd3RO3apypVUpqb51HMhZ6otl26dOknRfXKDgn33EVFRbVq1Urch2JiYuLh4REYGPjhqUxycnKdOnWIaKh67SCcnZ2JyCdrg8JcJSfzokU8axYfOaLBoyBvKBQ8bx6/ecNv3vCcOdkMiIyMFPU07O0rVajw4OjRfA8R4L+ioqK8vLxEMXcDAwMLC4srV65o8Pjdu5mIjY25TBk+fVrVyBMnWCZjiYSJlKVKRd6/r2Ls0qVLiahFixYaRAIfKyEhYwN7RAR/sPPsX1u2vCBiU1NWZzmmV69e4v4JiURy+/Zt1YP3798vbggjIisrq5kzZ06cOPF///vfiMWL3a5fb3LpklNIyNc3bky8f391VNTFHE6XAT5F//79iWjevHmxsbFpKq+37t69m+1WTYWCb9zglSu5Xz/+9VfesIEXL9Y4gVW/PhNpcAX3999/i80fxYsXz6xnKFSvXn3gwIFr164dOHAgEenr6x8+fFizaEBzd+9ySAiPH88pKdy8Offo8e+djl99dXPAgAEBAQHjx48XVxBtbWyUlpY8YoSqmRfgA5ivQCswX0GB4OXFBgbr27b9559/choSHh6+ZcuWuLg4TY+9YMECIurWrZvqYY8esbU1E/G4cbkcUFTrIiKHChUYt2J/lLS0tNatWxORo6PjyyJ54zIS7mpJT09v164dEU2cOFHFsJs3bxoZGRHRbhW3aTEnJSWdP3+eiCwtLdXcDp/h11/56lVmZg8P/u++Bsh/AQFMxDVr8u7dOZ6N7Nq1y8qqRNmye4jYzg7/aFAgxMXFTZgwoWPHjn9pVL+TmRUK7tWLly7lJUt41Sp+d39PNtavZ3191tNjIt68WfVRExISrKysiOjChQuaxQOau3KF69ThxET+8UdOSsp+zK1bt0xMTFu23Ll5s1o12c+cOSOWcIho1KhRKkaGh4eLvjoymUwikXy4r03J/CI19XFSEna4Qx5JT08Xa+G5Lg4JV69eFVsy3d3djx07NmvWrI4dO7q4PM1MVQwZwteu8eTJ/O23GoQREcESCZua5vgxzFZAQICpqalUKpXL5U5OTmKja/R/7/cfO3YsEZmZmV28eFGDQ4Owfz//9BNPmpRrfeurV7l0aS5Rgv39mZkvX+bbtzkkhL28uEYNdnKaJK7SJe8Q0bb58/PjJUAhgvkKVMF8BZ8dpXLJsGFEVK1aNdFz4tWrV8HBwatWrfLw8GjTpo21tTURmZqalipV6tGjRxod+5tv/ipevPqePXtyHRkczPr6XKIEP3+e45hr166JFcFcc4Cg2qtXr0xNTeVy+bYi2QoCCXd1zZ07l4h+/PFH1cMWL15MRCVKlIiKijp79uySJUumTp06aNAgV1fXBg0a2NnZ6enpjRs3rmvXrkQ0ZYqGZUYyb+Rbs4bPn/+o1wFa07QpE7FEwoaGqibr0aNfiz3BRDx2bD7GB6BSTEyMZgt+mYYN44gIvn+fPTyy+WtcHG/ezEFB/OefXLWqWtXemSdOnEhEffr0+Zh4QBNXrvCMGTxzJv/4I3t7c4cOPHgwz5jBW7dePXz4cGhoaHR0dN26dYlowIAB6h+2cePG4lLN2Ng4p/0LaWlpzZs3p3cF31WfvJ5Qr/MbgKZOnDghCgCq/5Bjx44ZGBhkbVro4nKgfHnu3ZuXL+eVK/n2bY6L46+/5gULWM272X77bYWj40BPT41XGR8/fhwQEJCcc/VlpVI5aNAgcS4aGhqq6fGLuu+///eHo0d5zBj+++8Pbzs/cya9WDEm4nbtONuK2aGht+bOndu0aVORupJIJIvzsmotFFaYr0AVzFfwGXr79m29evWIqFSpUmLH1XsMDQ1NTEyIqG7duqqbUmQVEiIa6SlTU9Xaih4QwGfOZCRnNm3iV6/47l3etYu9vXn48El169bV09PLDOn69esf/XohKSnJ3NyciE6ePKnrWHQACXd1LVq0tUwZl5kzl6keplQqO3bsSETt2rUbPXr0hzOIOG2SSCQGBgZPnz7VNAi+fJmZ2dOTi+QdGQVHcHAyERsaMhGrriH09i1XrcpE3LEjb93KzPz0KT94kD9hAuTo5s2bl8V8oqnMlb8P3/pKJffrx/fu8R9/8NKl/OYNJySoc8jw8HA9PT09Pb3w8PCPCQnUduUKb9zI3t7cvTt37vzvDcWNG8/L/JKSyWTq9yMRtm7dKjakENH8HHZFTZkyhYjEfWAqyvoD5KkJEyYQ0YQJE9QcHxoa2qJFCwcHB5lMVqFCBU9Pz+3bt0dEZHP+tncvE7GZGatzr0779u2JaMOGDRoFr6b09PTu3bsTkZ2d3cPs+h0/e/Zs165d48aNq1WrVvfu3dFM6F+Z32vDhnGXLhnzo5XV5YkTd+zYIWrCHj16tHLlGvb2j7p0yX3D7/fff09EPXr0yOO4oXDCfMWYr1TAfAWfp9DQUGNjYwsLC3GDrKmpqbhNNtPmzZsdHR2J6Ouvv/6w93K2Ro1iIs2KZd25w199xfv3888/s6vrv9dETZr0ECtPogqrnp7eR14yAzMzb9myhYgaN26s60B0Awl3dYleMdOn5z4yKipK3DJfvHjx9+aOTHZ2dpaWlrEa7eBbvZr37uVFi3jmTA4K+ugXAlrh5uZWu7ZLmTJfNGhwONcaxxcvsiic0KwZb9vGJ0/yH3/kQ4wAeUNsqFEq/91Zkykqin/+OeNnNVtrvdOzZ08imjp16qcHCDlJTOTLl3njRk5K4sqV+eZN3r+fV67kadN4zJilbdq0qVGjhr6+vlwu79u3r0ZHTktLK1OmjDhv/jnzPZDF8ePHRRkZkZfXoFUv6MThwzxlCv/wA1+6pOtQtKxq1aoa7bLx9vbOXCi6ceOGipEKBffpw0RcvDjfvv1KxciXL18aGBjIZLKYmBgNQtdESkqKqIVYuXJlsb0jLCzM39/f3d3dwcFBfBLpXQGB7777Ts0L2sJv3Dg+c4aDg3niRLayYrmcTU2ZaEiNGmJ+q1Onjtj1NnaslzoFXbdt20ZEnTp1yvvQizDMV+9gvipaMF/B58nPz4+IGjVqJLLqgpWVVYsWLYYNG7Z06dIHDx6EhYWJglrT1UjAKRRcqhQTsUa9ye7c4ZUreehQ/uknHjOGy5fnDh144kTetu3KxYsXxeb6IUOGEJGtrW1kZORHv94iTkzvv/32m64D0Q0k3NU1fToTcXZphPcplcoGDRqIJTuxJmZnZ9egQQNXV9dBgwZNmzbNx8enZs2ami0gx8aymRkTcbVqaJGsc7dv35ZKpeIMJte+HFmNH8+jR/O+fUi4g+6lpqbWqVMn/SM6wJw6lVEy8sPV/rdvM+rMJCWxylreHzp9+rQ42UpQb1M8aEqp5K5duVMnvnWLmTkhgZUfVGg/f/78F198IZFIzMzMXr9+rdHxReE1ExOTgQMHenl5+fn5HTx48ObNm2Jp+eDBgxYWFmLa3LJli3ZeEuQdUQxKoeDBg3UdijbdunVL7IdQ3XswqyZNmojTuYoVK+Y6OD2du3dnF5d5dnZ2D/57L9ubN28CAwO9vLzatGljaGhobGzctGnTj3kNanv9+nWDBg3Ep1Ls0spkZmbWrl27GTNmLFiwQPxpmIZLpIVWWhrv3s1793JEBDdpwlIpEymKFdOTSs3MzDJ7P06YMEH54QSanYiICCKytLREijAPYb56B/NV0YL5qrBKTuZx43j2bB43jgvj/aCiwqS/v/+aNWt8fX2DgoKyLfxw5MgRuVwukUi2iioBOdi9m1+94qgoVvteoAwi4X73Ltvb59hpLzU11cXFhYiaNm2qojoW5CQiIkImk+nr678oqs0MkXBX15QpTMSzZ+c+UvRHLlas2JEjR97rDJPp/v37ZmZmenrGO3bcV+vpvb2ZSKxa86ZNmgQO2pGUlPTo0aMzZ87s3r1bfEkYGhoS0ZkzZ9Q/yPjxHB7OrVsj4Q4FwoMHD9Q8BdeAnx///DOPHcuab2GuVauWXC6Xy+U2NjZOTk49e/b08PCYN2/ejh07goOD79+//zHLA/COjw8TsYEB29jwuXPZj9mwYYO43CUiX98lGh0/PDxc9Gr7kImJSY0aNcScOWTIEC28GMhrmemMwpLXSE9Pv3btWpcuXYiod+/eGzduVKdo0rNnz0THPyIaq14blsTElFatWhFRlSpVTp06JTZpOjo6ZuY+xD7NypUrn8/7ZjwxMTHlypUT1ZxtbGxcXV3nzZsXHByctSbDmTNnxEd+8uTJeR3P5+fZM/bzuz1ihNgyLP7tatasqdExypYtS0SoT52HMF8xM+YrwHxVaGzdmlHP4NChwpc1uHfvnkQiMTExUWda8/X1JSIjI6MLOVe/ElvU09N5/HjNIjl4kDduZGbeulVVDdQXL15UrFiRiPr166fZE8C7/Vi9evXSdSA6g4S7WlJSeOdOnjCBd+xg1Su+Fy5c0NfXl0gku3fvVn3MTZv21qjx1tw893LeycnJaQ4OGTWl7O0L5TpnQRMaGtqjR49vv/22ZcuW1atXz7xfQZBKpRYWFj/99JOnp6f6x3zxghcuZGZevlzdJkUAeerAgQOqtwzksz///LNUqVLZZmwFKyurY8eO6TrMz9KVK3eLF38jvkYMDLK5OUFITk4uXbo0EdWt61G79mtNdzg9ffr0999/9/Pz8/LyGjRoUPv27R0cHMzMzMQ/n7m5eaVKlXAHw+fB3Z2fPOHr1/mHH3QdSi7evOGzZ5mZb9zgqCheu5aZ+f59fviQ4+M5KIh//pkHDvxR9GsSeRx7e3siat26da6blVavXk3vmhOoP/m8efNGbNXMSk9Pz8nJycPDY8eOHXlXmeFDb9++9ff3V90e48iRI6IEYk4NGCAhIeHPP/8UXZrq16+v0WO/++47Ilq9enUexQaYrwTMVyBgvvrs/fYbX7vGzBwSwn5+uo5Gy6ZNm0ZEAwYMUHP80KFDicjW1jYiIoKZnz59GhR03NeX3d25WTOuXJnHj+fffuPgYI0T7s7OTMS7duU+8urVq2Jq/fXXX3Mak5aWdv78+du3b2sWRGFXo0YNIjpQhEt0IOGultBQrl2bk5J4+nRWcZN9bGxshQoVSO3mNt98w0Ts7Myqd236+flZGRmtqFgxtXz5jJQt5KW3b986Ojpm7g4QDAwM7O3tGzdu7ObmVqdOHSLStKlgjx5coQKfOpV3gQNo5tdff128eHFBS4AmJSXdvXv32LFjGzdunDNnzsiRI93c3OrVq2dkZCSVSr/77jtdB1gQvXjx4kHOi7fx8fFVq1a1ty9frlw3K6sXS5eqOtTChRuaNt1mappGxLktHKvr9evXN2/e/P3333Ez5udhzRpevJh//pn9/fnT2tP9888/69atGzRoUMWKFVu1aiUKYmrXkyf8yy/MzP7+fP4816/PAQG8bx8PHsxyecZehS++mE5ElSpV6tq1q6hIIDZRdu7cWXWtBjc3t8zVPvWrOjBzTEyMm5ubtbV1t27dFi5cePbs2QLe6C8gIEB0Wcg2z5Kamnr+/PnFixe3atWqSZMmBw8ezP8IC4LExER9fX2ZTCa6Eapp2bJlRNS/f/88i6tow3z1DuYrAfOVgPmq4NDo88jMfO8ee3ry/fs8ciRn10n486VUKkW6TP1FwZSUlJYtWxKRtbW1lZWVmOUsLZWZPU6HD+f0dB4yRLOE+717LJGwqSmrmc75888/pVKpVCrdt2+f+E1kDOr9zAAAIABJREFUZGRgYKCPj0/fvn2dnJzERC2Xy+fMmaNBHIXauXPniKhUqVIafwQKESTc1RIaypMmsbc3T5/OYWGc09fW119/TUQNGzZMUW8T+suXbG/PpUurKr2gVCrFuhARlbCyStbkKxM+zpIlS8TZ6rp16/7+++/Q0NCXL19mHZCcnFy3bl0i+v7DppE5CAtjmYwNDBj9NqDgiIuLGzBggLGxcc+ePbdv354X15Za9OjRI7lcrq+vn22Zv6Js7969dnZ2Yl2wUqVKLVu27NOnz6RJk5YsWbJnz56QkJBevXplXrH36TNEdRmh6Gg2NGSJhIm4Vav8eg2Ql27fvt26detnz56p+4AKFZiIpVIeOlTT50pLSwsJCfHx8enZs2fJkiXf2zX51VdfqXmCpL4nT7h9e549m3v04PPnefx4HjGCf/+dp0xhPT1u0oTHjuW9e59lzhvXrl0TOSyxR7JPnz45FatNSEgwNjYW/fr69Omj3bALoBUrVhCRTCbbvn07Zyni7OrqmvU+Pz09PRsbm7CwMF3H+wmCg9nbmwMDP+KhjRs3JqIgcbO/eq5cuSJ2aXzE0xVBmK8wX6kD85U6MF8VBJGRkS4uLitXrlT3AWfOcM+ePHs2b9yYeyWEz83Ro0eJqFy5cur3CVixYsXcuXNNTExED1UrK6uWLVv++OOLpUv56FGOjubFi5mZz5/nRYt43TpWM7U7fbqCiAcO1CD4WbNmiVkls05mVhKJpHjx4kSkr69/4sQJDY5beA0fPlz9vciFVRFNuD958mT37t05FVh/z+3bfOkSr1nDM2dy7948fDgbGrKrK/v7/yfzLipMFStWTMU2w0zbt/Pz58zMkybxoUO8aBEzc2bn3rQ0jozky5ev7d+/f9SoUeJzS0Q//fSTpq8UNJWenl6pUiUxb9aoUSOnCtehoaFiC7w4z8vVsGFMxGrn5wHy3IMHD6pUqWJiYiIuz4jodf36GVObht0y80337t1JvW71RUdUVJS1tTW9K7yeLQsLC1HrrEyZMu8tH2Zr0CAmYktLnjEjlypqUPCFhISIS5SGDRuqtc3t6lUmYkNDJsrYiqmeq1ev1q5dW2SFMtna2vbq1cvHx2f8+PHiS/Prr7/WbjOG93aMjh/PDx9ys2Z84AAnJmb/kMwqwOLkalQOHZ4PHDhARKIm0o4dO7QYc4Hl7e0tclhlypR5r4izg4PD4MGDV65c2bRpUyKqWLFi5Ge6g+DWLZ4yhZVKnj+fNWnDI4wbN46Ifv75Z/Ufkp6eLjKAWC3OFearD2G+ygnmq1xhvtK5a9euibN0DZKwgwczEcvlbGur7u7rnKWkpFy7dm3p0qVr164tCLeZ9uvXj4i8vLzUHJ+enm5jYyM+2o6OjqrflsOHMxGPHJn7YRUKReXKjk2aLAgO1mAzq1KpbNOmTeZsY2Nj06ZNGw8Pj1WrVgUHB4vKBxMmTCCi4sWL37+vXqfGz4eod3/3Lmd29AgOZlEq5sN8mFhQsbGxkUgkgR+1XlhoFMWE+5EjR6ysrPT19c3MzDw8PEQ1qJz4+7OJCX/3Ha9ZwwkJXKkSd+smeoAzETds2M7V1dXf3//kyZPipC0gIECdGH75hR8/ZmYeNYr37WM3N751i8eM4R49uFSpjK2FVarUynoWKJfLo6KitPJfAFQICIhr0mS+iYkpEc2cOVPFyKVLlxKRpaXlkydPVB8zOjra0bFn8+Z3b93SaqwAH+vChQvi/C/z4sTJ2vrfqc3UlB8+5IULecUKLgAnZ5mCg4OJqGTJkklJSbqOpUBQKBRt27YlImNj49GjR8fFxWVW4/H29h4xYoSbm1v16tXlcrm1tfW+fftOnz6tzmFDQ3ndOl69mt3dmZk16VUBBYtCoahdu3bmeswXX3yR+2dn5sx/E1iaVKJ0cHDQ19eXSqUODg59+/ZdtWrVzZs3xZ9evXolGuqKDUH9+/fXYrvm5895yxZm5kOH+MYNnjePmdnXN8fOwEJmFWDRYDDbfERMTMyMGTMkEom+vv7rgroMqXVDhgzR09OjnIs4JyQkODs7E1HNmjXVWcArcNavz6iife8e51yMNScBAQFE1K5dO/UfkpqaamlpaWJigrLIqmG+ygnmq5xgvlIN85XOzZ8/P3O1TK0kbGIiFyuWcTnWurWmT5eWlhYWFrZ379558+aJOieZy4oGBgY9evT4yJehDbGxsUFBQeJGnNDQUDV3voqlRPGlsGTJEtWDQ0LY2JiJeMWKXA4bFBREROXLl1d/o72Qnp6+bdu248eP57QkrFAoXF1dicjBwaGQTcViJSM4mLdty/jN6tXcpQu/esUjR/Lo0czMy5eznx8rFIrZs2czs2gQ0rRp04Kw2KMrRSvhnpiYOGrUKLGdU/Q9EKdT7u7udz6o6vL8+StX14zpbsAAjotjZn7xghUKjojgxYu5ffsYmUwmDiJWukaLN5oafvmFJ07k2bO5Qwfet48PHeIhQ9jTk9u3z7gtsnRp7tp1WIcOHfr37//ll1+KmRodw/OBkxMTpVepssHKyubFixcqRiqVSjGfuri4iMk6MTHxwYMHp0+f/vPPP5ctWzZ9+vQhQ4Zs3rxZ9Abp3Llzfr0IAFUOHuTWrUeJU/AdO3b07dvXzMzsTxcXJmKJhC0tuW7djFnvzp2C1oKsUaNGRLRWNBor8mbPVrZsecXY2FRcTCbmsEGuVq1aRLQt8/xIPevX89ixfOIEEu6ft/v374vNQWLDppubm+pCiu1btJju6HjZ0TG1USP1n+XZs2dSqVQul0+bNi3bjYR///23yF6Jy06NWo6rLyiIDQx4xAi1BmdWAZZKpSVLlhQ5msjIyB07dnh4eDg5OYlTO0NDw0aa/Kf43CkUimPHju3evVtFEefY2FhRWK9Ro0YaNbMpEI4dy9imdeAA//GHpo9+9uwZEZmbm6u58TkxMbFTp05EZGVl9bnusc1HmK9ygvkqW5ivVMN8VRAMGjSI3tV1dHBwiBNJpRxE7dzJRkZsZsZEGf/06rlx40alSpXE+tN7WzarVavWpEkTsVy3fPnyT35B75s2jZk5PJxXreLBg/n8eY6P519+4XPneM0aHjeO3d0Pi+bPRFSuXDkrK6sGDRo0adJEne1ToiqmVCrV19dXp4Hzpk1sahrfrFmPo0eP5jQmPDxcFIVXf6O9Rt68eVOzZk0i6tChg3bvkdKtZs149mweMuQ/Cfe//+ZJk9jTk2vV4uRkjozkrCspL168EKUj+vbtq5OYC4IilHC/efOm2Dehp6fn5eWlUChCQkL69u0rkuZSqdTV1fXUu46WR44csbW1bdHitoVFxk6EbD158sTHx8fZ2blSpUq1atVSf+nmvR3uf//Nhw+zgwM/ecJRUdn0UBUzdc2aNbGvM08FBv67UWbs2Nz7BUVHR5cuXZqIFi5cOHLkSMqOq6urKL8YHBycDy8BQLW1a1kuZ7k87YcffDOvTxISEmL//JN79WITEyZiX1/OXD4cNkxnsWZn06ZNYjLU4o6zz9SpUyyXs0SSWrXqZFNT03/++SenkatWrSKiBg0aaHT89ev52jUePFitezOhILt+/bpoMyVSSL17985pR09kZKREIhEXbBMnTlT/KVavXp25lSGnRlh79uwR13vif+eJvZ1aNXo0E/GUKeqOX79+vUQikUgkzs7OXbp0ea+Is4mJSatWrQYOHBgbG6v1UD93z58/r1q1KhF9+eWX2Z6aRkZG7ty509PTs27duj/++GP+R6jK/Pns7c2zZ/O5c6z5uZm4erx69WquI+Pj41u3bi2yV+dU72GGdzBf5QTz1UfDfIX5SoeSkpKaNGmSmXNXnYRt166dnanpomrV4ho1Yk16a3Xs2FEsEGbWOfH39w8JCUlISFAqlT169BCzh1wu16imvzrc3fn5cw4J4V9/5bFjeehQfvWKBwzgzKamDg4XxBzl5ORUqlQperdjPdckbFxcnJGRkdgs+/XXX6sZz88/LySiEiVKiOYNr169Cg4OXrVqlYeHR5s2bcQd3lKp1M7OLu+6Ozx48ECUR5s8eXJOY9LS0i5evHj58uU8ikHrsu5wVyg4NZVXr+bbt3npUu7YMcdHhYaGmpubE9GCBQvyLdQCpUgk3JVKpY+Pj7ihpnr16pcuXcr613/++WfIkCGZt9s0a9asc+fO4oPdsWPP8HC1nkLTm9SCgzO2zO/fz7dvs7i7aM2aHMe/fftWnCgU8Z4DeW3aNJZIWCZjmYzVrLt16NAhcf+mWDo2MTGxsLAwMzPL2knD3t7e1tY2j2MH+I/MvT6pqZyYmLGMt2FDRsWqqVM5+3x1QgL/8QdHR/OgQczMyck8fHg+Raye1NRUsUtC6+eLn5eUlIxGcfr6LJHw9u2qJqzExERx2ndW3JisnvXr+fp1vnKF69b95HBB186ePSuyS+J6bEQOmyqXL1+eeSF08uRJ9Y8v7vcSF+oqdqRu2rRJKpWKTZoSiUSDHmLqER+KzOKS6pgzZ46enl7mDYulS5d2dXWdN29ecHBwUb4BVh1hYWFiM3KXLl3EP3pYWJi/v7+7u7uDg0PmKZDIh67I9QbvfBYUxLVrMxHXr6/pQ0UJ2l9yqxj+8uVL0bHQxsbmxo0bHxtoUYT5KieYrz4a5ivVwzBf5amnT5+WKVMmcx0xpyRsRESETCYTH/CBmnTzjI+PNzU1lUgkffv2zXb/zYIFCzKXD62srO7du/eRryQ7Xbrw6tU8dy7/+iuPH88XLvCcOTxxItevz3368Ny5vH9/SlhYmFg6ffjwoVgsFMm3+fPnqziy6I0sWlPs3r1bzXgUCoWbm5v4dhDLt+8pUaJE3bp18/pjfvLkSdFAa/PmzeI3kZGRgYGBPj4+otqPWICRSqVjx47N00i0RbyOu3f5wgU+epTnzOHgYH76lNPScql39eeff0qlUqlUunfv3lye4+JFPnOGlUp+9iyjpdzdu1oKX2cKf8L92bNnHTt2FJ+uvn37vs1hqfD58+deXl7iM2ltbS2Xy8Uu+HyOVoWLFy/q6elJpdIinmbKI35+fOUKM/P48dyrF/fpo8FjXV1d39tmIsjlchsbm9q1a4sbY9etW5dHwQN8KLOr1siR7OXF3t4ZPzdv/m9/ZlWOHuVp0/jHH9VdespHs2fPJiI3NzddB6IbJ05wejonJbG/P5cuzURqzVeTJ08mom+++Uadp1AouGNH9vbO6OGWZ/s/IF8FBgZmrQK8YcMG8fvU1NSQkBAfH5+ePXuKs38isrCwUF3JIauEhARRlFOd/UrLli0TFxgijZVrRU71Xbv2zxdfnGjaNF7Tc7dFixZ169Zt06ZNDx8+1FYwRUTmZuTixYuLPnuZLCwsOnToMGvWrKFDh4p/661bt+o63iw8PJiIjYyYiI8cUf9xSqWyY8eOpUqVkkgkTk5OXl5et7Lrz/P48eNq1aoRUbVq1R6Le1pBE5ivcoL56qNhvsJ8pUOXL18WU5aoDbVz586sf1UoFGFhYWJ1REx9x48fV//ga9eupdx6Jg8ePJje7bKvXr266so2GhGXnA8fZiTcmXnChIw6M9kSmWgiEp81FUlYcWcAEZUsWVJF2agPvXnzxtnZWXTjKFasmJOTU9++fefNm7d3796829X+IbEqLJfLq1SpItJBWUkkEmtr61z/IxRMqamsUXUicfFuZmamaj3P25t37uQ9e/jHH3nLFhaNx3LoE/4ZKeQJ98OHD4tbGES/uFzHx8fHu7i4ENG4cePyITxNzZw5k4js7OxU1xaHjzB2LA8fzgpFRrVijYpViC+DL7744pdffvH39//rr7+uX7/+7NmzzJIXGzduFFPM/ftIXEE+GTqUT53iU6d46FD28uLJk/nOHfb0zKZi1WcnJibG0NBQJpPVrFnTzc1t5MiRc+bM2bhx4/Hjx+/du1fo62716MHLl/PTpzxnDsfE8PDhnEPbnv+IjIzU19eXy+XqXEpNn85EbGvL2jsbhwJh165dcrlcIpHUrFlz27Zt48aNa9KkyXsVP8XFmLGxsTp3oGcelojE6VZOV3pZzZgxw8DAoGbNmvr6+m3btv201/Svn3/+mYjcRatfyC/nz5+3sLAQbxsbG5uePXv6+PiEhIRk3bPi6+tLRHp6evv379dhqP/x5Anr6bFMxkTK9u3VfFB6errIWcjl8qwfnBo1akyaNOn06dPiVf/zzz9ly5Ylovr16z9//jwvX0ZhhvkKtA7zFeYrHfrjjz9EdrVGjRpXrlwRm53d3d2dnZ0zmwsKJUqU0KhypqhILpaOcurnlJqaKtJc4v3fvn17bZUXv3iRmfntW754kYcP5wcPODaW33Whzp5YIRDrqTklYe/du0fv7nMaM2aMplGlpqbu2bPn6dOnmj5Qu7p37y4abFCWaj+rVq0KDg4WLSXmzJkj/iNcv35dt6FqZOFC3ryZ1V7pZqVS+b///Y+IKlSokE0t/rg4XrWKhw7N+L9Dh/KWLTxqFC9YwC1bai1oHSnkCffWrVvr6+vXqFFD/b4fc+fOJaIfClifQEGhUIiJ8sMiVkql8tmzZzdu3Dhy5MiiRYty6psMORk/nk+f5pUrNW4PGBwcTERWVlaq+/D873+9W7TY1Ly5QpPVWYCPN3QoHz/Ox4+zuzt7eXF0NA8bVni6X37//ffv7U7KqlixYh06dCis0+D48fzjj3zpEs+Zo9kDe/fuTUSTJk0KCwvbt2/ftWvXsl27DQ5+Jcpq5VDYFj5vq1evlkgkmRUJiEgmkzk4OLi7u/v7+4eFhaWnp/fo0YOI7O0r3L+vVo2CAQMGEJFUKjUwMHgt7gDNzd27d7t06UJEv6l1x41anJyciKgApUiKjIcPH65YsSIqKkrFmJ9++omIjIyMTpw4ocWnjonJmKnOnOGICA0f3KeP0tT0YOXKdUuVuigSBiqlpPCYMb+JbMXBgwdfv369ffv23r17iz49grW1devWrcXXU8uWLbW4hbBownwFWof5CvOVDk2dOjUz0fwee3v7du3aicI+enp6Z86cUfOYDx8+zOxmMVxlIdBnz56JxRVR2Uaj1hfqWLOGibhVK7UGjxgxIjOS8uXLR2dttcnMzLGxscuXLxcDrohCBJ8hpVK5Z8+eoKCgnL5ulEqluEDL9j9CYZKYmNigQQMiqlevXlpa2oMHD5KSknjzZp4/nxMS2MyMBw/OGDp4MG/ZwsePc3Kyuo3FC7BCnnAfPnw4EWl0A+D69euJqF+/fnkX1ad4+PCh+F5s0qTJwIEDO3XqVL9+fVtb26wTt6mpafny5XMqngPZEjdA/fCDxh0iO3fuTETTp09XPSw2VlG2LBNxbgMBtOO9kjLx8bx/P7u66jQmrXr06NHFixd37969ZMmSH374oXfv3i1atKhQoYK4E1NPT0+LF8YFyvjx/OoV9+6tccI9JCSEiCwtLefNm5f5fWFkZFStWrVWrVr169dvxYoV0dHRtra2zZuvmzlT7U0L8LkZNWqUtbV1mzZtZsyYERgY+OFqcUpKiqtrrwYNosuU4VzviFAoFKIFFhF99dVXasaQmJhoYmIikUjC1WyVkwOlUhkaGurn5/ftt9/KZDIDA4Oc9naBznl6ehKRubl5SEjIh39NSUk5e/bswoULGzdu3LNnTzU3Wt6/z4sXM2cpDKi+lzdulLWwEEmKnj17qh6ckMBffcVyedqXX/YP/m/fwvT09ODg4EmTJlWvXl18EAwNDTt27Ii3olZgvgKdwHwFeUGpVLZu3drc3NzS0tLZ2dnd3d3HxycwMDDrW2jUqFFE1LTpMDXnGy8vL3E+T0S5dru9cuWK6Hshk8mkUumqVas+5eW8p0ULJuJ3BcBykZaWJpr0mpiYdOvW7dmzZyEhIf7+/pMmTXJ1da1YsaIo/GVgYFC1alUtBlkAJSUliYWW5s2bp6Sk6DqcPBQVFWVgYCCRSK5evXrw4MH4+HiOjmbx5k9J4d27edIknjqVN2/m339nsebk4aHbmD9dIU+4z5gxg4imqSgi9YFDhw4RUbt27fIuqk+0Zs2azKqFWZUoUcLR0fHLL78UBbxwl6JGRCG1mBheuVKDilT//POPVCo1NDRU536l48dZKmWZjLOrngegZY8e/ftDZCSHh/PixTxrlk5jyi/z588nopo1a2p0P+b/2bvvuCavLg7gJyFhgwgKIrg3blG0oqh1VLG0jlq1rrpwAiKOWgeuugeOqoijatWKWivuuhFcxb33YAgOFJAVSM77x6WU1zISGwjg7/uHHwr3eXJSws2T89x7DjMnJydPmDChS5cus2fP3rRp0/Hjx+/evVvYPoRMmcLMvHkzf0Sz99q1axsYGBgaGpqampqamop9mpnc3NzEHcSWLVtqa58pFFGJiRkfnKpX59wzCeKqSXx+U/8u1969e4nIyclJ/ZDWrmVR/mHePA4O5rlz+csv+bPP3mW+gEWNiJkzZ6p/TihISqWyd+/eRFS6dGlRRzguLu7o0aO+vr7t2rXLWt5ULpe3adNGnfpgDx+yqyvPns1du2qcwGJm0eRJVNTNJU/x9i07OzMR29hw7pVLbty4MWTIkFmzZiUmJmocDXwszFegdZivIP/kvi0+LS1t8ODNJiaqRo04z9+MSqWqUqWKeClWr15dnQ8+v//+u7Ozc506dQwNDYcOHapR5Ll49OidiQmbmHCuT+7/vHnzpmrVqpl3Cz5gYmLSuHHjjh07FmTVdV158eKFvb09EQ0aNCinMWlpaVeuXAkVlc2Lplu3bhGRmZnZ27dvsx+hUhWH6rf/r5gn3NesWUNEGk0lV65cIaJ69erlX1T/XWBg4ODBgwMCAoKCgi5evBgeHp71btitW7fEtKVOaULISqXiJk2YiNeuVWv84MGDKa/dW1nNmsVLl2Yk3LO90lIqleHh4didAFp38SITsYODruMoEAqFws7OjohOnDih0YE+Pj7ZtrO3srKqV6/el19+2aVLF/ULlOWryZO5ZEnNOto+fPjQ1NQ024vaTDKZrGTJkv9xER8UD7GxXK8eGxpmpI1SUzlzO+zjxxwYeGr48OF16tSRSCRlypSpU6fOjBkzItTeJC8Ky86ePVv9eKZPZ3d3jo3lkSNZJmMiJmK5nKtVq9+rV6/ly5e7urqKQplLlizR7KlCQVEoFCJnZGxsXK1aNbF+TZBKpbVr13Z3d585c2bZsmXFLcA8O2H+lxWjzHz69OmsE6C9vf2QIYunT+e9ezk8nJ894wULmJk3beKWLblCBb537yOeNBQEzFegdZivQFdiY7laNSbibt3yaCx36tSpzIT1HLW3viYnJ5cqVYqI1O9+kadp06aZmdlNnHhAo6Nu3LhRsmRJPT09uVzu4ODQo0cPX1/fwMDAmzdvKjXtKF3EXbp0SaypXblypfhOZGRk1kL/4qcSieT777/Xbagfbfz48UQ0XNOCEkVcMU+479mzR7wFqn/IixcviMja2jr/oioAfn5+RFSqVKncS9TBv+3cyURsYsJ37+YxMjo62tDQUCqV3r9/X/3zBwVxy5aclMRjxvDmzTxpEn//Pbu6crt2g2xtbUWdSjMzsw82AAL8R6mpbGDAUimrV7C0yBMtpr/++mv1Dzl06JBEIpHJZCNGjJg0aVK/fv1atWpVtWpVUT1QMDMza9WqVb5FrYHBg5lIg212CoVCtHf+5ptvYmNjb968efToUX9/f19fX3d393bt2jk4OBgaGhoZGbVRs/gifAJevODgYP7sMz55km/e5AkT+Jtv2NaWibhVq4Xij8LAwEBscp8xY4aap1UqlWXKlCEijTpETZ/Od+/yxIns5cW9e/Po0bxtGz9/nvHTs2fPiiSInp5ey5YBGzZo+lyhgCQlJdWrV09c7RgbGzs7O3t6egYGBmZtKXHv3j1R96NPnz65f+SOjuYDB5iZjx/nJ080DiYxMVG0qqtYsaJYceziEihyo0Tcuze3aMEXLvDkyfziheY1l6FgYb4CrcN8BQUvLY1VKr5zhy0suGdPFh2pPljqrlQqHz16FBQU1KRJEzGzSaVS9ZfL7Nq1i4gaNGigrZhVKlXlypU/YqkTMyclJV24cAE7a5l5+/btoq1u5cqV/71ASiKR2Nraiju1mzdv1nWwGktPT7e1tSWic+fO6TqWAlXME+7nzp0joiZNmqh/SHp6uqhppSjK3S1VKlXnzp2JqH379poWVYB+/bh8+Yfdug1VKBQpKSnPnz8/f/783r17/f39Z8yYMXLkyC5duvz8888//PCDyF5pdPKgIA4I4FmzeMwYdnHhzOukunVHiMlUTK/16tVLSVGr+xOAmho3ZiI+efKTmBBevnxpZGQklUrvqbfCJzKSHR37ENECsUDo/8XExFy+fHnnzp3io06eFRL/LSEhYfPmzX/++eft27c/2MJy7RqLe3Zqt0diZr55kyUSNjPjnPbkfcDb25uIypcvHxsbm9OY8PBwuVwul8ufZ2YFAJjHjmV3d758mT09M96wrK3Z3f32woULQ0NDU1NT//jjD9FIZvHixeqcUCSbKlSooFEY06dzXByvWME5vevOmTOHiGxt6xIp5XLet0+j00PBSUhICAgIOHnyZC5X2leuXLGwsCCikbn2y3r1ij/7jNPS2N+fHz/WOJIZM2YQUcOGDZVKpUqlun///p49kRMncvv2bGXFP/7I69fzkCH8ww+MnYdFBeYr0C7MV1DAli7lBw+Ymfv148OHuXNnZuYxY/jYMZ4/nwcM4MaN2d6+QmYe1sLCwtvb+/fff1f/Idzc3Ihoqdhw8d+kpqZevXpVNIMtX778p7YsXevc3d0z+4Tb2tq2a9fO09PT39//zJkzohLR+vXricjQ0LDIpa0PHDhAahc+Kk6KecL9yZMnRFSuXDmNjhILGQpJ3YCPFhMTI+63+/n56TqWIiYuTlW7dl0imjRpkuig/W8tW7YUJSDm6gCkAAAgAElEQVQ1neyCgvjkSZ49m93ceP16njWLN2zg/fv58uUnERERCoXi/fv3NWrUICJvb+98eoLwaZo6dY+9fYuFH1H5u2gSFZ881VgEnp7ObdqwiYlq2LDduV8EiNts3333nUaRpKSkVKtWTWw6Fj7/fHedOtypEw8ZwuPHc8+erFTymDEanZW7dn3VuvXsFSvW5zny4MGDYvH+2byS+r169RJTn2ahQLHm48PXr/OgQbxsGW/cmP0u9c2bN0skEolEsn59ji/I5OTk4ODgS5cuib8jLy8vjcJYuJAvXuS0NN69O8cxM2f+bGERK5dn7FSLjtboEaBwOXHihNhg5Ovrm+2A8HC+epX79uWlS3nZMs1KbDFzTEyMWCV6/PjxbAdcucIbN/L581yxIhJYRQbmK9AJzFegLUuX8m+/8Zkz/N13fOQI+/jwtm08Zgw3b/7PQr0GDVrb29t36NDBzc1NIpHo6ekdPnxYzfPHxMTI5XKZTBatyaRTrRpHRfGRI/zLL7xjB0+dyt27c/fuO8X9SyKqXLlyzZo18yysBHk6duzY4cOH48XWhuyMGjWKiMqUKVO0Fkh9++23pEnho2KjmCfck5OTiUhfX1+jGyl16tQholq1am3atKlIb28RSRYDA4Oc6nO9ffs2LCxMzRWgn5SQkBCx0aF+/frGxsYlSpQwMzMzMjLKWr9PX1//I2r979/P589zUhJ36pTjmEuXLunr60skkgMHNKuDBpCLDRs2ENG3336r60AKyI0bNyQSiZmZ2bt373IfOWNGRnepPJsfh4eHW1paff75RI326o4ePZqITE1NrayszMzMZDJZ2bIrMi+aPTx4925evVrjhPv+/fvFsruEhISlS5fu3Lnz7NmzERERH7xtRUREiEKN6txruXAhrHXrIzVrKtBACzL5+DAzjx/P/v65DVu+fDkR6enp7dq1K/ObMTExQUFBosucSEb06tVL3FTOKWuQLZWKy5ZlIra359Wrcxsmqi0ZG/Py5dyvHzPzli15/2lD4RQUFCQ+zC9atIiZ09PTr1y5snHj7e++4/LlmYhHjOB58/jHH3nCBI0TWO7u7kTUpUuXnAZERPDZs5yYyDNmMPYcFhWYr0BXMF+BVixdylu38qlT3Ls3HznChw/z6NE8eDAvXcpeXrx2LYeG8rt3/+y38PX1JSJzc/ObN2+qc/4lS5aQhlU3mXnIEPby4iNHeOjQf/L+Li43ZDJZjRo1OnfuLFYijtH0wwxoLj09vVOnTmK/S1Hp/Pfu3Tux9bxo3STQimKecFepVKICQNZSa7kLDAyUy+WZedVaNWsmb9hQdN+4xNuznZ3dokWLfH19hw0b9tVXXzVr1qx8+fKZhYn19PTmz5+v60jzxcqVHBzMzLxqlcbHfvvtt9bW1h8sbJdIJDY2NnXr1v3iiy9cXV01bS346hWXKMHff593++WffvqJiGxsbDS6+QyQi+vXrxNRpUqVdB1IwWnXrl2e28ZVKu7Zk6VSPnZMrXP27ZtKxOovAd+/f79EIpHL5cHBwZnvLM7OEzKvVseP58hIHj+eBwxQ95x/R66qWbMmES1YsCDrNCWTyezs7Jo3b96jR4/379+3bduWiDp27KjmjefPPmMiXrNGs2CgGBP5qJgYzrMyp9hTrK+vf+TIkV27dlWrVi3rK1MqlVpYWBgZGVlaWhobG6vfsZCZz5/PSEsRce7b9tLTuX9/PnCAIyK4c2feto0XLeJnz9R/KChcNm3aJBYjV61a1czMjIgcHX8Uk2fJkjxyJM+bx3FxXLUq37mjQVnk27dvy2QymUx2WzSyz8Hly2It4X9/HlBAMF+BDmG+gv8us6TM6NEZCffnzzmXolYqlUrsT61cufKrV6/yPH/9+vWJSKMSNMzs48ObN/OECbxwIXftypMn8/btfP16kiiBO2bMGPEBRCaTbdt2R6Mzw0eIjY0V71ndu3fP6fNdWlra9evXjx49WsCxZWv16tVE1KFDB10HogPFOeEeExMj6pgT0ezZs/MsKfXu3bvevXtnTa2amppOdHDIqP/n68tqZ+0Lj/fv35cvX140CPo3MzMzkVM2Nzd//BHF5AqftLS0yEjV5ct84ABv3Mje3jxyJKemarx0lJmdnJzEUoKNGzceOHDgypUrUVFR/3HHg68vE7E6TXyVSmWbNm0+4v4zQE6uXbsml8urV6+eyya1Ymbfvn1EZGRk1L9//ylTpqxevfrgwcRr1zLm8h9+4AsXWKXixYv5wgV1z/nXX0zElpZq7dgNDw+3srIiIj8/v7S0tJCQkN9++23x4sWzZp3u3p2bNeNy5XjTJn75kl+94iFDOCFBsyc4ZcoUAwMDcd/UxMTE1NQ060YcfX19T09PcevuhdpL5nbsYCKuXp1RhhGE0aOZmR8/5kWL8h4sugWYmZmJJVdyudzU1FS0eMok9sU3bNgwz90nmSZPZiI2MGAidUvfRkTw4sXs4cHTpiGBVYSlpqZmbR1WtWpVd/dxa9fyzZusUnFKCt+5w8x8/jx36MB2duq+PMTqMA8Pj9yHvXyZkSmDogLzFegQ5iv476KjM5Z6PnvGcXEcF8fMebxUkpKSRPfU9u3bZy3qktlbde7cuX379v3ss8+uXr1KRFZWVpr2ivPxYZWKO3Tg/fuz+envv/8ulUolEknVqkv09fnMGY3ODR/j7t27onVEZg/wyMjIo0eP+vn5ubu7Ozs7Gxsbi5Smmzq5J617/pwPH/ZbvHjQoEFOTk6iMP3WrVt1EImuFduE+4WDB0uXLi3SECL7ULVq1dWrVyclpWY7/tw5btAgvWzZZkZGRlOmTPH09BTvlxEVK2YsUSDiceP4zBneuLFoldm7cOFChw4dhgwZMm3atJUrV+7Zsyc0NPTRo0dJSUliQM+ePYnI2dm5iNbPOXz4cMeOHevWrWtjYyORSGxsojKXjg4fzjdu8Pz5Gifcjx8/TkTW1taZ/5f+u8RELlWKiTIW3ecpPDzc0tKSiNauXautGOCTdfz4cfGh0cnJSdexFBylUlmlShWxz0mwtFSJycHIiEeM4OHDWaFgTdslNGvGBgZ88mQew9LS0lq0aEFErq6uea4u37yZLS159mwNwkhMTKxVqxYRiZx7tkRpLI32wqelcc2aPGIEaoBCho4defZs9vFRK4GlVCr79OlDRFmTVnp6eg4ODu7u7ps2bXr8+HFMTEz16tWJqFOnPmq+xzZv7lK7dtuKFRv17LlBzbBFAis8nCtWRAKrCDty5AgRic+Ny5Yty2lYUhK3bs1EXKUKR0Xlcc4TJ06INKs6mwhNTJgoI+UBhR/mK9AhzFegK5GRkXZ2dkTk5eUVGxs7YMCAxo0bizIvWTVu3FidmzcfCA1lDw++dImjojinNfQzZ85s3ry3sXESEVtZ8aNHWnhSkLtDhw6JVGe5cuUya1dkkkgkdnZ2ItOd7z0dHz/mqVN52jS+coU7d+YSJUQyrn3FilnfW6PynO+Ko+KYcE9KYk/PdyVK2Flbizc8uVwu+qDa2VUoWzbtg6XqCgX/+CPr6TERf/tt1AOxh4f5xYsX62fP5pIlM3K3Zma8fDmvW8fh4dyvHycn6+bZ5YO3b9+WL1+eiGbOnKnrWDT27t07c3PzzNUEEonEzm6uuTmXKMHGxtyjBzOzry8PGaLZaUeO3GVjY6vd/yGrVwe4uIxwc9NgJ8HOnTuJyMTE5M4dbM6Cj7dr1y7xNty9e/fkYjR3qSM5OTkoKGjjxo0zZswYNmxk585cpw6XLMlGRjx2LF+8yMuXa5xwv3GDvbxYpeJLl/jvd4xsTJ48mYjs7OzU2eB57BgTcdmynJr9TeFsDB06lIjEloV3797dvHnz4MGDAQEBvr6+gwYN+uKLLxwcHGxsbEaL9X6a+PNP/uorZmYNu8RB8aTRilFmVigU7dq1s7W17dSp06xZs06cOPHvEpPh4eGNGrlWr57cvn3eRfuePHlCRPr6+kQ0ZcoUNcO+cYNXrODUVD54ENmHIkw0B5PJZHp6ernPpXFx7OjIhobJ33zj8/bt238PSElJCQkJWbBgQYUKFYho3rx56gTw9denXVxW3Lz5KX5KLIowX4EOYb4CHTp79qxYgvPzzz9nljcwMTGxsLDIWu3Ayspq6NChGp25b18mYpmMP/88x7q4KpWqV6+MalqlSvG5c1p4RpCnyZMnZ/5ybW1t27Vr5+np6e/vf+bMmYSEBGbevXu3aKubv60B3d05JYWVSh44kBs1yljaZm7uXaFC1nsAHTt2LKILfP+LYpdwv3qVa9ZkIpbLT5Yvr6enV7t27WvXrqWnpwcGBvbtGyiS5yYmPGIEr1jBzDx+PBsYsJ4eT53K2fRVTkhgPz8uX5779+fhw1msUly7li9dKuBnlq9Onz6tp6cnk8nOnj2brw+kUCgiIiLCwsL279+/YcOGWbNmeXh49OjRo1mzZjVr1tRoGaYwd+5cImrYsGGlSpX+vnPbMXOFu9hhk5DAAQHs66tuhYTr11kiYUvLlNevtbbCMz09vUqVKkS0e/dujQ7s2rWrVCpt1qyZtiKBYmP+/Plt2rRZsWJF7tVC/Pz8xNItT0/PPCtrfTqSknjsWGbmSZM0Trgzs7Mzb9jAO3fy3r186VI2u55OnTql6ery+vWZiH/9Va3Bv/3GLi6rLSxKXr9+XZPA1XLsGI8axTt3spcXr1vHU6fymjW8fz/fuBH55s0brT8cFHJLljAzR0Xx9u3aPO2dO1y6NBNx7955vDuLpiZiodbFixfVPP+0aRlNiaEIU6kiO3Zc6uBQ3cKiRYsWeQ5/+ZLd3IYSkbOzc2JiIjPHxcUdPXpUtMHMXJwh+tKPHz9enRA6duxIRPuz3UUPhQ/mK9AZzFega7/88otYSkz/Ym9v36FDh2+++Uasif7tt9/UPGdCApuaskTCRNy5c24jk5K4cWN2cODQUK5fnx884AsX1P1cAx/t3LlzBw8ezKVg7LRp04jI3Nz81q1b+RXE8OH/fPHdd5l9df9s1cra2vrzzz/v37+/KIAzadL0/IqhsCpGCfeICL51i2/eZEPDjCIwEsnBn376oEDVmTP85ZcskfC4cfzdd/z4MY8Zwxs25FVqSqHgN2/4hx8ydtHMmMHFrsHu+PHjiahy5cpx+bOy4sKFC2KfQU6MjIzKly+f7U3+nKSkpJQtW5aIjhw5MmXKlFGjRs2YMeOXX3YHBfH58/z8+T9rRRs3ZiKeM0et0/brx0Ts6an5k8zZb7/9Jv73anpbb9KkSfSJlQEBdYg0ulhCJZVKnZ2d/fz8IiMjs45RqVTi71oikRTXxsj/hciEP33KI0ZovKDMx4e9vXnNGh47NuOiwsCAu3bd6uLi0rdvXw8Pj1KlSlGWsnrq+OWXlNatz33xxaA8Rz56lLFXb926fCnHf+wYHzjAI0fykCHcrl3mVRM3ajRezNU1atTo06dPgqYl5wH+X1gYm5uzjQ2HhTEzx8f/8659/z7/+usfQ4cOrV27tkQiMTc379Kly9ixY9Xs/ct/38E6fDh/QoeCIZoAGhmxVPpu+XJ1jggPDxcLQitXrizaSmeSSqX16tUbMWJEnz59ZDIZEanzzjhs2DAiGjdu3H9+MlC0Yb6CPGC+gkKgT58+BgYGJUuWdHZ2dnd39/PzO3r0aExMTOaA5cuXE1G1al9cuqTWMqxt2x4aGydaWLwm4h078hj84kVGP6pRo3j4cCTcCwWVSiUqSNeoUSM2NjZfHmP0aH71it+/58GD+Y8/eMQI/vlnPnkyPUtdkZCQkLp1m5Ut+/xTK5ZcXBLu27bxggW8Ywd7e/OYMUzEdnacc0/eu3c5IIAvXuQRIzSp7h0VxWPH8qxZebScL5oUCoXotjFw4MBchn308sZu3brp6+tLpVJbW9sGDRq4uroOGDBg0qRJfn5+27dvP3HihGiZ3b17d/XPuW7dOiKqV69enle0f/7JEgnLZHz+fB7nTEpiGxuWydTtY6OmXr16GRgYjBw5UqOjUlJSbG1tiaiQNJiGwkClUo0bN06k0Rs0aPD1119nVm2TSqUuLi7Lly+PjIxMSUkRPesNDAy2a3ehV/Hi6spEvHSpZkf5+HB0NDdowD/+yA0aZLRnaN36h8xPSpUqVapSpUpAQMDDhw/VPGdKSoqNjQ0Rncmr2ZCzMxPxN99oFrP6RML9yRO2s+M9e3j6dB4yhDt14nbt3MXyBCIyNDRcpOaOfYCcnTnDV6+yvT2/fMnr1/PixdylC9vYMBE3bTpPvNjEFmkDA4Njx46pedqnT1kiYVPTvOs/QKEmes0bGjIR37un5kEPHjwwNzcX1WxNTEycnZ0nTpwYFBQkrmAjIyNNTEwkf/P398/9bN999514HX755Zfqz+dQLGG+gtxgvoLCIc+GqD/8sNnSUmlnl3cLAWZ2cXExMTHV05N9/nl/9euS+vjwvn08bhwS7oVCUlKSKN//QVvdrNLS0m7evLlv376P2RAfG8vz5/OcOfz/i/8+sG5dxhq1kBCNH6HoKi4Jd3f3jC+8vTkhgefM+b8y7dkJCOBbt3j3blZjy9en4vbt26LqvXinDwgImDlz5ujRo7t16+bs7Fy1alUTExNTU9N69epp2tj63r17UqnUwMDgec47Ax4+fGhmZla2bPnffovJaUxWKpXKwcGBiH5VbyIfM4Zr1Ehq23b0v4szZpo2jZk5OJjXrcu4hfv8uXZqkA0YMICIKlSooNEGgrVr1xJR/fr11V8jA8VbSkqKuEctk8kkEomxsXF4eHhcXNzWrVu7dOmSNfMuUrcWFhanTp3SddSF2t69TMQVK+ZYlPAD9+/zvn28cycz8+7d/9RwT07mBw+iTp48uXnzZrHCSPw6NCqhLjb9ffHFF1FRUf/+q795k0XHtsBAbtqUxXag27fVP71aBg3iCRNYLIXJtlzN+/fvRS0vBwcHTE3w371/zyNH8tixvH49u7tnbKcoU4a9vC4vWbLk3LlzCoXCx8eHiIyNjUPUu0hfuXJNgwbe3t7X8jt4yF9OThkviJo11T8oMjJSIpHo6+v7+fklZdfmcsWKFfR3n0ypVJrtznqlUhkUFOTk5JT5riqyqA/mzGFN9mJCMYP5CnKE+QqKCIUio22voyMnJuY28smTJxKJRFQJHzFihPoP4ePDzPztt0i4FxaRkZGiMsSYv5cbR0ZGHj161M/Pz93d3dnZWaQBxeq9/Pt8J5ZG59+isUKo2CXcPTzUbGcaGsqi7vEGdRvIfxJWrVollUolEkk2NV+I6O+iYD5iElXb8OEjiWhIXq1Lt28/WaLEW3NztVaX7927l4js7e0VCoU6MSQnqxwdmxORe+ar5W8xMXzjBt+9y02a8LZtvGMHh4RktF0KC9POKyQtLa1p06ZE1K9fPzUPUalUtWrVIqJt27ZpIQIo+mJjY11cXDLXT+np6a1cuTLrgKSkpKCgoH79+pmYmNjZ2ZUoUeLKlSu6iraoUCq5alU2NEw+cOBpnoNTUtjRkSWSvC8fxd+7uCmizsYgpVL55ZdfTp48WV9fX6wi19fXr1ixYpcu73v35nHjeNkyHjQooy6WlxdnpvFHjcrz3BrYvDmjzcmTJ7kNS0tLK1euHBEdOXJEmw8Pn6T373nqVF67lgcP5t27efNm/veyPJVKNXjwYHETMZdpLSkp6fTp05GRkW3btlX/fjwUXhcusLc3d+/OmpTnWr16NRGZmpoSUXBwcLZjxN1NcU0rl8sPHjyY+aPk5ORVq1aJvjtEZG5uLq6KjY2NO9nasp4eW1ryvHlYjfxpwnwFOcJ8BUXHy5dcsSK3b5/Rhio5mbOmWJVK5aNHj4KCgtq1aydeV0R0Tu1FiFeusLs737nDL19yro3GoECFhoaKHELZsmUzu0Rkkkgk5cqVEzdXpk6dmk8xpKeznx//8gsHBTEz//wz796d8SIR/TWLn+KScD9yhMeP58WL1a3SzbxkScYVkiarD4s/pVLp7u5evXr1zp07Dxw4cPLkycuWLduxY0dwcPDdu3fj4+PDwsLkcrlUKlV/p2RMDJcrl9669eG7d+/mObhnTyZiZ+e8V5u2aNGCiJZqUgni+vXrYs1pu3btOnfu3KhRo06dVsvlGcsRevZkHx/28OC1azkkhFu14tmzeeRIrd2SefDggZmZmfoJ9D179hBRxYoVc9r4A5+UyMjIevXqEZF4gzQwMNiRQyG9q1evenp6ilQvXjzq2LDhfKlS1q1atcpzpLgtX6VK3jXft23blvkJauHChXmeeebMmZm/XCsrK7FBgYhMTVWZJdQ9PHjuXL5zh728eNQonjOH58zh3r3Ve5JqePDgnakpE/H69XkPFovcXV1dtfbw8KkSCSylkps352s5L/FMT0/v0aOH+JzwOMtt+ejo6KCgoIkTJzo7O4sPEnPnztXX19fT03ud12ZHKNRmz+ZNm3jdOlZjCs3K1dVVzJ9WVla5vAmOHTtW3NoUyang4OD4+Hg/Pz+xBIyIKleuLNacXrx4sWXLlkQUU6tWRpsoIhbrJ9CK/BOD+Qqyh/kKiprnz/n2ba5fn1NSeMYM3r+f58/nAQPY0ZEbNhycmYe1sbH5+uuvd+/erf6ZR41iIh47Nv9ih4+0dOlSMY0Qka2tbbt27Tw9Pf39/c+cOSNac/35559iG32+rvicOZMHDuT4ePby4iVL+NEjZm2vISs8ikvCnZlTUliTJgBLlvDKlbxzJ2tSMxyYmWfNmkVEdnZ2al4aTp7MRNyli1onf/OG7e25bds8fpkXLlwgopIlS+bSkfkDcXFxgwcPdnNzs7a2znwLqVdvFBFbWbGDA3t7s48PR0Rww4baX+Eu+Pv7E1GJEiWePs17LW3z5s2JaNmyZVp7eCiyzp07JxYUi91eJUuWzGkVDDNv3LgxM9V79erVgoyziEpISBCLyv/6669chgUFBbVufczYmHMdlSFzDTgRVahQIfc7H8HBweLiRvzixL3JlJSUhw8fBQfzli08bx77+LCPDycn84gRGSvclUpWKrV2dZKSktKwYcPPPus5cKBaZa9iY2NFXdHbWi9qA5+Y8+e5d2/+809+9+6fJoTZSk1N/eKLL4ioSpUqUVFR8+fPz1zWJ0il0hIlShgaGhobG7dAxcCibtiwD79QQ0JCgqGhoZhOBwwYkMtIlUo1aNAg+rv8l5GRkYmJiXghNW7ceOfOnVnLmKpUqpA//uAqVTKyV2ZmvGMHjxjBs2fzjz9+zLODognzFWQP8xUUQffu8ZgxPG8ez5jB33zDmUt8WrTYYG9v36FDh++++07cGly9erWa50xNzWhthc+ghdONGzeCgoJyqXLs5+cnJpmLFy/mUwwzZ/K1azxlSkbC3ceHZ8/mDh3y6dF0rBgl3DW0ZAkfPcp37/L33+s6lKJGqVS2bt2aiLp27Zrn4Pfv2cqKiTg0VN3zP3nCW7bw3LnMzLNmsVLJL17wtWt86BBv2rR5/vz5Y8aMES3dJ02adOnSJTVPu2jRosws5Pfffx8UFHThwoXw8Kisl8ubNmX8e/duRp798WM+eVLdyNUhFry0bNkyPdc1/CEhIURkaWkpbjbCp0ylUllbW0skEvHqrVChQu4pzhs3bhCRuBBft25dgcVZpImKq/b29osWLdq+fXtISMjTp0+zlqsKDw+3srIiop9/3qXmOcUacPFbCwwMzGlYbGysmNDEioPNmzfnHCQz85EjPHIke3tnfFODvt+58vDwEHkB9ftMDB8+nDQs6Qjwb+PGMRGPG6fW4MTERGdnZyKqU6eOeAXK5XJTU1NRsjZTpUqVQtW/7IDCaejQD79Qw65du4hIbCjMc0VeWlqam5ubnp5etWrVxEJRZ2fnILHPOVsKBfv7c+nS3KdPRtcoZp43759uHlDcYb6C7GG+giLo3j1es4anT+eBAzkggL28eO1aDg3ld+/+SVMEBgaKMu7Hjx9X55xBQRHm5qoGDfItaMh/ohtZ2bJlIyIichqjUmWkzm7d4qgoFq+OFy/U6i42cya/fctLlnCPHljhXnyhpMx/8fz585IlSxLR+pxLDyiVnJjIb97wqFHcvr1m51+1iocO5bt3ecwYLlPmn9utlpalMi9PjYyM6tWrJ5VKT6qREVcoFOXLlxcHlilTJln9Htva9urVK1tbWyKaK24p5MDNzY3ys34WFCFpaWliPcu6devq168fHh6e+3ilUimu3YlomCYLbT5ljx8/rlmzJv0/qVRqa2vr5OTUtWtXsTPG1dVV/TYymWvAxYeibMeoVKqvv/6a/t678L2O7gAfOXLEyMhILpeHhYWpf5Tohm1sbIyN8PBfVK/ORHz6tLrjX79+Xbt27Q/+WuVyedOmTb29vXft2hUVFZWf8UJBWbeOZ87kadM06rnWv39/MXsbGBioswkyOTn56NGjnTt3JqJp06ap9RixsRwdzSNHZtRnWLUqt9oiULxgvoLsYb6CIkgk3N+/58qVOZdr+YkTJ4qFgA/UuFvj5uZmZGS5du0pbQYKBUuhUIj1tY6Ojtk2c2bm9HSuWpVPnuStW/nChYyc6vnzvGVL3uefPp0jIlih4E2b+OzZjMoW+/dr8RkUIhJmpk9SQgIZGZFMRm/fUsmSuo6mCPr1119Fb8bLly9Xr1793wOePSNXVwoLo+XLqV8/+rvEnFpWr6aWLWnlSjI0pJMnKTqarK2pTBmqXv0HQ8M0Ozu758+fL1u2TCaTpaen29vbX7t2zdLSMpcTbtq06fvvvzcxMUlMTJw/f/6ECRM0fb5adPTo0S+++EJPT2/RokWVKlV68eJFdHR0TExMVFTUy5cvX7x4ERMTI5fLk5OTw8PDM0s5w6esTZs2p06d2rdvX6dOnUTTpNy5uLicOXOGiBo1anTp0qX8D7A4OH/+/IEDBxITEyMjIyMiIp4/fx4dHZ2eni5+WqJECSo70ZgAACAASURBVBsbm+DgYI3+JEeMGLFmzRoDA4PU1NSrV6/Wr1//gwHLli0bM2aMGFCtWrVLly5l3iwpSHPnzp01a5aNjc2TJ080OtDV1fXQoUPz5s0T1+IAmrp//42LS2p6etmYGFJjbssQFBTUpUsXY2Pj1q1bN2/e3NnZuUmTJuKuFRQrSUkkkdC/WnvlRKlUlilT5vXr10Tk6up64MAB9R4kqXTp0ikpKREREWJJhFr+/JMuXqTPP6e1a2ndOpLJ1D0QiizMV5AbzFdQ1CxdSi9ekJcXqVRka5vj60KlUnXt2jUoKKhWrVrnzp0rUaKE+D4zP3ny5NatW7dv37558+bt27c3b97csGFDZg4PDy9TpkzBPRPQtjdv3jRt2vTRo0f9+vXbvHmzUql88uTJ/fuqq1er37xJd+5Qmzakp0dxcdS8OTk40Lhx9MUXFBFBzs7Ut29uZw4Pp4oVycyMoqPJ0LCgno8O6TjhD0XZd999R0SOjo7Pnz+/du3awYMHd+y4P3cue3lx7968dCl7evLs2TxvHkdGanbmVav4wQM+cIBbtOCclpOKSnbigrV7rpX4VSqVyHNJJBIzM7O3b99qFk0+8PT0lOV6rSPKUPj7++s6UigUxo8fT0S+vr5qjhe9laRSqVwuz+m+NOQpPT09IiLi7NmzO3funDRpUp57C/5NrAGXyWSzZs169OhRSkpK1p/Gx8eLMjVEZGBgcPnyZe3Frpn4+Hhx9XzlyhWNDjx8+DARWVlZpeZeyxYgB/Pnz5dIJJ6e6q3U+5u4a+6dWVkJgJmZT58+TX9XVFuzZo2aR4ke9c2aNdP48R484KNH+f17jQ+EognzFWgR5ivQLZWKK1VSd8tOfHx83bp1iahjx45paWkDBw5s3LhxZiOBTGLM119/nf/hQ767fv26SEnZ2NiI3faOjj9mVp5o3ZrHj+fr17lZM81WuM+ezUTcq1cBPINCAbc34eP9/PPPISEh165dy6zW4uKyIzi4mvjawIAaNqT4eHr4ULPTJidTWBg5O5OrK5UoQRJJ9sOWL18eEhJy//59fX393bt3b968WezL+7dDhw5du3bNyMgoOTl52LBhojuibs2YMSMiIuLq1au1a9e2sbGxtbW1trYuW7asjY2N+M9Dhw716NFj7NixrVq1qlGjhq7jBR1r0qQJEf31118ajTcxMUlISLh+/XrTpk3zMbjiS09Pz87Ozs7Ojoi++eabjzhD9erVHR0dr127NnXq1KlTpxJRmTJlxDnLly9vZ2fXr1+/NWvWpKSkLF68uGHDhlp+AmozMzMbNGjQ0qVLly9fvmHDBvUPNDQ0lEql8fHxH1SkBVBHQkLC9u3bmblVqw83f+QuKCiIiL766qv8iQuKqg0bNsjl8rJly9atW/fLL79U86iPfzlVrUpVq2p8FBRNmK9AuzBfgW6dPElPnlDFitSyZd6DzczMgoKCnJycDh8+PGXKlBMnTjx79oyITExM5HJ5YmJiWloaEd24caNMmTL9+vXL7+ChANStW9ff33/w4MExMTFEVKFChSpVEj7/nBwcqE4dqlGDNmygunXJ1ZUUCkpIICIyNaW/15LlaNs2IqIBA/I7/EJD1xl/KNq2bNlSrVo1GxubOnXqdOjQYdy438aN4yVL+Ndf+cgRXraMk5K4enWOjMxxofq/rVnDROzklPfIsLAwuVwuSiSbmprev38/22GiBJVY7fvs2TO1n5yOifeqRo0aYelosZGWlnbw4MGPOFAU+ihdurSa4x8+fEhERkZGRLRixYqPeETQlvDw8Pbt27do0aJChQpyufzf78IGBgadO3fWdZj85MkTPT09AwODFy9eqHlIbGysuNs6duzYfI0NipXw8IRduzw8PBo2bCgKZKlZuzbTgwcPiMjS0jItLS3/woSi6Oeff5bL5XK5/MCBA2oeolQqRaGwmzdv5mtsUCRhvoJ8g/kKdGvChBATk/dqb59mZj558qRMJjPMrg6Ivb19hw4dPD09V65cmV8Rgy48efLk5MmTubzxxcVx5cosl7MafRX57NmzNjYN3dyOpafnPTiTQqFo1qyZr69vUUyLIeEO+SU1lWNimJnv3OGePXnpUrWOUiq5WjUm4h071Bo/c+bMzMRikyZNFArFBwNevHhhYWGhr69PRAMGDNDkGehYQkJC1apViWjSpEm6jgW0Qyw8Dw4O/ohjRdPOp0+fqjNYpVINGTJELGxv27btRzwc5AeVShUVFXXx4sU9e/YsW7Zs/PjxHTt27NWr15MnT3QdGjNz165dSe3KRZntXps2bfrviRcgg0LB27bxli0cH8/ffcflyjER6+uXNDQUN8IlEom+vv6dO3fUP+XChQuJqF+/fvkXNRRdkyZNEpeFp9XraxkSEkJEVapUye/AoAjAfAUFC/MV6EpCQoKJiYmZmfnjx280OrBhw4ZGRkZmZmbOzs7u7u5+fn5Hjx6NEUkf+FRNnMhEbGnJeXbVHTZsGBFNmDBBo/OLnT0ODg4fH6LuIOEO+W7fPiZiQ0O1mqLv23enTJn0ypVZzbteSqVSLGA3MjJq0qRJdHS0+P7r169v3bp14sSJX3/9dfz48TKZTCKRXL169T88Dx0ICQnR09OTSqUnTpzQdSygBb6+vpRXy4GcdOrUiYh27typ5vg9e/YYGBjo6+uPGzfuIx4OPkGinKi1tXVycnKeg5cuXUpEJUuWVPMmEHyixo7lK1f4xg0eNYorV2YiNjBgM7N+dnZZF0ZVrFhR/R4JLVu2JKLAwMB8DRyKLi8vLyIyNzcPCwvLZdjLly+joqJEfW1s0wFmzFegA5ivoIClpqZev359+PDhROTi4qLRsdHR0TKZTF9f/9WrV/kUHhRFSiV/9RVXr36tVauO7969y3ZMWlpaZlH469eva3T+7t27E9GCBQu0EWxBQ8IdCsKwYUzEjo5JeeZxmjVrpq9vumFDqPonf/LkiWj3V7VqVScnJ3t7e7Ge/YOiDR/TXqYQmDZtmtil9eaNZvefoRCKiYkxNDTU09N79OiRpseKV8KECRMOHTp06tQppVKZy+B169aJrryDBg36oFEnQC7EJoyNGzfmPuzatWtiP+mePXsKJC4osoYPz/hi2DDu2JH/7rV0vG3bFi1aTJgwYefOnZ999hkRVatWLfOWeS5ev34tk8kMDAzi4uLyN3IospRKZa9evUQdttu3b2f90aNHjzZt2uTu7u7g4CCRSHx8fESbnFOnTukqWihEMF9BgcN8BfklIYHDw1ml4gcPODCQfX35m28md+qUtb7l+vXrNTrlggULiKhbt275FDIUXXFxqsaNnYjIzc1NpCkiIyOPHj3q5+fn7u7u7OxsbGxMRMbGxo0bN9bozG/evDEwMNDT01P/VnehgoQ7FITERG7f/nrVqrU9PT1zGSbWV1paWr7XsIX6ihUrPkiylyhRombNmi4uLr169fLy8vLy8kpISPhvT0I30tLSxMX9xy2LhsJmwIABH7c4Zd++fUTUoEEDBwcHIrKysurXr19QUNAHVUFVKpVYRy+RSNSsDQKQadOmTURUt25dVc5tNxISEsRHPg8Pj4KMDYqkIUMyvhg8mLdv56+/5gULODSUs9wIfPfuXaNGjYiofv36b9++/fc5UlJSQkNDFy5cOH369I0bNxJRx44dCyZ8KKIUCkXnzp3FeoWnT58ePHjQzc3N6v87eenr61tbW+vp6ZmamqK+NjBjvgLdwHwF2nfmDPv48IYN7OHB336beftwhYODVCo1NTWVSCRSqXT+/PkanbVu3bpEtHfv3nyKGoq0R48emZiYiDTFvwv9SySSSpUqOTo6NmrUSKO83IoVK4ioU6dO+Rd5vpIwMwHkv8uXL3/22WdpaWn79u3r3Lnz69evY2JiYmJiXrx4IXbJxcTE7N27Nz4+ftq0aTNmzND0/CEhIffu3XNwcChbtqyNjU223TyKqEePHtWrV4+ZN23a1KNHD12HA//JjRs36tevb2pqGhERYW5uruZRzDxhwgR/f/+kpCRRQjQpKUn8yMbGplu3bt98802rVq2YeeTIkQEBATKZbNWqVUOHDs235wHFk0KhqFChQnR0tKOjo6Ojo52dXYUKFezs7MQXYm1C//79t2zZUrdu3QsXLoj+GQA5Cg2lvXtJKqX27alt25xGvXr1ysXF5e7du61btz506JChoWF8fPzFixdDQkJCQ0NDQ0OTk5OJyNTU1MHB4eLFi6tWrRoxYkQBPg0oepKTkzt27BgcHFytWrURI0aMHTuWiIyMjCQSSeYbKBGZmZlNnDhx8uTJuosUCg3MV6AjmK9Ay0aMoJUrSU+P5syh69fp0CFKTCSlMszRscmlS5mj9PT0goKCXF1d1TllWFhYkyZNrK2tIyIisi6TBxDi4uJsbGxSU1PFf9ra2tauXdvBwUH826BBA1FPJiAgwM/PLywsTM1PkU2aNAkLC/vtt9969uyZj9HnH52m++HTMm/ePCISdaVzekHWrl0bbTc+IFrSGxsbb9++XdexgBaIrgPLli1Tc3xqamqfPn2ISC6X165dWyqVij8WmUxmZmaW+bdz/fp1UefdxMTkwIED+foUoBjz8PAwMDDIdn4uWbJkxYoVicjU1PTu3bu6jhSKlefPn1eoUIGI3NzcwsLCJBJJ5gtPIpGUKFFC3EeXSCQWFhbTpk3TdbxQBLx7965hw4ZEpKenl3UqMzc379ix48yZM48fP15Etz+CbmG+Aq3DfAXaNHx4RkO8n37i1auZiKVSrlIletiwSZMmbd269fLly6Jnr7m5+a1bt9Q55ejRo4nI29s7fyOHImvNmjXiQyIRbd26NZeRbdq0cXJySk1NzfOct27dIqISJUokJSVpL9IChRXuUHBUKlXv3r2PHDkSFxdnYWFha2trbW1dtmxZa2trW1vbMmXK6Ovrt2nTpkyZMrqOtHDZu3dvly5dypUr9+jRI9xPLgb++OOPrl27VqpU6cGDBx9cVf/b+/fve/TocfjwYVNT0507d3bs2DEqKmr37t27du0KCQlRqVREJJPJLC0tDQwMwsPDbWxsDhw44OjoWCBPBYqnM2fOxMXFhYeHR0ZGhoeHZ34hVu2VKlXKxcVl9+7dug4TipsHDx60bNkyJiamcePGt27dkslkSUlJSqUyc4C9vX358uXPnTsnkUg2bdrUt29fHUYLRcLLly+bNWv25s0bExOTFi1aODs7t2jRomHDhpm3rgE+DuYr0DrMV6A1wcG0fz/Vq0cXL9LMmfT4MdWqRf+/oJiZe/fuvWPHjho1apw/f97CwiKX8ykUCjs7u9evX1+9erV+/fr5HD0USW1atjwVEkJEVlZWkZGROa3fIqK4uLgGDRrUqlUrKChItJ3LyYQJExYuXDh8+PDVq1drP+ICgYQ7FLT79++XL1++OJV8yW9u7drtP3586dKlY8aM0XUsoAUqlap69eqPHj3au3fvV199lcvI6Ojozp07X758uUyZMgcOHBBlQzO9fv364MGDO3fuPHz4cHp6uoGBga2t7Z9//lmtWrV8fgbwiXrz5s3evXsHDx5sbW397NkzTOOgdWFhYa1bt1YoFGlpaUSkp6dXo0YNkXdwdHSsXbs2Ea1cudLDw0NPT2/Hjh3du3fXdchQBISHh5crV07XUUBxg/kK8gPmK9COuDh69YqqVKEsW3A+kJyc7OLiEhYW1r59+4MHD2amPt++fXvr1q3bt2+Lf9+9ezdp0qTu3bvXrVv3+vXrBfUEoEi5f1/12WfHLS1/0dOz6tBh+fLluQ8/duxY7969mzRpsm/fvmwXIKanpz948KBNmzYxMTHnzp1r1qxZ/sSd75BwByjczp3jzp1PNWvWJDBQ7NCBYmDu3LmTJ082Nzdv2bKlo6NjixYtmjdvLgpkZ3r48GGnTp0ePnxYpUqVw4cPV61aNaezRUdHL1u2TKFQuLu7i26WAPlHlNLbtGlT//79dR0LFEOBgYEBAQFNmzZt3bp106ZNsxbOyjRt2rRZs2bp6+vv27evQ4cOBR8kAABhvgKAIu758+dOTk4xMTHe3t6jRo0aOnTojRs3Xr9+/cGwihUrPn36dMmSJd7e3jqJEwq7yZNpzhwyNKSUlPRLl2T/v0wwW6NGjVq/fr2bm9uOHTukUmlUVFTmPZ5bt25duXIlKSnJzs4uMTHxyZMnue/AKMyQcAco3Lp2pT/+oMmTafZsXYcCWjNs2LCAgICs069MJqtevbpIvjs7OyckJHz11VevXr1q2rTpvn37SpcurcNoAbL65ZdfBg4c2LBhw8uXL+s6Fvh0jR07dunSpWZmZidOnGjcuLGuwwEAyBHmKwAotM6ePfv555+npqbOmDHD19eXiAwMDAwNDRUKhSgmKZiYmIwYMWLhwoW6ixQKK5WKKlWi58+JiGrXpps31TkoLS3N2dn50qVLFhYWycnJWV9sRCSRSCpWrJiYmPjy5UtXV9egoKA8K/EWTki4AxRi9+9TrVokl9OTJ2Rrq+toQDtu3brVoEEDIvL09FyyZIm+vr5cLk9OThYF2QVzc/P4+Pivv/56+/btarbwBigYqampFStWjI6OPnXqVKtWrXQdDnyimHnIkCEbNmwoVarU6dOnHRwcdB0RAED2MF8BQGEmFtPo6ellbURBRBYWFg4ODrVr15bJZAEBAUqlctu2bb169dJVnFBIRUVRt2508yYlJtKiReTjo+Zxd+/edXJySkhIICJbW9vatWuL15uDg0ODBg1MTU2fPXvm5OT08uXLcePGFdGbPUi4AxQa796RhQUxU3w8lShBqal09y4FBlJaGi1YoOvgQGu+/37Dpk2Dvby86tat6+vrGxkZmfkjiURibm4ukUjS09OrV69+8eLFInovF4q3GTNmTJ8+vWvXrr///ruuY4FPl1Kp7Nmz5+7du+3s7EJCQipWrKjriAAAsof5CgAKs0GDBh08eLBSpUp16tSpVatWnTp1HBwc7O3tMwf4+fl5e3sbGRmdPn26SZMmOgwVCp29e+nWLbK0pOfPydOTypRR/9ATJ06YmJjUqlXL3Nw82wGhoaFt27ZNTU0NCAgYMmSIliIuOEi4AxQaHh60YgUlJdH06TR4MPn50WefUXAwrVlDubZvhiIkMJB69qS2baN27TIWxciioqIuXbp06dKl0NDQs2fPJiUlrVq1auTIkV26dNmzZ4+u4wXIxqtXr8qXL69QKO7fv1+lShVdhwOfruTkZFdX13v37h09elS0KAQAKJwwXwFAkTZs2LC1a9eWLVv24sWLdnZ2ug4HCocTJ+j+fRo+nKZOpT59qGZNrT+C2IGhr69/9OhRFxcXrZ8/XyHhDlBo9OtHPXqQQkEXL5JcTmPGUOnStGULVapELVroOjjQguRkqlWLnj2jgADK9gZtWlra9evXFQpF8+bNnZycLly4UOAxAqhl0KBBGzduHDNmzNKlS3UdC3zS4uPjY2NjsVwUAAo/zFcAUHSlpaV16NDh1KlTjo6OZ86cQdVTICKaN4969aKKFSkkhJ49oz598uNBvLy8li9fbmNj89dff5UrVy4/HiKfSHUdAAD8zcCAKlWiChUy/lMi+edfKBaOHKHwcGrUiAYNyn6AXC53dHS0s7OtUaOyvb1ZwUYHoAEvLy8iWrt2bXR0tK5jgU+aubk5slcAUCRgvgKAoksul+/atatKlSqXLl0aNmyYrsOBwqFyZbp7l4jo9m3Kt33PS5YscXV1jYmJ+eqrrxITE/PpUfIDEu4AhYaREdWtS2KTad++NH067dxJp05Rs2a6jgz+q2XLKDaW2rWjxYspIICkuU695crZbt36ZPLkYCJVbuMAdMfExEQul1taWn7QUx4AAAAAAIofKyuroKAgc3PzLVu2FNEmlqBl3bvT1as0ezYpFPmXttLT0/v111+rV69+9epVX1/ffHqU/ICSMgCFxuvXVKoUMdPbt2RpSe/fU0QEVa2KAu7FwJAhZGVF48fT6tU0dWre469ds0pPj61f/6VMVjr/owPQTFpaWsuWLS9cuNCjR4/AwEBdhwMAAAAAAAXhjz/+6N69e8OGDc+dOyeXy3UdDnwq7t27N336dH9//5w6rBZCWOEOUGiUKkVEJJGQpSURkakp1ayJbHvxYGFBlSvT2bPqjpfLyxJRWtqLfIwJ4GNdXrDg0l9/Va5cOSAgQNexAAAAAABAAenSpcvOnTtPnz6NbDsUpBo1amzfvr0IZdsJCXcAgIIxdCj99hsR0du31L07PXyY2+BSpYba2IzV0ytRMLEBaGD//qZTp75o0uS37dtLlMBLFAAAAADgE9KtWzcTExNdRwFQ2CHhDgCQ7+rVI6mUxo0ja2tq1Ih+/506dqSXL3Mcn5b2wsCgelLSlQKMEUANkZE0cCAxl+rRo4mTk66jAQAAAAAAACh0kHAHAMh3/fsTETVqREeO0NOnZGJCz56ljx07J9su20rlO+Z0qVTf3LxDQQcKkLvHj4mIXF1p7FhdhwIAAAAAAABQGCHhDgBQcPz9qVo1Skwka+thW7dO/vbbb9PT08WPmBVv3+68e7fZo0fd7O0XGBhUDQ/30G208ElbuDDji0WLKCWFpk2jn36i8+fpyhX65ReSSHQaHAAAAAAAAEAhhYQ7AEDBKV2aDh+mZs1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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Uniprot</th>\n",
-       "      <th>E3 Ligase</th>\n",
-       "      <th>Cell Line Identifier</th>\n",
-       "      <th>Dmax (%)</th>\n",
-       "      <th>DC50 (nM)</th>\n",
-       "      <th>Active (Dmax 0.6, pDC50 6.0)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>Q16288</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>Hs578T-Dox</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>1000.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>321</th>\n",
-       "      <td>Q92830</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>PSC-THP1</td>\n",
-       "      <td>91.00</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>793</th>\n",
-       "      <td>P10276</td>\n",
-       "      <td>cIAP1</td>\n",
-       "      <td>HT1080 EGFP</td>\n",
-       "      <td>90.00</td>\n",
-       "      <td>10000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>924</th>\n",
-       "      <td>P62937</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>Huh7 IFITM2-/-</td>\n",
-       "      <td>90.00</td>\n",
-       "      <td>30.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1249</th>\n",
-       "      <td>O43924</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>PancTu-I</td>\n",
-       "      <td>83.40</td>\n",
-       "      <td>48.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1606</th>\n",
-       "      <td>O14744</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>MCF-7</td>\n",
-       "      <td>74.00</td>\n",
-       "      <td>1100.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1624</th>\n",
-       "      <td>P35348</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>HEK293</td>\n",
-       "      <td>94.00</td>\n",
-       "      <td>2860.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1956</th>\n",
-       "      <td>P14679</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>A375-C5</td>\n",
-       "      <td>61.00</td>\n",
-       "      <td>50000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1980</th>\n",
-       "      <td>P07900</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>MCF-7</td>\n",
-       "      <td>83.43</td>\n",
-       "      <td>990.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2084</th>\n",
-       "      <td>P04035</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HepG2 hALR</td>\n",
-       "      <td>76.00</td>\n",
-       "      <td>120.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>90.00</td>\n",
-       "      <td>25.30</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>5.90</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>129</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>87.00</td>\n",
-       "      <td>45.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>137</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>98.00</td>\n",
-       "      <td>34.80</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>504</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>79.10</td>\n",
-       "      <td>215.80</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1036</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>OVCAR-8</td>\n",
-       "      <td>97.60</td>\n",
-       "      <td>39.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1037</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HeLa</td>\n",
-       "      <td>68.80</td>\n",
-       "      <td>736.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1038</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>98.90</td>\n",
-       "      <td>11.70</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1039</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>NCI-H3255</td>\n",
-       "      <td>96.60</td>\n",
-       "      <td>22.30</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1040</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>79.10</td>\n",
-       "      <td>215.80</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1198</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>87.00</td>\n",
-       "      <td>45.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1199</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>98.00</td>\n",
-       "      <td>34.80</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1200</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1201</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1202</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>cIAP1</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1203</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>cIAP1</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1205</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1206</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1207</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1209</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1213</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1214</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1215</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1216</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1217</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>MDM2</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1219</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1221</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1222</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1223</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1225</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1227</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1229</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1230</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1231</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>A431 siYAP</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2000.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1415</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>PC9-BrM3</td>\n",
-       "      <td>68.00</td>\n",
-       "      <td>161.00</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1642</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>90.30</td>\n",
-       "      <td>126.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1643</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>80.40</td>\n",
-       "      <td>0.51</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1644</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>80.30</td>\n",
-       "      <td>151.20</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1645</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>74.80</td>\n",
-       "      <td>3.54</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1646</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>82.00</td>\n",
-       "      <td>1.91</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1721</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>CRBN</td>\n",
-       "      <td>PC9-BrM3</td>\n",
-       "      <td>40.00</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1824</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827-Luc [JCRB]</td>\n",
-       "      <td>98.00</td>\n",
-       "      <td>7.95</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1825</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827-Luc [JCRB]</td>\n",
-       "      <td>91.00</td>\n",
-       "      <td>3.57</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1945</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>H1975/WR</td>\n",
-       "      <td>86.00</td>\n",
-       "      <td>1.56</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1946</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>VHL</td>\n",
-       "      <td>HCC827</td>\n",
-       "      <td>82.00</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Uniprot E3 Ligase Cell Line Identifier  Dmax (%)  DC50 (nM)  \\\n",
-       "104   Q16288      CRBN           Hs578T-Dox    100.00    1000.00   \n",
-       "321   Q92830      CRBN             PSC-THP1     91.00       3.00   \n",
-       "793   P10276     cIAP1          HT1080 EGFP     90.00   10000.00   \n",
-       "924   P62937       VHL       Huh7 IFITM2-/-     90.00      30.00   \n",
-       "1249  O43924      CRBN             PancTu-I     83.40      48.00   \n",
-       "1606  O14744       VHL                MCF-7     74.00    1100.00   \n",
-       "1624  P35348      CRBN               HEK293     94.00    2860.00   \n",
-       "1956  P14679      CRBN              A375-C5     61.00   50000.00   \n",
-       "1980  P07900      CRBN                MCF-7     83.43     990.00   \n",
-       "2084  P04035       VHL           HepG2 hALR     76.00     120.00   \n",
-       "60    P00533       VHL             H1975/WR     90.00      25.30   \n",
-       "69    P00533       VHL             H1975/WR    100.00       5.90   \n",
-       "129   P00533      CRBN               HCC827     87.00      45.20   \n",
-       "137   P00533       VHL               HCC827     98.00      34.80   \n",
-       "504   P00533       VHL             H1975/WR     79.10     215.80   \n",
-       "1036  P00533       VHL              OVCAR-8     97.60      39.20   \n",
-       "1037  P00533       VHL                 HeLa     68.80     736.20   \n",
-       "1038  P00533       VHL               HCC827     98.90      11.70   \n",
-       "1039  P00533       VHL            NCI-H3255     96.60      22.30   \n",
-       "1040  P00533       VHL             H1975/WR     79.10     215.80   \n",
-       "1198  P00533      CRBN               HCC827     87.00      45.20   \n",
-       "1199  P00533       VHL               HCC827     98.00      34.80   \n",
-       "1200  P00533      CRBN             H1975/WR       NaN    2000.00   \n",
-       "1201  P00533      CRBN           A431 siYAP       NaN    2000.00   \n",
-       "1202  P00533     cIAP1             H1975/WR       NaN    2000.00   \n",
-       "1203  P00533     cIAP1           A431 siYAP       NaN    2000.00   \n",
-       "1205  P00533      MDM2           A431 siYAP       NaN    2000.00   \n",
-       "1206  P00533      MDM2             H1975/WR       NaN    2000.00   \n",
-       "1207  P00533      MDM2           A431 siYAP       NaN    2000.00   \n",
-       "1209  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1213  P00533      MDM2           A431 siYAP       NaN    2000.00   \n",
-       "1214  P00533      MDM2             H1975/WR       NaN    2000.00   \n",
-       "1215  P00533      MDM2           A431 siYAP       NaN    2000.00   \n",
-       "1216  P00533      MDM2             H1975/WR       NaN    2000.00   \n",
-       "1217  P00533      MDM2           A431 siYAP       NaN    2000.00   \n",
-       "1219  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1221  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1222  P00533       VHL             H1975/WR       NaN    2000.00   \n",
-       "1223  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1225  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1227  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1229  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1230  P00533       VHL             H1975/WR       NaN    2000.00   \n",
-       "1231  P00533       VHL           A431 siYAP       NaN    2000.00   \n",
-       "1415  P00533      CRBN             PC9-BrM3     68.00     161.00   \n",
-       "1642  P00533       VHL             H1975/WR     90.30     126.20   \n",
-       "1643  P00533       VHL               HCC827     80.40       0.51   \n",
-       "1644  P00533       VHL             H1975/WR     80.30     151.20   \n",
-       "1645  P00533       VHL               HCC827     74.80       3.54   \n",
-       "1646  P00533      CRBN               HCC827     82.00       1.91   \n",
-       "1721  P00533      CRBN             PC9-BrM3     40.00     100.00   \n",
-       "1824  P00533       VHL    HCC827-Luc [JCRB]     98.00       7.95   \n",
-       "1825  P00533       VHL    HCC827-Luc [JCRB]     91.00       3.57   \n",
-       "1945  P00533       VHL             H1975/WR     86.00       1.56   \n",
-       "1946  P00533       VHL               HCC827     82.00       0.49   \n",
-       "\n",
-       "     Active (Dmax 0.6, pDC50 6.0)  \n",
-       "104                          True  \n",
-       "321                          True  \n",
-       "793                         False  \n",
-       "924                          True  \n",
-       "1249                         True  \n",
-       "1606                        False  \n",
-       "1624                        False  \n",
-       "1956                        False  \n",
-       "1980                         True  \n",
-       "2084                         True  \n",
-       "60                           True  \n",
-       "69                           True  \n",
-       "129                          True  \n",
-       "137                          True  \n",
-       "504                          True  \n",
-       "1036                         True  \n",
-       "1037                         True  \n",
-       "1038                         True  \n",
-       "1039                         True  \n",
-       "1040                         True  \n",
-       "1198                         True  \n",
-       "1199                         True  \n",
-       "1200                        False  \n",
-       "1201                        False  \n",
-       "1202                        False  \n",
-       "1203                        False  \n",
-       "1205                        False  \n",
-       "1206                        False  \n",
-       "1207                        False  \n",
-       "1209                        False  \n",
-       "1213                        False  \n",
-       "1214                        False  \n",
-       "1215                        False  \n",
-       "1216                        False  \n",
-       "1217                        False  \n",
-       "1219                        False  \n",
-       "1221                        False  \n",
-       "1222                        False  \n",
-       "1223                        False  \n",
-       "1225                        False  \n",
-       "1227                        False  \n",
-       "1229                        False  \n",
-       "1230                        False  \n",
-       "1231                        False  \n",
-       "1415                         True  \n",
-       "1642                         True  \n",
-       "1643                         True  \n",
-       "1644                         True  \n",
-       "1645                         True  \n",
-       "1646                         True  \n",
-       "1721                        False  \n",
-       "1824                         True  \n",
-       "1825                         True  \n",
-       "1945                         True  \n",
-       "1946                         True  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "active_df = protac_df[protac_df[active_col].notna()]\n",
-    "# active_df = protac_df.dropna(subset=['Dmax (%)', 'DC50 (nM)']).copy()\n",
-    "\n",
-    "# Get all entries with the UniProt ID set to P00533\n",
-    "uniprot_id = 'P00533'\n",
-    "uniprot_df = active_df[active_df['Uniprot'] == uniprot_id]\n",
-    "# Add it to the test set\n",
-    "test_df = pd.concat([test_df, uniprot_df])\n",
-    "# Reporting\n",
-    "print(f'Number of entries with UniProt ID {uniprot_id}: {len(uniprot_df)}')\n",
-    "print(f'Number of entries in the test set: {len(test_df)}')\n",
-    "img = Draw.MolsToGridImage(\n",
-    "    [Chem.MolFromSmiles(s) for s in test_df['Smiles']],\n",
-    "    molsPerRow=5,\n",
-    "    subImgSize=(400, 200),\n",
-    "    legends=[f'{u}\\n({s})' for u, s in zip(test_df['Article DOI'], test_df['Database'])],\n",
-    ")\n",
-    "display(img)\n",
-    "# Histogram plot the the E3 ligases hue-ed by the active and inactive PROTACs\n",
-    "sns.countplot(data=test_df, x='E3 Ligase', hue=active_col)\n",
-    "plt.title('E3 ligase distribution in the test set')\n",
-    "# Add the values on top of the bars\n",
-    "for p in plt.gca().patches:\n",
-    "    plt.gca().annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
-    "                       ha='center', va='center', fontsize=11, color='black', xytext=(0, 5),\n",
-    "                       textcoords='offset points')\n",
-    "plt.tight_layout()\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()\n",
-    "display(test_df[['Uniprot', 'E3 Ligase', 'Cell Line Identifier', 'Dmax (%)', 'DC50 (nM)', active_col]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 424,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "# Histogram plot the the E3 ligases hue-ed by the active and inactive PROTACs\n",
-    "sns.countplot(data=train_val_df, x='E3 Ligase', hue=active_col)\n",
-    "plt.title('E3 ligase distribution in the train+val set')\n",
-    "# Add the values on top of the bars\n",
-    "for p in plt.gca().patches:\n",
-    "    plt.gca().annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
-    "                       ha='center', va='center', fontsize=11, color='black', xytext=(0, 5),\n",
-    "                       textcoords='offset points')\n",
-    "plt.tight_layout()\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 425,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "protac_df.shape: (2141, 37)\n",
-      "active_df.shape: (857, 37)\n",
-      "train_df.shape: (644, 38)\n",
-      "val_df.shape: (158, 38)\n",
-      "test_df.shape: (55, 37)\n",
-      "Number of leaking Uniprots: 0\n",
-      "\n",
-      "Active/inactive PROTACs in the training set:\n",
-      "True     0.51\n",
-      "False    0.49\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "\n",
-      "Active/inactive PROTACs in the validation set:\n",
-      "True     0.51\n",
-      "False    0.49\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "\n",
-      "Active/inactive PROTACs in the test set:\n",
-      "True     0.51\n",
-      "False    0.49\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Split the dataset into train, val, and test\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# The train and validation sets will be created from the active PROTACs only,\n",
-    "# i.e., the ones with active_col column not NaN, and that are NOT in the test set\n",
-    "active_df = protac_df[protac_df[active_col].notna()]\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)].copy()\n",
-    "\n",
-    "# Get the 20% amount of the train_val_df\n",
-    "val_len = int(0.2 * len(train_val_df))\n",
-    "# Create a new column with the corresponding Uniprot frequency\n",
-    "train_val_df['Uniprot Frequency'] = train_val_df['Uniprot'].map(train_val_df['Uniprot'].value_counts())\n",
-    "# Isolate the least frequent Uniprots as the validation set\n",
-    "least_freq_uniprot = train_val_df['Uniprot Frequency'].nsmallest(val_len).index\n",
-    "val_df = train_val_df.loc[least_freq_uniprot]\n",
-    "# Get the rest as the training set\n",
-    "train_df = train_val_df[~train_val_df.index.isin(val_df.index)]\n",
-    "# Isolate the entries in the val set that have leaking Uniprot in the train set\n",
-    "leaking_uniprots = val_df['Uniprot'].isin(train_df['Uniprot'])\n",
-    "# Remove the leaking Uniprots from the validation set and add them to the training set\n",
-    "leaking_uniprots = val_df[leaking_uniprots]\n",
-    "val_df = val_df[~val_df.index.isin(leaking_uniprots.index)]\n",
-    "train_df = pd.concat([train_df, leaking_uniprots])\n",
-    "\n",
-    "# Reporting\n",
-    "print(f\"protac_df.shape: {protac_df.shape}\")\n",
-    "print(f\"active_df.shape: {active_df.shape}\")\n",
-    "print(f\"train_df.shape: {train_df.shape}\")\n",
-    "print(f\"val_df.shape: {val_df.shape}\")\n",
-    "print(f\"test_df.shape: {test_df.shape}\")\n",
-    "# Measure leaking Uniprots in the validation set\n",
-    "leaking_uniprots = val_df['Uniprot'].isin(train_df['Uniprot'])\n",
-    "print(f'Number of leaking Uniprots: {leaking_uniprots.sum()}')\n",
-    "print('')\n",
-    "print(f'Active/inactive PROTACs in the training set:\\n{train_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "print('')\n",
-    "print(f'Active/inactive PROTACs in the validation set:\\n{val_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "print('')\n",
-    "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 476,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of parameters: 9,650,529\n",
-      "Number of parameters: torch.Size([128, 3040])\n"
-     ]
-    }
-   ],
-   "source": [
-    "hidden_dim = 128\n",
-    "batch_size = 4\n",
-    "join_embeddings = 'beginning'\n",
-    "apply_scaling = True\n",
-    "dropout = 0.5\n",
-    "learning_rate = 1e-5\n",
-    "use_smote = True\n",
-    "model_log_label = f'hidden_dim_{hidden_dim}_join_{join_embeddings}_scaling_{apply_scaling}_dropout_{dropout}_lr_{learning_rate}_smote_{use_smote}'\n",
-    "\n",
-    "train_ds = PROTAC_Dataset(\n",
-    "    train_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_smote=use_smote,\n",
-    "    active_label=active_col,\n",
-    ")\n",
-    "val_ds = PROTAC_Dataset(\n",
-    "    val_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    active_label=active_col,\n",
-    ")\n",
-    "test_ds = PROTAC_Dataset(\n",
-    "    test_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    active_label=active_col,\n",
-    ")\n",
-    "\n",
-    "model = PROTAC_Model(\n",
-    "    hidden_dim=hidden_dim,\n",
-    "    smiles_emb_dim=fingerprint_size,\n",
-    "    poi_emb_dim=1024,\n",
-    "    e3_emb_dim=1024,\n",
-    "    cell_emb_dim=768,\n",
-    "    batch_size=batch_size,\n",
-    "    learning_rate=learning_rate, # 2e-5,\n",
-    "    dropout=dropout,\n",
-    "    train_dataset=train_ds,\n",
-    "    val_dataset=val_ds,\n",
-    "    test_dataset=test_ds,\n",
-    "    join_embeddings=join_embeddings,\n",
-    "    apply_scaling=apply_scaling,\n",
-    ")\n",
-    "# Get model parameters size\n",
-    "print(f\"Number of parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
-    "print(f\"Number of parameters: {model.fc1.weight.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 477,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    }
-   ],
-   "source": [
-    "logger = pl.loggers.TensorBoardLogger(\n",
-    "    save_dir='../logs',\n",
-    "    name='protac_playground_model',\n",
-    "    sub_dir=model_log_label,\n",
-    ")\n",
-    "\n",
-    "callbacks = [\n",
-    "    pl.callbacks.EarlyStopping(\n",
-    "        monitor='train_loss',\n",
-    "        patience=10,\n",
-    "        mode='min',\n",
-    "        verbose=True,\n",
-    "    ),\n",
-    "    pl.callbacks.EarlyStopping(\n",
-    "        monitor='val_loss',\n",
-    "        patience=5,\n",
-    "        mode='min',\n",
-    "        verbose=True,\n",
-    "    ),\n",
-    "    pl.callbacks.EarlyStopping(\n",
-    "        monitor='val_acc',\n",
-    "        patience=10,\n",
-    "        mode='max',\n",
-    "        verbose=True,\n",
-    "    ),\n",
-    "    # pl.callbacks.ModelCheckpoint(\n",
-    "    #     monitor='val_acc',\n",
-    "    #     mode='max',\n",
-    "    #     verbose=True,\n",
-    "    #     filename='{epoch}-{val_metrics_opt_score:.4f}',\n",
-    "    # ),\n",
-    "]\n",
-    "# Define Trainer\n",
-    "trainer = pl.Trainer(\n",
-    "    logger=logger,\n",
-    "    callbacks=callbacks,\n",
-    "    max_epochs=50,)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 478,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name    | Type       | Params\n",
-      "---------------------------------------\n",
-      "0 | fc0     | Linear     | 9.2 M \n",
-      "1 | fc1     | Linear     | 389 K \n",
-      "2 | fc2     | Linear     | 16.5 K\n",
-      "3 | fc3     | Linear     | 129   \n",
-      "4 | dropout | Dropout    | 0     \n",
-      "5 | metrics | ModuleDict | 0     \n",
-      "---------------------------------------\n",
-      "9.7 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "9.7 M     Total params\n",
-      "38.602    Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d6a1f505427d4f14a2b18eca89484cea",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a6e84eaa2a64413b8a394f77c65a69de",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "370ef785e97b4a159a62c5c6ba1ab868",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.681\n",
-      "Metric val_loss improved. New best score: 0.688\n",
-      "Metric val_acc improved. New best score: 0.500\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "687b4870256a4029a6a9545328ace8a1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.046 >= min_delta = 0.0. New best score: 0.635\n",
-      "Metric val_loss improved by 0.009 >= min_delta = 0.0. New best score: 0.680\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9d47361e4d7340089f19eecc8cdf7197",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.044 >= min_delta = 0.0. New best score: 0.591\n",
-      "Metric val_loss improved by 0.005 >= min_delta = 0.0. New best score: 0.675\n",
-      "Metric val_acc improved by 0.019 >= min_delta = 0.0. New best score: 0.519\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3a59575fee141a7b4899a4f5d45b021",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.037 >= min_delta = 0.0. New best score: 0.555\n",
-      "Metric val_loss improved by 0.003 >= min_delta = 0.0. New best score: 0.672\n",
-      "Metric val_acc improved by 0.070 >= min_delta = 0.0. New best score: 0.589\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "418562b2c27b494ea83d395e736132f0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.045 >= min_delta = 0.0. New best score: 0.509\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49ec8419d82c4e68b5c2cd284ebb8245",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.024 >= min_delta = 0.0. New best score: 0.485\n",
-      "Metric val_acc improved by 0.006 >= min_delta = 0.0. New best score: 0.595\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5a8b929c37434bdf87234c275ce46abf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.019 >= min_delta = 0.0. New best score: 0.465\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd679c3ce79e4b3b8ac62c6cd9d6d0f3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.004 >= min_delta = 0.0. New best score: 0.461\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4c56ee8b153e48f29b780108078a703a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved by 0.030 >= min_delta = 0.0. New best score: 0.431\n",
-      "Monitored metric val_loss did not improve in the last 5 records. Best score: 0.672. Signaling Trainer to stop.\n"
-     ]
-    }
-   ],
-   "source": [
-    "trainer.fit(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Open tensorboard\n",
-    "%load_ext tensorboard"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d5cc11148b214db5b691d9961b11df00",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d131da206451436bae85574009738b4c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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3KSIiQm3atNGsWbNkGIbds+Pvv/9+TZkyRS+//LK6deumffv2afLkyQoODrY+Bq20qlSpoilTpujxxx/XQw89pD//+c86c+aMEhMT7ZbW9+7dWzNnztSgQYM0atQonTp1Sq+//nqxfwG/Nku8bNky3XrrrapRo4bNAPu/kpOT1aNHD91999169tlnVb16daWkpOi7777TkiVLSvVX84owefJkZWVlKSoqSk899ZRatGihX375RYcOHdLatWv11ltvqVGjRvLx8VGTJk30wQcf6N5771WdOnVUr1496yP4Sqtp06Z64YUXNGXKFF26dEkDBw5UrVq1tHv3bp08eVKTJk3SmjVrlJKSogcffFC33nqrDMPQypUrdebMGfXo0aN8OgIA4LYeeeQRvffee7rvvvv09NNPq2PHjqpWrZp++uknff7553rggQf00EMP6a233tJnn32m3r17KygoSL/88ovmz58vSerevbskldl4V1pdunTRqFGjNHz4cG3ZskVdu3aVl5eXcnNztXHjRt1+++0aO3aspKvfQ1auXKnU1FSFh4erSpUqioiIuOH3nDlzpu6880516tRJzz//vJo1a6bjx49r9erVevvtt+Xj46NOnTrp/vvvV5s2bXTLLbdoz549Wrx4sSIjI20mYgBXRyIPlIGRI0fq6aefVqtWrdSpUyebfQkJCbp48aLeeecdTZ8+Xa1atdJbb72lVatW3dCNXv73vSRp2rRp6tevnzWBzM7OtjnfPffco/nz52vatGnq06ePGjZsqD//+c/y8/Oz+2PDpEmTlJubqz//+c86d+6cmjRpokOHDhX7/t26ddNnn32ml19+WY899piKiorUtm1brV692u6GOM4UGBioLVu2aMqUKXrttdf0008/ycfHR8HBwfrDH/6gW265xVr3nXfe0V//+lf17dtXBQUFGjZsmN1zaUvj2qNuZs+erUcffVRVq1ZVaGionnrqKUlSaGioateurenTp+vnn39W9erV1aJFCy1cuFDDhg0rq6YDACoJs9ms1atX680339TixYuVnJysqlWrqlGjRurWrZv1j+7t2rVTZmamXn75ZeXl5cnb21thYWFavXq19X45UtmNd6X19ttvq3Pnznr77beVkpKioqIiNWjQQF26dFHHjh2t9Z5++mnt2rVLL7zwgs6ePSvDMGxWHpZW27Zt9fXXX+vll1/WhAkTdO7cOQUEBOiee+6xXsN/zz33aPXq1XrjjTd08eJFNWzYUEOHDrXeQwhwFybDkZ8SAAAAAADgFFwjDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ER4/V4yioiL9/PPP8vHxcZlnYgMAft8Mw9C5c+fUoEEDVanC3+FvFmM9AMDV3MhYTyJfjJ9//lmNGzd2dhgAANg5evSoGjVq5Oww3B5jPQDAVZVmrCeRL4aPj4+kqx3o6+vr5GgAAJDy8/PVuHFj6xiFm8NYDwBwNTcy1pPIF+PaEjtfX18GdwCAS2EZeNlgrAcAuKrSjPVcZAe4mJSUFAUHB6tGjRoKDw/Xhg0brlu/oKBACQkJatKkiTw8PBQSEqL58+db969cuVIRERGqXbu2vLy81K5dOy1evPim3xcAAABwthv5Drt+/XqZTCa71969e6115s2bp7vuuku33HKLbrnlFnXv3l1ff/21zXnOnTunuLg4NWnSRJ6enoqKitLmzZvLrY3FIZEHXMiyZcsUFxenhIQEbd++XXfddZd69eqlI0eOlHhM//799emnn+qdd97Rvn37tGTJErVs2dK6v06dOkpISNCXX36pb7/9VsOHD9fw4cO1bt26m3pfAAAAwJkc/Q67b98+5ebmWl+hoaHWfevXr9fAgQP1+eef68svv1RQUJBiYmJ07Ngxa53HH39cWVlZWrx4sXbu3KmYmBh1797dpk55MxmGYVTYu7mJ/Px81apVS2fPnmW5HSpUp06ddMcddyg1NdVadtttt+nBBx9UcnKyXf2PP/5YjzzyiA4cOKA6deqU+n3uuOMO9e7dW1OmTHHofQFUPMamskV/AoD7u9HvsOvXr9fdd9+t06dPq3bt2qV6D4vFoltuuUVz5szR0KFDdenSJfn4+OiDDz5Q7969rfXatWun+++/X1OnTnW4PTcyNjEjD7iIwsJCbd26VTExMTblMTExysnJKfaY1atXKyIiQtOnT1fDhg3VvHlzPfvss7p06VKx9Q3D0Keffqp9+/apa9euDr8vAAAA4Ew38x22ffv2CgwM1L333qvPP//8unUvXryoy5cvWyfNrly5IovFoho1atjU8/T01MaNGx1oiWO42R3gIk6ePCmLxSJ/f3+bcn9/f+Xl5RV7zIEDB7Rx40bVqFFDq1at0smTJxUbG6v//ve/NtfJnz17Vg0bNlRBQYHMZrNSUlLUo0cPh98XAAAAcCZHvsMGBgYqLS1N4eHhKigo0OLFi3Xvvfdq/fr11kmuX3v++efVsGFDde/eXdLVp55ERkZqypQpuu222+Tv768lS5boq6++slmiX95I5AEX8+u7VBqGUeKdK4uKimQymfTee++pVq1akqSZM2fq4Ycf1ty5c+Xp6Snp6i+cHTt26Pz58/r0008VHx+vW2+9VdHR0Q69LwAAAOAKbuQ7bIsWLdSiRQvrdmRkpI4eParXX3+92ER++vTpWrJkidavX28zA7948WKNGDFCDRs2lNls1h133KFBgwZp27ZtZdSq38bSesBF1KtXT2az2e4viCdOnLD7S+M1gYGBatiwoTWJl65eF2QYhn766SdrWZUqVdSsWTO1a9dOzzzzjB5++GHrdUOOvC8AAADgTGX1HbZz587av3+/Xfnrr7+upKQkZWZmqk2bNjb7QkJClJ2drfPnz+vo0aP6+uuvdfnyZQUHBzvWGAeQyAMuonr16goPD1dWVpZNeVZWlqKiooo9pkuXLvr55591/vx5a9n333+vKlWqqFGjRiW+l2EYKigocPh9AQAAAGcqq++w27dvV2BgoE3Za6+9pilTpujjjz9WREREicd6eXkpMDBQp0+f1rp16/TAAw/cWCNuAkvrARcSHx+vIUOGKCIiQpGRkUpLS9ORI0c0ZswYSdKECRN07NgxLVq0SJI0aNAgTZkyRcOHD9ekSZN08uRJ/fWvf9WIESOsy+qTk5MVERGhkJAQFRYWau3atVq0aJHN3T1/630BAAAAV3Oj351nzZqlpk2bqnXr1iosLNS7776rFStWaMWKFdZzTp8+XS+99JIyMjLUtGlT64y/t7e3vL29JUnr1q2TYRhq0aKFfvjhB/31r39VixYtNHz48AprO4k84EIGDBigU6dOafLkycrNzVVYWJjWrl2rJk2aSJJyc3Ntnovp7e2trKwsPfnkk4qIiFDdunXVv39/m8deXLhwQbGxsfrpp5/k6empli1b6t1339WAAQNK/b4AAACAq7nR786FhYV69tlndezYMXl6eqp169b617/+pfvuu89aJyUlRYWFhXr44Ydt3uvll19WYmKipKs3kp4wYYJ++ukn1alTR3/84x/1yiuvqFq1auXf6P+P58gXg2fLAgBcDWNT2aI/AQCuhufIAwAAAABQSZHIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbqersAIDfgy6zuzg7hHL17yf/7ewQAAAAUEm8MvhhZ4dQrhLe/edNn4MZeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAODCUlJSFBwcrBo1aig8PFwbNmwo1XH//ve/VbVqVbVr186m/PLly5o8ebJCQkJUo0YNtW3bVh9//LFNnaZNm8pkMtm9xo0bV1bNwk0gkQcAAAAAF7Vs2TLFxcUpISFB27dv11133aVevXrpyJEj1z3u7NmzGjp0qO699167fS+++KLefvttzZ49W7t379aYMWP00EMPafv27dY6mzdvVm5urvWVlZUlSfrTn/5Utg2EQ0jkAQAAAMBFzZw5UyNHjtTjjz+u2267TbNmzVLjxo2Vmpp63eNGjx6tQYMGKTIy0m7f4sWL9cILL+i+++7TrbfeqrFjx6pnz56aMWOGtU79+vUVEBBgfa1Zs0YhISHq1q1bmbcRN45EHgAAAABcUGFhobZu3aqYmBib8piYGOXk5JR43IIFC/Tjjz/q5ZdfLnZ/QUGBatSoYVPm6empjRs3lhjHu+++qxEjRshkMt1gK1AeSOQBAAAAwAWdPHlSFotF/v7+NuX+/v7Ky8sr9pj9+/fr+eef13vvvaeqVasWW6dnz56aOXOm9u/fr6KiImVlZemDDz5Qbm5usfXff/99nTlzRo899thNtQdlh0QeAAAAAFzYr2fBDcModmbcYrFo0KBBmjRpkpo3b17i+d58802FhoaqZcuWql69up544gkNHz5cZrO52PrvvPOOevXqpQYNGtxcQ1BmSOQBAAAAwAXVq1dPZrPZbvb9xIkTdrP0knTu3Dlt2bJFTzzxhKpWraqqVatq8uTJ+uabb1S1alV99tlnkq5e//7+++/rwoULOnz4sPbu3Stvb28FBwfbnfPw4cP65JNP9Pjjj5dPI+EQEnkAAAAAcEHVq1dXeHi49Y7x12RlZSkqKsquvq+vr3bu3KkdO3ZYX2PGjFGLFi20Y8cOderUyaZ+jRo11LBhQ125ckUrVqzQAw88YHfOBQsWyM/PT7179y7bxuGmFH/RBAAAAADA6eLj4zVkyBBFREQoMjJSaWlpOnLkiMaMGSNJmjBhgo4dO6ZFixapSpUqCgsLsznez89PNWrUsCn/6quvdOzYMbVr107Hjh1TYmKiioqKNH78eJtji4qKtGDBAg0bNqzE6+3hHPzfAAAAAAAXNWDAAJ06dUqTJ09Wbm6uwsLCtHbtWjVp0kSSlJub+5vPlP+1X375RS+++KIOHDggb29v3XfffVq8eLFq165tU++TTz7RkSNHNGLEiLJqDsqI05fWHzt2TIMHD1bdunVVs2ZNtWvXTlu3brXuNwxDiYmJatCggTw9PRUdHa1du3bZnCM+Pl516tRRUFCQli5darNv+fLl6tOnT4W0BQAAAADKWmxsrA4dOqSCggJt3bpVXbt2te5buHCh1q9fX+KxiYmJ2rFjh01Zt27dtHv3bv3yyy86efKkFi1aVOyN7GJiYmQYxnVvnAfncGoif/r0aXXp0kXVqlXTRx99pN27d2vGjBk2fwmaPn26Zs6cqTlz5mjz5s0KCAhQjx49dO7cOUnShx9+qIyMDGVmZmratGkaPny4Tp06JUk6c+aMEhISNHfuXGc0DwAAAACAMufUpfXTpk1T48aNtWDBAmtZ06ZNrf9tGIZmzZqlhIQE9evXT5KUnp4uf39/ZWRkaPTo0dqzZ4+io6MVERGhiIgIxcXF6cCBA6pbt67Gjx+v2NhYBQUFXTeOgoICFRQUWLfz8/MlXX18g8ViKcMW4/fKrOIf5VFZ8HMClD9+zgAAwDVOTeRXr16tnj176k9/+pOys7PVsGFDxcbG6s9//rMk6eDBg8rLy1NMTIz1GA8PD3Xr1k05OTkaPXq02rZtq7S0NJ0+fVoHDhzQpUuX1KxZM23cuFHbtm1Tamrqb8aRnJysSZMm2ZX/+OOP8vb2LrsG43erc63Ozg6hXO3fv9/ZIQCV3vnz550dgkMSExPtxlh/f3/ro5QMw9CkSZOsY3mnTp00d+5ctW7d2lo/Pj5eCxculLe3t6ZPn65HHnnEum/58uVavHixPvzww4ppEAAALsCpifyBAweUmpqq+Ph4vfDCC/r666/11FNPycPDQ0OHDrUO8r9+RqK/v78OHz4sSerZs6cGDx6sDh06yNPTU+np6fLy8tLYsWO1cOFCpaamavbs2apXr57S0tJsvhhcM2HCBMXHx1u38/Pz1bhxY4WEhMjX17ccewC/F5uyNjk7hHKVHJrs7BCASu/aajF31Lp1a33yySfWbbP5/1YpXbuEbuHChWrevLmmTp2qHj16aN++ffLx8bG5hG7//v0aPny4evToobp161ovofv000+d0SwAAJzGqYl8UVGRIiIilJSUJElq3769du3apdTUVA0dOtRaz2Qy2RxnGIZNWWJiohITE222u3fvrmrVqmnq1KnauXOn1qxZo6FDh9rcSO8aDw8PeXh42JWbzWabLxuAoyyq3Eti+TkByp87/5xVrVpVAQEBduUVdQkdAACVjVMT+cDAQLVq1cqm7LbbbtOKFSskyTro5+XlKTAw0FrnxIkTdrP01+zdu1fvvfeetm/frvnz56tr166qX7+++vfvrxEjRig/P59ZdgAAKtD+/fvVoEEDeXh4qFOnTkpKStKtt95aYZfQSdwPBwDciamK0x+uVq5KGnduZDxyaiLfpUsX7du3z6bs+++/tz4TMTg4WAEBAcrKylL79u0lSYWFhcrOzta0adPszmcYhkaNGqUZM2bI29tbFotFly9fliTrv0VFReXZJAAA8D86deqkRYsWqXnz5jp+/LimTp2qqKgo7dq1q8IuoZO4Hw4AuJP6LcOcHUK5Kun+UjdyPxynJvJ/+ctfFBUVpaSkJPXv319ff/210tLSlJaWJunqkvq4uDglJSUpNDRUoaGhSkpKUs2aNTVo0CC7882bN09+fn7q27evpKt/KEhMTNSmTZv00UcfqVWrVjaPtgMAAOWrV69e1v++/fbbFRkZqZCQEKWnp6tz56s3Ai3vS+gk7ocDAO5k5d7vnB1CuQoNTSi2/Ebuh+PURL5Dhw5atWqVJkyYoMmTJys4OFizZs3So48+aq0zfvx4Xbp0SbGxsda72WZmZsrHx8fmXMePH1dSUpJycnKsZR07dtQzzzyj3r17y8/PT+np6RXWNgAAYM/Ly0u333679u/frwcffFBSxVxCx/1wALiaOc9U/qdtPDGjj0PHGZV8FXVJ486NjEdOv/jg/vvv186dO/XLL79oz5491kfPXWMymZSYmKjc3Fz98ssvys7OVliY/VILf39/HTp0SA0aNLApnzhxok6dOqU9e/aoY8eO5doWAABwfQUFBdqzZ48CAwNtLqG75toldFFRUXbHcgkdAABXOT2RBwAAldezzz6r7OxsHTx4UF999ZUefvhh5efna9iwYTaX0K1atUrfffedHnvssRu6hO6zzz7Tpk2b9MYbb3AJHQDgd4NEHgAAlJuffvpJAwcOVIsWLdSvXz9Vr15dmzZtst7Ydvz48YqLi1NsbKwiIiJ07Nix615C97e//c1a9r+X0C1fvlwLFiyo0Lb9r5SUFAUHB6tGjRoKDw/Xhg0bSnXcv//9b1WtWlXt2rWzKY+OjpbJZLJ79e7d21rniy++UJ8+fdSgQQOZTCa9//77ZdgiAIArc+o18gAAoHJbunTpdfdfu4Tuf29mV5xrl9D92sSJEzVx4sSbiPDmLVu2THFxcUpJSVGXLl309ttvq1evXtq9e/d1n3F/9uxZDR06VPfee6+OHz9us2/lypUqLCy0bp86dUpt27bVn/70J2vZhQsX1LZtWw0fPlx//OMfy75hAACXRSIPAABwE2bOnKmRI0fq8ccflyTNmjVL69atU2pqqpKTk0s8bvTo0Ro0aJDMZrPdbHqdOnVstpcuXaqaNWvaJPK9evWyeSoAAOD3g6X1AAAADiosLNTWrVsVExNjUx4TE2PzJJ1fW7BggX788Ue9/PLLpXqfd955R4888oi8vLxuKl4AQOXAjDwAAICDTp48KYvFYve4PH9/f+Xl5RV7zP79+/X8889rw4YNqlr1t7+Kff311/ruu+/0zjvvlEnMAAD3x4w8AADATTKZTDbbhmHYlUmSxWLRoEGDNGnSJDVv3rxU537nnXcUFhbGY3QBAFbMyAMAADioXr16MpvNdrPvJ06csJull6Rz585py5Yt2r59u5544glJUlFRkQzDUNWqVZWZmal77rnHWv/ixYtaunSpJk+eXL4NAQC4FWbkAQAAHFS9enWFh4crKyvLpjwrK0tRUVF29X19fbVz507t2LHD+hozZoxatGihHTt2qFOnTjb1ly9froKCAg0ePLhc2wEAcC/MyAMAANyE+Ph4DRkyRBEREYqMjFRaWpqOHDmiMWPGSJImTJigY8eOadGiRapSpYrCwsJsjvfz81ONGjXsyqWry+offPBB1a1b127f+fPn9cMPP1i3Dx48qB07dqhOnTrXfewdAMD9kcgDAADchAEDBujUqVOaPHmycnNzFRYWprVr16pJkyaSpNzcXB05cuSGz/v9999r48aNyszMLHb/li1bdPfdd1u34+PjJUnDhg3TwoULb7whAAC3QSIPAABwk2JjYxUbG1vsvt9KqhMTE5WYmGhX3rx5cxmGUeJx0dHR190PAKi8uEYeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIA4OZSUlIUHBysGjVqKDw8XBs2bCix7vr162Uymexee/futdZZuXKlIiIiVLt2bXl5ealdu3ZavHixzXmuXLmiF198UcHBwfL09NStt96qyZMnq6ioqNzaCQAAgKt4/BwAuLFly5YpLi5OKSkp6tKli95++2316tVLu3fvVlBQUInH7du3T76+vtbt+vXrW/+7Tp06SkhIUMuWLVW9enWtWbNGw4cPl5+fn3r27ClJmjZtmt566y2lp6erdevW2rJli4YPH65atWrp6aefLr8GAwAAgEQeANzZzJkzNXLkSD3++OOSpFmzZmndunVKTU1VcnJyicf5+fmpdu3axe6Ljo622X766aeVnp6ujRs3WhP5L7/8Ug888IB69+4tSWratKmWLFmiLVu23HyjAAAAcF0k8gDgpgoLC7V161Y9//zzNuUxMTHKycm57rHt27fXL7/8olatWunFF1/U3XffXWw9wzD02Wefad++fZo2bZq1/M4779Rbb72l77//Xs2bN9c333yjjRs3atasWTfdLsAVhP91kbNDKHdbXxvq7BAAAA4ikQcAN3Xy5ElZLBb5+/vblPv7+ysvL6/YYwIDA5WWlqbw8HAVFBRo8eLFuvfee7V+/Xp17drVWu/s2bNq2LChCgoKZDablZKSoh49elj3P/fcczp79qxatmwps9ksi8WiV155RQMHDiyfxgIAAMCKRB4A3JzJZLLZNgzDruyaFi1aqEWLFtbtyMhIHT16VK+//rpNIu/j46MdO3bo/Pnz+vTTTxUfH69bb73Vuux+2bJlevfdd5WRkaHWrVtrx44diouLU4MGDTRs2LCybyQAAACsSOQBwE3Vq1dPZrPZbvb9xIkTdrP019O5c2e9++67NmVVqlRRs2bNJEnt2rXTnj17lJycbE3k//rXv+r555/XI488Ikm6/fbbdfjwYSUnJ5PIAwAAlDMePwcAbqp69eoKDw9XVlaWTXlWVpaioqJKfZ7t27crMDDwunUMw1BBQYF1++LFi6pSxXYIMZvNPH4OAACgAjAjDwBuLD4+XkOGDFFERIQiIyOVlpamI0eOaMyYMZKkCRMm6NixY1q06OqNu2bNmqWmTZuqdevWKiws1LvvvqsVK1ZoxYoV1nMmJycrIiJCISEhKiws1Nq1a7Vo0SKlpqZa6/Tp00evvPKKgoKC1Lp1a23fvl0zZ87UiBEjKrYDAAAAfodI5AHAjQ0YMECnTp3S5MmTlZubq7CwMK1du1ZNmjSRJOXm5urIkSPW+oWFhXr22Wd17NgxeXp6qnXr1vrXv/6l++67z1rnwoULio2N1U8//SRPT0+1bNlS7777rgYMGGCtM3v2bL300kuKjY3ViRMn1KBBA40ePVoTJ06suMYDAAD8TpkMwzCcHYSryc/PV61atXT27Fn5+vo6OxxUAl1md3F2COXq30/+29khAJUeY1PZ+q3+5PFzAMrbnGc+dHYI5e6JGX0cOu6VwQ+XcSSuJeHdfxZbfiNjPdfIl7OUlBQFBwerRo0aCg8P14YNG0qsu379eplMJrvX3r17rXXmzZunu+66S7fccotuueUWde/eXV9//bXNeZKTk9WhQwf5+PjIz89PDz74oPbt21dubQQAV8XvYAAAUBmRyJejZcuWKS4uTgkJCdq+fbvuuusu9erVy2aZa3H27dun3Nxc6ys0NNS6b/369Ro4cKA+//xzffnllwoKClJMTIyOHTtmrZOdna1x48Zp06ZNysrK0pUrVxQTE6MLFy6UW1sBwNXwOxgAAFRWLK0vRlktX+zUqZPuuOMOmxtE3XbbbXrwwQeVnJxsV3/9+vW6++67dfr0adWuXbtU72GxWHTLLbdozpw5Gjq0+CVy//nPf+Tn56fs7Gyb50Sj4rC0Hqh4le13MEvryxZL61laDzgbS+tLxtJ6ltY7TWFhobZu3aqYmBib8piYGOXk5Fz32Pbt2yswMFD33nuvPv/88+vWvXjxoi5fvqw6deqUWOfs2bOSdN06AFCZ8DsYAABUZiTy5eTkyZOyWCzy9/e3Kff391deXl6xxwQGBiotLU0rVqzQypUr1aJFC91777364osvSnyf559/Xg0bNlT37t2L3W8YhuLj43XnnXcqLCzM8QYBgBvhdzAAAKjMePxcOTOZTDbbhmHYlV3TokULtWjRwrodGRmpo0eP6vXXXy92Oeb06dO1ZMkSrV+/XjVq1Cj2nE888YS+/fZbbdy48SZaAQDuid/BAACgMmJGvpzUq1dPZrPZbubnxIkTdjNE19O5c2ft37/frvz1119XUlKSMjMz1aZNm2KPffLJJ7V69Wp9/vnnatSo0Y01AADcGL+DAQBAZUYiX06qV6+u8PBwZWVl2ZRnZWUpKiqq1OfZvn27AgMDbcpee+01TZkyRR9//LEiIiLsjjEMQ0888YRWrlypzz77TMHBwY41AgDcFL+DAQBAZcbS+nIUHx+vIUOGKCIiQpGRkUpLS9ORI0c0ZswYSdKECRN07NgxLVp09c64s2bNUtOmTdW6dWsVFhbq3Xff1YoVK7RixQrrOadPn66XXnpJGRkZatq0qXW2ydvbW97e3pKkcePGKSMjQx988IF8fHysdWrVqiVPT8+K7AIADqrsd7J19C62N4LfwQAAoLIikS9HAwYM0KlTpzR58mTl5uYqLCxMa9euVZMmTSRJubm5Ns8zLiws1LPPPqtjx47J09NTrVu31r/+9S/dd9991jopKSkqLCzUww/bPpLh5ZdfVmJioiRZH7UUHR1tU2fBggV67LHHyr6hAOCC+B0MAAAqKxL5chYbG6vY2Nhi9y1cuNBme/z48Ro/fvx1z3fo0KHffE/DMEobHgBUavwOBgAAlRHXyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjVR1dgDuKPyvi5wdQrnb+tpQZ4cAAHZeGfyws0Modwnv/tPZIQAAABfHjDwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN+LURD4xMVEmk8nmFRAQYN1vGIYSExPVoEEDeXp6Kjo6Wrt27bI5R3x8vOrUqaOgoCAtXbrUZt/y5cvVp0+fCmkLAAAAAAAVwekz8q1bt1Zubq71tXPnTuu+6dOna+bMmZozZ442b96sgIAA9ejRQ+fOnZMkffjhh8rIyFBmZqamTZum4cOH69SpU5KkM2fOKCEhQXPnznVKuwAAAAAAKA9OT+SrVq2qgIAA66t+/fqSrs7Gz5o1SwkJCerXr5/CwsKUnp6uixcvKiMjQ5K0Z88eRUdHKyIiQgMHDpSvr68OHDggSRo/frxiY2MVFBTktLYBAAAAAFDWqjo7gP3796tBgwby8PBQp06dlJSUpFtvvVUHDx5UXl6eYmJirHU9PDzUrVs35eTkaPTo0Wrbtq3S0tJ0+vRpHThwQJcuXVKzZs20ceNGbdu2TampqaWKoaCgQAUFBdbt/Px8SZLFYpHFYrGrbzbdZKPdQHHthuPMMjs7hHLF56UcmAxnR1CuHP3MmKo4/e/P5a6kvuHnDAAAXOPURL5Tp05atGiRmjdvruPHj2vq1KmKiorSrl27lJeXJ0ny9/e3Ocbf31+HDx+WJPXs2VODBw9Whw4d5OnpqfT0dHl5eWns2LFauHChUlNTNXv2bNWrV09paWlq3bp1sXEkJydr0qRJduU//vijvL297crvbOxxs013efv373d2CJVK51qdnR1CueLzUvZ8gyp3Iu/oZ6Z+y7AyjsT1lNQ358+fr+BIAACAq3JqIt+rVy/rf99+++2KjIxUSEiI0tPT1bnz1cTHZLKd/jYMw6YsMTFRiYmJNtvdu3dXtWrVNHXqVO3cuVNr1qzR0KFDtXXr1mLjmDBhguLj463b+fn5aty4sUJCQuTr62tXf+P8rx1qrzt5LTTU2SFUKpuyNjk7hHKVHJrs7BAqnU/+/r2zQyhXoQ7+jlm597syjsT1hIYmFFt+bbUYAACA05fW/y8vLy/dfvvt2r9/vx588EFJUl5engIDA611Tpw4YTdLf83evXv13nvvafv27Zo/f766du2q+vXrq3///hoxYoTy8/OLTcw9PDzk4WE/y242m2U22y+JtlTuiTJJKrbdcJxFlXtJLJ+XcmBU7mt4HP3MGEVFZRyJ6ympb/g5AwAA17jUxYYFBQXas2ePAgMDFRwcrICAAGVlZVn3FxYWKjs7W1FRUXbHGoahUaNGacaMGfL29pbFYtHly5clyfpv0e/gCyAAAAAAoHJzaiL/7LPPKjs7WwcPHtRXX32lhx9+WPn5+Ro2bJhMJpPi4uKUlJSkVatW6bvvvtNjjz2mmjVratCgQXbnmjdvnvz8/NS3b19JUpcuXfTZZ59p06ZNeuONN9SqVSvVrl27glsIAAAAAEDZcurS+p9++kkDBw7UyZMnVb9+fXXu3FmbNm1SkyZNJF19hNylS5cUGxur06dPq1OnTsrMzJSPj4/NeY4fP66kpCTl5ORYyzp27KhnnnlGvXv3lp+fn9LT0yu0bQAAAAAAlAenJvJLly697n6TyWR3M7vi+Pv769ChQ3blEydO1MSJE28iQgAAAAAAXItLXSMPAAAAAACuj0QeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAKDCJCcny2QyKS4uzlpmGIYSExPVoEEDeXp6Kjo6Wrt27bI5Lj4+XnXq1FFQUJDd42uXL1+uPn36VET4AAC4BBJ5AABQITZv3qy0tDS1adPGpnz69OmaOXOm5syZo82bNysgIEA9evTQuXPnJEkffvihMjIylJmZqWnTpmn48OE6deqUJOnMmTNKSEjQ3LlzK7w9AAA4C4k8AAAod+fPn9ejjz6qefPm6ZZbbrGWG4ahWbNmKSEhQf369VNYWJjS09N18eJFZWRkSJL27Nmj6OhoRUREaODAgfL19dWBAwckSePHj1dsbKyCgoKc0i4AAJyhqrMDAAAAld+4cePUu3dvde/eXVOnTrWWHzx4UHl5eYqJibGWeXh4qFu3bsrJydHo0aPVtm1bpaWl6fTp0zpw4IAuXbqkZs2aaePGjdq2bZtSU1N/8/0LCgpUUFBg3c7Pz5ckWSwWWSwWu/pm08201j0U124AFchkODuCcufo7xlTlco931xSv9xIf5HIAwCAcrV06VJt27ZNmzdvttuXl5cnSfL397cp9/f31+HDhyVJPXv21ODBg9WhQwd5enoqPT1dXl5eGjt2rBYuXKjU1FTNnj1b9erVU1pamlq3bm33PsnJyZo0aZJd+Y8//ihvb2+78jsbezjUVneyf/9+Z4cA/K75BlX+RN7R3zP1W4aVcSSupaR+OX/+fKnPQSIPAADKzdGjR/X0008rMzNTNWrUKLGeyWQ7BW4Yhk1ZYmKiEhMTbba7d++uatWqaerUqdq5c6fWrFmjoUOHauvWrXbnnzBhguLj463b+fn5aty4sUJCQuTr62tXf+P8r2+kmW7ptdBQZ4cA/K598vfvnR1CuQt18PfMyr3flXEkriU0NKHY8murxUqDRB4AAJSbrVu36sSJEwoPD7eWWSwWffHFF5ozZ4727dsn6erMfGBgoLXOiRMn7Gbpr9m7d6/ee+89bd++XfPnz1fXrl1Vv3599e/fXyNGjFB+fr5dcu7h4SEPD/tZdrPZLLPZbFduqfwTZcW2G0AFMir/NTyO/p4xiorKOBLXUlK/3Eh/Ve6LDwAAgFPde++92rlzp3bs2GF9RURE6NFHH9WOHTt06623KiAgQFlZWdZjCgsLlZ2draioKLvzGYahUaNGacaMGfL29pbFYtHly5clyfpvUSX/AggAADPyAACg3Pj4+CgszPZaRy8vL9WtW9daHhcXp6SkJIWGhio0NFRJSUmqWbOmBg0aZHe+efPmyc/PT3379pUkdenSRYmJidq0aZM++ugjtWrVSrVr1y73dgEA4Ewk8gAAwKnGjx+vS5cuKTY2VqdPn1anTp2UmZkpHx8fm3rHjx9XUlKScnJyrGUdO3bUM888o969e8vPz0/p6ekVHT4AABWORB4AAFSo9evX22ybTCa7m9kVx9/fX4cOHbIrnzhxoiZOnFh2AQIA4OK4Rh4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAIAKlJKSouDgYNWoUUPh4eHasGFDiXU3btyoLl26qG7duvL09FTLli31xhtv2NU7c+aMxo0bp8DAQNWoUUO33Xab1q5dW+w5k5OTZTKZFBcXV1ZNAlDBuGs9AAAAUEGWLVumuLg4paSkqEuXLnr77bfVq1cv7d69W0FBQXb1vby89MQTT6hNmzby8vLSxo0bNXr0aHl5eWnUqFGSpMLCQvXo0UN+fn765z//qUaNGuno0aN2j3CUpM2bNystLU1t2rQp97YCKD8k8gAAAEAFmTlzpkaOHKnHH39ckjRr1iytW7dOqampSk5Otqvfvn17tW/f3rrdtGlTrVy5Uhs2bLAm8vPnz9d///tf5eTkqFq1apKkJk2a2J3r/PnzevTRRzVv3jxNnTq1PJoHoIKwtB4AAACoAIWFhdq6datiYmJsymNiYpSTk1Oqc2zfvl05OTnq1q2btWz16tWKjIzUuHHj5O/vr7CwMCUlJclisdgcO27cOPXu3Vvdu3e/+cYAcCpm5AEAAIAKcPLkSVksFvn7+9uU+/v7Ky8v77rHNmrUSP/5z3905coVJSYmWmf0JenAgQP67LPP9Oijj2rt2rXav3+/xo0bpytXrmjixImSpKVLl2rbtm3avHlz2TcMQIUjkQcAAAAqkMlkstk2DMOu7Nc2bNig8+fPa9OmTXr++efVrFkzDRw4UJJUVFQkPz8/paWlyWw2Kzw8XD///LNee+01TZw4UUePHtXTTz+tzMxM1ahRo9zaBaDikMgDAAAAFaBevXoym812s+8nTpywm6X/teDgYEnS7bffruPHjysxMdGayAcGBqpatWoym83W+rfddpvy8vKsy/lPnDih8PBw636LxaIvvvhCc+bMUUFBgc2xAFwf18gDAAAAFaB69eoKDw9XVlaWTXlWVpaioqJKfR7DMFRQUGDd7tKli3744QcVFRVZy77//nsFBgaqevXquvfee7Vz507t2LHD+oqIiNCjjz6qHTt2kMQDbogZeQAAAKCCxMfHa8iQIYqIiFBkZKTS0tJ05MgRjRkzRpI0YcIEHTt2TIsWLZIkzZ07V0FBQWrZsqWkq8+Vf/311/Xkk09azzl27FjNnj1bTz/9tJ588knt379fSUlJeuqppyRJPj4+CgsLs4nDy8tLdevWtSsH4B5I5AEAAIAKMmDAAJ06dUqTJ09Wbm6uwsLCtHbtWuvj4nJzc3XkyBFr/aKiIk2YMEEHDx5U1apVFRISoldffVWjR4+21mncuLEyMzP1l7/8RW3atFHDhg319NNP67nnnqvw9gGoGCTyAAAAQAWKjY1VbGxssfsWLlxos/3kk0/azL6XJDIyUps2bSp1DOvXry91XQCuh2vkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGqjo7AAAAAMDddZndxdkhlLt/P/lvZ4cA4P9jRh4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbcZlEPjk5WSaTSXFxcdYywzCUmJioBg0ayNPTU9HR0dq1a5fNcfHx8apTp46CgoK0dOlSm33Lly9Xnz59KiJ8AAAAAAAqhEsk8ps3b1ZaWpratGljUz59+nTNnDlTc+bM0ebNmxUQEKAePXro3LlzkqQPP/xQGRkZyszM1LRp0zR8+HCdOnVKknTmzBklJCRo7ty5Fd4eAAAAAADKi9MT+fPnz+vRRx/VvHnzdMstt1jLDcPQrFmzlJCQoH79+iksLEzp6em6ePGiMjIyJEl79uxRdHS0IiIiNHDgQPn6+urAgQOSpPHjxys2NlZBQUFOaRcAAAAAAOWhqrMDGDdunHr37q3u3btr6tSp1vKDBw8qLy9PMTEx1jIPDw9169ZNOTk5Gj16tNq2bau0tDSdPn1aBw4c0KVLl9SsWTNt3LhR27ZtU2pqaqliKCgoUEFBgXU7Pz9fkmSxWGSxWOzqm02OttZ9FNduOM4ss7NDKFd8XsqByXB2BOXK0c+MqYrT//5c7krqG37OAADANU5N5JcuXapt27Zp8+bNdvvy8vIkSf7+/jbl/v7+Onz4sCSpZ8+eGjx4sDp06CBPT0+lp6fLy8tLY8eO1cKFC5WamqrZs2erXr16SktLU+vWrYuNIzk5WZMmTbIr//HHH+Xt7W1Xfmdjjxtuq7vZv3+/s0OoVDrX6uzsEMoVn5ey5xtUuRN5Rz8z9VuGlXEkrqekvjl//nwFRwIAAFyV0xL5o0eP6umnn1ZmZqZq1KhRYj2TyXb62zAMm7LExEQlJibabHfv3l3VqlXT1KlTtXPnTq1Zs0ZDhw7V1q1bi32PCRMmKD4+3rqdn5+vxo0bKyQkRL6+vnb1N87/urTNdFuvhYY6O4RKZVPWJmeHUK6SQ5OdHUKl88nfv3d2COUq1MHfMSv3flfGkbie0NCEYsuvrRYDAABwWiK/detWnThxQuHh4dYyi8WiL774QnPmzNG+ffskXZ2ZDwwMtNY5ceKE3Sz9NXv37tV7772n7du3a/78+eratavq16+v/v37a8SIEcrPzy82Mffw8JCHh/0su9lsltlsvyTaUrknyiSp2HbDcRZV7iWxfF7KgVG5r+Fx9DNjFBWVcSSup6S+4ecMAABc47SLDe+9917t3LlTO3bssL4iIiL06KOPaseOHbr11lsVEBCgrKws6zGFhYXKzs5WVFSU3fkMw9CoUaM0Y8YMeXt7y2Kx6PLly5Jk/bfod/AFEAAAAABQuTltRt7Hx0dhYbbXOnp5ealu3brW8ri4OCUlJSk0NFShoaFKSkpSzZo1NWjQILvzzZs3T35+furbt68kqUuXLkpMTNSmTZv00UcfqVWrVqpdu3a5twsAAAAAgPLk9LvWX8/48eN16dIlxcbG6vTp0+rUqZMyMzPl4+NjU+/48eNKSkpSTk6Otaxjx4565pln1Lt3b/n5+Sk9Pb2iwwcAAAAAoMy5VCK/fv16m22TyWR3M7vi+Pv769ChQ3blEydO1MSJE8suQAAAAAAAnKzyP5AXAAAAAIBKhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAA5SY1NVVt2rSRr6+vfH19FRkZqY8++si63zAMJSYmqkGDBvL09FR0dLR27dplc474+HjVqVNHQUFBWrp0qc2+5cuXq0+fPhXSFgAAXAWJPAAAKDeNGjXSq6++qi1btmjLli2655579MADD1iT9enTp2vmzJmaM2eONm/erICAAPXo0UPnzp2TJH344YfKyMhQZmampk2bpuHDh+vUqVOSpDNnzighIUFz5851WvsAAHAGEnkAAFBu+vTpo/vuu0/NmzdX8+bN9corr8jb21ubNm2SYRiaNWuWEhIS1K9fP4WFhSk9PV0XL15URkaGJGnPnj2Kjo5WRESEBg4cKF9fXx04cECSNH78eMXGxiooKMiZTQQAoMJVdXYAAADg98Fisegf//iHLly4oMjISB08eFB5eXmKiYmx1vHw8FC3bt2Uk5Oj0aNHq23btkpLS9Pp06d14MABXbp0Sc2aNdPGjRu1bds2paamluq9CwoKVFBQYN3Oz8+3xmSxWOzqm0032Vg3UFy74TizzM4OodzxmSljJsPZEZQ7Rz8zpiqVe765pH65kf4ikQcAAOVq586dioyM1C+//CJvb2+tWrVKrVq1Uk5OjiTJ39/fpr6/v78OHz4sSerZs6cGDx6sDh06yNPTU+np6fLy8tLYsWO1cOFCpaamavbs2apXr57S0tLUunXrYmNITk7WpEmT7Mp//PFHeXt725Xf2djjZpvt8vbv3+/sECqVzrU6OzuEcsdnpmz5BlX+RN7Rz0z9lmFlHIlrKalfzp8/X+pzkMgDAIBy1aJFC+3YsUNnzpzRihUrNGzYMGVnZ1v3m0y209+GYdiUJSYmKjEx0Wa7e/fuqlatmqZOnaqdO3dqzZo1Gjp0qLZu3VpsDBMmTFB8fLx1Oz8/X40bN1ZISIh8fX3t6m+c/7WjzXUbr4WGOjuESmVT1iZnh1DukkOTnR1CpfLJ3793dgjlLtTB3zMr935XxpG4ltDQhGLLr60WK42bSuQLCwt18OBBhYSEqGpV/iYAAEBlUxZjffXq1dWsWTNJUkREhDZv3qw333xTzz33nCQpLy9PgYGB1vonTpywm6W/Zu/evXrvvfe0fft2zZ8/X127dlX9+vXVv39/jRgxQvn5+cUm5h4eHvLwsJ9lN5vNMpvtl0RbKv9EWbHthuMsqvzLzvnMlDGj8l/D4+hnxigqKuNIXEtJ/XIj/eXQxQcXL17UyJEjVbNmTbVu3VpHjhyRJD311FN69dVXHTklAABwIeU51huGoYKCAgUHBysgIEBZWVnWfYWFhcrOzlZUVFSxx40aNUozZsyQt7e3LBaLLl++LEnWf4sq+Zc/AAAkBxP5CRMm6JtvvtH69etVo0YNa3n37t21bNmyMgsOAAA4R1mN9S+88II2bNigQ4cOaefOnUpISND69ev16KOPymQyKS4uTklJSVq1apW+++47PfbYY6pZs6YGDRpkd6558+bJz89Pffv2lSR16dJFn332mTZt2qQ33nhDrVq1Uu3atW+67QAAuDqH1si9//77WrZsmTp37mxzDVurVq30448/lllwAADAOcpqrD9+/LiGDBmi3Nxc1apVS23atNHHH3+sHj16SLr6CLlLly4pNjZWp0+fVqdOnZSZmSkfHx+78yQlJVlvkCdJHTt21DPPPKPevXvLz89P6enpN9lqAADcg0OJ/H/+8x/5+fnZlV+4cMHuhjUAAMD9lNVY/84771x3v8lksruZXXH8/f116NAhu/KJEydq4sSJpY4HAIDKwKGl9R06dNC//vUv6/a1AX3evHmKjIwsm8gAAIDTMNYDAOC6HJqRT05O1h/+8Aft3r1bV65c0Ztvvqldu3bpyy+/tHmcDAAAcE+M9QAAuC6HZuSjoqKUk5OjixcvKiQkRJmZmfL399eXX36p8PDwso4RAABUMMZ6AABc1w3PyF++fFmjRo3SSy+9xE1lAACohBjrAQBwbTc8I1+tWjWtWrWqPGIBAAAugLEeAADX5tDS+oceekjvv/9+GYcCAABcBWM9AACuy6Gb3TVr1kxTpkxRTk6OwsPD5eXlZbP/qaeeKpPgAACAczDWAwDguhxK5P/+97+rdu3a2rp1q7Zu3Wqzz2QyMbgDAODmGOsBAHBdDiXyBw8eLOs4AACAC2GsBwDAdTl0jfz/MgxDhmGURSz4nUlJSVFwcLBq1Kih8PBwbdiwocS6GzduVJcuXVS3bl15enqqZcuWeuONN+zqrVixQq1atZKHh4datWpV7M2ajh07psGDB6tu3bqqWbOm2rVrZzfbBAD4P4z1AAC4FocT+UWLFun222+Xp6enPD091aZNGy1evLgsY0MltmzZMsXFxSkhIUHbt2/XXXfdpV69eunIkSPF1vfy8tITTzyhL774Qnv27NGLL76oF198UWlpadY6X375pQYMGKAhQ4bom2++0ZAhQ9S/f3999dVX1jqnT59Wly5dVK1aNX300UfavXu3ZsyYodq1a5d3kwHA7TDWAwDgmhxaWj9z5ky99NJLeuKJJ9SlSxcZhqF///vfGjNmjE6ePKm//OUvZR0nKpmZM2dq5MiRevzxxyVJs2bN0rp165Samqrk5GS7+u3bt1f79u2t202bNtXKlSu1YcMGjRo1ynqOHj16aMKECZKkCRMmKDs7W7NmzdKSJUskSdOmTVPjxo21YMECm3MBAGwx1gMA4LocmpGfPXu2UlNTNW3aNPXt21cPPPCApk+frpSUFP3tb38r6xhRyRQWFmrr1q2KiYmxKY+JiVFOTk6pzrF9+3bl5OSoW7du1rIvv/zS7pw9e/a0Oefq1asVERGhP/3pT/Lz81P79u01b968m2gNAFROjPUAALguhxL53NxcRUVF2ZVHRUUpNzf3poNC5Xby5ElZLBb5+/vblPv7+ysvL++6xzZq1EgeHh6KiIjQuHHjrDP6kpSXl/eb5zxw4IBSU1MVGhqqdevWacyYMXrqqae0aNGiMmgZAFQejPUAALguhxL5Zs2aafny5Xbly5YtU2ho6E0Hhd8Hk8lks20Yhl3Zr23YsEFbtmzRW2+9ZbNkvrTnLCoq0h133KGkpCS1b99eo0eP1p///GelpqbeZGsAoHJhrAcAwHU5dI38pEmTNGDAAH3xxRfq0qWLTCaTNm7cqE8//bTYQR/4X/Xq1ZPZbLabfT9x4oTdjPqvBQcHS5Juv/12HT9+XImJiRo4cKAkKSAg4DfPGRgYqFatWtnUue2227RixQqH2wMAlRFjPQAArsuhGfk//vGP+uqrr1SvXj29//77WrlyperVq6evv/5aDz30UFnHiEqmevXqCg8PV1ZWlk15VlZWscs4S2IYhgoKCqzbkZGRdufMzMy0OWeXLl20b98+mzrff/+9mjRpciNNAIBKj7EeAADX5dCMvCSFh4fr3XffLctY8DsSHx+vIUOGKCIiQpGRkUpLS9ORI0c0ZswYSVfvOH/s2DHrtetz585VUFCQWrZsKenqc+Vff/11Pfnkk9ZzPv300+rataumTZumBx54QB988IE++eQTbdy40VrnL3/5i6KiopSUlKT+/fvr66+/Vlpams1j7AAAVzHWAwDgmhxK5NeuXSuz2ayePXvalK9bt05FRUXq1atXmQSHymvAgAE6deqUJk+erNzcXIWFhWnt2rXWmfHc3FybZ8oXFRVpwoQJOnjwoKpWraqQkBC9+uqrGj16tLVOVFSUli5dqhdffFEvvfSSQkJCtGzZMnXq1Mlap0OHDlq1apUmTJigyZMnKzg4WLNmzdKjjz5acY0HADfAWA8AgOtyKJF//vnn9eqrr9qVG4ah559/nsEdpRIbG6vY2Nhi9y1cuNBm+8knn7SZfS/Jww8/rIcffvi6de6//37df//9pY4TAH6PGOsBAHBdDl0jv3//frsbhklSy5Yt9cMPP9x0UAAAwLkY6wEAcF0OJfK1atXSgQMH7Mp/+OEHeXl53XRQAADAuRjrAQBwXQ4l8n379lVcXJx+/PFHa9kPP/ygZ555Rn379i2z4AAAgHMw1gMA4LocSuRfe+01eXl5qWXLlgoODlZwcLBatmypunXr6vXXXy/rGAEAQAVjrAcAwHU5dLO7WrVqKScnR1lZWfrmm2/k6emptm3b6q677irr+AAAgBMw1gMA4LpuaEb+q6++0kcffSRJMplMiomJkZ+fn15//XX98Y9/1KhRo1RQUFAugQIAgPLHWA8AgOu7oUQ+MTFR3377rXV7586d+vOf/6wePXro+eef14cffqjk5OQyDxIAAFQMxnoAAFzfDSXyO3bs0L333mvdXrp0qTp27Kh58+YpPj5ef/vb37R8+fIyDxIAAFQMxnoAAFzfDV0jf/r0afn7+1u3s7Oz9Yc//MG63aFDBx09erTsooPbOTL5dmeHUK6CJu50dggAUK4Y6wEAcH03NCPv7++vgwcPSpIKCwu1bds2RUZGWvefO3dO1apVK/X5UlNT1aZNG/n6+srX11eRkZHW6/IkyTAMJSYmqkGDBvL09FR0dLR27dplc474+HjVqVNHQUFBWrp0qc2+5cuXq0+fPjfSRAAAftfKeqwHAABl74YS+T/84Q96/vnntWHDBk2YMEE1a9a0uXvtt99+q5CQkFKfr1GjRnr11Ve1ZcsWbdmyRffcc48eeOABa7I+ffp0zZw5U3PmzNHmzZsVEBCgHj166Ny5c5KkDz/8UBkZGcrMzNS0adM0fPhwnTp1SpJ05swZJSQkaO7cuTfSRAAAftfKeqwHAABl74YS+alTp8psNqtbt26aN2+e5s2bp+rVq1v3z58/XzExMaU+X58+fXTfffepefPmat68uV555RV5e3tr06ZNMgxDs2bNUkJCgvr166ewsDClp6fr4sWLysjIkCTt2bNH0dHRioiI0MCBA+Xr66sDBw5IksaPH6/Y2FgFBQXdSBMBAPhdK+uxHgAAlL0buka+fv362rBhg86ePStvb2+ZzWab/f/4xz/k7e3tUCAWi0X/+Mc/dOHCBUVGRurgwYPKy8uz+bLg4eGhbt26KScnR6NHj1bbtm2Vlpam06dP68CBA7p06ZKaNWumjRs3atu2bUpNTS3VexcUFNg8Sic/P98ak8VisatvNjnURLdSXLtLo8hk/u1KbszRfjGLfsENMhnOjqBcOfqZMVW5ob8/u6WS+qaifs7Kc6wHAABl44YS+Wtq1apVbHmdOnVu+Fw7d+5UZGSkfvnlF3l7e2vVqlVq1aqVcnJyJMnmhjvXtg8fPixJ6tmzpwYPHqwOHTrI09NT6enp8vLy0tixY7Vw4UKlpqZq9uzZqlevntLS0tS6detiY0hOTtakSZPsyn/88cdiv6zc2djjhtvpbvbv3+/QcWf9upVxJK7lFwf7pXOtzmUciWtx9POCkvkGVe5E3tHPTP2WYWUciespqW/Onz9foXGU5ViP36+UlBS99tprys3NVevWrTVr1iybSzX+18qVK5WamqodO3aooKBArVu3VmJionr27GmtM2/ePC1atEjfffedJCk8PFxJSUnq2LGjtc65c+f00ksvadWqVTpx4oTat2+vN998Ux06dCjfxgJABXIokS9LLVq00I4dO3TmzBmtWLFCw4YNU3Z2tnW/yWQ7/W0Yhk1ZYmKiEhMTbba7d++uatWqaerUqdq5c6fWrFmjoUOHauvWrcXGMGHCBMXHx1u38/Pz1bhxY4WEhMjX19eu/sb5XzvaXLfxWmioQ8cd/Uf2b1dyY41DZzp03KasTWUciWtJDuWZ0mXtk79/7+wQylWog79jVu79rowjcT2hoQnFll9bLQa4i2XLlikuLk4pKSnq0qWL3n77bfXq1Uu7d+8u9tLHL774Qj169FBSUpJq166tBQsWqE+fPvrqq6/Uvn17SdL69es1cOBARUVFqUaNGpo+fbpiYmK0a9cuNWzYUJL0+OOP67vvvtPixYvVoEEDvfvuu+revbt2795trQMA7s7piXz16tXVrFkzSVJERIQ2b96sN998U88995wkKS8vT4GBgdb6J06csJulv2bv3r167733tH37ds2fP19du3ZV/fr11b9/f40YMUL5+fnFJuYeHh7y8LCfZTebzXZLCiXJUrknyiSp2HaXRhWjci+xdrRfLKJfcIOMyn0Nj6OfGaOoqIwjcT0l9Q0/Z3A3M2fO1MiRI/X4449LkmbNmqV169YpNTVVycn2fwCeNWuWzXZSUpI++OADffjhh9ZE/r333rOpM2/ePP3zn//Up59+qqFDh+rSpUtasWKFPvjgA3Xt2lXS1Ume999/X6mpqZo6dWo5tBQAKp7LXWxoGIYKCgoUHBysgIAAZWVlWfcVFhYqOztbUVFRxR43atQozZgxQ97e3rJYLLp8+bIkWf8t+h18AQQAAHC2wsJCbd261e7GiDExMdbLJ39LUVGRzp07d93LOS5evKjLly9b61y5ckUWi0U1atSwqefp6amNGzfeYCsAwHU5dUb+hRdeUK9evdS4cWOdO3dOS5cu1fr16/Xxxx/LZDIpLi5OSUlJCg0NVWhoqJKSklSzZk0NGjTI7lzz5s2Tn5+f+vbtK0nq0qWLEhMTtWnTJn300Udq1aqVateuXcEtBAAA+P05efKkLBZLsfc6ysvLK9U5ZsyYoQsXLqh///4l1nn++efVsGFDde/eXZLk4+OjyMhITZkyRbfddpv8/f21ZMkSffXVVw5f0gMArsipifzx48c1ZMgQ5ebmqlatWmrTpo0+/vhj9ejRQ9LVR8hdunRJsbGxOn36tDp16qTMzEz5+PjYnScpKcnmL7wdO3bUM888o969e8vPz0/p6ekV2jYAAIDfu9+611FJlixZosTERH3wwQfy8/Mrts706dO1ZMkSrV+/3mYGfvHixRoxYoQaNmwos9msO+64Q4MGDdK2bdturjEA4EKcmsi/8847191vMpnsbmZXHH9/fx06dMiufOLEiZo4ceJNRAgAAIAbVa9ePZnNZrvZ9+vd6+iaZcuWaeTIkfrHP/5hnWn/tddff11JSUn65JNP1KZNG5t9ISEhys7O1oULF5Sfn6/AwEANGDBAwcHBN9coAHAhLneNPAAAANxb9erVFR4ebnOvI0nKysoq9l5H1yxZskSPPfaYMjIy1Lt372LrvPbaa5oyZYo+/vhjRURElHguLy8vBQYG6vTp01q3bp0eeOABxxoDAC7I6XetBwAAQOUTHx+vIUOGKCIiQpGRkUpLS9ORI0c0ZswYSVcf/3vs2DEtWrRI0tUkfujQoXrzzTfVuXNn62y+p6enatWqJenqcvqXXnpJGRkZatq0qbWOt7e3vL29JUnr1q2TYRhq0aKFfvjhB/31r39VixYtNHz48IruAgAoN8zIAwAAoMwNGDBAs2bN0uTJk9WuXTt98cUXWrt2rZo0aSJJys3N1ZEjR6z13377bV25ckXjxo1TYGCg9fX0009b66SkpKiwsFAPP/ywTZ3XX3/dWufs2bMaN26cWrZsqaFDh+rOO+9UZmamqlWrVnGNB4Byxow8AAAAykVsbKxiY2OL3bdw4UKb7fXr1//m+Yq7J9Kv9e/f/7p3ugeAyoAZeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcSFVnBwAAAAD3cWTy7c4OoVwFTdzp7BAA4DcxIw8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAEC5SU5OVocOHeTj4yM/Pz89+OCD2rdvn00dwzCUmJioBg0ayNPTU9HR0dq1a5dNnfj4eNWpU0dBQUFaunSpzb7ly5erT58+5d4WAABcBYk8AAAoN9nZ2Ro3bpw2bdqkrKwsXblyRTExMbpw4YK1zvTp0zVz5kzNmTNHmzdvVkBAgHr06KFz585Jkj788ENlZGQoMzNT06ZN0/Dhw3Xq1ClJ0pkzZ5SQkKC5c+c6pX0AADhDVWcHAAAAKq+PP/7YZnvBggXy8/PT1q1b1bVrVxmGoVmzZikhIUH9+vWTJKWnp8vf318ZGRkaPXq09uzZo+joaEVERCgiIkJxcXE6cOCA6tatq/Hjxys2NlZBQUHXjaOgoEAFBQXW7fz8fEmSxWKRxWKxq2823WzLXV9x7S6NIpO5jCNxLY72i1mVu18kx/sGJTAZzo6g3Dn6mTFVqdzzzSX1y430F4k8AACoMGfPnpUk1alTR5J08OBB5eXlKSYmxlrHw8ND3bp1U05OjkaPHq22bdsqLS1Np0+f1oEDB3Tp0iU1a9ZMGzdu1LZt25Samvqb75ucnKxJkybZlf/444/y9va2K7+zsYejTXQb+/fvd+i4s37dyjgS1/KLg/3SuVbnMo7E9Tj6mUHxfIMqfyLv6GemfsuwMo7EtZTUL+fPny/1OUjkAQBAhTAMQ/Hx8brzzjsVFnb1S1peXp4kyd/f36auv7+/Dh8+LEnq2bOnBg8erA4dOsjT01Pp6eny8vLS2LFjtXDhQqWmpmr27NmqV6+e0tLS1Lp1a7v3njBhguLj463b+fn5aty4sUJCQuTr62tXf+P8r8us3a7qtdBQh447+o/sMo7EtTQOnenQcZuyNpVxJK4nOTTZ2SFUKp/8/Xtnh1DuQh38PbNy73dlHIlrCQ1NKLb82mqx0iCRBwAAFeKJJ57Qt99+q40bN9rtM5ls17IbhmFTlpiYqMTERJvt7t27q1q1apo6dap27typNWvWaOjQodq6davd+T08POThYT/LbjabZTbbL4m2VP6JsmLbXRpVjMq9vNrRfrGocveL5HjfoARG5b+Gx9HPjFFUVMaRuJaS+uVG+qtyX3wAAABcwpNPPqnVq1fr888/V6NGjazlAQEBkv5vZv6aEydO2M3SX7N371699957mjJlitavX6+uXbuqfv366t+/v7Zt23ZDMxoAALgjEnkAAFBuDMPQE088oZUrV+qzzz5TcHCwzf7g4GAFBAQoKyvLWlZYWKjs7GxFRUUVe75Ro0ZpxowZ8vb2lsVi0eXLlyXJ+m9RJZ/JAQCApfUAAKDcjBs3ThkZGfrggw/k4+NjnXmvVauWPD09ZTKZFBcXp6SkJIWGhio0NFRJSUmqWbOmBg0aZHe+efPmyc/PT3379pUkdenSRYmJidq0aZM++ugjtWrVSrVr167IJgIAUOGYkQfgNlJSUhQcHKwaNWooPDxcGzZsKLHuypUr1aNHD9WvX1++vr6KjIzUunXr7OqtWLFCrVq1koeHh1q1aqVVq1bZ7E9OTlaHDh3k4+MjPz8/Pfjgg9q3b1+Ztw2orFJTU3X27FlFR0crMDDQ+lq2bJm1zvjx4xUXF6fY2FhFRETo2LFjyszMlI+Pj825jh8/rqSkJP3tb3+zlnXs2FHPPPOMevfureXLl2vBggUV1jYAAJyFRB6AW1i2bJni4uKUkJCg7du366677lKvXr105MiRYut/8cUX6tGjh9auXautW7fq7rvvVp8+fbR9+3ZrnS+//FIDBgzQkCFD9M0332jIkCHq37+/vvrqK2ud7OxsjRs3Tps2bVJWVpauXLmimJgYXbhwodzbDFQGhmEU+3rsscesdUwmkxITE5Wbm6tffvlF2dnZ1rva/y9/f38dOnRIDRo0sCmfOHGiTp06pT179qhjx47l3SQAAJyOpfUA3MLMmTM1cuRIPf7445KkWbNmad26dUpNTVVysv3jcGbNmmWznZSUpA8++EAffvih2rdvb63To0cPTZgwQdLVx1NlZ2dr1qxZWrJkiSTp448/tjnPggUL5Ofnp61bt6pr165l3UwAAADgNzEjD8DlFRYWauvWrYqJibEpj4mJUU5OTqnOUVRUpHPnzqlOnTrWsi+//NLunD179rzuOc+ePStJNucBAABl40Yuo8vNzdWgQYPUokULValSRXFxccXWO3PmjMaNG6fAwEDVqFFDt912m9auXWvd37RpU5lMJrvXuHHjyrp5QJkhkQfg8k6ePCmLxWL3KCp/f3+7R1aVZMaMGbpw4YL69+9vLcvLy7uhcxqGofj4eN15553FLvsFAACOu9HL6AoKClS/fn0lJCSobdu2xdYpLCxUjx49dOjQIf3zn//Uvn37NG/ePDVs2NBaZ/PmzcrNzbW+rj1F409/+lPZNxIoIyytB+A2TCaTzbZhGHZlxVmyZIkSExP1wQcfyM/Pz+FzPvHEE/r222+1cePGG4wcAAD8lhu9jK5p06Z68803JUnz588v9pzz58/Xf//7X+Xk5KhatWqSpCZNmtjUqV+/vs32q6++qpCQEHXr1u2m2wSUF2bkAbi8evXqyWw2282Unzhxwm5G/deWLVumkSNHavny5erevbvNvoCAgFKf88knn9Tq1av1+eefq1GjRg62BAAAFKcsLqMrzurVqxUZGalx48bJ399fYWFhSkpKksViKTGOd999VyNGjCjVZAHgLCTyAFxe9erVFR4ebl3qdk1WVpaioqJKPG7JkiV67LHHlJGRod69e9vtj4yMtDtnZmamzTkNw9ATTzyhlStX6rPPPlNwcPBNtgYAAPxaWVxGV5wDBw7on//8pywWi9auXasXX3xRM2bM0CuvvFJs/ffff19nzpyxebIG4IpYWg/ALcTHx2vIkCGKiIhQZGSk0tLSdOTIEY0ZM0bS1TvOHzt2TIsWLZJ0NYkfOnSo3nzzTXXu3Nn6JcDT01O1atWSJD399NPq2rWrpk2bpgceeEAffPCBPvnkE5ul8+PGjVNGRoY++OAD+fj4WM9Tq1YteXp6VmQXAABQ6Tl6GV1JioqK5Ofnp7S0NJnNZoWHh+vnn3/Wa6+9pokTJ9rVf+edd9SrVy+7x1wCroZEHoBbGDBggE6dOqXJkycrNzdXYWFhWrt2rfU6t9zcXJub4bz99tu6cuWKxo0bZ3PX2WHDhmnhwoWSpKioKC1dulQvvviiXnrpJYWEhGjZsmXq1KmTtX5qaqokKTo62iaeBQsW8Nd6AADKyM1cRnc9gYGBqlatmsxms7XstttuU15engoLC1W9enVr+eHDh/XJJ59o5cqVDr8fUFFI5AG4jdjYWMXGxha771pyfs369etLdc6HH35YDz/8cIn7DcMobXgAAMBB/3sZ3UMPPWQtz8rK0gMPPODwebt06aKMjAwVFRWpSpWrVxV///33CgwMtEnipat/pPfz8yv2cjzA1XCNPAAAAACni4+P19///nfNnz9fe/bs0V/+8he7y+iGDh1qc8yOHTu0Y8cOnT9/Xv/5z3+0Y8cO7d6927p/7NixOnXqlJ5++ml9//33+te//qWkpCS7Z8QXFRVpwYIFGjZsmKpWZa4Tro9PKQAAAACnu9HL6CSpffv21v/eunWrMjIy1KRJEx06dEiS1LhxY2VmZuovf/mL2rRpo4YNG+rpp5/Wc889Z3OeTz75REeOHNGIESPKt5FAGSGRBwAAAOASbuQyOql0l8BFRkZq06ZN160TExPD5XRwKyytBwAAAADAjZDIAwAAAADgRkjkAQAAAABwI05N5JOTk9WhQwf5+PjIz89PDz74oPbt22dTxzAMJSYmqkGDBvL09FR0dLR27dplUyc+Pl516tRRUFCQli5darNv+fLl6tOnT7m3BQAAAACAiuDURD47O1vjxo3Tpk2blJWVpStXrigmJkYXLlyw1pk+fbpmzpypOXPmaPPmzQoICFCPHj107tw5SdKHH36ojIwMZWZmatq0aRo+fLhOnTolSTpz5owSEhI0d+5cp7QPAAAAAICy5tS71n/88cc22wsWLJCfn5+2bt2qrl27yjAMzZo1SwkJCerXr58kKT09Xf7+/srIyNDo0aO1Z88eRUdHKyIiQhEREYqLi9OBAwdUt25djR8/XrGxsQoKCnJG8wCUQnbXbs4OoVx1+yLb2SEAAACgknGpx8+dPXtWklSnTh1J0sGDB5WXl6eYmBhrHQ8PD3Xr1k05OTkaPXq02rZtq7S0NJ0+fVoHDhzQpUuX1KxZM23cuFHbtm1Tamrqb75vQUGBCgoKrNv5+fmSJIvFIovFYlffbLqpZrqF4tpdGkUmcxlH4loc7Rez6JeSGGb6plimyv0IHEf7xVSl8t/apaS+uZmfMwAAULm4TCJvGIbi4+N15513KiwsTJKUl5cnSfL397ep6+/vr8OHD0uSevbsqcGDB6tDhw7y9PRUenq6vLy8NHbsWC1cuFCpqamaPXu26tWrp7S0NLVu3druvZOTkzVp0iS78h9//FHe3t525Xc29rjp9rq6/fv3O3TcWb/KPbv6i4P90rlW5zKOxLU4+nmRpF86dizDSFyPo33jG1S5E3lH+6V+y7AyjsT1lNQ358+fr+BIAACAq3KZRP6JJ57Qt99+q40bN9rtM5lsp8ANw7ApS0xMVGJios129+7dVa1aNU2dOlU7d+7UmjVrNHToUG3dutXu/BMmTFB8fLx1Oz8/X40bN1ZISIh8fX3t6m+c/7UjTXQrr4WGOnTc0X9U7mXEjUNnOnTcpqxNZRyJa0kOTXb42LyvK/fPU2jSKw4d98nfvy/jSFxLqIO/Y1bu/a6MI3E9oaEJxZZfWy0GAO6Gy+iAsucSifyTTz6p1atX64svvlCjRo2s5QEBAZKuzswHBgZay0+cOGE3S3/N3r179d5772n79u2aP3++unbtqvr166t///4aMWKE8vPz7ZJzDw8PeXjYz7KbzWaZi1n2a6ncE2WSVGy7S6OKUbmXfjraLxbRLyUxVfLlwg73jVG5r+FxtF+MoqIyjsT1lNQ3N/NzBgAAKhenXmxoGIaeeOIJrVy5Up999pmCg4Nt9gcHBysgIEBZWVnWssLCQmVnZysqKqrY840aNUozZsyQt7e3LBaLLl++LEnWf4t+B18CAQAAAACVl1Nn5MeNG6eMjAx98MEH8vHxsV4TX6tWLXl6espkMikuLk5JSUkKDQ1VaGiokpKSVLNmTQ0aNMjufPPmzZOfn5/69u0rSerSpYsSExO1adMmffTRR2rVqpVq165dkU0EAAAAAKBMOTWRv3ZH+ejoaJvyBQsW6LHHHpMkjR8/XpcuXVJsbKxOnz6tTp06KTMzUz4+PjbHHD9+XElJScrJybGWdezYUc8884x69+4tPz8/paenl2t7AAAAAAAob05N5A3jty82N5lMdjezK46/v78OHTpkVz5x4kRNnDjRwQgBAAAAAHAtlf+BvAAAAAAAVCIk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAOXmiy++UJ8+fdSgQQOZTCa9//77NvsNw1BiYqIaNGggT09PRUdHa9euXTZ14uPjVadOHQUFBWnp0qU2+5YvX64+ffqUdzMAAHApJPIAAKDcXLhwQW3bttWcOXOK3T99+nTNnDlTc+bM0ebNmxUQEKAePXro3LlzkqQPP/xQGRkZyszM1LRp0zR8+HCdOnVKknTmzBklJCRo7ty5FdYeAABcQVVnBwAAACqvXr16qVevXsXuMwxDs2bNUkJCgvr16ydJSk9Pl7+/vzIyMjR69Gjt2bNH0dHRioiIUEREhOLi4nTgwAHVrVtX48ePV2xsrIKCgn4zjoKCAhUUFFi38/PzJUkWi0UWi8WuvtnkSGvdS3HtLo0ik7mMI3EtjvaLWZW7XyTH+8YwV+6+cbRfZDLKNhAX5GjfmKpU7vnmkvrlRvqLRB4AADjFwYMHlZeXp5iYGGuZh4eHunXrppycHI0ePVpt27ZVWlqaTp8+rQMHDujSpUtq1qyZNm7cqG3btik1NbVU75WcnKxJkybZlf/444/y9va2K7+zsYfjDXMT+/fvd+i4s37dyjgS1/KLg/3SuVbnMo7E9Tj6mfmlY8cyjsS1ONovvkGVP5F3tG/qtwwr40hcS0n9cv78+VKfg0QeAAA4RV5eniTJ39/fptzf31+HDx+WJPXs2VODBw9Whw4d5OnpqfT0dHl5eWns2LFauHChUlNTNXv2bNWrV09paWlq3bp1se81YcIExcfHW7fz8/PVuHFjhYSEyNfX167+xvlfl1UzXdZroaEOHXf0H9llHIlraRw606HjNmVtKuNIXE9yaLJDx+V9Xbl/nkKTXnHouE/+/n0ZR+J6Qh38PbNy73dlHIlrCQ1NKLb82mqx0iCRBwAATmUy2a5jNwzDpiwxMVGJiYk22927d1e1atU0depU7dy5U2vWrNHQoUO1devWYt/Dw8NDHh72s+xms1nmYpb9Wir/RFmx7S6NKoaDy4jdhKP9YlHl7hfJ8b4xObr03E042i8yKv81PI72jVFUVMaRuJaS+uVG+qtyX3wAAABcVkBAgKT/m5m/5sSJE3az9Nfs3btX7733nqZMmaL169era9euql+/vvr3769t27bd0GwGAADuikQeAAA4RXBwsAICApSVlWUtKywsVHZ2tqKiouzqG4ahUaNGacaMGfL29pbFYtHly5clyfpvUSWfxQEAQGJpPQAAKEfnz5/XDz/8YN0+ePCgduzYYX0ufFxcnJKSkhQaGqrQ0FAlJSWpZs2aGjRokN255s2bJz8/P/Xt21eS1KVLFyUmJmrTpk366KOP1KpVK9WuXbuimgYAgNOQyAMAgHKzZcsW3X333dbtazecGzZsmBYuXKjx48fr0qVLio2N1enTp9WpUydlZmbKx8fH5jzHjx9XUlKScnJyrGUdO3bUM888o969e8vPz0/p6ekV0ygAAJyMRB4AAJSb6OhoGUbJd44zmUx2N7Mrjr+/vw4dOmRXPnHiRE2cOPEmowQAwL1wjTwAAAAAAG6ERB4AAAAAADfi1ET+iy++UJ8+fdSgQQOZTCa9//77NvsNw1BiYqIaNGggT09PRUdHa9euXTZ14uPjrTfMWbp0qc2+5cuXq0+fPuXdDAAAAAAAKoxTE/kLFy6obdu2mjNnTrH7p0+frpkzZ2rOnDnavHmzAgIC1KNHD507d06S9OGHHyojI0OZmZmaNm2ahg8frlOnTkmSzpw5o4SEBM2dO7fC2gMAAAAAQHlzaiLfq1cvTZ06Vf369bPbZxiGZs2apYSEBPXr109hYWFKT0/XxYsXlZGRIUnas2ePoqOjFRERoYEDB8rX11cHDhyQJI0fP16xsbEKCgqq0DYBAAAAAFCeXPau9QcPHlReXp5iYmKsZR4eHurWrZtycnI0evRotW3bVmlpaTp9+rQOHDigS5cuqVmzZtq4caO2bdum1NTUUr1XQUGBCgoKrNv5+fmSJIvFIovFYlffbLrJxrmB4tpdGkUmcxlH4loc7Rez6JeSGGb6plimku/yXRk42i+mKpX/1i4l9c3N/JwBAIDKxWUT+by8PElXHzfzv/z9/XX48GFJUs+ePTV48GB16NBBnp6eSk9Pl5eXl8aOHauFCxcqNTVVs2fPVr169ZSWlqbWrVsX+17JycmaNGmSXfmPP/4ob29vu/I7G3vcbPNc3v79+x067qxftzKOxLX84mC/dK7VuYwjcS2Ofl4k6ZeOHcswEtfjaN/4BlXuRN7RfqnfMqyMI3E9JfXN+fPnKzgSAADgqlw2kb/GZLKd/jYMw6bs18+eTUxMVPfu3VWtWjVNnTpVO3fu1Jo1azR06FBt3bq12PeYMGGC4uPjrdv5+flq3LixQkJC5Ovra1d/4/yvb7JVru+10FCHjjv6j+wyjsS1NA6d6dBxm7I2lXEkriU5NNnhY/O+rtw/T6FJrzh03Cd//76MI3EtoQ7+jlm597syjsT1hIYmFFt+bbUYAACAyybyAQEBkq7OzAcGBlrLT5w4YTdLf83evXv13nvvafv27Zo/f766du2q+vXrq3///hoxYoTy8/OLTcw9PDzk4WE/y242m2UuZtmvpXJPlElSse0ujSpG5V766Wi/WES/lMRUyZcLO9w3RuW+hsfRfjGKiso4EtdTUt/czM8ZAACoXFz2YsPg4GAFBAQoKyvLWlZYWKjs7GxFRUXZ1TcMQ6NGjdKMGTPk7e0ti8Wiy5cvS5L136LfwRdAAAAAAEDl5tQZ+fPnz+uHH36wbh88eFA7duywPhc+Li5OSUlJCg0NVWhoqJKSklSzZk0NGjTI7lzz5s2Tn5+f+vbtK0nq0qWLEhMTtWnTJn300Udq1aqVateuXVFNAwAAAACgXDg1kd+yZYvuvvtu6/a169SHDRumhQsXavz48bp06ZJiY2N1+vRpderUSZmZmfLx8bE5z/Hjx5WUlKScnBxrWceOHfXMM8+od+/e8vPzU3p6esU0CgAAAACAcuTURD46OlqGUfIF5yaTye5mdsXx9/fXoUOH7MonTpyoiRMn3mSUAAAAAAC4Dpe9Rh4AAAAAANgjkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRkjkAQAAAABwIyTyAAAAAAC4ERJ5AAAAAADcCIk8AAAAAABuhEQeAAAAAAA3QiIPAAAAAIAbIZEHAAAAAMCNkMgDAAAAAOBGSOQBAAAAAHAjJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADdCIg8AAAAAgBshkQcAAAAAwI2QyAMAAAAA4EZI5AEAAAAAcCMk8gAAAAAAuBESeQAAAAAA3AiJPAAAAAAAboREHgAAAAAAN0IiDwAAAACAGyGRBwAAAADAjZDIAwAAAADgRtwikU9JSVFwcLBq1Kih8PBwbdiwwbrv9ddfl7+/v/z9/fXGG2/YHPfVV18pPDxcFoulokMGAAA3iPEeAIDSqersAH7LsmXLFBcXp5SUFHXp0kVvv/22evXqpd27d+vs2bOaOHGi1qxZI8MwdP/996tHjx4KCwvT5cuXNWbMGKWlpclsNju7GQAA4DoY7wEAKD2XT+RnzpypkSNH6vHHH5ckzZo1S+vWrVNqaqrat2+vNm3a6J577pEktWnTRnv27FFYWJhee+01de3aVR06dPjN9ygoKFBBQYF1++zZs5Kk06dPF//X/cJLZdAy13b69GmHjjtb8Nt13Jmj/WJcMso4EtfiaL9I0nmDvinOpcILZRyJa3G0XwquVP4Z15L6Jj8/X5JkVNKfmfIe7xnr7THWF4+xvmSO9g1jffEq+1gvMd6XpEzGesOFFRQUGGaz2Vi5cqVN+VNPPWV07drV2L17t3HLLbcYhw8fNg4dOmTUrl3b2L17t7F//34jNDTUyM/PL9X7vPzyy4YkXrx48eLFy+VfR48eLY8h16kqYrxnrOfFixcvXu7yKs1Y79Iz8idPnpTFYpG/v79Nub+/v/Ly8nTbbbcpKSlJPXr0kCQlJyfrtttuU/fu3TV9+nStW7dOiYmJqlatmt5880117dq12PeZMGGC4uPjrdtFRUX673//q7p168pkMpVfA0shPz9fjRs31tGjR+Xr6+vUWFwNfVM8+qVk9E3x6JeSuVLfGIahc+fOqUGDBk6NozxUxHjvymO95FqfNVdCv5SMvike/VI8+qVkrtQ3NzLWu3Qif82vB1jDMKxlY8aM0ZgxY6z7Fi5cKB8fH0VGRqpFixbavHmzfvrpJz3yyCM6ePCgPDw87M7v4eFhV167du2yb8hN8PX1dfoHy1XRN8WjX0pG3xSPfimZq/RNrVq1nB1CuSrP8d4dxnrJdT5rroZ+KRl9Uzz6pXj0S8lcpW9KO9a7dCJfr149mc1m5eXl2ZSfOHHC7q/20tW/6E+ePFlffPGFvvrqKzVv3lyhoaEKDQ3V5cuX9f333+v222+vqPABAEApMN4DAHBjXPrxc9WrV1d4eLiysrJsyrOyshQVFWVXPy4uTn/5y1/UqFEjWSwWXb582brvypUrPJYGAAAXxHgPAMCNcekZeUmKj4/XkCFDFBERocjISKWlpenIkSM2y+ukq4P9/v37tWjRIklSx44dtXfvXn300Uc6evSozGazWrRo4Ywm3BQPDw+9/PLLxV4S8HtH3xSPfikZfVM8+qVk9E3FYbzns1Yc+qVk9E3x6Jfi0S8lc9e+MRmG6z8PIiUlRdOnT1dubq7CwsL0xhtv2NzI5tKlS2rXrp2WLVumdu3aWcv//ve/68UXX5SHh4dSUlLUu3dvJ0QPAABKg/EeAIDScYtEHgAAAAAAXOXS18gDAAAAAABbJPIAAAAAALgREnkAAAAAANwIiTwAAAAAAG6ERB4AAAAAADfi8s+RB36tsLBQBw8eVEhIiKpW5SN8TVFRkX744QedOHFCRUVFNvv+9/FNgCStXbtWZrNZPXv2tClft26dioqK1KtXLydFBgD/55dfflGNGjWcHYZLKSwsLHasDwoKclJEcFWbN29WUVGROnXqZFP+1VdfyWw2KyIiwkmRoSyQBbmg5ORk+fv7a8SIETbl8+fP13/+8x8999xzTorMuS5evKgnn3xS6enp/6+9e4/L+fz/AP66i5JKZSRkKflGKcQIm0NOWRPabE4zjMxsjHLaJiunmVHsu9Fmc5ocUiw7qL4xps1pnUSS0oEVymIdkO7P7w9f9697ldl3dH3u+/N6Ph57PHR97vHicdf7vj6f63pfAIALFy7AwcEBs2bNQqtWrbBw4ULBCcU5fvw4xo0bh9zcXPz5REmVSoWqqipByerf+vXrH/m1s2bNeoJJ5G3hwoX48MMPa4xLkoSFCxcqfiJfUlKCvXv3IisrC/PmzUPTpk2RmJiIFi1aoHXr1qLjkR7YvHkzzMzMMHr0aK3xiIgIlJeX47XXXhOUTDy1Wo3ly5dj48aNuHr1qqbeL168GG3btsXrr78uOqIQmZmZmDJlCn7++WetcUmSFFfro6OjH/m1Pj4+TzCJvM2cORPz58+vMZG/cuUKVq1ahRMnTghKJg+6Xut5jrwMtW3bFuHh4ejdu7fW+IkTJzBmzBhcunRJUDKxZs+ejYSEBISGhsLLywupqalwcHBAdHQ0lixZgqSkJNERhenSpQv+9a9/ISgoCC1btoRKpdK6bmFhIShZ/bO3t3+k16lUKmRnZz/hNPJlYmKC9PR0tG3bVms8JycHLi4uKCsrExNMBlJTUzFo0CBYWFggJycHGRkZmklEbm4utm3bJjoi6QEnJyds3LgRAwYM0Bo/cuQI/Pz8kJGRISiZeMHBwdi6dSuCg4Mxbdo0pKWlwcHBAXv27EFISAh++eUX0RGF6NOnDxo0aICFCxfWWus7d+4sKFn9MzB4tN3BSrvB8WdmZmaaz8vVXbp0CW5ubvjjjz8EJRNPH2o9n8jLUGFhIVq2bFljvHnz5igoKBCQSB7279+P3bt3w8PDQ6t4OTs7IysrS2Ay8TIzM7F37144OjqKjiKcUm90/V0WFhbIzs6uMZG/ePEiTE1NxYSSiblz52LSpEn46KOPYG5urhkfNmwYxo0bJzAZ6ZPc3Nxabzza2dkhLy9PQCL52LZtGz7//HMMHDgQb7zxhmbczc0N58+fF5hMrOTkZPz666/o0KGD6CjC/XlbAdXO2NgYV69erTGRLygoUPz2VH2o9Wx2J0Nt2rRBQkJCjfGEhAS0atVKQCJ5uH79OqytrWuMl5WV1bgrrTQ9e/bExYsXRccgHeLj44N33nlH6ybYxYsX4e/vr+hliMD9PYXTp0+vMd66dWsUFhYKSET6yNraGqmpqTXGU1JS8NRTTwlIJB9Xrlyp9ca0Wq1GZWWlgETy4OzsjKKiItExSIcMHjwYixYtws2bNzVjJSUlePfddzF48GCBycTTh1qv7FsxMjV16lS88847qKyshKenJwAgPj4e8+fPh7+/v+B04jzzzDP47rvv8PbbbwOAZvL+xRdfoFevXiKjCff222/D398fhYWFcHV1RcOGDbWuu7m5CUpW/+bOnfvIr127du0TTCJvq1evhpeXFzp06ABbW1sAwOXLl/Hcc8/h448/FpxOrEaNGuHWrVs1xjMyMtC8eXMBiUgfjRkzBrNmzYK5ubmmIemRI0cwe/ZsjBkzRnA6sVxcXPDTTz/Bzs5OazwiIgJdu3YVlEq8VatWYf78+VixYkWttb5JkyaCktU/9sN5NGvWrEHfvn1hZ2en+d5JTk5GixYtsH37dsHpxNKHWs898jL0oNnU+vXrcffuXQD332wLFizA4sWLFfv0+eeff4aXlxfGjx+PLVu2YPr06Th79ix++eUXHDlyBN26dRMdUZja9oqpVCpFNsD5837TuqhUKhw6dOgJp5E3SZIQFxeHlJQUmJiYwM3NjSccAPDz88P169exZ88eNG3aFKmpqTA0NMTIkSPRt29fhIaGio5IeuDu3bt49dVXERERoVniqlarMXHiRGzcuBFGRkaCE4pz4MABvPrqq1i0aBGCg4MRFBSEjIwMbNu2Dd9++61inyQ+qPV//hyoxFrPfjiPrqysDDt27NCq9WPHjq1xI0hp9KHWcyIvY6WlpUhPT4eJiQnat28PY2Nj0ZGES0tLw+rVq/Hrr79CrVbD3d0dCxYsgKurq+hoQuXm5j70+p+fahBR3W7duoXnn38eZ8+exR9//IFWrVqhsLAQvXr1wvfff6/4HgL0eGVmZiI5ORkmJiZwdXXlz+v/iomJwYoVK7TqfWBgIIYMGSI6mjBHjhx56PV+/frVUxIi3acPtZ4TeRm6efMmqqqq0LRpU63xGzduoEGDBopaOvVAZWUl/Pz8sHjx4hoNO4jo7wsODn7o9cDAwHpKIl+HDh1CYmKiZhIxaNAg0ZGI9N69e/ewfPlyTJkyBW3atBEdh0in/VXn9YkTJ9ZTEvnS5VrPibwMDRs2DMOHD8ebb76pNb5x40ZER0fj+++/F5RMLEtLSyQmJnIiX4esrCyEhoYiPT0dKpUKHTt2xOzZs9GuXTvR0YQ6deoUIiIikJeXp9mq8kBUVJSgVOL9eZ9pZWUlLl26hAYNGqBdu3ZITEwUlIxIGV566SV0794dCxcu1BpfvXo1Tp48iYiICEHJxDMzM0NaWlqNUzXofqOyL7/8UlPrnZ2dMWXKFEUdM1uby5cvIzo6utZar+R+OFZWVlpfV1ZWory8HEZGRmjcuDFu3LghKBk9DpzIy1DTpk2RkJCAjh07ao2fP38effr0QXFxsaBkYk2ePBmurq5/q5mZUsTExMDHxwddunRBnz59IEkSfv75Z6SkpODAgQOK3U+4a9cuTJw4EUOGDEFcXByGDBmCzMxMFBYWYtSoUdi8ebPoiLJy69YtTJo0CaNGjcKrr74qOo4wdTVRUqlUaNSoERwdHdG3b18YGhrWczLSJ82bN8ehQ4dqbA07c+YMBg0ahKtXrwpKJt7IkSMxcuRITJo0SXQUWTl9+jSGDh0KExMT9OjRA5Ik4fTp06ioqEBsbCzc3d1FRxQiPj4ePj4+sLe3R0ZGBjp16oScnBxIkgR3d3fF98P5s8zMTMyYMQPz5s3D0KFDRccRRh9qPSfyMmRqaorjx4/XWtx79uyJ8vJyQcnEWr58OT7++GMMHDgQ3bp1q7F3RcldSbt27YqhQ4fiww8/1BpfuHAhYmNjFft01c3NDdOnT8fMmTNhbm6OlJQU2NvbY/r06WjZsiWCgoJER5SdtLQ0vPDCC8jJyREdRRh7e3tcv34d5eXlsLKygiRJKCkpQePGjWFmZoZr167BwcEBhw8f5tJf+p+ZmJggOTkZTk5OWuPnz59H165dUVFRISiZeGFhYfjggw8wfvz4Wuu9Uo/IfO655+Do6IgvvvhC0yDx3r17mDp1KrKzs3H06FHBCcXo0aMHvLy8EBwcrKn11tbWGD9+PLy8vDBjxgzREWXn9OnTmDBhAs6fPy86ijB6Ueslkp1+/fpJb731Vo3xN998U3r22WcFJJKHtm3b1vmfvb296HhCGRsbSxcuXKgxnpGRIRkbGwtIJA+NGzeWLl26JEmSJD311FNSamqqJEmSdO7cOcnGxkZgMvn66aefJEtLS9ExhAoPD5f69+8vXbx4UTOWmZkpeXp6Srt27ZLy8/OlPn36SC+++KLAlKTrunfvLgUFBdUYX7JkieTu7i4gkXyoVKo6/zMwMBAdT5hGjRpJ6enpNcbPnj0rmZiYCEgkD2ZmZpqf15aWllJaWpokSZKUnJws2dnZCUwmX4mJiZK5ubnoGELpQ63nOfIytHz5cgwaNAgpKSkYOHAggPvLhk6dOoXY2FjB6cS5dOmS6Aiy1bx5cyQnJ6N9+/Za48nJybC2thaUSrymTZvijz/+AAC0bt0aaWlpcHV1RUlJiWJXtjzw5yVlkiShoKAA27dvh5eXl6BU8vD+++8jMjJSq7+Eo6MjPv74Y7z44ovIzs7GRx99hBdffFFgStJ1ixcvxosvvoisrCx4enoCuF/rd+7cqej98cD9Y/iopiZNmiAvLw8dOnTQGs/Pz4e5ubmgVOKZmprizp07AIBWrVohKysLLi4uAICioiKR0YSLjo7W+vpBrf/3v/+NPn36CEolD/pQ6zmRl6E+ffrgl19+werVq7Fnzx7NmY9ffvlljYmaUkn/3RHy57NUlWratGnw8/NDdnY2evfuDZVKhWPHjmHVqlXw9/cXHU+Y5557DnFxcXB1dcXLL7+M2bNn49ChQ4iLi9PcJFOqkJAQra8NDAzQvHlzvPbaa1i0aJGgVPJQUFCAe/fu1Ri/d+8eCgsLAdz/sPjgJhHR/8LHxwf79+/HihUrsHfvXk2t/89//sNjxKhWr7zyCl5//XV8/PHHWrV+3rx5GDt2rOh4wnh4eCAhIQHOzs7w9vaGv78/zpw5g6ioKHh4eIiOJ9TIkSO1vlapVGjevDk8PT2xZs0aMaFkQi9qveAVAUR/y9atW6VOnTpJxsbGkrGxseTq6ipt27ZNdCzh1Gq1tHbtWql169aa5YetW7eWQkNDJbVaLTqeMMXFxdKVK1ckSZKkqqoqadWqVdLw4cOlOXPmSDdu3BCcjuTq+eefl9zd3aXExETNWGJiotStWzfJ29tbkiRJio6Oljp16iQqIpHe+/HHH6UXXnhBateuneTo6CgNHz5cOnr0qOhYQt25c0eaNWuWZGRkJBkYGEgGBgaSsbGx9M4770i3b98WHU+YrKwsKSUlRZIkSSorK5NmzJghubq6SqNGjZJycnIEpyO50odaz2Z3MldRUYHKykqtMSWeIw/cPz5k8eLFeOuttzSd2RMSEvDpp59i2bJlmDNnjuiIsvDgzqGSl9kR/ROFhYV49dVXER8fj4YNGwK4f4d+4MCB2L59O1q0aIHDhw+jsrISQ4YMEZyWSP98/fXXmDx5Mnx9fbVOYtm3bx+2bNmCcePGiY4oVHl5ObKysiBJEhwdHdG4cWPRkYh0jj7Uek7kZai8vBzz58/Hnj17aj1qrqqqSkAq8ezt7REUFISJEydqjW/duhUffPAB99BTDd9//z0MDQ1rHK8SGxuLqqoqDBs2TFAyeTh16hQiIiJqPXc3KipKUCr5OH/+PC5cuABJktChQ4ca3cWJ/omqqiqEhIRgz549tX4PKvl8544dO8LPz6/GDfq1a9fiiy++QHp6uqBkJEenTp2CWq1Gz549tcZPnDgBQ0NDdO/eXVAyebh8+TKio6Nr/Tmzdu1aQankQ5drPffIy9C8efNw+PBhfPbZZ5g4cSI+/fRTXLlyBWFhYTWOF1OSgoIC9O7du8Z47969UVBQICCRWO7u7oiPj4eVlRW6du360H4BSj1+buHChbV+z6jVaixcuFDRE/ldu3Zh4sSJGDJkCOLi4jBkyBBkZmaisLAQo0aNEh1PFjp06FCjqRTR4xIUFIRNmzZh7ty5WLx4Md577z3k5ORg//79CAwMFB1PqOzsbAwfPrzGuI+PD959910BicTx9fXFli1b0KRJE/j6+j70tUq9ATtz5kzMnz+/xkT+ypUrWLVqFU6cOCEomXjx8fHw8fGBvb09MjIy0KlTJ+Tk5ECSJLi7u4uOJwu6XOs5kZehAwcOYNu2bejfvz+mTJmiOTfUzs4OO3bswPjx40VHFMLR0RF79uypUcR3796tyCaAI0aMgLGxMYCazUzovszMTDg7O9cY79ChAy5evCggkXysWLECISEhmDlzJszNzbFu3TrY29tj+vTpaNmypeh4wvEJBj1pO3bswBdffAFvb28EBQVh7NixaNeuHdzc3HD8+HHMmjVLdERh2rRpg/j4eDg6OmqNx8fHy/c85yfEwsJCc6PewsJCcBp5OnfuXK2T0q5du+LcuXMCEsnHokWL4O/vj+DgYJibmyMyMhLW1tYYP3684k+oAXS/1nMiL0M3btyAvb09gPv74R8sr3v22WcxY8YMkdGECgoKwiuvvIKjR4+iT58+mm6t8fHx2LNnj+h49W7JkiW1/pr+n4WFBbKzs9G2bVut8YsXL8LU1FRMKJnIysqCt7c3AMDY2BhlZWVQqVSYM2cOPD09ERQUJDihOHyCQfWhsLAQrq6uAAAzMzPcvHkTAPDCCy9g8eLFIqMJ5+/vj1mzZiE5OVmrO/uWLVuwbt060fHq1ebNm2v9Nf0/Y2NjXL16FQ4ODlrjBQUFaNBA2VOd9PR07Ny5EwDQoEEDVFRUwMzMDMHBwRgxYoSi5xX6UOsNRAegmhwcHJCTkwMAcHZ21kxSDxw4AEtLS3HBBHvxxRdx4sQJNGvWDPv370dUVBSaNWuGkydPKn4pcH5+Pi5fvqz5+uTJk3jnnXfw+eefC0wlno+PD9555x1kZWVpxi5evAh/f3/4+PgITCZe06ZNNY0RW7dujbS0NABASUkJysvLRUYT7sETjLS0NDRq1AiRkZHIz89Hv379MHr0aNHxSE/Y2tpqtoU5OjoiNjYWwP39vg9WWynVjBkzsGvXLpw5cwbvvPMOZs+ejbS0NOzevRvTp08XHU+YiooKrZ/Pubm5CA0N1bx3lGrw4MFYtGiR5mYYcL+Wvfvuuxg8eLDAZOKZmprizp07AO4fpVb981BRUZGoWLKgF7VeTLN8epi1a9dK69atkyRJkg4dOiSZmJhojhoJDQ0VnI7k6Nlnn9Ucw1dQUCCZm5tLvXr1kp566ikpKChIcDpxSkpKJA8PD6lBgwZS27ZtpbZt20oNGjSQBgwYIP3++++i4wk1duxYac2aNZIkSdKyZcuk5s2bS1OnTpXs7OykUaNGCU4nlpmZmXTx4kVJkiTJ0tJSSktLkyRJkpKTkyU7OzuByUifLFiwQFq+fLkkSZIUEREhNWjQQHJ0dJSMjIykBQsWCE5HcjR48GBpw4YNkiRJ0u+//y5ZW1tLtra2UqNGjaTPPvtMcDpxLl++LDk4OEgWFhZS//79pf79+0uWlpaSk5OTlJeXJzqeUCNGjJA+//xzSZIkad68eZKjo6O0bNkyyd3dXRo4cKDgdGLpQ61n13odkJeXh9OnT6Ndu3bo3Lmz6DjC1NWBPCYmBmq1WtGNy6ysrHD8+HE4OTlh/fr12L17NxISEhAbG4s33ngD2dnZoiMKI0kS4uLikJKSAhMTE7i5uaFv376iYwl348YN3L59G61atYJarcbHH3+MY8eOwdHREYsXL4aVlZXoiMLY2Njg0KFDcHZ2houLC1auXAkfHx+kpKSgT58+KC0tFR2R9NCJEyeQkJAAR0dHxa8YYhfy2jVr1gxHjhyBi4sLNm3ahE8++QRJSUmIjIxEYGCgorv5l5WVYceOHVq1fuzYsZpjxZQqOzsbpaWlcHNzQ3l5OQICAjS1PiQkBHZ2dqIjCqMXtV7wjQT6Bzp16qSoO42urq7Sd999V2P8hx9+kNzc3AQkkg9TU1Pp0qVLkiRJ0vDhw6UPP/xQkiRJys3NlRo1aiQwmXxUVFRIarVadAyds3LlSsWtXuATDJKT559/Xvrtt99Ex6hXzzzzjBQREVFjPDIyUurRo4eARPJgYmIi5ebmSpIkSaNHj5Y++OADSZIkKS8vTzIxMREZjXRceHi4VFpaKjpGvdKHWs898josJycHlZWVomPUG3Ygr5uLiws2btyIn376CXFxcZpOpL/99hueeuopwenEUavVWLp0KVq3bg0zMzNcunQJALB48WJ8+eWXgtPphhUrVijuPOu1a9dqngR+8MEHGDx4MHbv3g07Ozu+b6jeHT16FBUVFaJj1Ct2Ia+do6Mj9u/fj/z8fMTExGDIkCEAgGvXrqFJkyaC04m1fft2PPvss2jVqhVyc3MBACEhIfjmm28EJ9MN06dPx9WrV0XHqFf6UOs5kSed8aAD+Z+xAzmwatUqhIWFoX///hg7dqxmC0Z0dDR69OghOJ04y5Ytw5YtW/DRRx/ByMhIM+7q6opNmzYJTKY7JAXuvnJwcICbmxsAoHHjxvjss8+QmpqKqKgoRS9DJKovD7qQ/5nSu5AHBgYiICAAbdu2Rc+ePdGrVy8AQGxsLLp27So4nTgbNmzA3LlzMWzYMPz++++oqqoCcH/bYWhoqNhwOoK1XjdrPSfypDPYgbxu/fv3R1FREYqKivDVV19pxv38/LBx40aBycTatm0bPv/8c4wfPx6GhoaacTc3N5w/f15gMpIzBwcHFBcX1xgvKSmpcbwRET1+7EJeu5deeknTN+ngwYOa8YEDByIkJERgMrE++eQTfPHFF3jvvfe0bvR0794dZ86cEZiM5Ewfar1yb2uSzlm9ejW8vLzQoUMH2NraAgAuX76M5557Dh9//LHgdOIZGhrWaFD25/PTlebKlStwdHSsMa5WqxW1LYX+npycHM0Tneru3LmDK1euCEhEpCxr1qxB3759YWdnp3nSnJycjBYtWmD79u2C04llY2MDGxsbrTElr7wDgEuXLtW6IsHY2BhlZWUCEpEu0Idaz4k86QwLCwv8/PPP7ED+X+7u7oiPj4eVlRW6du0KlUpV52sTExPrMZl8uLi44KeffqqxRCoiIkLRyxCpdtHR0Zpfx8TEwMLCQvN1VVUV4uPjFX9zjKg+tG7dGqmpqVpdyCdPnqzILuS+vr7YsmULmjRpAl9f34e+Nioqqp5SyYu9vT2Sk5Nr1Poffvih1t5KpGz6VOs5kSedolKpMGTIEE2Dl5KSErGBBBoxYgSMjY0BACNHjhQbRqaWLFmCV199FVeuXIFarUZUVBQyMjKwbds2fPvtt6Ljkcw8+D5SqVR47bXXtK41bNgQbdu2xZo1awQkI1IeU1NT+Pn5iY4hnIWFheZGffUJB/2/efPmYebMmbh9+zYkScLJkyexc+dOrFy5kv1wqAa9qvWCu+bTP7Bjxw5FHRXx4YcfSrt27dJ8PXr0aMnAwEBq1aqVlJycLDAZydnBgwelvn37SqamppKJiYnUp08fKSYmRnQsnTFs2DDFHX3Vtm1b6fr166JjkJ47cuSIVFlZWWO8srJSOnLkiObrFStWKO4IyC1btkjffvut5ut58+ZJFhYWUq9evaScnByByUiuPv/8c+npp5+WVCqVpFKpJFtbW2nTpk2iY+kMFxcXRR1pLUn6UetVkqTANoUytH79+kd+7axZs55gEvlycHDA119/jd69eyMuLg4vv/wydu/ejT179iAvLw+xsbGiIwpz6tQpqNVqzTEaD5w4cQKGhobo3r27oGTi3Lt3D8uXL8eUKVPQpk0b0XFk5/vvv4ehoSGGDh2qNR4TEwO1Wo1hw4YJSkakDIaGhigoKIC1tbXWeHFxMaytrWvdu6kUTk5O2LBhAzw9PfHLL79g4MCBCA0NxbfffosGDRoodgn5pUuXcO/ePbRv315rPDMzU/MkUWnu3buHHTt2YOjQobCxsUFRURHUanWN7yul4udD/caJvEzY29s/0utUKlWtR7ApgYmJCS5cuIA2bdpg9uzZuH37NsLCwnDhwgX07NkTv//+u+iIwvTo0QPz58/HSy+9pDUeFRWFVatW4cSJE4KSiWVmZoa0tDRFfrj5K25ubvjwww/x/PPPa40fPHgQCxYsQEpKiqBk8hAfH4/4+Hhcu3YNarVa61r1kyGI/lcGBga4evUqmjdvrjV+4cIFdO/eHbdu3RKUTLzGjRvj/PnzePrpp7FgwQIUFBRg27ZtOHv2LPr374/r16+LjihEv379MGXKlBrLgb/++mts2rQJP/74o5hggjVu3Bjp6ek6c2RYfeLnw4fT9VrPPfIycenSJdERZM/Kygr5+flo06YNDh48iGXLlgG4f/alkp9cAMC5c+fg7u5eY7xr1644d+6cgETyMGjQIPz444+YNGmS6Ciyk5mZWWsToA4dOuDixYsCEslHUFAQgoOD0b17d7Rs2fKhjSSJ/q4HDctUKhUmTZqk6XUC3G+0lJqait69e4uKJwtmZmYoLi7G008/jdjYWMyZMwcA0KhRI1RUVAhOJ05SUhL69OlTY9zDwwNvvfWWgETy0LNnTyQlJXEiXwt+PqybPtR6TuRJZ/j6+mLcuHFo3749iouLNUt/k5OTaz1iTEmMjY1x9erVGudeFhQUaJ2pqjTDhg3DokWLkJaWhm7dusHU1FTruo+Pj6Bk4llYWCA7O7vGaoWLFy/W+HdSmo0bN2LLli149dVXRUchPfSgYZkkSTA3N4eJiYnmmpGRETw8PDBt2jRR8WRh8ODBmDp1Krp27YoLFy7A29sbAHD27FlFr7BSqVT4448/aozfvHlT0Q803nzzTfj7++Py5cu11no3NzdBycTj58O66UOt59J6mbp8+TKio6ORl5eHu3fval1bu3atoFRiVVZWYt26dcjPz8ekSZM0x4eFhobCzMwMU6dOFZxQnDFjxqCwsBDffPON5kNiSUkJRo4cCWtra+zZs0dwQjEMDAzqvKZSqRT9wcfPzw/Hjx/Hvn370K5dOwD3J/EvvvginnnmGUV3+n3qqadw8uRJzb8L0ZMQFBSEgIAAxd84q01JSQnef/995OfnY8aMGfDy8gJw/yQSIyMjvPfee4ITivHCCy+gcePG2LlzJwwNDQHcX8XxyiuvoKysDD/88IPghGLUVutVKhUkSVJ8refnw7rpQ63nRF6G4uPj4ePjA3t7e2RkZKBTp07IycmBJElwd3fHoUOHREeUNW9vb2zatAktW7YUHaXeXLlyBX379kVxcbHmBkdycjJatGiBuLg4NnujGm7evAkvLy+cPn0atra2AO7fQHzuuecQFRUFS0tLsQEFWrBgAczMzLB48WLRUUiPVVRUQJIkNG7cGACQm5uLffv2wdnZWXPEKj3cm2++ieDgYDRr1kx0lHpx7tw59O3bF5aWlnjuuecAAD/99BNu3bqFQ4cOoVOnToITipGbm/vQ60pecs/Ph3XTh1rPibwM9ejRA15eXggODoa5uTlSUlJgbW2N8ePHw8vLCzNmzBAdUdYe/Jv9eRmRvisrK8OOHTuQkpICExMTuLm5YezYsWjYsKHoaLLn6uqK77//XnEFTZIkxMXFab1n+vbtKzqWcLNnz8a2bdvg5uYGNze3Gt9DSl0VRY/XkCFD4OvrizfeeAMlJSVwcnKCkZERioqKsHbtWtb6R9CkSRMkJycrqt7/9ttv+Pe//631c/utt95C06ZNRUeTPSU+6AH4+bAu+lDrOZGXIXNzcyQnJ6Ndu3awsrLCsWPH4OLigpSUFIwYMQI5OTmiI8qaUify9L/je4aqGzBgQJ3XVCoVV0XRY9GsWTMcOXIELi4u2LRpEz755BMkJSUhMjISgYGBSE9PFx1R9vizm/4Ovl+oOn2o9cruciBTpqamuHPnDgCgVatWyMrKgouLCwCgqKhIZDSSse3btyMsLAzZ2dn45ZdfYGdnh5CQEDg4OGDEiBGi45EMHTlyBB9//DHS09OhUqnQsWNHzJs3T7NkU6kOHz4sOgIpQHl5OczNzQEAsbGx8PX1hYGBATw8PP5yqTAp108//aSp9REREWjdujW2b98Oe3t7PPvss6LjkQxlZWUhNDRUq9bPnj1bp/eGPw76UOvr7gRFwnh4eCAhIQHA/WVA/v7+WL58OaZMmQIPDw/B6UiONmzYgLlz52LYsGH4/fffNY1drKysEBoaKjYcydLXX3+NQYMGoXHjxpg1axbeeustmJiYYODAgQgPDxcdTxYuXryImJgYzXFXXMBGj5OjoyP279+P/Px8xMTEaPbFX7t2DU2aNBGcjuQoMjISQ4cOhYmJCRITEzUPff744w+sWLFCcDqSo5iYGDg7O+PkyZNwc3NDp06dcOLECbi4uCAuLk50PFnQ6VovkexkZWVJKSkpkiRJUllZmTRjxgzJ1dVVGjVqlJSTkyM4nfyZmZlJWVlZomPUq44dO0r79u2TJEn773/mzBnpqaeeEphMNyjxPdOhQwdp7dq1NcbXrFkjdejQQUAi+SgqKpI8PT0llUolGRgYaN4bU6ZMkebOnSs4HemLiIgIqWHDhpKBgYE0aNAgzfiKFSskLy8vgcl0h9J+dnfp0kXaunWrJEnaf/ekpCSpRYsWIqPpBKW9XyTp/ntmwYIFNcYXLFggde3aVUAi+dCHWs8n8jK0dOlSXL9+XdPN9rPPPkNqaiqioqIU3XmT6nbp0iVNN9LqjI2NUVZWJiARyV12djaGDx9eY9zHxweXLl0SkEg+5syZg4YNGyIvL0/TURwAXnnlFRw8eFBgMtInL730EvLy8nD69GnExMRoxgcOHIiQkBCByUiuMjIyam1I2qRJE5SUlNR/IJK99PR0vP766zXGp0yZgnPnzglIJB/6UOs5kZeh4uJieHt7w9bWFv7+/khOThYdSae8++67iuveam9vX+v75IcffoCzs3P9ByLZa9OmDeLj42uMx8fHK657/5/FxsZi1apVmmP5Hmjfvj33LtNjZWNjA3Nzc8TFxWmWdT7zzDPo0KGD4GS6YcKECYrahtCyZUtcvHixxvixY8fYwI1q1bx581o/HyYnJ8Pa2rr+A8mIPtR6NruToejoaJSUlGDPnj0IDw9HaGgonJycMGHCBIwbNw5t27YVHVFWfv/9dxw4cAATJ04EACxatEhwovo3b948zJw5E7dv34YkSTh58iR27tyJlStXYtOmTaLjyV5YWBhatGghOka98vf3x6xZs5CcnIzevXtDpVLh2LFj2LJlC9atWyc6nlBlZWVad+cfKCoqgrGxsYBEpI+Ki4vx8ssv4/Dhw1CpVMjMzISDgwOmTp0KS0tLrFmzRnRE2SkrK8Ovv/6qeSq9YcMGwYnq1/Tp0zF79mx89dVXUKlU+O233/DLL78gICAAgYGBouMJc/ToUfTu3RsNGmhPa+7du4eff/5Z835R4oOeadOmwc/PD9nZ2Vq1ftWqVfD39xcdTyh9qPU8fk4HXL58GTt37sRXX32FzMxM3Lt3T3QkWUlJSYG7u7umwZtSffHFF1i2bBny8/MBAK1bt8YHH3xQ65IqfbZ+/fpHfu2sWbOeYBL527dvH9asWaM55upB13qln3Lg7e0Nd3d3LF26FObm5khNTYWdnR3GjBkDtVqNvXv3io5IemDixIm4du0aNm3ahI4dO2qOxYqNjcWcOXNw9uxZ0RFlh/UeeO+99xASEoLbt28DuL+FLiAgAEuXLhWcTBxDQ0MUFBTUeMJcXFwMa2trRb9fJElCaGgo1qxZg99++w3A/ROx5s2bh1mzZkGlUglOKI4+1HpO5GWusrIS3333Hb7++mt89913aNq0Ka5cuSI6Vr26devWQ6+npqaiX79+iv1Bfe/ePezYsQNDhw6FjY0NioqKoFarFbtkyt7e/pFep1KpkJ2d/YTTyNO9e/c0J2EofRl9bc6dO4f+/fujW7duOHToEHx8fHD27FncuHEDCQkJij+yhx4PGxsbxMTEoHPnzlrnW1+6dAmurq4oLS0VHVF2lDyRr6qqwrFjx+Dq6opGjRrh3LlzUKvVcHZ2hpmZmeh4QhkYGODq1ato3ry51viFCxfQvXv3v/wcqa/+/Pnwjz/+AADNsZdKpw+1nhN5mTp8+DDCw8MRGRmJqqoq+Pr6Yvz48fD09ISBgbJaGxgYGDz0jqEkSVCpVIos7A80btwY6enpbIZIj8zMzAxpaWncqlOHwsJCbNiwAb/++ivUajXc3d0xc+ZMtGzZUnQ00hPm5uZITExE+/bttSbyp06dgpeXF4qLi0VHrHd/tey5qqoKpaWliq33jRo1Qnp6+iPfsNZ3vr6+AIBvvvkGXl5eWsuhq6qqkJqaCicnJ51pXPYk8PPhw+l6receeRmytbVFcXExhg4dirCwMAwfPhyNGjUSHUsYc3NzvPfee+jZs2et1zMzMzF9+vR6TiUvPXv2RFJSEn9Q0yMbNGgQfvzxR0yaNEl0FFmysbFBUFCQ6Bikx/r27Ytt27ZplkSrVCqo1WqsXr0aAwYMEJxOjDt37mDGjBlwdXWt9Xpubq6ivy9dXV2RnZ3Nifx/WVhYALj/QMfc3BwmJiaaa0ZGRvDw8MC0adNExZMFfj58OF2v9ZzIy1BgYCBGjx4NKysr0VFkwd3dHQDQr1+/Wq9bWlpC6QtL3nzzTfj7++Py5cvo1q0bTE1Nta67ubkJSibe5cuXER0djby8PNy9e1fr2tq1awWlEm/YsGFYtGgR0tLSan3P+Pj4CEom3ubNm2FmZobRo0drjUdERKC8vByvvfaaoGSkT1avXo3+/fvj9OnTuHv3LubPn6+1rFOJunTpgjZt2tT5PZaSkqLTH7r/qeXLl2v2w9f2c1tJHfyB+z+rAaBt27YICAio8e9B/Hz4MPpQ67m0nmTviy++QEVFRZ2Nya5evYqNGzdiyZIl9ZxMPmrbbqFSqRS/7SA+Ph4+Pj6wt7dHRkYGOnXqhJycHEiSBHd3dxw6dEh0RGEetkVHye8ZAHBycsLGjRtrPBU9cuQI/Pz8kJGRISgZ6ZO8vDw0aNAAYWFhNZZ1VlZW4umnnxYdsd6tWLEClZWVddbz/Px8BAYGaiZwSlP953b1LYdKr/UVFRWQJEnTgTw3Nxf79u2Ds7MzhgwZIjidWPx8WDd9qPWcyBPpgb8671KpS6p69OgBLy8vBAcHa/agWltbY/z48fDy8sKMGTNERyQZatSoEc6fP1+jf0BOTg46duyoOe+b6J9gp236u44cOfLQ63WtXNR3Q4YMga+vL9544w2UlJTAyckJRkZGKCoqwtq1axVd6/n5sG76UOu5tJ5krWnTprhw4QKaNWuGKVOmYN26dey2WQsl/yB+mPT0dOzcuRMA0KBBA1RUVMDMzAzBwcEYMWKEoov7o3J1dcX333+vqO721tbWSE1NrVHcU1JS8NRTT4kJRXqnrucopaWliuyL4+7ujvj4eFhZWSE4OBgBAQG1nvGsZEqdqP+VxMREhISEAAD27t0LGxsbJCUlITIyEoGBgYqu9Y/6+dDb2xubNm3SmSZvj4M+1HpO5EnW7t69i1u3bqFZs2bYunUrVq1axYl8HTIyMvDJJ58gPT0dKpUKHTp0wNtvvw0nJyfR0YQxNTXFnTt3ANw/NzUrKwsuLi4AgKKiIpHRdEZOTg4qKytFx6hXY8aMwaxZs2Bubo6+ffsCuP8kbPbs2RgzZozgdKTr5s6dC+D+8tbAwECtyWpVVRVOnDiBLl26CEonTnp6OsrKymBlZYWgoCC88cYbnMjX4vfff8eXX36pqfUdO3bE5MmT/7Ljvz4rLy/XfDaMjY2Fr68vDAwM4OHh8ZdPpOm+o0eP6sQT6MdJH2o9J/Ika7169cLIkSPRrVs3SJKEWbNmaXUlre6rr76q53TysXfvXowdOxbdu3dHr169AADHjx9Hp06dEB4eXqORh1J4eHggISEBzs7O8Pb2hr+/P86cOYOoqCh4eHiIjkcytWzZMuTm5mLgwIFo0OB+mVSr1Zg4cSJWrFghOB3puqSkJAD3n8ifOXMGRkZGmmtGRkbo3LkzAgICRMUTpkuXLpg8eTKeffZZSJKEjz/+uM7z0QMDA+s5nTwcOXIEPj4+sLCwQPfu3QEA69evR3BwMKKjoxX7xN7R0RH79+/HqFGjEBMTgzlz5gAArl27prgGgPTo9KHWc488ydrVq1cREhKCrKwsREVFYejQoVrnhFa3b9++ek4nHw4ODpgwYQKCg4O1xpcsWYLt27cjOztbUDKxsrOzUVpaCjc3N5SXlyMgIADHjh2Do6MjQkJCuCXhEVQ/31oJJElCXl4emjdvjitXriA5ORkmJiZwdXXl+4Ueq8mTJ2PdunWcaPxXRkYGlixZgqysLCQmJsLZ2Vnz4bo6lUqFxMREAQnF69SpE3r37o0NGzbA0NAQwP1VHG+++SYSEhKQlpYmOKEYe/fuxbhx41BVVQVPT0/ExcUBAFauXImjR4/ihx9+EJxQ/ljrdbPWcyJPOsPe3h6nT5/WmX0r9alx48ZITU2Fo6Oj1nhmZiY6d+6M8vJyQcnEmjx5MiZMmABPT0+tDr/06JRW3NVqNRo1aoSzZ8+iffv2ouMQKZKBgQEKCwtrNAJUOhMTEyQnJ9fYMpeRkYEuXboobml0dYWFhSgoKEDnzp01ndpPnjyJJk2aoEOHDoLTyR9rvW6q+/whIpm5dOnSI03iXV1dkZ+fXw+J5KN///746aefaowfO3YMzz33nIBE8lBcXAxvb2/Y2trC398fycnJoiORzBkYGKB9+/YoLi4WHYVIsdRq9SNN4r29vVFQUFAPieTB3d0d6enpNcbT09MV2VehOhsbG5ibmyMuLk5zQ+OZZ57hJJ5qpS+1nnvkSe8osTmXj48PFixYgF9//VWz9/v48eOIiIhAUFAQoqOjtV6rFNHR0SgpKcGePXsQHh6O0NBQODk5YcKECRg3blyNTqVEAPDRRx9h3rx52LBhAzp16iQ6DhHVQWkNumbNmoXZs2fj4sWLWrX+008/xYcffojU1FTNa93c3ETFrHfFxcV4+eWXcfjwYahUKmRmZsLBwQFTp06FpaUl1qxZIzoiyZA+1HourSe9o7TlQQA0y8j+ikqlUvTZxJcvX8bOnTvx1VdfITMzE/fu3RMdSfbCw8MxYsQImJqaio5Sb6ysrFBeXo579+7ByMioRoPNGzduCEpGRNUprd7/Va1XqVSQJElxtX7ixIm4du0aNm3ahI4dO2reE7GxsZgzZw7Onj0rOqIwR48eRe/evWv0m7h37x5+/vlnTbf2lStXYsaMGbC0tBSQUgx9qPV8Ik+kB9RqtegIsldZWYnTp0/jxIkTyMnJQYsWLURHqnfr169/5NfOmjULADBu3LgnFUe2QkNDRUcgIqrh0qVLoiPIUmxsLGJiYmBra6s13r59e8UfPzdgwAAUFBTU2Kpy8+ZNDBgwQHPDZ9GiRSLiCaUPtZ4TeSIFcXV1xffff482bdqIjlJvDh8+jPDwcERGRqKqqgq+vr44cOAAPD09RUerdyEhIY/0OpVKpZnIK9Frr70mOgIRUQ2P2k3b29sbmzZtQsuWLZ9wInkoKytD48aNa4wXFRXVedKRUjxYofFnxcXFilppVxt9qPWcyBMpiNL6B9ja2qK4uBhDhw5FWFgYhg8fjkaNGomOJQyf5jy6rKwsbN68GVlZWVi3bh2sra1x8OBBtGnTBi4uLqLjERHVSWm9A/r27Ytt27Zh6dKlAO7fjFar1Vi9ejUGDBggOJ0Yvr6+AO7/W0yaNEnrhkZVVRVSU1PRu3dvUfFkQ9drPbvWE5HeCgwMxG+//Yb9+/dj9OjRip7E06M7cuQIXF1dceLECURFRaG0tBQAkJqaiiVLlghOR0RE1a1evRphYWEYNmwY7t69i/nz56NTp044evQoVq1aJTqeEBYWFrCwsIAkSTA3N9d8bWFhARsbG/j5+eHrr78WHVMofaj1fCJPeicsLEyR+5+pJj8/P9ERZO3y5cuIjo5GXl4e7t69q3Vt7dq1glKJt3DhQixbtgxz586Fubm5ZnzAgAFYt26dwGREyvCoDbreffddNG3aVEREkhEzMzMkJycjLCwMhoaGKCsrg6+vL2bOnKmoVYjVbd68GQDQtm1bBAQEKH4ZfW30odazaz3J2v/SnIvqprQOv1S3+Ph4+Pj4wN7eHhkZGejUqRNycnIgSRLc3d1x6NAh0RGFMTMzw5kzZ2Bvb6/1PZOTk4MOHTrg9u3boiMS6TVDQ8NaG3QVFxfD2tpaUR3Z/xdKq/V8v9StoqICkiRpegjk5uZi3759cHZ2xpAhQwSnE0sfaj2fyJOssTkX0ZOxaNEi+Pv7Izg4GObm5oiMjIS1tTXGjx8PLy8v0fGEsrS0REFBAezt7bXGk5KS0Lp1a0GpiJSDDbro76jrmWRpaanit9SNGDECvr6+eOONN1BSUoIePXrAyMgIRUVFWLt2LWbMmCE6ojD6UOs5kSdZY3MuoicjPT0dO3fuBAA0aNAAFRUVMDMzQ3BwMEaMGKHo4j5u3DgsWLAAERERmqZJCQkJCAgIwMSJE0XHI9JbbNBFf8fcuXMB3H+/BAYGanWur6qqwokTJ9ClSxdB6eQhMTFR81Bs7969sLGxQVJSEiIjIxEYGMhar+O1nhN5IgVh/wB6wNTUFHfu3AEAtGrVCllZWZoOrUVFRSKjCbd8+XJMmjQJrVu3hiRJcHZ2RlVVFcaNG4f3339fdDwivWVhYQEAmgZdJiYmmmtGRkbw8PDAtGnTRMUTjr0DtCUlJQG4/345c+YMjIyMNNeMjIzQuXNnBAQEiIonC+Xl5Zr937GxsfD19YWBgQE8PDyQm5srOJ1Y+lDruUeedAqbc/0/9g+gf2LkyJHw9vbGtGnTMH/+fOzbtw+TJk1CVFQUrKys8J///Ed0ROGysrKQlJQEtVqNrl27on379qIjESlCUFAQG3TVgnvBazd58mSsW7cOTZo0ER1Fdtzc3DB16lSMGjUKnTp1wsGDB9GrVy/8+uuv8Pb2RmFhoeiIwulyredEnnQGm3Np+/OenrqoVCpkZ2c/4TSka7Kzs1FaWgo3NzeUl5cjICAAx44dg6OjI0JCQmBnZyc6oiw8KJG17dcloieDDbpqZ2BggKtXr6J58+Za4xcuXED37t1x69YtQclIrvbu3Ytx48ahqqoKnp6eiIuLAwCsXLkSR48exQ8//CA4oTzoaq3nRJ50Ro8ePeDl5aVpzpWSkqLVnEvJ+3yI/q7JkydjwoQJ8PT01LnCVR++/PJLhISEIDMzEwDQvn17vPPOO5g6dargZET6b8iQIVoNupycnBTdoOtB74BvvvkGXl5etfYOcHJywsGDB0VFJBkrLCxEQUEBOnfuDAMDAwDAyZMn0aRJE3To0EFwOrF0vdYbiA5A9KjS09Px2muvAajZnGvVqlWC0xHpluLiYnh7e8PW1hb+/v5ITk4WHUk2Fi9ejNmzZ2P48OGIiIhAREQEhg8fjjlz5ujMvjkiXZaYmIjnnnsOwP836MrNzcW2bdv+1rYyfWFhYQELCwtN74AHX1tYWMDGxgZ+fn74+uuvRcckmbKxsYG5uTni4uJQUVEBAHjmmWcUP4nXh1rPJ/KkM2xsbHDo0CE4OzvDxcUFK1euhI+PD1JSUtCnTx+UlpaKjigU+wfQ31VSUoI9e/YgPDwcP/30E5ycnDBhwgSMGzcObdu2FR1PmGbNmuGTTz7B2LFjtcZ37tyJt99+W/HNAImetMaNG+P8+fN4+umn8fLLL8PFxQVLlixBfn4+nJycUF5eLjqiEOwdQH9XcXExXn75ZRw+fBgqlQqZmZlwcHDA66+/DktLS6xZs0Z0RGH0odbziTzpDA8PDyQkJAAAvL294e/vj+XLl2PKlCnw8PAQnE6s+Ph4ODk54bPPPsOaNWtw+PBhbN68GV999RWftFKdLC0t4efnhx9//BG5ubmYPHkytm/fDkdHR9HRhKqqqkL37t1rjHfr1g337t0TkIhIWRwdHbF//37k5+cjJiZGsy/+2rVrim5oNn/+fK2tULm5uQgNDUVsbKzAVCRnc+bMQcOGDZGXl6d1PN8rr7yi+K0Y+lDrOZEnnbF27Vr07NkTAPDBBx9g8ODB2L17N+zs7PDll18KTifWokWL4O/vj7S0NDRq1AiRkZHIz89Hv379MHr0aNHxSOYqKytx+vRpnDhxAjk5OYo/onDChAnYsGFDjfHPP/8c48ePF5CISFkCAwMREBCAtm3bokePHujVqxeA+8dnde3aVXA6cUaMGIFt27YBuL+iqkePHlizZg1GjBhR688sotjYWKxatQq2trZa4+3bt1f88XP6UOu5tJ50Bptz1c3c3BzJyclo164drKyscOzYMbi4uCAlJQUjRoxATk6O6IgkQ4cPH0Z4eDgiIyNRVVUFX19fjB8/Hp6enpqGOEr09ttvY9u2bWjTpo1mtc/x48eRn5+PiRMnomHDhprXctsK0ZPBBl01NWvWDEeOHIGLiws2bdqETz75BElJSYiMjERgYCDS09NFRySZMTc3R2JiItq3b69pFO3g4IBTp07By8sLxcXFoiMKow+1voHoAESP6kFzrqeeegpjxozBq6++ii5duoiOJQumpqa4c+cOAKBVq1bIysqCi4sLAOjEHh+qf7a2tiguLsbQoUMRFhaG4cOHo1GjRqJjyUJaWhrc3d0B3D9fFgCaN2+O5s2bIy0tTfM63lAkenJsbGxQWlqKuLg49O3bFyYmJnjmmWcU/X1XXl4Oc3NzAPeftPr6+sLAwAAeHh6Kf7pKtevbty+2bduGpUuXArhft9RqNVavXo0BAwYITieWPtR6TuRJZ0RHR2s15woNDWVzrv960D/A2dlZ0z/gzJkziIqKUnz/AKpdYGAgRo8eDSsrK9FRZOfw4cOP9LrLly9DrVYrevUC0ZNQV4OuqVOnKrpB14PeAaNGjUJMTAzmzJkDgL0DqG6rV69G//79cfr0ady9exfz58/H2bNncePGDU3fKaXSh1rPpfWksy5fvoydO3fiq6++QmZmps40pngSsrOzUVpaCjc3N5SXlyMgIADHjh2Do6MjQkJCYGdnJzoikd5p0qQJkpOT4eDgIDoKkV6ZOHEirl27hk2bNqFjx46a5cCxsbGYM2cOzp49KzqiEHv37sW4ceNQVVUFT09PxMXFAQBWrlyJo0eP4ocffhCckOQmLy8PDRo0QFhYGH799Veo1Wq4u7tj5syZqKysxNNPPy06ouzJudbziTzpJDbn0rZ06VJMmDABkiShcePG+Oyzz0RHItJ7vA9O9GTExsYiJiaGDbr+5KWXXsKzzz6r6R3wwMCBAzFq1CiByUiu7O3tUVBQgKCgIK3x4uJi2NraoqqqSlAy3SHnWi+/NQJED3H48GFMmzYNLVq0wGuvvQZzc3McOHAA+fn5oqMJ9aB/gK2tLfz9/XnkHBER6ayysjKto7IeKCoqgrGxsYBE8mFjYwNzc3PExcWhoqICAPDMM88otgEgPVxdk9DS0lL2xdEDfCJPOoPNuerG/gFERKQv2KCrduwdQI9q7ty5AO5/7wQGBmrdGKuqqsKJEyfYMFoPcCJPOoPNuR7O0tISfn5+8PPz0+ofEBgYqOj+AUREpFvYoKt2c+bMQcOGDZGXl4eOHTtqxl955RXMmTOHE3nSSEpKAnD/ifyZM2dgZGSkuWZkZITOnTsjICBAVDx6TDiRJ53h5+cnOoJOYP8Aovoh5yNpiHSZmZkZkpOTERYWBkNDQ5SVlcHX11fToEup2DuAHtWDjuyTJ0/GunXreKrBPyDnWs+JPJGeOHz4MMLDwxEZGYmqqir4+vriwIED8PT0FB2NSC/JuQEOkS5jg67asXcA/V2bN28WHUHnybnWs9kdkR6wtbXF888/j+vXryMsLAxXr17F5s2bMWjQIFmee0mkCy5evIiYmBhNQ6k/F/Nz587xaEeiJ4ANumr3oHfAA+wdQPTP6XKt5znyRHrg888/Z/8AosekuLgYr7zyCg4dOqTVUOr1119nQymiJ+hBg65169Zh2rRptTboMjQ0VOw++XPnzqF///7o1q0bDh06BB8fH63eAe3atRMdkUhn6EOt56M6Ij3g5+fHSTzRYzJnzhw0aNAAeXl5WhOJV155BQcPHhSYjEi/JSUlISkpSdOg68HXSUlJOH/+PDp37owtW7aIjinMg94BPXr0wODBgzW9A5KSktCwYUPR8Yh0ij7Uej6RJyIiqsbGxgYxMTHo3LkzzM3NkZKSAgcHB1y6dAmurq4oLS0VHZFIr7FBV+0MDQ1RUFAAa2trrfHi4mJYW1srtncA0f9CH2o9m90RERFVw4ZSRGKxQVft2DuA6PHRh1rPiTwREVE1DxpKLV26FAAbShGRWA96B6hUKgQGBtbaO6BLly6C0hHpJn2o9ZzIExERVbN69Wr0798fp0+fxt27dzF//nythlJERPUpKSkJADS9A4yMjDTXjIyM0LlzZwQEBIiKR6ST9KHWc488ERHRnxQWFmLDhg349ddfoVar4e7ujpkzZ6Jly5aioxGRQrF3ANHjpeu1nhN5IiIiIiIiIh3C4+eIiIiqsbe3x+LFi5GRkSE6ChERET0B+lDrOZEnIiKq5u2338bBgwfRsWNHdOvWDaGhoSgoKBAdi4iIiB4Tfaj1XFpPRERUiwsXLmDHjh3YtWsXsrOzMWDAAEyYMAETJ04UHY2IiIgeA12u9ZzIExER/YXjx49jxowZSE1NRVVVleg4RERE9JjpWq3n8XNERER1OHnyJMLDw7F7927cvHkTL730kuhIRERE9Bjpaq3nE3kiIqJqHiyzCw8PR05ODgYMGIDx48fD19cX5ubmouMRERHRP6QPtZ4TeSIiomoMDAzQvXt3jBs3DmPGjIGNjY3oSERERPQY6UOt50SeiIiomgsXLuBf//qX6BhERET0hOhDredEnoiIiIiIiEiHsNkdEREpXtOmTXHhwgU0a9YMVlZWUKlUdb72xo0b9ZiMiIiIHgd9q/WcyBMRkeKFhIRomtuEhIQ8tLgTERGR7tG3Ws+l9UREREREREQ6xEB0ACIiIjkxNDTEtWvXaowXFxfD0NBQQCIiIiJ6nPSh1nMiT0REVE1dC9Xu3LkDIyOjek5DREREj5s+1HrukSciIgKwfv16AIBKpcKmTZtgZmamuVZVVYWjR4+iQ4cOouIRERHRP6RPtZ575ImIiADY29sDAHJzc2Fra6u1tM7IyAht27ZFcHAwevbsKSoiERER/QP6VOs5kSciIqpmwIABiIqKgpWVlegoRERE9AToQ63nRJ6IiIiIiIhIh7DZHRERUTUvvfQSPvzwwxrjq1evxujRowUkIiIiosdJH2o9n8gTERFV07x5cxw6dAiurq5a42fOnMGgQYNw9epVQcmIiIjocdCHWs8n8kRERNWUlpbWevRMw4YNcevWLQGJiIiI6HHSh1rPiTwREVE1nTp1wu7du2uM79q1C87OzgISERER0eOkD7We58gTERFVs3jxYrz44ovIysqCp6cnACA+Ph47d+5ERESE4HRERET0T+lDreceeSIioj/57rvvsGLFCiQnJ8PExARubm5YsmQJ+vXrJzoaERERPQa6Xus5kSciIiIiIiLSIdwjT0RERERERKRDuEeeiIiomqqqKoSEhGDPnj3Iy8vD3bt3ta7fuHFDUDIiIiJ6HPSh1vOJPBERUTVBQUFYu3YtXn75Zdy8eRNz586Fr68vDAwM8MEHH4iOR0RERP+QPtR67pEnIiKqpl27dli/fj28vb1hbm6O5ORkzdjx48cRHh4uOiIRERH9A/pQ6/lEnoiIqJrCwkK4uroCAMzMzHDz5k0AwAsvvIDvvvtOZDQiIiJ6DPSh1nMiT0REVI2trS0KCgoAAI6OjoiNjQUAnDp1CsbGxiKjERER0WOgD7WeE3kiIqJqRo0ahfj4eADA7NmzsXjxYrRv3x4TJ07ElClTBKcjIiKif0ofaj33yBMRET3E8ePH8fPPP8PR0RE+Pj6i4xAREdFjpou1nhN5IiIiIiIiIh3Cc+SJiIj+5MKFC/jxxx9x7do1qNVqrWuBgYGCUhEREdHjouu1nk/kiYiIqvniiy8wY8YMNGvWDDY2NlCpVJprKpUKiYmJAtMRERHRP6UPtZ4TeSIiomrs7Ozw5ptvYsGCBaKjEBER0ROgD7WeE3kiIqJqmjRpguTkZDg4OIiOQkRERE+APtR6Hj9HRERUzejRozXnyRIREZH+0Ydaz2Z3RERE1Tg6OmLx4sU4fvw4XF1d0bBhQ63rs2bNEpSMiIiIHgd9qPVcWk9ERFSNvb19nddUKhWys7PrMQ0RERE9bvpQ6zmRJyIiIiIiItIhXFpPRESKN3fuXCxduhSmpqaYO3duna9TqVRYs2ZNPSYjIiKix0Hfaj0n8kREpHhJSUmorKzU/Lou1c+ZJSIiIt2hb7WeS+uJiIiIiIiIdAiPnyMiIiIiIiLSIZzIExEREREREekQTuSJiIiIiIiIdAgn8kREREREREQ6hBN5IpINlUqF/fv3i45BRERETxDrPdE/x4k8EWmZNGkSVCoV3njjjRrX3nzzTahUKkyaNOmRfq8ff/wRKpUKJSUlj/T6goICDBs27G+kJSIiov8F6z2RbuNEnohqaNOmDXbt2oWKigrN2O3bt7Fz5048/fTTj/3Pu3v3LgDAxsYGxsbGj/33JyIioppY74l0FyfyRFSDu7s7nn76aURFRWnGoqKi0KZNG3Tt2lUzJkkSPvroIzg4OMDExASdO3fG3r17AQA5OTkYMGAAAMDKykrrzn7//v3x1ltvYe7cuWjWrBkGDx4MoOZSu8uXL2PMmDFo2rQpTE1N0b17d5w4ceIJ/+2JiIiUgfWeSHc1EB2AiORp8uTJ2Lx5M8aPHw8A+OqrrzBlyhT8+OOPmte8//77iIqKwoYNG9C+fXscPXoUEyZMQPPmzfHss88iMjISL774IjIyMtCkSROYmJho/t+tW7dixowZSEhIgCRJNf780tJS9OvXD61bt0Z0dDRsbGyQmJgItVr9xP/uRERESsF6T6SbOJEnolq9+uqrWLRoEXJycqBSqZCQkIBdu3ZpCntZWRnWrl2LQ4cOoVevXgAABwcHHDt2DGFhYejXrx+aNm0KALC2toalpaXW7+/o6IiPPvqozj8/PDwc169fx6lTpzS/j6Oj4+P/ixIRESkY6z2RbuJEnohq1axZM3h7e2Pr1q2QJAne3t5o1qyZ5vq5c+dw+/ZtzTK5B+7evau1HK8u3bt3f+j15ORkdO3aVVPUiYiI6PFjvSfSTZzIE1GdpkyZgrfeegsA8Omnn2pde7Dk7bvvvkPr1q21rj1KAxtTU9OHXq++LI+IiIieHNZ7It3DiTwR1cnLy0vTYXbo0KFa15ydnWFsbIy8vDz069ev1v/fyMgIAFBVVfW3/2w3Nzds2rQJN27c4F16IiKiJ4j1nkj3sGs9EdXJ0NAQ6enpSE9Ph6GhodY1c3NzBAQEYM6cOdi6dSuysrKQlJSETz/9FFu3bgUA2NnZQaVS4dtvv8X169dRWlr6yH/22LFjYWNjg5EjRyIhIQHZ2dmIjIzEL7/88lj/jkRERErHek+keziRJ6KHatKkCZo0aVLrtaVLlyIwMBArV65Ex44dMXToUBw4cAD29vYAgNatWyMoKAgLFy5EixYtNMv2HoWRkRFiY2NhbW2N559/Hq6urvjwww9rfMAgIiKif471nki3qKTazoEgIiIiIiIiIlniE3kiIiIiIiIiHcKJPBEREREREZEO4USeiIiIiIiISIdwIk9ERERERESkQziRJyIiIiIiItIhnMgTERERERER6RBO5ImIiIiIiIh0CCfyRERERERERDqEE3kiIiIiIiIiHcKJPBEREREREZEO4USeiIiIiIiISIf8H6/T49olOZCUAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "warnings.filterwarnings(\"ignore\", \".*FixedLocator*\")\n",
-    "\n",
-    "val_metrics = trainer.validate(model, verbose=False)\n",
-    "test_metrics = trainer.test(model, verbose=False)\n",
-    "# Add percentage of inactive PROTACs per dataset\n",
-    "val_metrics[0]['inactive_percentage'] = val_df[active_col].value_counts(normalize=True)[0]\n",
-    "test_metrics[0]['inactive_percentage'] = test_df[active_col].value_counts(normalize=True)[0]\n",
-    "# Remove useless metrics\n",
-    "val_metrics[0].pop('val_loss')\n",
-    "val_metrics[0].pop('val_hp_metric')\n",
-    "val_metrics[0].pop('val_opt_score')\n",
-    "test_metrics[0].pop('test_loss')\n",
-    "test_metrics[0].pop('test_hp_metric')\n",
-    "test_metrics[0].pop('test_opt_score')\n",
-    "# Plot the metrics as bar plots: first melt them into a Score and Metric column\n",
-    "val_metrics_df = pd.DataFrame(val_metrics)\n",
-    "test_metrics_df = pd.DataFrame(test_metrics)\n",
-    "val_metrics_df = val_metrics_df.melt(var_name='Metric', value_name='Score')\n",
-    "test_metrics_df = test_metrics_df.melt(var_name='Metric', value_name='Score')\n",
-    "# Plot the metrics as bar plots in seaborn\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "sns.barplot(data=val_metrics_df, x='Metric', y='Score', ax=ax[0])\n",
-    "sns.barplot(data=test_metrics_df, x='Metric', y='Score', ax=ax[1])\n",
-    "for a in ax:\n",
-    "    a.grid(axis='y', alpha=0.5)\n",
-    "    # Rotate x-axis labels\n",
-    "    a.set_xticklabels(a.get_xticklabels(), rotation=90)\n",
-    "    # Set the y-axis as a percentage\n",
-    "    a.set_yticklabels(['{:,.0%}'.format(x) for x in a.get_yticks()])\n",
-    "    # Write the Score on top of the bars\n",
-    "    for p in a.patches:\n",
-    "        a.annotate(f'{p.get_height():.3f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
-    "                        ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n",
-    "ax[0].set_title('Validation metrics')\n",
-    "ax[1].set_title('Test metrics')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b2789d0680e14d518e15e0ae72f2132d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "expected Tensor as element 0 in argument 0, but got numpy.ndarray",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[168], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Get model predictions on the validation set using the trainer\u001b[39;00m\n\u001b[0;32m      2\u001b[0m train_dl \u001b[38;5;241m=\u001b[39m DataLoader(train_ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m----> 3\u001b[0m train_preds \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m train_preds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mvstack(train_preds)\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m      6\u001b[0m val_dl \u001b[38;5;241m=\u001b[39m DataLoader(val_ds, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:845\u001b[0m, in \u001b[0;36mTrainer.predict\u001b[1;34m(self, model, dataloaders, datamodule, return_predictions, ckpt_path)\u001b[0m\n\u001b[0;32m    843\u001b[0m     model \u001b[38;5;241m=\u001b[39m _maybe_unwrap_optimized(model)\n\u001b[0;32m    844\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m_lightning_module \u001b[38;5;241m=\u001b[39m model\n\u001b[1;32m--> 845\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    846\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predict_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_predictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[0;32m    847\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\call.py:42\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[1;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m     40\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     41\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 42\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[0;32m     45\u001b[0m     _call_teardown_hook(trainer)\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:887\u001b[0m, in \u001b[0;36mTrainer._predict_impl\u001b[1;34m(self, model, dataloaders, datamodule, return_predictions, ckpt_path)\u001b[0m\n\u001b[0;32m    882\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_connector\u001b[38;5;241m.\u001b[39mattach_data(model, predict_dataloaders\u001b[38;5;241m=\u001b[39mdataloaders, datamodule\u001b[38;5;241m=\u001b[39mdatamodule)\n\u001b[0;32m    884\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[0;32m    885\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn, ckpt_path, model_provided\u001b[38;5;241m=\u001b[39mmodel_provided, model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    886\u001b[0m )\n\u001b[1;32m--> 887\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    889\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[0;32m    890\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredicting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:975\u001b[0m, in \u001b[0;36mTrainer._run\u001b[1;34m(self, model, ckpt_path)\u001b[0m\n\u001b[0;32m    970\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[0;32m    972\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m    973\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[0;32m    974\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m--> 975\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    977\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m    978\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[0;32m    979\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m    980\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:1013\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1011\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evaluation_loop\u001b[38;5;241m.\u001b[39mrun()\n\u001b[0;32m   1012\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredicting:\n\u001b[1;32m-> 1013\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1014\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining:\n\u001b[0;32m   1015\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m isolate_rng():\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\loops\\utilities.py:177\u001b[0m, in \u001b[0;36m_no_grad_context.<locals>._decorator\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    175\u001b[0m     context_manager \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mno_grad\n\u001b[0;32m    176\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context_manager():\n\u001b[1;32m--> 177\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mloop_run\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\loops\\prediction_loop.py:112\u001b[0m, in \u001b[0;36m_PredictionLoop.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    110\u001b[0m     batch, batch_idx, dataloader_idx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(data_fetcher)\n\u001b[0;32m    111\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mis_last_batch \u001b[38;5;241m=\u001b[39m data_fetcher\u001b[38;5;241m.\u001b[39mdone\n\u001b[1;32m--> 112\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predict_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    113\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m    114\u001b[0m     \u001b[38;5;66;03m# this needs to wrap the `*_step` call too (not just `next`) for `dataloader_iter` support\u001b[39;00m\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\loops\\prediction_loop.py:228\u001b[0m, in \u001b[0;36m_PredictionLoop._predict_step\u001b[1;34m(self, batch, batch_idx, dataloader_idx)\u001b[0m\n\u001b[0;32m    225\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_started()\n\u001b[0;32m    227\u001b[0m \u001b[38;5;66;03m# configure step_kwargs\u001b[39;00m\n\u001b[1;32m--> 228\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpredict_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mstep_kwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_processed()\n\u001b[0;32m    232\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m predictions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\call.py:287\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[1;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[0;32m    284\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    286\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 287\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[0;32m    290\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n",
-      "File \u001b[1;32mc:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\strategies\\strategy.py:397\u001b[0m, in \u001b[0;36mStrategy.predict_step\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    395\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mpredict_step_context():\n\u001b[0;32m    396\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, PredictStep)\n\u001b[1;32m--> 397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[1;32mIn[161], line 203\u001b[0m, in \u001b[0;36mPROTAC_Model.predict_step\u001b[1;34m(self, batch, batch_idx)\u001b[0m\n\u001b[0;32m    200\u001b[0m         cell_emb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscalers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCell Line Identifier\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mtransform(cell_emb)\n\u001b[0;32m    201\u001b[0m         smiles_emb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscalers[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSmiles\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mtransform(smiles_emb)\n\u001b[1;32m--> 203\u001b[0m y_hat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoi_emb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me3_emb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcell_emb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msmiles_emb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39msigmoid(y_hat)\n",
-      "Cell \u001b[1;32mIn[161], line 127\u001b[0m, in \u001b[0;36mPROTAC_Model.forward\u001b[1;34m(self, poi_emb, e3_emb, cell_emb, smiles_emb)\u001b[0m\n\u001b[0;32m    125\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmiles\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdisabled_embeddings:\n\u001b[0;32m    126\u001b[0m         embeddings\u001b[38;5;241m.\u001b[39mappend(smiles_emb)\n\u001b[1;32m--> 127\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    129\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoi\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdisabled_embeddings:\n",
-      "\u001b[1;31mTypeError\u001b[0m: expected Tensor as element 0 in argument 0, but got numpy.ndarray"
-     ]
-    }
-   ],
-   "source": [
-    "# Get model predictions on the validation set using the trainer\n",
-    "train_dl = DataLoader(train_ds, batch_size=32, shuffle=False)\n",
-    "train_preds = trainer.predict(model, train_dl)\n",
-    "train_preds = torch.vstack(train_preds).numpy().flatten()\n",
-    "\n",
-    "val_dl = DataLoader(val_ds, batch_size=32, shuffle=False)\n",
-    "val_preds = trainer.predict(model, val_dl)\n",
-    "val_preds = torch.vstack(val_preds).numpy().flatten()\n",
-    "\n",
-    "test_dl = DataLoader(test_ds, batch_size=1, shuffle=False)\n",
-    "test_preds = trainer.predict(model, test_dl)\n",
-    "test_preds = torch.vstack(test_preds).numpy().flatten()\n",
-    "\n",
-    "# NOTE: The test set consists of the unlabeled PROTACs\n",
-    "unlabeled_df[active_col] = False\n",
-    "unlabeled_ds = PROTAC_Dataset(\n",
-    "    unlabeled_df,\n",
-    "    protein_embeddings,\n",
-    "    cell2embedding,\n",
-    "    smiles2fp,\n",
-    "    use_ored_activity=True if active_col == 'Active - OR' else False,\n",
-    ")\n",
-    "unlabeled_dl = DataLoader(unlabeled_ds, batch_size=32, shuffle=False)\n",
-    "unlabeled_preds = trainer.predict(model, unlabeled_dl)\n",
-    "unlabeled_preds = torch.vstack(unlabeled_preds).numpy().flatten()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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AwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACschRGCgsL9d3vflfdu3dXUlKSbr75Zu3ateuc+1RUVMjj8TS7fPTRRxdUOAAA6BgchZHNmzdr9uzZeuutt1ReXq7Gxkbl5OTo5MmT5913165dqqmpCVyGDBnS5qIBAEDHEe1k8oYNG4K2n3jiCSUlJendd9/Vddddd859k5KS1LNnT8cFAgCAjs1RGPmm48ePS5J69ep13rmjR4/WqVOnNHLkSN1///2aMGFCi3N9Pp98Pl9gu66uTpLk9/vl9/svpOSIivIYV9ZtTz04G/0AgM6ltb+f2xxGjDHKy8vTtddeq4svvrjFeampqXrssceUmZkpn8+n1atXa+LEiaqoqGjx1ZTCwkIVFBQ0G9+7d6/i4+PbWnLETep3xpV19+zZ48q6bqMfANC51NfXt2qexxjTpn9XZ8+erXXr1unNN99U//79He07depUeTwerV27NuT1oV4ZSUtL09GjR5WQkNCWcq0Yev8rrqy7+8HJrqzrNvoBAJ1LXV2devXqpePHj5/z73ebXhm59957tXbtWr3xxhuOg4gkjR07VmvWrGnxeq/XK6/X22w8KipKUVFRjm/PFr/xuLJue+rB2egHAHQurf397CiMGGN077336sUXX1RFRYXS09PbVFxVVZVSU1PbtC8AAOhYHIWR2bNn65lnntHLL7+s7t27q7a2VpLUo0cPxcXFSZLy8/N18OBBPfXUU5KkoqIiDRo0SKNGjVJDQ4PWrFmjsrIylZWVhfmuAACA9shRGFmxYoUkafz48UHjTzzxhO68805JUk1NjaqrqwPXNTQ0aP78+Tp48KDi4uI0atQorVu3TlOmTLmwygEAQIfg+DDN+axatSpoe8GCBVqwYIGjogAAQOfBd9MAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACschRGCgsL9d3vflfdu3dXUlKSbr75Zu3ateu8+23evFmZmZmKjY3V4MGDtXLlyjYXDAAAOhZHYWTz5s2aPXu23nrrLZWXl6uxsVE5OTk6efJki/vs27dPU6ZM0bhx41RVVaVFixZpzpw5Kisru+DiAQBA+xftZPKGDRuCtp944gklJSXp3Xff1XXXXRdyn5UrV2rAgAEqKiqSJI0YMUKVlZVatmyZpk2b1raqAQBAh+EojHzT8ePHJUm9evVqcc62bduUk5MTNHbDDTeopKREp0+fVkxMTLN9fD6ffD5fYLuurk6S5Pf75ff7L6TkiIryGFfWbU89OBv9AIDOpbW/n9scRowxysvL07XXXquLL764xXm1tbVKTk4OGktOTlZjY6MOHz6s1NTUZvsUFhaqoKCg2fjevXsVHx/f1pJDKnptd1jXO9ukfu6su2fPHncWdtmkfmdcWbc99sPNx93c64e6tjYAOFFfX9+qeW0OI/fcc4/+93//V2+++eZ553o8nqBtY0zI8Sb5+fnKy8sLbNfV1SktLU0ZGRlKSEhoa8khlT/517CuFwnFQ4bYLqFN3Op1e+yHm4+79tgPAB1T05GN82lTGLn33nu1du1avfHGG+rfv/8556akpKi2tjZo7NChQ4qOjlZiYmLIfbxer7xeb7PxqKgoRUVFtaXkFvlN6ED0bRbuHkSKW71uj/1w83HXHvsBoGNq7e8jR++mMcbonnvu0QsvvKDXX39d6enp590nKytL5eXlQWMbN27UmDFjQp4vAgAAOhdHYWT27Nlas2aNnnnmGXXv3l21tbWqra3V119/HZiTn5+v6dOnB7Zzc3N14MAB5eXlaefOnSotLVVJSYnmz58fvnsBAADaLUdhZMWKFTp+/LjGjx+v1NTUwOW5554LzKmpqVF1dXVgOz09XevXr1dFRYUuv/xy/eIXv9Cjjz7K23oBAIAkh+eMNJ14ei6rVq1qNpadna3t27c7uSkAANBJ8N00AADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArHIcRt544w1NnTpVffv2lcfj0UsvvXTO+RUVFfJ4PM0uH330UVtrBgAAHUi00x1Onjypyy67TD/96U81bdq0Vu+3a9cuJSQkBLb79Onj9KYBAEAH5DiMTJ48WZMnT3Z8Q0lJSerZs6fj/QAAQMfmOIy01ejRo3Xq1CmNHDlS999/vyZMmNDiXJ/PJ5/PF9iuq6uTJPn9fvn9/rDWFeUxYV0vEsLdg0hxq9ftsR9uPu7aYz8AdEyt/X3kehhJTU3VY489pszMTPl8Pq1evVoTJ05URUWFrrvuupD7FBYWqqCgoNn43r17FR8fH9b6JvU7E9b1ImHPnj22S2gTt3rdHvvh5uOuPfYDQMdUX1/fqnkeY0yb/0XzeDx68cUXdfPNNzvab+rUqfJ4PFq7dm3I60O9MpKWlqajR48GnXcSDkPvfyWs60XC7gedHyb7NnCr1+2xH24+7tpjPwB0THV1derVq5eOHz9+zr/fETtMc7axY8dqzZo1LV7v9Xrl9XqbjUdFRSkqKiqstfiNJ6zrRUK4exApbvW6PfbDzcdde+wHgI6ptb+PrHzOSFVVlVJTU23cNAAA+JZx/MpIfX29/vrXvwa29+3bpx07dqhXr14aMGCA8vPzdfDgQT311FOSpKKiIg0aNEijRo1SQ0OD1qxZo7KyMpWVlYXvXgAAgHbLcRiprKwMeidMXl6eJGnGjBlatWqVampqVF1dHbi+oaFB8+fP18GDBxUXF6dRo0Zp3bp1mjJlShjKBwAA7Z3jMDJ+/Hid65zXVatWBW0vWLBACxYscFwYAADoHPhuGgAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWOw8gbb7yhqVOnqm/fvvJ4PHrppZfOu8/mzZuVmZmp2NhYDR48WCtXrmxLrQAAoANyHEZOnjypyy67TL/97W9bNX/fvn2aMmWKxo0bp6qqKi1atEhz5sxRWVmZ42IBAEDHE+10h8mTJ2vy5Mmtnr9y5UoNGDBARUVFkqQRI0aosrJSy5Yt07Rp05zePAAA6GAchxGntm3bppycnKCxG264QSUlJTp9+rRiYmKa7ePz+eTz+QLbdXV1kiS/3y+/3x/W+qI8JqzrRUK4exApbvW6PfbDzcdde+wHgI6ptb+PXA8jtbW1Sk5ODhpLTk5WY2OjDh8+rNTU1Gb7FBYWqqCgoNn43r17FR8fH9b6JvU7E9b1ImH2inW2S2iTSf3cWXfPnj3uLCyp6LXdrqzrVi8k9/rhVi8kae71Q11bG+hI3HoeuvUcrK+vb9U818OIJHk8nqBtY0zI8Sb5+fnKy8sLbNfV1SktLU0ZGRlKSEgIa23lT/41rOsh8oqHDHFt7fb4+HCrH272ws2fIdCRuPU8dOs52HRk43xcDyMpKSmqra0NGjt06JCio6OVmJgYch+v1yuv19tsPCoqSlFRUWGtz29CByK0H+F+TJytPT4+3OqHm71w82cIdCRuPQ/deg62dl3XP2ckKytL5eXlQWMbN27UmDFjQp4vAgAAOhfHYaS+vl47duzQjh07JP3trbs7duxQdXW1pL8dYpk+fXpgfm5urg4cOKC8vDzt3LlTpaWlKikp0fz588NzDwAAQLvm+DBNZWWlJkyYENhuOrdjxowZWrVqlWpqagLBRJLS09O1fv16zZs3T8XFxerbt68effRR3tYLAAAktSGMjB8/PnACaiirVq1qNpadna3t27c7vSkAANAJ8N00AADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq9oURpYvX6709HTFxsYqMzNTW7ZsaXFuRUWFPB5Ps8tHH33U5qIBAEDH4TiMPPfcc5o7d64WL16sqqoqjRs3TpMnT1Z1dfU599u1a5dqamoClyFDhrS5aAAA0HE4DiMPP/ywZs6cqVmzZmnEiBEqKipSWlqaVqxYcc79kpKSlJKSErhERUW1uWgAANBxRDuZ3NDQoHfffVcLFy4MGs/JydHWrVvPue/o0aN16tQpjRw5Uvfff78mTJjQ4lyfzyefzxfYrqurkyT5/X75/X4nJZ9XlMeEdT1EXrgfE2drj48Pt/rhZi/c/BkCHYlbz0O3noOtXddRGDl8+LD8fr+Sk5ODxpOTk1VbWxtyn9TUVD322GPKzMyUz+fT6tWrNXHiRFVUVOi6664LuU9hYaEKCgqaje/du1fx8fFOSj6vSf3OhHU9RN6ePXtcW7s9Pj7c6oebvXDzZwh0JG49D916DtbX17dqnqMw0sTj8QRtG2OajTUZNmyYhg0bFtjOysrSJ598omXLlrUYRvLz85WXlxfYrqurU1pamjIyMpSQkNCWkltU/uRfw7oeIq/YxfOP2uPjw61+uNkLN3+GQEfi1vPQredg05GN83EURnr37q2oqKhmr4IcOnSo2asl5zJ27FitWbOmxeu9Xq+8Xm+z8aioqLCfa+I3oUMU2g83zz9qj48Pt/rhZi84hwxoHbeeh249B1u7rqMTWLt27arMzEyVl5cHjZeXl+vqq69u9TpVVVVKTU11ctMAAKCDcnyYJi8vT3fccYfGjBmjrKwsPfbYY6qurlZubq6kvx1iOXjwoJ566ilJUlFRkQYNGqRRo0apoaFBa9asUVlZmcrKysJ7TwAAQLvkOIzcdtttOnLkiB544AHV1NTo4osv1vr16zVw4EBJUk1NTdBnjjQ0NGj+/Pk6ePCg4uLiNGrUKK1bt05TpkwJ370AAADtVptOYL377rt19913h7xu1apVQdsLFizQggUL2nIzAACgE+C7aQAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFjVpjCyfPlypaenKzY2VpmZmdqyZcs552/evFmZmZmKjY3V4MGDtXLlyjYVCwAAOh7HYeS5557T3LlztXjxYlVVVWncuHGaPHmyqqurQ87ft2+fpkyZonHjxqmqqkqLFi3SnDlzVFZWdsHFAwCA9s9xGHn44Yc1c+ZMzZo1SyNGjFBRUZHS0tK0YsWKkPNXrlypAQMGqKioSCNGjNCsWbP0T//0T1q2bNkFFw8AANq/aCeTGxoa9O6772rhwoVB4zk5Odq6dWvIfbZt26acnJygsRtuuEElJSU6ffq0YmJimu3j8/nk8/kC28ePH5ckffnll/L7/U5KPi9Pw8mwrofI+/LLL11buz0+Ptzqh5u9cPNnCHQkbj0P3XoO1tXVSZKMMeec5yiMHD58WH6/X8nJyUHjycnJqq2tDblPbW1tyPmNjY06fPiwUlNTm+1TWFiogoKCZuODBg1yUi46iV6/tl3Bt0t77Ed7rBnoSNx+Dp44cUI9evRo8XpHYaSJx+MJ2jbGNBs73/xQ403y8/OVl5cX2D5z5oyOHj2qxMTEc96OU3V1dUpLS9Mnn3yihISEsK3b3tGXltGb0OhLaPQlNPoSWkfsizFGJ06cUN++fc85z1EY6d27t6Kiopq9CnLo0KFmr340SUlJCTk/OjpaiYmJIffxer3yer1BYz179nRSqiMJCQkd5gcfTvSlZfQmNPoSGn0Jjb6E1tH6cq5XRJo4OoG1a9euyszMVHl5edB4eXm5rr766pD7ZGVlNZu/ceNGjRkzJuT5IgAAoHNx/G6avLw8Pf744yotLdXOnTs1b948VVdXKzc3V9LfDrFMnz49MD83N1cHDhxQXl6edu7cqdLSUpWUlGj+/PnhuxcAAKDdcnzOyG233aYjR47ogQceUE1NjS6++GKtX79eAwcOlCTV1NQEfeZIenq61q9fr3nz5qm4uFh9+/bVo48+qmnTpoXvXrSR1+vVkiVLmh0S6uzoS8voTWj0JTT6Ehp9Ca0z98Vjzvd+GwAAABfx3TQAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKoOH0aWL1+u9PR0xcbGKjMzU1u2bDnn/M2bNyszM1OxsbEaPHiwVq5cGaFKI8tJX2pqanT77bdr2LBh6tKli+bOnRu5QiPMSV9eeOEFTZo0SX369FFCQoKysrL06quvRrDayHLSmzfffFPXXHONEhMTFRcXp+HDh+vXv+6YX0Dj9HdMkz//+c+Kjo7W5Zdf7m6BljjpS0VFhTweT7PLRx99FMGKI8Pp48Xn82nx4sUaOHCgvF6vMjIyVFpaGqFqI8h0YP/1X/9lYmJizO9//3vz4Ycfmvvuu89cdNFF5sCBAyHnf/zxx6Zbt27mvvvuMx9++KH5/e9/b2JiYswf//jHCFfuLqd92bdvn5kzZ4558sknzeWXX27uu+++yBYcIU77ct9995mHHnrI/OUvfzG7d+82+fn5JiYmxmzfvj3ClbvPaW+2b99unnnmGfP++++bffv2mdWrV5tu3bqZ3/3udxGu3F1O+9Lk2LFjZvDgwSYnJ8dcdtllkSk2gpz2ZdOmTUaS2bVrl6mpqQlcGhsbI1y5u9ryePn+979vrrrqKlNeXm727dtn3n77bfPnP/85glVHRocOI1deeaXJzc0NGhs+fLhZuHBhyPkLFiwww4cPDxq76667zNixY12r0QanfTlbdnZ2hw0jF9KXJiNHjjQFBQXhLs26cPTmlltuMT/5yU/CXZpVbe3LbbfdZu6//36zZMmSDhlGnPalKYx8+eWXEajOHqd9eeWVV0yPHj3MkSNHIlGeVR32ME1DQ4Peffdd5eTkBI3n5ORo69atIffZtm1bs/k33HCDKisrdfr0addqjaS29KUzCEdfzpw5oxMnTqhXr15ulGhNOHpTVVWlrVu3Kjs7240SrWhrX5544gnt3btXS5YscbtEKy7k8TJ69GilpqZq4sSJ2rRpk5tlRlxb+rJ27VqNGTNGv/zlL9WvXz8NHTpU8+fP19dffx2JkiPK8cfBtxeHDx+W3+9v9m3CycnJzb5FuEltbW3I+Y2NjTp8+LBSU1NdqzdS2tKXziAcffnP//xPnTx5Uj/84Q/dKNGaC+lN//799cUXX6ixsVFLly7VrFmz3Cw1otrSlz179mjhwoXasmWLoqM75q/ftvQlNTVVjz32mDIzM+Xz+bR69WpNnDhRFRUVuu666yJRtuva0pePP/5Yb775pmJjY/Xiiy/q8OHDuvvuu3X06NEOd95Ix3w2nMXj8QRtG2OajZ1vfqjx9s5pXzqLtvbl2Wef1dKlS/Xyyy8rKSnJrfKsaktvtmzZovr6er311ltauHChvvOd7+gf//Ef3Swz4lrbF7/fr9tvv10FBQUaOnRopMqzxsnjZdiwYRo2bFhgOysrS5988omWLVvWYcJIEyd9OXPmjDwej55++mn16NFDkvTwww/r1ltvVXFxseLi4lyvN1I6bBjp3bu3oqKimiXOQ4cONUumTVJSUkLOj46OVmJiomu1RlJb+tIZXEhfnnvuOc2cOVPPP/+8rr/+ejfLtOJCepOeni5JuuSSS/T5559r6dKlHSaMOO3LiRMnVFlZqaqqKt1zzz2S/vbHxhij6Ohobdy4Ud/73vciUrubwvU7ZuzYsVqzZk24y7OmLX1JTU1Vv379AkFEkkaMGCFjjD799FMNGTLE1ZojqcOeM9K1a1dlZmaqvLw8aLy8vFxXX311yH2ysrKazd+4caPGjBmjmJgY12qNpLb0pTNoa1+effZZ3XnnnXrmmWd04403ul2mFeF6zBhj5PP5wl2eNU77kpCQoPfee087duwIXHJzczVs2DDt2LFDV111VaRKd1W4Hi9VVVUd4tB4k7b05ZprrtFnn32m+vr6wNju3bvVpUsX9e/f39V6I87SibMR0fQ2qpKSEvPhhx+auXPnmosuusjs37/fGGPMwoULzR133BGY3/TW3nnz5pkPP/zQlJSUdOi39ra2L8YYU1VVZaqqqkxmZqa5/fbbTVVVlfnggw9slO8ap3155plnTHR0tCkuLg56O+KxY8ds3QXXOO3Nb3/7W7N27Vqze/dus3v3blNaWmoSEhLM4sWLbd0FV7TluXS2jvpuGqd9+fWvf21efPFFs3v3bvP++++bhQsXGkmmrKzM1l1whdO+nDhxwvTv39/ceuut5oMPPjCbN282Q4YMMbNmzbJ1F1zTocOIMcYUFxebgQMHmq5du5orrrjCbN68OXDdjBkzTHZ2dtD8iooKM3r0aNO1a1czaNAgs2LFighXHBlO+yKp2WXgwIGRLToCnPQlOzs7ZF9mzJgR+cIjwElvHn30UTNq1CjTrVs3k5CQYEaPHm2WL19u/H6/hcrd5fS5dLaOGkaMcdaXhx56yGRkZJjY2Fjzd3/3d+baa68169ats1C1+5w+Xnbu3Gmuv/56ExcXZ/r372/y8vLMV199FeGq3ecx5v/O0AQAALCgw54zAgAA2gfCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKz6fydJR71hwY01AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plot histogram of predictions\n",
-    "plt.hist(train_preds, bins=20)\n",
-    "plt.title('Train predictions')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.hist(val_preds, bins=20)\n",
-    "plt.title('Validation predictions')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.hist(test_preds, bins=20)\n",
-    "plt.title('Test predictions')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.hist(unlabeled_preds, bins=20)\n",
-    "plt.title('Unlabeled predictions')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "TODO: Which data are in the most unsure middle region?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:261: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n",
-      "  warnings.warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:264: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
-      "  mols = mols[:maxMols]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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o3jwiomnTKCDgg9VsbUmYvNvVlZ48oYcPqUYN2r6dlBMDNG7c2HzIEOrVizZvpvXradkySk8v2UOBsqJiRapVS/G31aQJrVhBixaRvT01b07h4cIqGRkZQUFBEyZMqFatWtOmTZVzt0skEhsbm/w7s7Oz+/rrr/Py8qZPnx4UFLRlS3ZyMjk60uLFihXevqWQEOWOy4L9+/d37NgxPj7e0tKSiB48aNenDzk6UkQExccT5gQHwYMHD549m6WpefLyZXlu7vv2U6fI1FRxLSiKMD22qalp4UVZWVnCC6HqaGFGRkZDhgxp3769yqU6OqSvT99/Tyq37tKly5MnT+7evbtu3Tqdqh/eY2poOI0Z07t376ioKIlE4ujoKHTnUVdX9/DwePLkSZ8+ferXr5+env706dOPHVtpVa1aNYlE8vr16xo1ahgZVTQ2Jh0dcnWlvXspJ0fs4D4LsbNCpUJcXJyampqWltbbt29VrjB58uQCFTSJqGrVqkQkzJpZYH25XN6woZyoyJ5vUNBff/GqVXz9uvCva9eYmZ8+5RYtFMOoY2L4wYP3qwt56D59+pR8pFBirlxhZk5N5Xv3+No1fvyYFy0qcli9cI2pWrVqSUYoOqEiPRHt2rWr8NLY2FgiMjIyKuVdFeALNWgQe3pyeDivXat6BWEQeM+ePUs2rkJyc3nDBvb25suXCywJDg4uXDhw3z5m5mPH2MeHd+9Wsb+mTeV16hTxXtnZPGQI5+VxRASjzDZ8ApmMMzOZmTMyOC+PmfnNG05O5rQ0trV9aWjo5eX1zTffKOtEEJFEIrG2tn37VvX3fG5urpWVlVALPygoaOXKzKQk9vBgf3+ePJkvXOAffuDjx3nt2jLSU8bX11foTD1ixIirV6+2a9dHR0eur8+jRvGYMRwdzcuWiR0ilA7u7u5EpKmp6eExLX/7vHk3zM3H1K07Yd++G0VtKzwblslkhRcpf5j9+eefnxCVsoBm/kqa/96AAQOEdz+r6hY0ICCAiAYMGPApuy5+H/+xKlTTJ6LevXsLLb/9xo8e8YMH3LJlicRXzNBThoioatWq//d//5ednX3gwIHCSwMD6fbtYSkpGR06dPjqq6+Us71kZWVVrFjxyJEjFStWFFrCwiglhYjo5k3JsGGSyZOpSRO6erWkDuPLFRFBmzfTkCH0558UGEjTpjUO/nXz5s0zZ46Ljo4TOjBVr06NG7/fwsPDQ0dH5+DBgw8fPhQraihubdoQEenr01dfUevWdPIkzZhBt26pXtnAwEBDQyMvL09ln5GySugLQ6q6w8TFxQ0cOFBPTy81NfXu3bslHhqUfZUqUY0a9JGeWKNHj9bW1j58+HBEREQJxlXIxo1kaUmzZ9Pu3XTwIE2cGL1+/e7du0+ePBkSEtJG+KLJR+ikqa1Nnp60YsVN5VdKfHz8vXv3du7cGR3tfe5cnur3io+nunVJKiVLS3r9uhgPCsoqdXXFI3IdHZJKiYhMTMjAgPT0KDj4twkTFi9efPr0aZlMZmtrO3369PPnz7u4/Pz3339OnixRub9ff/01LCysevXqS5YscXR0nDhR29CQiGjYMMX11MyMOncmB4cSOr5i5ePjM336dIlEsnLlSn9/f3t7+7Nn982fLzl1inR1qXNn2rdP7BCh1DA0NNTQ0DAzq/7DD1Pyt5uZ/fXmzcanT1cdOrRK5YYymSwvL09DQ2P79u0ZGRkFlv5jT5mPu3lT8eLTejl//N3z8vI+ObBikr/TX9WqVXfu3FnUmtvfdXLr37+/8MLVlSIjaeBAysggmawkoi1eYmeFSovff/+diP7v//5P2fL6NQsJ0GHDmIhXr34ttGdlZd2+fXvTpk1Tpky5efNm/p2sXs2PHjEzu7vz6dPcqhUnJ3NZn1X9c9iyRdEp4skTofp5lJkZEUkkElPTqo8fq56+avTo0UQ0ePDgEg0VxBMc/KZNm0GOjj0KL4qKihKmemnWrFm56hVy79494Zvc3t4+f3tYWJiFhYWwyMLCIjk5WawIoQzz8ODcXO7WrcieMh4eHubm5kZGRrOLqgVVMkaPVvQ42L6dHRyYKLB2bSLq2rXriRMnPrLdsGH+6urqhoaG1atXF2Y2JSI1NbWKFSuGhYWp3iY7W1F4MCaGZ8z47IcC5dyGDRuIqG3btq9fv1Y2Pn3KTZvyqlXMzHFxnL/QxIsXLwwNDYkoMDAw/36Eevlr1nCjRnz1KjNzWhqXgSqIw4cPl0qlRkZGM2fOLLBo8mRm5kGD0FMGFNLT07t3/6FJk6uVKnF0tKIxLo7nzLmjrq4uDE3q2rVrtHIZMzM/f/582LBhFSpU0NHRIaJatWoVmE5hxYoVwsxKnzZ/848/8oIFvGCBotjTf9W1a1fhUhUSElJ46apVq4iolBTg9/HxcXJyKtDpT01Nbfv27YVXjoqKEnrAGRkZZeWb17h9e1ZTy9PWfnnxYmLhrb4sSMooJCQkaGhoVKhQoX79+hMmXFm0iAcOZF9fZuaJE/nIkX+1k9WredIkXrCAv/mGT59mf3+eMYMnTOCBA7lnT16+fO/BgwfjysBF77M7fZqFwRdBQdy/PxM9trAQfvtaWVkVtVFkZKRw6S3N863CZ+S3fr3wfT1u3LjLly/nCXdZzA8ePKhevToRNWnS5O+//xY3yBKWl5cnFG/74YcfXr6rOHrz5k2hBB0RGRoaXrx4Udwgoaw6epSZef9+trPjbt0KLj148KDwRyiVSg8ePFjy4b23cqWiYOOMGdypExP9VqsWEWlra69YseIj2w0bNkz4mSgcSMWKFRs3bixMod28efP8vwuTk5M9PDxSU1OZmS9fZi8vXrCAkQyFz23NmjVENFbV7Vr9+nzxIh8/zvv3c1QUJyZmJSYmCtO19OrVS+XeevRgE5O8uXNVFxz9Eim7hZqammZnZ+df5OHB+/fzkyf8bloqAM7O5l692NmZO3bkvDyeN48NDVlbm6tXt3dwcBBmlzc0NFy7dm1eXl58fPy4ceOEhIuGhoby0kBEffv2ffHiRVRU1MCBA4XcQc2aNYuqifFx7u6KF5+WlHFyciIiPT298PDwAosOHTo0ZswYIpo6deqn7Pqz8vX1FQaaSKVSodPfjBkzlKmZX3/9Nf/Kjx8/7tKli7B05MiR+RetWrVbKtVQU9MoDQf1P0JSRmHIkCH0bi732rXDiLhfP54zh6Oi/kNJ5wI9ZY4f5xUr2MmJK1ViIq5Vqz4RWVpaFj5Pyju5nOfO5YULeeZMfvKEt217GhAwdepU4W7zqvAQp5ALFy5IJBJzc3PlvSiUZT4+EY0aSfLdIJmZmY0ePXr16tXC13qHDh2SkpLEjvJ/kpfHv/3GzBwSoug6xsyHDikyloWL6cjlcqG4kvAgVLiweXl5NWvWTPiIqlatevcjkwcA/M9kMq5alYnSra1/Cw5+/8cWHR2tLMTm6ekpYoTMzBkZvGgRe3vzvn388CHv3n3az69Hjx4mJpV69vTPN3HNhxISkpo1G2dgoK6mdv78eWUKJi0trX79+kTk4eEhtFy9erV27dpENHr06BI5Hii/li1blv9vL79Jk3jECA4MVExV2aHDDSJSV1dXU1MLLTBn7zthYck6OqZSqbRPnz5ubm4TJkyYPn36rFmzVq5cqXzsUZTHjx8nJiYmJib+45olTDmqd8+ePUJLbi5nZPD48Vy3LsfGKir2ZGdzkec+lCc5ORwaysOH8/r17OXFw4dzz54cGprBzNHR0T169BD+nGrXrq2npyf81ho0aFBUVNTFixcbNWqkzCP07t1bGBakoaHh5uYWGxv7afHs2KF4oaq/yD/r2rVrhQoVTExMtmzZomy8evWqUFdYU1PT3Ny8du3aT8TOTQpT6NSoUSM4OFhoycrKUn7aEolk6dKlp0+fnj59uvJDrlSpkqmp6dmzH/RvvXfvnp2dnY6Ozk8//STGcXxOSMowMy9atEj4/9bT0wsKCtq8OXfSJHZ355QUHjfuPyRltmzhyEhmZi8vvnyZz5/nnBzu3JkfPuSdO+Wenj8LT9h++eWX4juWssTT07OoJzypqal16tQhIi8vrxKPC0pWairLZPz990x0rWHDdu3azZw5U7gpIiIhc9e7d+/8T62/ULm5itGOp0/zoUOKxtWruV8/fv264BeRTCYbOnSocIkdNWpUt27dlIOEhfpzDRo0iIqKKuljgPJn8eKn5uYNiWjhwoVCi0wmc3hXoKJFixYFnleXEnl5eVZW2UT8xx9FrLFxIxMxUfqgQQWWBAcHa2pqSiSSI0eO+Pr6CiOb7OzsHgmPZQCKzYIFC4io8NgcZvbw4Dt3+LvveMgQtrDg7767KFwUJETHli5VubdDO3fm7wumZGFhMX/+/I+E0a1bN3Nz8/yb6OjoODk5fXyo7B9//NG/f38h7+Pj47N48WIfH5/P3r9VKEdARF9//bXQ8tdfbGfHOTk8cybfv8/LlzMzb9zIqoZ3QHkUFsb+/uzhwWPGcKG5WzgwMNDc3NzMzIyInJyc7ty5o1yUk5Pj4+OjpaUlecfZ2VnlPNklJiQkpG3btsIp0KlTpwcPHnTu3Dn/qWpgYFDDwCC3WjWeN49TUvj6dRbjiabZuzoV8fHxysbs7OxevXoJceYvfFOxYsWePXs2aNCgQ4fad+9Wef16o1yeI5dnp6ZeOX/e3tDQQEtLS+Wgpy8LkjK8b98+oacZES1atEjZLjyH2L2bhw79bO915MgRImratOln22OZFh8fr6OjI5FI7t+/X2CRcDtqY2OTk5MjSmxQQjw9ecUKHjuWL1/mVq14xAj5u2dbISEhkydPlkgkOjo6ZePPIDeX27ThBQt4yJAPkjI3b7KHB0+cyB068JgxfO5ccFJSknCV1dfXVxbFSElJ2b17d58+fXR1dTU0NNCDDEpGdHS0mppahQoVNDQ0atas2bZtWxcXF6HGk6GhYaTwpKJUOnCAhwxhmYzv3OGYGMVorJgY/usv5sePecUK9vDgb79lVb01hWc5Qj92qVQ6ffr0svEtBKXc7NmzicjNzS1/Y3Iy37mjmOzLw4PPnFG0X7x40a1BgxQdHdbTU1EwJjFRbm5+vFIlPXX1/v37+/n5rVy50sfHR+g53sXenvOVrclvx44dwi2TsbGxsbGxMqejpqY2Z86coiI/d+6ccloMJT09vQYNGnzeXq4ZGRnCTMB2dnYeHnu++YYnTOApU3jZMkVSZtQoPn2ap0xBUgYUhKTM69dcvTonFqpMcvNd6d38hUfze/z4cc+ePXv27PmX2N1PBHK5fOPGjcLppqOjIzy8JKLKlSuvXr06ISEhbMECVlNjHx8eOpRPnuThwzkmpiQjDA8PF0Jq0qRJgUW5ubmDBg1q0KCBpqZmrVq1Zs6ceeXKldzcXGbOzMwMDZ0cHEwREb0iI/vHxfmEhdkGB9PmzS737t0ryfiLSXlPyhw9elSZiitQMjYhgWUynjyZGzbkz/WcLycnR6j1UDb+ekqAMF+dnZ1dQEDAH3/8cfny5bCwsC1bthCRlpZW4WQNlCnR0eztzcyck8NjxrCqvjCOjn06dPAIC0sv6diKQYGeMsnJnJjIq1fzs2fs58ft2jERm5gkqKurC+XlTE1Nb9++XXg/trYtbWyc7t5FUgZKwrJlt6RS9QoVKhS43TI0NFxaxPP5UkImYysr/vNP3r6db95UDOC/do33+r/l4cM5LY1//10xP3Yh+/bt09DQMDIy0tLSOn78eInGDeXY3LlzhYGBDRs29PLyevLkiVwuHzw4Q1v7fSr/A927MxG3aKHipuvBA2F0fXrnzrkfjuS56ubGRKxqkFRSUpLQR2bTpk3520+ePGlr62RjczvfY+/3srOzGzZsKNwWzpkzx9vbe/r06aNGjRJ+gc/43CWxmzVrVqNGDSJq3340EX//PW/ezLNm8cCBfP8+T5jA16/zrFlIyoBCSgqvXs1z5/KePSru+C5cuCBc1FxdXcWI7hMlJia6ublJJBIjIyNdXd3p06d/kP28f583b+bLl5mZ793jD2u4FDc/Pz/hI1VZHouZmzRpQkSXLl0qvCgxcXdq6pWoKLecnLiMjPsvXsyVy8vIE5HynpQZOnSooaGhoaFh+/btVQ5/aN6cK1XKO378s2UQhTmDfv7558+1w7Lt0KFDFSpUKNC3VhidsXLlSrGjg2IWEcGLFzMz5+XxqFEqV+nbl4n4998/uh9hHrVSTy5noYT/06ccGspXr/LSpXzlCicmcl4eb9zIt2/zsmUhDRo0MDY21tLSGjNmjMr9DBjARCpq0AB8diEhLJWynV3CxYuXs7KyIiIiLl68uH379tatWxORp2epnuZEJuOpU3nkSA4I4Js32cGBFyzgkSP51KwLimH9OTnviy6+k5qaKnTVVPaUWbNmjQjRQ7mUnZ3t5uYmzAsjdP6vVauWgUHN9u1fRkSo2uDxYz59midN4vnzedYsFmYnTE9nb29esIADAnjqVC6c3P/rLx46lHft4l27OCUl/5JRo0YRUdu2bQtPdNijB+vq8uHDKqLw9vYWAq5Ro0ZaWprQeOPGDeHXnba2dszne1B/8uRJYZ9z5849cODKyZN85Ahv3swpKWxhgeFLoNqPPzIR796tYtGJEyeEv95hw4aVeFz/q0OHDv3yyy+vXr1SsWzPHj59mpn5+nUOCCiZeHJzcy9duiTkXChf4af83rx5I5VKtbW1M4X6T6pkZoY9ezYwK+tJcQZb0sp7UkbohGlvb1/UONi1a4+qq2tXrFhxurs7//Yb37jBr17x2rV84AB/0sy7ly5dEh4hbtu27cGDB7JScLu4dKmi2llpq3WTlsbdux82Njaxt7cfOnRo9+7d27Rp06BBAwsLi759+5a22nLw+cnl7ObGR46wlxefOqVylaVL2deXY2M5KEjVYpmMx43jxYvZza2ontilVnIyb92qepFQnUoqlar84lq/nseO5Zs3OTycnz1TFDUsos4jwD87d07x8PDkyfeNGzcyM0dGsre3IneaX3h4lJXVHV1dfvOmpKL872QynjaNw8PZ1vaDnjKBy58oLodPnrCPT/5Nbt26JRS00tbW9vX1PXDggNBtExW1oSTl5eVdunTJzc2tQoUKampqderU+dgEZytXstCteONGRQ35JUv44UNm5pkzixy2sGIFBwZyWBjnq6l08+ZNqVSqrq6ucphGVBQPHszXrnFuLr8rMMXM/PTpU2Wf9MMf5mx++OEHob3AjCrCWKu0NM7MVHyHZGcXyA6plpWVJZyhS5YsUTZmZCgKhTx/zllZit8CiYlc9B0flDtubr+2bLlg3z4Vg1UPHTr08W4dX6r0dB42jNes4WHDiruszMuXL/fu3TtgwABhaKFwI2xtbf3ixYvCKwsX1qIGizFzZmZoUtKxqKgRGRllasBEeU/KpKen6+vrSySSosa9C12UiaiXsTETcd26PGAAx8dzUJCK36H/glwut7GxEWZZE37Y2dnZubq6nhaylWKYOFFRhb60fdscOMBSKXfsGFs660RCScjJ4eBgLrqI/aRJigq4wsCfgk6eVIw+ePiQPzr3bSm0bBnv2cMqc49du3YVvkDOnTtXeKmXl+Lp/oQJvHQpC9V+S9vZDV8QT0/F7VD+XiMtW/KePfznn/znn6q36tGDXV05Lo5jY0vjLCd//807drC/PzPzypUcHs6rVzMzh4ZyUBDz6tXs7c3Tpxf4qeri4kJE1tbWD4V7WmY3NzciatSoUUZGRkkfA5R7qamp/v7+Kp82vzd7NgvPyY8eVfRjGTNGcWnZtk0xfqEwZe/UGTP4+fP0kSMDAgJat24tlUrHjx9f1FtNnsyjRnFmJg8Zwn37sodHspubm7Iwf+/evQus/+jRI6Hvs5qa2rFjxyIjY1+84KQkrlaNnz3jAwf4/HnF187du4qzVRAbqyj4+PvvH1RmnTVrllCoAjWe4N979uyZMN7NwsLiu+86P336wQ+vo0ePGhgYENGUKVPEirC4rFvHXbooCqoVm2PHjtWqVUs53KFBgwZDhgwRCv22atUq8V0Vn8jIyLVr13bp0qVbt25ENHfu3KJ2KJfL0tJuZGc/K9awS56UyjddXd2ePXsy865duwovDQsLGzFihEwmI6KvK1cmItLSIktLqlKFHB0pMvIT3lEikQQGBg4fPrxXr16WlpZZWVnBwcF79uxxc3N7+/bt/3Y0n27ZMlq6lF69Euv9C/rpJ8rJoapVacMGWr3aXOgiDuWRhgbZ2tKHszzkJ5VS27Z0+DAR0fnzlJf3bsHNm9SzJ71+TXp6RET6+pSZWfzhfk4eHtS3L0lVfUkrp/y8ceOGym0bN6ZTpxSv162jX36hiIhiCRLKiWXLaOFCio1939KuHV26RCkpRW7i40N375K+Pvn7U2pqCcT4H+Tl5U2dmtC/P2VnExFNnEgNGtC4cUREVlbk6Eg0bhzNnk0+PmRomH/DtWvXzpkz58aNG8pJOleuXNmwYcPQ0NClS5eW9GFAuaevrz9ixIh69erlvb/4FdKpE61fTzExdOgQtWlDRGRjQ0FBxExXr5KVleqtJBKSyYiIkpLIxETD2dnR0VFfX18ulwtplKI2Gj6c1q2jtDTau5eOH1f39/dPSEjQ0dH56quvfH19C6xfv379oUOH6urqamlpdenSpXt3L3Nzsrenbt1IeT7FxNDBg3T27AcbZmdTfDzt309xcRQQQNOn0+LFpydOnLhkyRKJRLJ+/XrheSqUW1dTUjbExQXExeUxCy3Km7bISAoPp4wMIqLr12niROrXLyAmJkZbWzs6OvrevZpNmkgvX1asHB1Nly/npaWl1ahRQ5gSu0y5d4+OHaOYmGJ9k02bNkVHRzdr1mzNmjVPnz4NDw/ftGnT9evX69ate+PGjY4dO27btq1Ro0aWlpZjx449duzY0aNHjY2NW7ZsWdQOJRJ1Pb2Wmpq1ijXskqcudgDic3V13b59+/bt2yMjI2vUqGFtbW1tbV27du2EhITu3bsnJSX98MMP3t7eBo8f040bZGtLQsGnjAz61ExB9erVV65cKbxOTk6+d+/e8OHDHz9+fPDgwWHDhn2u4/pPpk0jdXVydxflzYmIAgMDQ0ND5fKaqamuJiaUlESrV1O9elS7NjVuLFpxRUORAAAgAElEQVRU8EXo3ZvGjqXEROrYkRo3zuvTZ+GTJ4+9Hz60vHuX4uPJyooMDWnvXho5UuxIP5uPJGUSE4mIXF1p2jQSfjmPHk0WFh/cTgP8V1OmUIUKirSF0rRp5OJCs2YVuVXv3rRkCUmlNGsWRUaSjc0pDY1rNjY2wnMwES1cuHD//sXdul348Ue7/7ShsbHxvHnz8rfo6uru3LnT39/fw8Pjs8YI8G+FhYXp6ekpO6QU5OBA+vp09ix5eZEw/9GQIbRlCy1cSIMHk4mJ6q3GjqXp08nQkOzsSF9fw9GxFtGyZctsbGx+++23SZMmCT0LCrO1pYMHqWJF2r2bsrJysrP9fv311/v37zs6OlavXr3w+vPnzw8KCnr27JmZmZm5uVlCAlWuTHp61KYNHTlCgweToSFZWZFUSq9ff7Dhd9/RuXNkZES7d1NkJJmaroiPP1GzZk1TU1MHB4d//eFB2fQwPd25cmUj9ff3uStX0po1REQrVpCaGlWsSHPm0OzZFBREjRrN+Omn53Xr1j1/Pvb8+WWamnTwILVtS+PH04YNVKVKZx0dg+7du3t6eop2PMXDTSbbb2T0q46Oa+Flcjl5epKBAb18SfPn07thRwX99Rdt307a2mRnRz16FF7OTFlZNlZWf+/bt71u3brKdgsLi3Pnzjk5Od29e3f06NHp6enKRZqamh07dvz222//18P74ojdVUd8MpnMwMBAS0sr/8diaWnZpk0bIrK1tVXWJFPYsYPnzuUpU/jzzfT5+++/E1HHjh2LWiE9Pd3FxcXFxUU5/W0BwcHBXl5eu1WWqPonR48qBtYGBn7C1p/B7t27hUt1kyYjiNjKiidPZi8v9vXls2fFCQlKkdRUXrKE583j4OD8za9fc6dOLJTMfvKEBwzgBg24Q4dg4RSuqa4u19Dg6tU5Lo7PnlUxG+iXLCIiQiKRaGpquri4BAUFKUtTnTkjNzJiZ2dOSeGHD7lrV165kp8/Z2bFIC+AT7B+PaenMzOvWMExMbxrF587x0Kl9V9/VTlhNPO7SUaXLGFnZ7azYyK2s5tFRBYWFquFkULisbW11dXV9fX1FTcMgM8iMjLS3d39M9bK/Yh+/foR0fDhwwsv8vDg6dM5L4/fvuVr196337t3TyqVampqRqiqRTxnzhwiaty4cf4BR8K8T1268OnTqocvRUbymjX87BlbWHCjRkzE2tqmBgYGEolER0enZD4KKM38X7xY/fffB/IVE3R15VWreNUqHjuWJ0/m5cv5r794/HgeNYqVJZJiY9nZmefM4UGD+No1njiRHRzYxYUjI4u35IpYhg4damBgYG9vH1941rSzZ3n7dmbme/f4I9fKESMU45OF8YSFPHjARFy9uuqt4+PjrayshDqJVatWHTBgwN69e4sq81rmISnDsb//bqilpSzyIhAKwltYWMSVyL1ccnKyjo6OVCpVeSFJTk5u3769EJWeXpVevbLOnlUMEI6O5vPnedSoHURUpUoViUTi5uZWMIvE/Oeff3br1m3YsGEpquqkzZnDM2cyF1WVo/gJ/YM6dOiwatUeHx/evJknT+bUVLaxQVIGFAPaOS+PBw9mPz8eOPDu0qXLli3r3HmYjk7Gt9++XzEvj//884KpqalUKpVKpTcWLizu0mXF58IFzsnh7Gx+V7nivefPnwtziypnJatYseKQIUM8PDwaNXIxMJAPGSJGxFBu/PUXq6pl9IHkZL51i/39OTubraz45k0+epR//z2wc+fOwg1Y4albStKMGTOIqG/fviLGAPC5hIeHC7kSv27dePBgPnOGjx7lhQuL4ydUZGSkpqammpragwcP8rdfvXq3bt0sLS1WOQnUTz/9REQDBgwIDw9//PhxREREXFxcYmJieHi4tra2RCIpUB9NKBMzbx5XqqSYIyknh1NTFUtfvuT+/Vl4RnnkCJ84wXv38rZtO/fu3dunTx8qVDYYyiH/Fy/efjiVypgxnJLCKSmKpEx2Nru5qb7xOXCAL17kYcN43DhWNTFv2XHs2DGhXI6JiUlAQIBwXZYfPcpt23JAgKJiXGwsz5tX5C6Uxafc3blQIafr1zksjLduZU9PDg5W9AAoMM/169evAwICCnyflE/lPikTH896eilNm/7u53fp0iVfX183NzcHBwd1dXV1dfWlS5eWWCDOzs5EtGxZwQlEExMThblFiahVq1adO18g4ubN+bvvFHepEgkbGCSZm1dv3bq1MIa2YcOGN2/eZOa8vLzDhw8Lo/LU1dWJqGbNmgWufHI5z5nDixZxSEiRSZm0tDRlHabPTi6Xm5ubE9H9++9raB89yllZ7O39wYMRKKeU3/je3ly/PhOtetdr2tGxb+Gsaffu3enLn6TWxYWXL+c3bwpeCh8+fCh0Gm/cuPGZM2e8vLxsbW2FT0M4/SdPXo95yaD4PH7Ms2fz06f/sNqPP+bVrcu3bjEzZ2S8n6swJydH6BcpYm17Zn7x4oVwY1lUjX+AL0VMTHadOvWF0nu3hCkpnJ0VZatnzVKR1/+fjR07loj09PRsbW1tbW2dnJycnJyMjY01NfVXrLilcpNnz55paWlJpdL89T6JSPiBqrLfDTMPHcqWlqGTJxd8Sj91apBUmtenj4pNHj16pKGhoaamFooZB8u342/ePElPT8yXl1EWqheSMsx89iz/9JOKbQ8c4MuX+cYNbtiwBCIVWWRkpPCwhIiaN29uY2MT3LQpE3GLFjx0KIeGsqenIjO6fDnPn8+enu/zo8w8axZfu8bPnvGIERwdzR/+9p46VXFrOXEiz52rmPgM804UpbwkZV69erV69ep169Y9uHZNMY3Erl28cCH/8QdfvMj5Zs4T+Pr6dmjRZOPiaSUWoTDjmo6OjouLy+LFi0+ePPny5cuXL19+9dVXyjuuAwcOJCbytGm8fDkvXcr+/jxxIg8Zwt7enJgoY+aQkJDGjRsLKZhOnToJj9OJyMzMbNKkSc2aNROerru5uaWnp2dmZvr55dSrx+7unJTEI0bw+PG8axfn72eTlpbm6+trZmZmbGzcu3dvlamZhISEgwcPRkdHf9qB37x5U+iUVKD9jz9YSD9BeefpyX//zXI5Dx3KNjZMNM3CQvhLnjRpUuHV7ezsiOj69esF2nNyePNmZubbtwsMhCqNhBF8wcHs7s5durCPT8bx48dPnDhRqVIloVtZUr5OQKGhocrUzKeNYQT4jPz8/Jo1+7pOnb8fPVKxdOHChUTUpUuXEo/rA8Kj+6lTp4obBsD/4tUrrl+f7e2jq1WrVatWrbRq1ZiIBw5U5E2vXOEtWz77m8bExFhYWBQohmBsbGxra5tZ9CzTI0eONPywbLawlYGBQUL++ZPyiYp6o6mpKZFI7O3tnZycevbs6ezs3KlTJyKytf2/AlPk5H8jIuqjMmcD5UZQYqJtcPDUfB23lM+r8vI4NZVtbdnJSfW2f/+tSB8UNTVZ2bN7925hOiQiqqqmJtfU5Hr1ODqaDxzgJ0+Yme/cUaR679zhVaveb5mby3v28IYN/Pgx6+iwuvqRxYvT09OPH5ePGcPjx/PevRwYqEjKzJjBCxZwt27iHGPpVy6SMrm5uZ07dzY1NSWin7/+miUSnjKFAwKYmX18FPm/D6W+fe3do8b8nhYZKW9LJsiMjIx69eoVuFwZGRkJL3R1dfM/V9y/n69cYQ8PHjCg4H4yMzOnT5+upqYmDMiqVauWr6+vMFVnTk7OnDlzhC4z1apVq1SpUocOd4i4bVtOS+OLF7llS5ZIuFWruKtXryYmJnp7ewt3gPk72pzN1xv21atXXl5exsbGlStXrly5cqCqmjQxMTGzZ8/eunVrUQfu5eVFRGMLJU6zs7lz59UWFnX9/f0/cpmHsi85mRcuZG9vvn6dHz/mP/88tWPH8OHDJRKJrq7uc6FiSj5Cx6vCWcLMTBZmMzx+vLin//sMhBF8P/0kpKG4WrU9wu9XIvrhhx8KnxGnt2zZVLduhJlZujAWEUA8rVu3VlNT27Ztu8qlb9680dPTk0gkYWFhJRxYfrdv3yYiQ0PDwt8hAKVQdDQLPwNPnODYWB43juVyPn+eu3ZlGxtOSpIzM8fE8I4dvHev4iK3cWMx3VampKTcv38/ODj41q1bp0//P3t3Hldz9v8B/Nx7W7VrIYqKSqVsRSmSYuzRkH3JWMbeZEwUGsJgbFO2su8KjSFTsoWQhkKbpAWlUint2+38/jjzvb8795aJwRWv5x/zuH3u53PuueN+7udz3+d93ufSpUuX9u/f361bt7fcreXn50+dOnXw4MFOTk52dnY9evQwMTGRlpaWk5MryMpq8JDzAQGkIa1bt27VqlWDozLV1dVubm5SUlKtW7duLNYDX4M/U1MnXr++/q+GU7dycykhtFWrT9ypz1pRUZG+vj4hRFFRMSEwUHTi1oULf1ceLSqinp4NHL92LeVyl6uosDQCc/MSQuiIEZTPp3Pm0LlzkSnz776KoAzLtFRRURk+fHjE2LFURobOmfN35O/OHXrgQINHRexffXabe3xkSD2/7tP0s6ysLCYmZu/evQsWLOjbt6+KioqGhoaUlJSxsXHUPy+rLCjz6hXt3Lnhps6ePevq6hoQEFD7zxmVlNKYmBg1NTUul0sI6dNn/KlTtKDg79zy69dply60Z8/hXC5XTk6OXfx69+4dGhr69OlTOzs7QaJNcnLyggULWrRoIRw84nA4M2fOLP1fVlt6evrChQvl5ORY6bUxY8YUFhaKdCYhIaF9+/aEkBMnToi/i169erGqGaH9+tGRI+mNG/SPP+jmzX//28HXjU0dnzhxovDGuro6Ho/H5XJrxKa2VlZSOzu6Zg2dOvVzD8qkpNCVKymlNCSE9uxJCaEtWzpKSUlxuVwDA4O6uoa+kR48oIRQQmjv3p+4twAi2HLRB1lmWkPGjBnD5XIlXvfBwMCADTzIyckZGRk5ODhMmTJl5cqVDZ9iABIVH0937aKUUn9/mpREbW3pgQP02DF6+7ZY8bS6OrpuHfX1fVt5zo9g7ty5bu9Y0mzNggW3zM1pg9OXsrKoklKyqqqeoqKXl9elS5dCQkKCg4PXrFnD7mClpaWfik2kXLduHbsvbd++fXV19Xu/F2ju1q9fTwgZO3Zsg89mZGQQQjp2NPzEvfrMzZ69q3Pn+SdPhjXwXG4u/f57WllJt2+nDa45U19/cfVqQoi6ujqXy508+ZKPD128mPL5NDWVzp9Pd+ygRUWUUurr+1HfRDP2RQVl6uvr84XqbDNs8Wk5Oblbt279vammhp49S0+dopTSXbv+USNeSNKt0JSYiL/+PJR8Jyz/RerzpJiP2PVGDB061NraZd060Unvt29TU1O6bNnfK2K8K8F6upmZmSJPVVXVenp6tm/fXllZ2dbW9ty5c4KnamtrV69ezepW8Hg8FoVxdnaOjo6ur68PCAhgMRo9Pb29e/eOHz+e7cPj8bp06SIvL08I0dHRiYiIYK3FxcVNnjyZx+NxOBwul6uhofH7778LXuvNmzesyA4LyryRk6OE0GnTaHAwraig06bRhioWw1fl+fPnLVq04HA4N27cyM7OPnfunI+PT9++faWlpRUVFcXH65pLpkxdHTU3p23bUlZkqbSUXr9Of/11s4fHEmPjrsOHf9/oYZ6edMQIunEjFSv1DfApHTx4kBBibm7eYDXfigrarZuVjIzM+vXrP33fBMLCwjgcDruiCVNXV1+xYoUEOwbQoPh4OmIEXbOGDhtGk5LokiX0hx+ovz8Vm6orMQUFBYqKirENpZ83KjWVKivTpUsbqKeakUE7dKCE1Do7izwzefJkdrZO+mdFkMzMTMFg4dmzZ9/nPUDzV1hY6OnpKSMj065dOx6P5+npKXJD+OLFi7FjxyooKNja2kqqk5+ngQMpIQ2HXCil1N+f9u9P33rhXrZsWUBAwCu2GA28oy8nKJOenm5iYiIrK2tgYODi4rJq1aqzZ88eOHCA/ew/evSfedT19fS33+iaNXTPnsYaTLoVen77koNeo1/nZB5fPeXV84Ymx39kd+/Wsvw64VH/pCR66xYNDKTLl79nszU9e5bLyBSamNQI12oS8vTp0/v37zf41P37983Nzdu2bTts2DCRfRISErp3704IYeuLy8jIzJgxIzU1lVKamprKlhjncDguLi79+/dnV015efkhQ4YIFpFxc3N78+ZNcnIyG2hVUlJyc3Ob4uxMZWWptDSdOvXvlJ79+z+j2xCQHLaUpsh69mwMbdSoUSLD3bW1f6deJiV9jNKHH8z27Tt69hzTs2emyD1qSQklhLZo0fiRs2bRV6/o8+d0wYKP3EeAt6murmaz0681tErTnDm0Vavs4cNniC8U+FFkZ1Oxar65ubmtW7cmhCxevNjMzExJSYl9e7BKGW3bthVPtQOQLJFMmSVLaG4uNTf/vO6Gvv32W0tLy3fLNUtNpbNn040b6bx5f6+tW1BAf/qJ+vrSLVvonj3iydGZmZmssDGXyxWOAQ0bNoydyCNHjvwAbwaam7KyMl9fX1a6iMvlsrIVhBBDQ8MrV65QSgsLC5csWcLGiaWlpTeKVRT92jx58v8Pnj2jmzdTD4/GgzKqqpQQKi//lgYjIyOfCBqFd/SFBGWKi4vZz3jBz3tGQUGBEOL7XplSSbdCM+NvF2SlXT+xJXzPyg/e5yZauZLu3EkppYcOURcX2q0b9famzs60ro66u79vo+y8kpN77141dsNaU1OzaNGijh07LliwQGR5bz6fv23bNhkZGVYXQ0lJaeHChdnZ2YmJiSyUw2hqarKZU126dEkVXIlLSujNm9THh7JCIT4+tKG1w+Frk5mZKS8vL5hqxwwYMKBly5aEkFmzZonsr6tLX72ip0/T69cl0t9GZWdnBwcHL1y40NbWVl1dvcGpH/n5dMIEumIFTUigxcV/T83NzKT/P0NRsEzV941k0wB8KqtWrSKEOAsNcaelUZaaefw47dyZxsV9kn4EBtJt2+jevcIJ03w+f8CAAYQQBwcHwa/HkpKSxMTExMREVlwfBbPhc/Py5d9LyUZG0txcevo0rayk3t5vW/Z6w4YN48aN27FjR0JCQhMXoV+yZImcnNwuFv55d4mJiR07dszOzn6HY9av/zvscuIEvXKFUkpXraKs0tO6dY1NV3d3d2dDMr6+vsHBwREREb///nvbtm3ZaF9GRsb79R+ar+joaEGp2kGDBsXFxc2YMUNwZ8jhcHr27CkotjB27FjEDqhQeZf58+nmzXTaNEppo78ub9nb31NRSWisPDKllNItW7Z82B5+Vb6EoExNTQ27wTI1NbW3txf+eSYrKystLS1eyqQp0h/evLhvVfgen7yMpNsh73l9+u/CwqiDA83MpIsWUR6PEkL796dHj1J///cNylRU0DVr6Jw59H0vuv8Fu1Nv2bKl8C1vTU3N8uXL2XQnOTk5WVnZyZMnl4tPzSospJ6edPVqGhDwSTsNn6vdu3cTQvr06WNqaurm5rZ79+64uLja2tro6GgWkPXx8aGUlpfX37xJL16kbm7Uw+PzCsps2rRJZEEKFlm2sbEROQVCQ6m9Pa2poYsW0chIGhxMKf3/wmmUUjp9OqWU8vkNz88H+ITy8vLk5OR4PF7fvn29vGLZsgujRlE+n7q700+3arsgMjt7NnV3pyNH+i1bxlZv0dLSevnypfgR7FvFxsbmU3UR4D0tXkwJoUuXNvwsq73CMkoIIRoaGiNHjlyzZs3bIyZeXl6EkDVr1nyUHjdo1SrKuvTHH/TCBUopnTfv79GGw4cbK1Scn5/fqlUrdqEXue3ftGnTJ+s7fD7YjCRdXd1woUyPixcvGhgYsDsrtgqEk5PTvc9/Ac5P5fvv6ebNdPNmOmkS3byZ+vvT339v+NdlVVUVuztVV1d/S4ORkZEfq69fgS8hKDNnzhxCSOvWrVmFlIKCgsuXL2/evHnatGlOTk6EkD2Nz1H6/IWF0ZAQOncuXbSItmtHCaHDh9OUFOrpSf9Z4bTJ1q2jSUmUUuruTj95afoNGzawC+fWrVtFnoqOjp4+fTohpEOHDp+4V9BMDRw4kBCyf/9+8af++OMPtmSYgYGBldUgQqi5OV28mO7ZQ93dP5egTElJiby8vEhVCwcHBz09PULI0KFDBYW6KypoSAjdupVu2fJ3UGbqVLplCx00SCgoEx1Nvb2pl9ff1WgAJGrWrFnGxsaEkN69Mwmh48fTvXtpYOB/yPF8DzNn/v+Dzp0pIR3U1GRkZFq0aOHq6trg/KmKigqWrRb9WU0LARBz5UqZvf2vI0Z8J/6Un58fu6Do6elNmDCBpZCwLBJLS8u3tLl582ZCSIMLG30s8fHU3Z3euEGnTqVsTv2ZM3TvXvr6NXVzo43MsqeURkVFTZgwYcyYMWPGjHFycnJycurcufO0adOqxMvTwFdgxIgR7EMuUlqhoqLC29t78ODBhJBRo0ZJqnufp3nzaEUFraig8+bRzZvpixd0zhwqUoKfz+cHBwd36NCB/G8pXnf3OyILplVUVB44cGDMmDFTpkwRX2EGmkiKNHPLly/ftWtXixYt/vjjD7aOj7q6uqOjo6OjIyHk0KFDly9fPnbsmHAOW7OjqEj69SPr1pE//ySqqiQykkhLk59+InPmvFdzz54RExNCCDE1JRkZRF39g3b2XxQWFrIHgsW2BXr16lVTU7N//35B/iHAWxQXF0dGRkpJSQ0fPlz8WQMDg+7du6ekpKSnpxcXv+nendrYcAgh06cTBwfSrx+5dYvY2n7cHqampiYnJw8ZMoRdxsSFhoZWVlb27NmTUtqzZ89evXr16tXL0NAwLS3Nzs7uwoUL06ZN27Rp05EjZOPG1t9+S1xdydWrJDubEEIGDCCjRpH/nU+EEEJ69SL/q+ENIHEBAQGJiYnPnj0rKFB2ciJ8PrG1JXv2kNevGz2ktrZ28uTJcXFxenp6bdu21dXV1dHRUVJScnZ2ZlUA3s2bN8TenqxZQxQUiKkpmT2bZmYuyMry9/dPS0sLDg62t7efO3euyEGXLl3q0KFDaWnprl27euGEgs+YnZ10bOzqsrKy6dPrBw8e3LdvX1ZE48CBA4sWLWL7ODg47Nu3j8PhpKenb9myZffu3ffu3UtJSWEBU3EsIln4j0vLxxQRQXx9ia8voZT4+xNFRUIIcXEhV6+S48eJj8/fWxpia2tr+7Gv4tB8VFZWsgciFwt5efk1a9Zs3LgxLCysY8eOkuja54vDIez/Fpf795bFi8nMmWTtWsLhPF261CA8PHzZsmWPHj0ihJiZmVlZWcXGVu7caX30KBk+nOzfTwICyMOHJDxcOifnh+rqN4QQRUXFHTt2SOwtNWuSjgr9V5aWljIyMtbW1ikpDRTiLSkpadGiBZfLfc5qkTRDcXH04UNKKf1gy0Fs2UL/+otSSufOFVtH8aNjuTCEkPCGCkmdOXOGoEIbNM2hQ4cIIU6NzG719vZmn7SpU6cK5gGxGcQPHlB9faqqSh88eP6/7U+OHj06d+7cYcOG3WlkObamyM3NDQsL+/nnn4cMGcJubfX19Z2cnBobuGNLjG1raNXSu3fvKioqEkKkpKQsLVcQQvv0oVeu0DdvqJlZI9OXAD5jBw/SJ09oXh51dGx09TxPT0+RwnCEECUlJSsrK/47Tnmqz8ykGhr055/p69ciOaHnz59nLXfs2FGk2WfPnrGqZxwOx9PT8x3fIsCn5unpqaWlJThZOnXq5OjoyP3fD6xJkyYJf8IXLlzItm/evLmxBtnZMXTo0E/Q+YqKihAXl3olJfrrr5/g5eDL1qdPH/bxbrCiECuegJX1mqimhp45Ez506FC2kka7du0OHDjASrBlZdW7uNAffqDDh9OzZ+nPP1NLS0oInTw5yMfHhy1/5u/vL+l30Cw1+6CMh4cHOwkbPNPKy8tHjRpFCJkwYUJSUlIdn08pjXj9es/Ll8Ff7XpdNTX0t9/o6tV/l4z7tATphQ1O6QwICCCEzBQknAM0ztXVVUZGplOnTg3Ofjdh6WCEiC/PWV9Pp0yps7HZoqys7ODgoC6ULKaoqKinp/duRQr/Jy0tTV9fX/jHZOvWrRUUFHR09GbPfib+i7KyslJJSYnD4TxjFQ3/6enTpzY2NgoKCvLy8qNGudy8iXRQ+BIcOEBVVGiDxfevXr3Kfkxqa2uHhITs3bvXx8dn3LhxbH7fuXPnGmszNja2S5cuRkZG/fv3nzJlyvLly3fs2NG1S5dtffvSoUOpWH3T+vr6Tp06sZM0NDRUsL22tlYw8G5lZVVdXf2B3jTARxEbG/vixYvw8HBfX98BAwawAitcLpeVc/r2229F5hFcvHiRfbwdHR0ba/O3336TkpLS1tZubAnOD+jUqVMcDmeigwPFfAf4zywtLdnHOycnR/zZZcuWEULWrl376TvWfLHEooULF4qPLCYn04AA+v339KefaHj4/1fuDw4O5nA4PB7v/Pnzn7q7zV+zD8rcu3ePnYQGBgYiteX5fP6oUaNUVVXZTBkpaen+cXFTkpO9xVbHhE9m4MCB8vLy3bp1E/khGhcXN3nyZBbnXrZsmaS6B83IqlWr2KA6h8PZu3ev8FOJiYnsa6F9+/YNLjlRU1PzzTffsKpv7EfgyJEjfXx82MIrnTt3fv369bv2x9fXlxCipaW1ePHi4OBglp338GG8oWG2jAy9cYNSSquq/r/E6fHjxwkhPXv2bDAFYP369axvs0Vm9wI0Z1evUj296qFDT4jc5OXl5bE1qgkh8+fPF2zfuXPnv/yMLCtz6NGDjeYJ09TU1NXVfdNImf/t27ez3UaPHi3YyEqcEkJUVFTScZ8An7ewsDCRa0dtbe2dO3fYvKS1a9eKRxWrqqpY4EZKSqq4oUTpgIAAKSkpdmHt1Wv4woWU1cLm82ll5Ud5F1evXo2JiehWcMEAACAASURBVPkoTcNXhn3yLSwsSv9Zhyg7O3v27NmsvvuGDRsk1b3myNDQkBDS4EyUx49pQABNS6M6OlTkMrty5UpCiJKS0n9JPP86NfugDKWULYZNxMryLV68mG3ncrl2dnYOY8f2uHevx717y9LSAl++vFZUJKkOf82OHTvGJmVoa2tfuHCBUnr16lW2eBajrq7+KxJZoQnq6+sFtaK4XO6ZM2cET61bt45tX7x4cWOHFxcX//bbb8eOHROODxYUFLAUm759+1b+7yb0zZs3ly9f9vDw2LJly1sqCLq6Puvd2//PPy+LbL92jQYH0w4d6Js31N+fbt1Kp0+nAwfO53A4ampqhoaGDaaGCYpZXLt2rWn/PwCah65du7Nw5KJFizZv3nzy5MkbN24IVk40MzOrqKgQ7FxeXs5WuCeEPHjwoIHm3Nxq2rVbNmRIXFxcRETEgQMHVq1aNXPmzDFjxrxlqL+srExVVZXH47Vv375fv36TJ0/28vIS9OH48eMf440DfChv3rxpLHds48aNhJCpU6c2+KyzszP7kM+YMePRo0eCsE59fb2Pjw97isPhuLqO1dNLJ4R26UIvX6bZ2XT9+o/0VgA+DEdHRzk5OSkpqYULF7K4TFFR0dKlS1mJGQ6Ho6+vb2Fh0WA4Ehqkq6tLCGmwAEhhIb18mZ4+TdeuFY3Y1tfX9+7dW05ObsqUKZ+oo1+KLyEowyYKEkJ++uknwca9e/cKfufv2LGDbSypq7tfWrqx2daX+TJkZmb269eP/dOwGSiCf6kWLVrMnj0bhbuhierq6lxcXNiHR0FBISMj4+bNm9u2bfv222/ZlIeoRlbTfIsXL160a9eO5ctMnTrVzMxMMD9fTU1t8uTJDabePHtGCaHKyrTBoM2rV3T+fOrlRf39af/+lBDap89BaWlpQfkMkdmXWVlZgqUHcTrAF+bkyZOsKr8wFRUVLperoKDw+PFjkf09PT3ZPtPZou8Cjx7RyEh6+zbt3PnvJQXfxcqVK9mZLtINLM8Bn7lr164tWLCgsWcTEhIIIZqamg3mYF65ckVDQ0OwGJO6urqzs/OGDRuEx8ZGjRpVVlb24AH19KQeHnT2bJqejqAMfO5ycnLmzZvHbtgMDAyCgoIEAX1CCI/HU1ZWJoRYWVkVNpJBCSLYRJNXjZT7CAmhhFDxKqAlJSUsD73B2gLwFl9CUCY+Pp7D4cjJyZmamk6ePHnbtm2hoaGCROgff/xRZP+gvDyJ9BME+Hz+r7/+KicnJ3wrvHz58nzULIV3VFFRYWdnRwiRlZUVRE+Ynj17vl9EIyEhQVVVlV2/WcvW1tYjR45kn1hvb2+R/QMCaFYWTUykYl82f3v1iq5bR7dsoTNm0N27qZ8f/euv11VVVatXrxb0VrjcLyuuRFBfCb5Qt2/fDgoK2rJli7u7+5gxYzp37szhcHR1dRssAP/8+XMej0cIkZGRGTFiRPry5XTjRrpjBw0MpJcu0e+/p+9YA1igtLQ0OTn58uXLBw8eXL16NStJMGLEiP/25gA+urfXvdbT0yOEHDt27Nq1a8J5Z3l5eayakrGx8bhx49gwuOAejI0EeHp6Cje+eDFNTKTu7gjKQPNw9+7drl27sk81W/iSw+GMGjUqKSnp2bNnrEiKqanpSzYxD/7pxo0b0dHRbIG2tLQ0NtuxsRhWVFRWv37Lpk3bKLKdLf32HuX5odkHZfiUus2fTwgR+T3Gri4jR47EZ+KzlZCQYGtra25uvn79+jdv3ki6O9BcvX79ukePHm3btpWWljY1NZ01a9ahQ4cSEhIaTGlpoitXrsycOdPPzy86OlowMz80NJRd47du3SrYs76ezptH3d0pn089PBpujQVlampoly40NfUfTwkWLpWSknrw4MGlS5d8fHyGDRsmJSXF5XLZFD+AL1tdXR0rkv3nn382uMO2bdt4PB6ryZ3Zti3lcKggXrlkCf1Al4+8vDw5OTkul/v06dMP0iCARLCF3lVVVVko087OzsvL6+HDh927dyeEWFhYCH5lpaenHzx4kIUje/bsuX//fuF2+HzKZgB7eSEoA81GbW3ttm3blJSUVFVVLSwsbrCSfpRSSrOzs9kUdRMTk+TkZAl28vOkqalJGqGoqKimpmZiYiLYOT4+nhCirKx88OBBwYpXDx8+lJKSkpKSEl9kA/5Vsw/KbH3xYmBsrN348X/99df169f9/Py+++479gvN0dGxrKxM0h0EgI+urq4uJibmE6yWcvToUQ6Hw+VyFy9evHz5igED6tTU6IIF9MQJGhraaFDm7Flqb0/PnqUvX4rOb+Lz+WxhbDbfSpiVlZVIvTqALxUrhPHNN9+IP1VUVMRCNlOnTj19+nTNb7/R5cvprFl/J8jMm0c/3Inv5uZGCPnhhx8+VIMAn965c+dUVFRELig6Ojrst2ieWLZ4VlbWnj39Y2M71NT8I33AxYWqqdHx4+mzZ7ShBW0APl+TJ08mhBzcs0dke15enq6uroKCgpeXl0Q69jlzdna2srLq0aOHoaGhYDlRNhjJaGlpCXYuLi5mi2Mw7du3nzRpEjtKfJIKNAWHUtpYVOxzxqd0c1ZWSymppIqKO2/e+BsaWiopSbpTAPDlmzVr1p49e3g8Hp/P79gx/unTzmPHkpMnyZw5pEULsn49EYuuEHf3Yj8/1XXryNKlDTRYXV09dOjQlJSU4uLiLl269OjRo0ePHn379mUp6ABfg+LiYl1d3bKysqNHj5aWluro6LRr165t27bq6urjx48/efKklZVVVFTU/9cgu32bBAWRli2JmhpZuPBDdSMhIcHCwkJRUfHFixfiP2sBmovIyMhbt27duHHj1q1b5eXlhBAVFRVlZeWoqCjxUkqEkLS0b0tLrxgYnFRWHsS21NfXDxp0+upVO0Jav37N/d90XoDm4eWuXfJeXmoTJpAdO4S319fXGxsbP336dPPmzR4eHpLq3uevtLRUWVlZUVGxtLSU/VlXV1dZWSlYt5QQ8uDBg+vXr0dGRt68ebOwsJAQIi8v37Jly5SUFDb1Cd5Jcw3K1FC6Kzt7kY4OISS9stJAXl7SPQKAr4LHvHlbd+4khDg5OXl47O3WrX1EBJkyhaSkkIMHSXAwuXCBdOr0//vn5uYaGxsrKppERoYZGqo11mx6erqenp7INEyAr8f8+fN37NihpKTEbgEZFxeXkJAQFRWVuLg4wcDd/6urI0KDeB+EjY1NTEyMubn5uHHfzpnTXUHBuqTkUlVVsqKinbLygH8/HuBzUldXd//+/WvXrqWlpc2bN09QbkNETU1WWdn1ysr4Fi26qamNJYQ8efLE0tKyrKzcxWX96dNLPm2vAf6zK1fIrFlk3Diydq3w5uDg4LFjxxoYGKSkpEh96MvHl+TVq1etWrXS1NR89erVv+5MKU1ISIiMjDx48GCfrl237dv3CXr45WnGQZk5T55YKysPbtlSR1ZW0t0BgK8Dn0/atct78+bPTp0cz5xp989FZEaNIrdv55qazlqy5Pu0tLSYmJi7d++mpqa2bNnS0NAwOjpaUr0G+PzFx8c7OTmJ3P9JS0vX1tYGBQW5urp+gj5QSrt27frkyZOqqqp+/bpv2hTL4ykrKw/W1z/K4eD2Hb5kmZnT27ZdKy2tnZhoUlPzfO7cDtHR8aqqqlOmTPntt98k3TuAd5ScTAICiKoqUVYm/8uIoZRaWlrGxsYGBATMmjVLsh38zD1//rx9+/bt2rV79uxZ04/Kj4lp2a8f7+VLoqr68fr2pWrGNxkWCgoztbUl3QsA+Jo8e0aqq1uVl7txOERsWd+jR8n48T9cuHAhMvK8YKOCgoKpqem2bds+bUcBmhlzc/PQ0NC4uLicnJysrKzs7Oznz59TSvv37/9pIjKEkMOHDz969IgQMnjw4Jkz7RUVFaWkVLW0Fjx/Pk9ZeYCa2uhP0w2AT69Nm9WvXvnzeIr19RX19RXa2ipt27bt27fvTz/9JOmuAby7nTvJr78SOTni6UnOnCGFhU+NjSNTU2NjY3V0dKZOnSrp/n3uKisrCSHC6+Q2haaVFVFQIJcvk9G4XL6z5hqU4RFioago6V4AwNekupqcPk0WLCCVlWTgQPHnFRTI/v3+P/4oe/v2bTs7u169ellbW5uZmSFFFqAprKysrKysJPXqpaWly5YtY49dXFxGjZpBiCchpL6+TEFh+7NnMxGUgS8a1db2fvZsZqdOd3k8pZAQlISA5qyujrCAgqoqOXqUnD27u1u3zXFxXC53yZIlsphj8W+4XK6xsXGHDh3e7TAOh2zYQB4+JLdukQkTyMWLxN2dKCoST0+yYcPH6emXo7n+VOBxOA7IjAKATykggPTvTywtiZcXMTJqcBcNDY2DBw9+2m4BwAfg6+ubk5NDCOnevfv06dMF2wsK9ldWxquqjpRc1wA+uqqqlMrKuNatvaWlW0u6LwD/ma0t2bOHWFmR1FQyciRRUysrLGyTl2dqajpt2jRJd64ZMDQ0fPz48TsfVldHYmLI7t2Ezydz5xJ1dfLsGVFQIK9ff4Q+fmmaa1AGAOBTe/KEzJ1LCCHm5uTpU6KjI+kOAcB7KiwsjImJycvLy8rKysvLS09Pv3z5MiGEw+H4+/sLl9yWle3w4sWimposxGXgC6as7KSs7CTpXgB8IJMmkfv3yfPnxN+fKCiQqVN3E7Jb0p368lVXE7buEo9HOBxCCImOJnJyRKh+PzQGQRkAgKbp0oXcvEkcHMhffxFPT0n3BgDeX1RU1MiR/wiyaGtrFxQULFvm27t3b+HthYUq2dn2KSlqhoaftosAAPDeevQgPXpIuhNfGQUFUlpK7t8nmZmkUyfy6hVxdSVKSuTOHUn3rBlorqsvAQB8anw+CQgghYXE1pb07y/p3gDA+4uPj1+8eLG2tna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Nnk1EtHkzHTlCWVlERJs2qTYoULXlyyk4mObPp+3b337dBID3Y906mjaN5syhly/p5UtVRwPwKWvQYHpAgGNJScTt282aNevXr9933323ZMmSzZs3P3v2TNXBARDV3DFlVq4kW1syMKCQEFq5UrG8qKhoxYoV4eHhFRUV+vr6YWFh+/bti4uLY4xFRUUdO3asQ4cOuRkZ15KStK9fp2+/fd1Z0ciIrl27oKEROG9eTk5Obm4utzc9Pb2DBw/+8ssv27Zti4iI2L17t66ubmZmJhH5+vquWbPm7XMk/UstW0bp6WRkRIaGREQvX9K8eURE48bR7t2UlLRq2jTS1pZIqEsXqqwkXV3avJlq1aLx48nNzWj48KcaGpK+ffueOnWqS5cu165d+9+83WVlNGUKubvTnTu0Zg3NmUMREZSeTgcP0owZKnu/n7Xs7OzRo1saGTncu7dn/XqboCDS0SEiqqoqzsjIIKJmzZqpOET4K6pZdUxxjJ5Qz1LDskJa4a7r/pc29/CgX355/XrjRjIxIVRaarqqKjIyIiKqU4eKisjYWNUBAXzuCgupdm0iImtryskhKytVBwTwCRNYuxHtV1dXT0pKSkpK4pfb29vfunXLzMxMhbHBO6qoqFBXV9fQ0FB1IB9ETW0pk5REgwZRr15UXKy0PCQkZOPGjRUVFb169YqPj585c+b169dnzZqlpaVFRFVVVQ8ePLgTH3945Eg6epQmTqQ5c2j5cpoxo/rHHyclJb148SIvL4+IBALBsGHDnj592rNnzx9++CEuLq5nz56lpaXl5eVOTk6nTp06dOjQJ5mR4djavs7IEBE/Jg5jNGgQzZ5N2tpEpKZGY8aQgQE1b04ZGZSbS5s20fjxpKsr1NDQiI6Obt26dXp6eo8ePV5yD4IqKmjXLhoyhCZNonHjKDqaCgrowgW6cUMlb7GG2Lt3r1gsqqq65e7+9cSJ/1teXV3NvfjEutfVeMEvgvWF+hXSiseVj9PF6X91cx8fOnmSKiuJiCZNorAwcnJ6/0HCp8TYmNLSSCSizExkZAA+Bi8vioykwkK6eZPq16c3DE0IAH8qLo5++CGkQ4dz4eHrr127Fh0dvX79+hkzZpiZmaWmpm7ZskXVAcKfyMnJWbFihaOjo52d3fz58980VOsnraYmZfgMwhv+qTk5OcXFxY6OjitWrOCSJgYGBkuXLn327NmwYcO8vLwyMzO7d+8+ZNMmcnb+32Zqaupjxhw6caKwsLBOnTqdO3e+evXq7t27LS0tuXIXF5fi4mKJRFJUVHTq1Klu3bp90Hf5UXl704IFNGcOffmlYqGWFqmpUUgI/dF46DVdXd1jxzosczMAACAASURBVI5ZWlqmpqYuXry4cN8+Wr+ezp4lTU0iIk1Nqq4moZC0tUlL66O8jRrKzc0tKyurSxe7/ftDtbX/txxJmX8tfiwmpYMylUvLOxh06GHUw1jtL98/6+iQQEBBQZSbSxoar6ff0tKiggI6cIBu3fonUcMna/ZsOnyYVq2iwYOpSxc6fFjVAQF87kaMIEND2rOHevcmLy9avlzVAQF8AqqqXo8qkZVFJSWv60g6OtSlC33xxZeTJk309vb28fGZPHnyihUrDh48SEQbNmyo5B5Dwb9PXFzc6NGj7ezsZs6cmZWVlZ2dvWDBgq+++urlZ9eps6YmZfr2pRkzaO5cat1aabmmpiYRJScnv+JHNiEiIhsbm927d48cOZKIrK2tlW5raGhIRNra2mfPnvX29pYrdXHxNTP7xsVlv1CofPNP1ciRNHMmzZlD/v6KhQEBdO8eublR06byRZqamiKRqLS0tFGjRsZDhtDMmbRtG23fTqdP08aN1LcvGRuTlxd5eHyMd1FTdenSJSZmwZw5SaWls2SXIynzAT14QGFhFBpK9+79ja137nz94qeflJSKmfhvxxUURBER1KMH+fjQd99R3bpERGvWkKkp9ehBjx//7R3Dp0xPj4KCqF8/GjiQzp17sHVrrkyKvaSkpKCgQIXRfQgxMTGenp6f3/uCT0n37tS7Nw0dSvfvXzt5Mj4+nlssFovz8vJevHih2ug+MolEsmrVqqdPn6o6EPhXy8qiyEgiopgYevKEfH3p3j2Kj6eJE2njRvmVv/zyS3d395ycHC47A/8qUqm0b9++bm5uO3bsEIvF/fv3j4iIYIwR0fnz593c3I4rzv/7KaupSRkdHYqJof376Y97Tjlvn6laTU3tTUX8tm8qTUqanJe39fHjQRKJzt8I/F9NS+t1Cxdl1NSovFxaWCg/5vmSJUvy8/O9vLwmTJjwelGtWrRhA5mY0Jo1ZGlJM2cSEVla0tdff6DAgYg6d55mahpgZ7cpM3NBTs6G6uq8/PyfHj68paGhYWZmduDAAbkE5UclkdD27bR4McXGUmkpnT9PRHT/PqWmqiykf27DBlq0iBYvpoiIv7F1aSktWUJLlpDSpzv9jfsHZwSHvgytYBVagr/cyqykhLKyWG5uuezC7GwKDSUMLlSjOTtXeXqOqlWryZkzTZo0adiwoYWFhaampqGhoZOT009KE4SfoKKiolGjRnXv3v3WrVsbFWvxAB+TvX3xiBE9TU3bXL/u5eVlaWmpp6enqalZu3Ztd3f3NWvWqDq+jyQ+Pt7b23vGjBljxoxhGGsc3uryZVq7lk6fJiLq25c2bnzdQULpUCSBgYFEtGbNGnyv/m3OnDlz48YNoVAYEBAQHx9/5MiRb7/9dteuXfr6+kSUn5/PDdis6jDfm888KZORkbFnz57S0lL5guxseviQkpIoMVHphlzahYiUdlp7e9rl7aWDB9PixZSeTmfOvNNb+GzY2V0vKFC7etVLtolgSkrK+vXrBQJBeHi4QCD439r6+uTp+XrAGq5FkoYGmZt/5JhrFKFQx95+V0nJBSOjnrVrT3z6tGNq6kiJJKO6urqysjI2NrZFixZy85fHxcWNHz/+iy++GDVqVNUHnRtryxaqV49CQ2nrVnr5kq5fJyJKSKCMjA940A+N6xokFCqvJvwZfX0KC6OwMJLtbsbrYtDlcunl44XHuxt272rYVXGFqqrEnJz15eV3lO68Tp1VpaXClJQw2YWmprR4Mdnb/41g4XOhofF03bpdr16pq6tnZmY+evQoOztbLBZra2sXFhbOmDEjJydH1SH+U+fOnXNzc9u5c6e2tvby5ctDQ0NVHRHUdFWLFp0rLVVXVy8pKcnKyiovL1dTUzMwMMjPz1+wYEFaWpqqA/ywqqurV6xY0axZs99//93KymratGn/V10EUODtTRMmUKdORETq6jRihPI2xZyAgAATE5PHjx8fO3bsYwUI76SwsDA3N1cikdSpU8fFxYVbOHz48IcPH3p5eQkEAjU1tZiYGNUG+R59zkmZiooKV1fXUaNGjRw5Ui79eVpLq7mxsZuu7ro33A7x6yvmVg4dOnTjxg0iKi8vl9+MiP5IylRUVPj5+cUqjPdQUkJxcaSn9znOZsIYPXjwpsYLtra2FhYWCQkJhoaG7u7u33zzzZYtW8aMGVNZWTl06FBPT8+PGysoZ2zsk5u7NScn3Nh4gKFh12nTRj5//jwuLq5ly5ZJSUmtW7dev369RCL5+eefO3Xq1Lhx4y1btqSlpe3cubNNmzbJyckfKqwHD6hDBxIKqWVLSkqiS5doyRI6evRDHe7jEAgoL4/y8uhv1S+5mXBkX8jSEepkVmWKq8RV1cqTZS9fzjc1HaqhoaQTZWFh4YkTJywsLLZu3bps2Xq+k4qGBpmYkK7u3wgWPh8VFfUbNw7V0dE9depUfHx8ZmZmVVVVeXl5165dc3Nzv/nmG1UH+I9s2LChR48ez58/b9my5d27d0NCQvgnNACqkpRUy9PzgLGxxY0bN168eFFSUlJdXV1cXOzj41NSUjJq1KjP+Al/UVGRp6fnzJkzRSLRuHHjEhIS+vTpo+qg4N9OXZ10dP73wKtNGzIxIcUH9Jz09PTKykojIyNtpc+4QHX4HppyA4bY29tfvHhx165d1dXVH/aR8EfGPl+RkZH0R5uXsLAwfrlYLOYrjm5ubtnZhbJbSaVs376jRkZGVlZW1tbWP//8M1/08OHDTp06EZFQKLSxsdHU1Fy4eHG5RCJ33JycHEtLSy41IxQKhw8fnpGRwRUlJLC1a9l//sNmzGCBgR/snavK9Ons4EEWHs62b5crqa6u7tKlyxdffNGwYUPZOq6mpqaOjk56erpK4oU3ef58slicL7uksrJy4h/TMhn/MfeKgYHBxIkT7f9oO2FgYBAZGflBAlqyhD19yhhjISHs4UO2aBFjjB04wK5c+SCH+ziysth33zEfH3b69N/YOiKCxcUxxtjOncpX4MYXf8p9bvIkOTkb09ImlZfHvXp1TCzO4wtiYmK4DbnaiaPjcB0d1r8/O3KEMfb6g4earF8/RsQWLy6RW56RkWFiYkJEe/fuVUlg74WPj4+2tvbw4cPFYrGqYwF4bcgQRsTmzZP/Tubm5pqbmxPRtm3bVBLYR7B161ZbW1tjY+OzZ8+qOhb4VBUUsK5dmbU1q6qSLxKLxa1atSIiX19fVYT2OcvKypozZ46bm1vfvn3z8/P/fAMFQUFB3P2F0nrF1atXiahVq1b/ONJ/i885KdO3b18i+u6777hRe7ds2cIY++233xo2bMj9j4VCoZWVXZ065bNnszt3GGMsKop5ebHWrZOJyOiPB9C9e/d+/PjxjBkzZLMJ5ubmAoHA9/vvu9+/H/HixZaXL7e9fFn1R4Jm9erVpqam/Mq6urqHDh0KDS1QV2eDBrHnz1lEBOvdW4WfzQcgErFJk16/HjeOLV7Mli0rePKEMZaXl2dra+vt7c0VlpaWXr16NTw8fOjQoe3atfv+++9VFTIoKio68/Ll/MzM5UpLjxw5oq+vb2pq6uDgsHz58oKCAsbYL7/8wn/bBQLBN998I5VK33NYd+6wefPYwoUsMpK9esUOHGCMsUuXWHz8ez7QxxQfz4gYEevc+W9sHRjIJkxgUqmS9K5EIgkPD9fU1DQzMzM1NZ03b15e3uu0i1QqfvFiXmJiD8akEkl5UtLA2FitO3f0Vq+e8+TJk5CQEC6b7OXlNWvWLCIyNf2FiPn5sSlTWFHR55hKhreKiWGMsYIC9vvvLDKSlZSw5GQWEMByc5WsvHPnTi5p++nm2b/+eoKFRbMDB35VdSBQc+3bxxhj2dns7FkWHs5yclh+PpswgSm9qdm/fz8RGRoaPn/+/CPH+XH8+OOPRDR06FBVBwKfMKmUNWkiadnyyMGD8s/AQkJCiMjW1par0MJ78fDhw1GjRmlpadEf84TY2NhcunRJdh2RSLR379727dv37NkzLS1N6X4CAgK4m4sLFy4olnJtLwYMGPAh3oJKfLZJmYKCAi0tLTU1tczMzG3btnFfi3bt2nH/XScnp+PHjyckJIwff4GI9ejBOnViZWVs/HhWqxazsmL7958qLy9ftmwZN5WShoZG7dq1uW2NjIzCw8PFYvHVq1fHxcc3u307MDExWyH7WlBQEBgYyH0XBQKBnp6eu3uwujobPZoVFjKxmH1uDzaqq9m4ca9fjx7N1NUZkXvt2vb29rq6uh4eHu//Rh0+OolEwqU469SpI7s8LS3Ny8uL+6rXqlXrCNes4v8VF3N7YKWlf/Go8fFMXZ0p+0X+pFXn5mY1bHjP2Pg/AQFKV8jOzg4ODl6yZEllZaX8ttUsOJhduMB27GBTprCICMZXJ5KTk1u3bs39L3R0Xo8mrqenN3Xq1AsXLgwd+tWdO0axsWrp6cEZGTOzslYmJvY4eLARf+HU1NRctmxZdXU1Y+z33+/4+Ei//JIFB7O0NDZ7NpIyNQ6XaU9MZOHhbMQINm8ey89nCxa8cf2WLVvq6emNHTv2o0X4fk2ezIjY+vV/Z9szZ1433YuMZJcvs+xsxhg7dOh9hgc1gexJ9803bNq0PznpvL29dXV1P92T7u2OHi22t68aPlyhhcM7EItZRQVjjJWVserq160kKiuZQgN3+PwtWbKCiExMTCZPnrxs2bLdu3f/9ttvP/30k5qamrq6+rVr11Qd4GciPT29Xbt23KhPQqFwwIABbdq04VtCTJ48WSQSFRUVhYeH29nZ8W0XTExMoqOj5XZ169YtruG2s7NzcnKyXGl+fv6SJUu4thcf6819cJ9tUmbLli1E1KBBgwkTJnh7e2toaHBfCF1d3Xnz5lVwv9OMMcZOn2bbt7P9+9mCBSwwkF2//vrukZOXlzd58mQ1NTVjY2OBQDBs2LCsrCy+VMrYmYKCHS9frktPj8rJUQzj6dOnAwcO1NbWrlOnTseOHePiihXX+XysXs2WLWMzZrAlSxhRyR+tJxwdHbl7PPjUZfwxsK6Hh4dckUgkmjNnjqurKxFdvHhRcdtvv2WMsezst1UueZWVlenp6b///vvhw4fntmiR2aXLe4j+X0YqlXKN7/T19RXTLqdPn7aysjIwMODa5c2bN49/jBMVxZyd2fjxjDEWFMR69mRErGHDJ8HBwT/88APXuczCwuLYsWN8GprDjVe/enWfkpKrssd6/Phhv379jIyMdHV1Fyn0UBKLWXAwY4ytWcNGj/4wnwX8W3XpwhYvZkFBLDycBQWxNWvY+fN/kpQhohkzZnzEGN+n8HDWqxc7cICVlSkpHTeO3brFKivZypVKSletYn5+rKKCBQay779nCQmM/fG7B/Du5E66HTvY4cN/ftIFBQV9xBg/njNnmJ4e8/NTXnrjBnv0iDHGzp1TUnr9OmvbllVXsylT2J07rx+Frl3LkpI+WLjw71RePrlzZ+5hlWylSCgUCgSChQsXqjq+z8egQYOMjY11dHTGjh2bkJDAGIuLi2vUqBH/mdvb23N1USJq2LDhhg0bfH19uT+HDRtWWloqkUiOHz/euXNnbqG2tra2tvb333/PP9pPSkqaPHmyrq4u13lz6dKlKn3H79Nnm5Tx8/OTO/e4vMzGjRsVV46MZDdvsqVL39il6Pr16yEhIadOnVJauiszU7GljKxz585d+aQHv3h3xcWsqopVV7NLl35fsqROnTpE1LFjR1WHBe/HtWvXuLOpf//+SlewtbUlopSUFMWiQYPYr7++zn6+yc2bN1u0aGH0/0PXamtryw7t9AkRi8WBgYGDBw/evn171f//RFRVsWXLjgoEAq59imzapaysbMKECdzPl6ura4MGDbjPwdDQcMqUKQEBAR063CZi/v6MMZaZyRYtYl99xdq2/YZrEUNEAwcO5Lvv3r59e9iwYUKhUF9fX1NTs379+sXFSlLDOTk5mpp6RNSkSRPF0nv32IkTbPFipqwJFHzOZB/aBwUxkYgNGvTGU/j69etcY9LCwkLla/wbJCSwvXvZGxpLOzqyJ0/YmTPsxAklpYGBbNw4VlrKZs6UL5JK2apV7Px5tmTJ66RMYCBbvPjv9U2EGk3upJNK2ZAhbzzprly5QkTGxsavXr36mEG+T6Wl7PJl9uKF0sJRo9idO6y4mIWHKyldupQNGcIkEuWtOK9fZ9Ons02bXidlgoPZ2bNswgQkZWqeSZPEGhoratUyMTFZvHhxcHDwkCFD2rVrx9U2o6Ki3rbtuXNs8WL2gQZM/Oxw7WIMDAxkeySVlZWNGTOGiNTU1LgGMq1btz5+/DifZ9m4cSPXstvW1pYfp9LY2DgoKGjUqFHcn127dr17926/fv24XvYcMzOzT/QGQanPNimTn5/fpk0bf3//1atXnzt3Ljc3NyIigoi6deumuPKZM+zhQ1ZRwYYN+zvHulJYWISWIMpkZ+e2a3emdescfDyfB677OhEFKqsBiUQiNTU1NTU1kUikWPr11+zOHfbbb29LyrRo0YL7XdbU1LS2tvbw8HB0dCSiXr16vcd38XGkpKRwFyddXV0i6t792KpVbPNmxhh79oy5uzMdnXJnZ7d+/fo1bdqU+1SNjIwGDRpUr149ItLS0lq5cqVEImGMXblypVevXnxzUAMDsx9/rJDrDnjnzh0nJyciqlev3n/+8x+5YOLj41evXk1EPXr0UBqtRMLU1auIRA4Oyht29+3LiJCUqXHWrmWMsYwMdujQ62/vpUts3Trlz6W7du1KRPPmzfuYEf41d++ymTNZcjIbO1bpTeDEiWzcOOVJmfR0FhTEbt5ka9aw4GA2YwbjR865f581b86mTGEvXrCFC5m/P1rKwN+3axdjjGVns9OnX7++d48tWsQUWvczxliHDh2ISLGF4yejvJyNGMEuXWLTp7Pff1cs5zKhBQVM6e/K0qXs+HG2aRMLDGTPnv1f0cWLLDqaHTrEpk9ngwaxO3dYWBi7eZMFBiIpU8NUVbHYWKatzYTCosuXZUu2bNnVocOcHj2y3rQpe/KEzZ7NGGM7drCTJz9woJ8DBwcHrkJbXl4uVxQVFcU1ipk6dariho8ePXJ1dTUzMyMibthKPtEcHR3NTSNgbm6u8ceUWkKhsG/fvqf/1kQZ/1qfbVJGUX5+vpaWllAoVByD8MQJlpjIGFOeif8ve/cdF8W1xQH87FJFQKRLhNhLjCZ2Y1c0NlCxRWONgl3ALoqSp8ZuxBILVsTesWLvsSCKLaIoiiJILy6d3fP+uLhBWAzYVuH3/eN9NntnZu/6mJ2Zc+89Bz5ExYpMxHfuqLsf8DFMmzZNTNlYJZ7P3vb48WMR51a5738uX8rIyNDT05NIJI9z3FtFR0fr6upKJJKHDx9+eP8/G29vbzE/09raesqUKe3adZdK5fr6XKcO79rFfn7s5MSVK/O1a9nTZ0TYRUzok0gk33333U2RezyHlStXiktR165dVX6ou7u72GB9nvJnzHz9+nUiqlevnsp9MzK4XTu2tORBg1S0KhQKd/fTP/3kuWGDn8rdHz9+PHr06KFDh0ZEROTzTwJFxMOHbGDAZcqkhoe/lXc0ICBAIpEYGhq+q8hCVBTPm8fz5rGq1b6fw7x5LJamnzvH27axry+/qbL08iWfPMnjxvHRo+zoyFOn8s8/88WL6cycmJg4fHh6iRLcvTsz84QJ3KYNE3H9+rGOjo6TJk1q1WoPETduzHFxnJzMs2bxsWMsii6uW8eJibxtG797LBbgHQICWCrl8uWjXrx46wf2zJkz/z1NJiWFN2/m3bv5yxwcO3s2O3N/YiJPmsRBQZxjwOGff3j8eL58mefPZzc3njKFlfkD7t3jli151CiOiuKJE7l3bzYw4CZN+OTJ80lJSR4e/hJJ9gU3JoYtLXMvXzp8mPMMXkCRk5nJo0fz/Pk8dCiHhOQdVpLJ2NiYraz4VX5hmZ07+cwZZs6OuMM7KRQKkdzXyMhI5QYicW9+JRq3bNlCRCVKlPhTDAfl8Pz582bNmunq6mpoaGhra/fv3/8fsXCxaNGkYsPY2NjOzm7v3r0bNmyYMWNGzqZXr0hUQH/8WD19K8JatCBfXzI1paNHKTqaBg4kItq0iQYNUnPH4D2UKVPGxMQkLi7u2bNncrlcWY/s0qVLy5Yte/78ORFZWlqq3LdBA8rMJG1tatVK9cFv3ryZkpJSo0YNMVVEMDU17d2796ZNm9asWbN48eKP/H0+gfj4+F9//dXPz4+I+vTps3LlSiMjI2by86Pnzyk4mC5coBYtqEsXWrKESpbUFns1bdq0adOm/v7+y5YtMzU1nTt3rqhInZN98+Y6tWpZvnpVwcJC5UeL5bVEFBkZmV+ryiYiOnWKNDQoPJze1B/8V2Rk5JAhQ549e3b//n1NzWYdO/5o8XYHfHyiR478kYhkMtmmTZt++eWXqVOnVqtW7V3/TPDVqlKFunW7c/58z5Ytyd7ePu4NcYfk5OSUs/Jgbu7uNH8+EdHkybRmzWfrM6WlkTihTEzo1SsqX54iI+niRVq1Stas2fb+/fX0zFxd7bOyNHr2pA4daMMGunqV7t2jlJTNKSmrIiIidHRqp6bu9/fXlsvJzY1OniQzM3r48I9169bp6emlp2d4eIRPnmwmMmu/iY4SEQ0ZQkT044/04sXn+7pQxNSpQ0OGHNu9u1/TpgatW7dOTExMTExMSEgIDg4mIldXV5FKTLVx48jFhRITado0mjfv83W6gPT0SCYjInr9mrKyqF49rlz56IwZP9Sr5+xsduyYTp8+1Lgx7d5Nd+/S6dN04UJ0gwZzdHR0Ll6U//333IAAzf/9jyZNouHDSVeXFIrnbdu2MDIySknJql49qkOHEq1akYkJXblClpYkVkX070+GhmRpST4+1KCBWr87fGpnz1Lz5tSzJwUF0b59NH58rvaSJWnVKrp8mSwsaNEimjAhR5tcTgcPUqVK9Pff1KoV3b1LlSt/zr5/jWJjY9PT04mobNmyKjcIDw8nIisrK5WtyckdbGx85fJrEknuDaytrb29vStUqGBiYvLw4UMTE5OP2vEvhrqjQp/VwYMHxbeuUq4c//wzjxvHZ8/ytGm3O0z+49d7s2dzy5bq7mKRM2gQT5nCT56wp+e/E7mVtbPh65Kenu7u7i5iMa1atQoLC1u1apWyxjwRlS1bVktLy1PVlLMePXjJEn76lPNEwLOJmEveEhI3b94kIiMjI1mh6zapgaenp5WVlZ6e3po1a/K2ijJGzZrxkfcouRsZmV1Cu3Ztle2iOiDls7js6dOnRKSjo6OyDtrRo+zmxj4+uVfm7969W8waJSLxwtZ234gRvHo1x8VxZib37MlE3KzZpjZt2tjb24u1vlKptEePHrnKH0KRsWTJEolEoozJKmlpaTk5DXvXnsoKfcPeudlHlJLCw4bx4sXs5MQJCSyT8dixPHs2T57MtWox0ew3IaSWLed36pSdNFQm45cvec6cdHNzKy0tLTFfukGDaW5unHNp5tq1a0V9xncnGnz5kidMYH//T/xNoUibMGECEeU96TQ0NCZPdnvXniInfM4XajdtGs+axSNHckgIKxTs6soLF/Lw4dyvHxPdNDUlIm1t7ZYt3UqX5sWLmZljYnjbNu7alb//fgi9yRFpbT3D1JQfPco+qkzGf/65TV9f38rKqkKFCnfvvmsg/dAhvnfv039TUC9f3+ybrefPOZ8lfpcucffufPo0u7jwo0cs8qGlpKREOzkxEXfvzqtX86xZPH++clol5EekONDQ0FCZKiQjI0OMueY3833ixOyb3M2bVbSKpHX169f/uH3+ohSjmTJElJgok0qlCoXCJDycnj0jf3+Kj6cNGwLWZg3zG2kyzevVK3V3scgxMSFra7p2jYgoJobmzFF3h+ADaGtrz5o1q3Xr1n379j179qxIhhITEyNadXV1S5cuHRYW5urqeuPGjVWrVoklPCkpdOMGffstxcVRaGi+BxdZhBs3bpzr/dq1a1erVu3Ro0erV68en2eg40uTmpoaHh7+22+/DR06NG9r2bJkY0P29vR2LuOCMTGhMWMoIID69lXZrpzAkms6TGZm5h9//DFv3jwNDQ0dHZ3Y2FixajeX1q3p0CF6/Zr69CETE5owQbZgwaRVq1aJVl1d3UGDBoWGhl292vn0aerUiUJDacYM0tMjMzMaMaJfnz4DiSgkJGTp0qVr167ds2fPiRMn7t+/n9+ACXyt5PK2Fy+W19QMycz89ddfbW1tjY2NTUxMYmJi5s5ddfLklOPHqV27fPbNzKSsLHGQz9TbI0fIwYHatSN/f9q1i5yc6M8/iYiYqWbN5A0bfj93TiKR6OrqDhxYRjl/s2RJKlmS3Ny0R48OKlu2bFJSUu/evV1c7Bo1euvYT58+TUpKovzH/QQrK1q48JN8OSguEhOd/f3PaGvfzMiYPHlyvXr1jIyMjIyMgoODPT23bts2smdPqls3n30zM4mImLNPPbV78IBKl6bx4yk+nubOpQULaMmS7LlsmzfzpUsjkpKISC6Xly798P59LlNGQkQmJtSnD/XpQ4GBo9u1OxQVFWVjY9O8eeiSJaS8mpUsSYaGKTKZTCaTTZs27fvvq+fXhawsSk2lFy8ox4gSFEUtW5KLC5UqRbt307Bh+W3Vpw9t305aWjRoEF25Ql26rLp2bVZmbGyotrbe/v00cya9qbcAuRyPi8tgttbVjc/MPHzkyJ/9+pmamsbExBgYGCQkJChn8Ik7w507d8bFxVE+V8zDh6lxY7p4kfbsoTeVl94iZtl8Ixa2FFXqjgp9PgEBXL58po1NN1NT0z/r1hUrwsXQwa1bnDJgGDP7qU6YAO/j9Wves4fHj+esLO7aFTNlipTIyMi2bdvSmwGrMmXKeHh4REdHM7OPj4+IxVStWvXo0aNTp841NeUSJXjkSE5KI/HW1gAAIABJREFUYgcH/uMPnjWL8+YCrlq1qkQiyTvLZsOGJ1Kp1MLC4sCBA5/n232I7dsfN2nyePz4cJWtcXGspcWlS7/XoRMTedAgDg7mP/5QmWf11q1bhoaGWlpaY8aMiYmJEW/eu3dPmUiYiBo1aqTy2EeP8smTHBbG1tasocH6+kkmJqbKKE/Dhg0fvRmOfPCA//c/Hj+e//qLz53jyZNVFA8ODw8X+165cuW9vip8wXbuZCKWSG5165b1dpaK2bP5m2+yByZTUvitKVnbt/Pw4Xz7Nru7s7t7dhbcz8DHh0Vmx3v3ePnyXI1yuVxUAKhQoUJ+B6hcuTLlM7InkmcTUd4F8P9KTVXxYwdQKEuXipPu0ZAhuVqcndnaOjvrRe6f4l27eOdOPnyY3dx44sQvJYeKvz+vXcv8Jt/H2zLS0lasWCGRSCQSSVY+SXBEQvG9e/fmbdq9e7c4JZ2dnT92v+HrlJDA587lnzOGL13ivXv57l22sGBbWy5Rgi0saou/olpmZpkbN+bdRZkl8/btT9Pnr8esZ89epadvjIioe+NG+5s3zSwsOnbsKMqAVq5c2d/f/8qVK126dFHWS9LQ0KhatarKQy1axL17c2Ki6kpqzLx06VIiGjly5Cf8PupWNIMy4eEs6s8+fcoJCdkn4/79bGHBo0ZlMbMiOZlv3OCbN3nIEI6K4uBgnjBBrV0uaqKjo9u3fyGRsMixHRDAp0/zjh3ZrSKtG3zVsrKyJk6c2KNHj/379+e6efrnn3++//57IhIZvxo0iGjUiCdPZmbet48bNGAiHjjwwPPnz5lZLpffuXPnzJkzI0aMIKKyZct17Jh67RqnpTEzz5rFRNyixcGvpbrEkSNMxKpmbjIzKxRcrVpqnTox6emFnwd7+nR2UcbkZB43TtXBFSJSRkQlS5Z0cXHx8/MT/xcI/fv3f/36tcpjJyRwYiIz89OnfP8+u7kdFQWwNDU1J0+enLeclgi2/vZb9v+tednb2xORr69vob8mfMmSk/ncOf71VzYz4zx3q5mZ/PffXLs2p6ezuzsra6/fu3cvvGFDJuJatVhVtewzZ1hkiN627WN3OCqKBw/mCxd4+HCVZbBjYzMqVkyrXDnfHKiNGjXKL7y4YcMGcWa5i/IceS1blr1U6msIKMMX6vFj3rqVhw7lihX54sVcjcnJvHs3d+7MzOzi8u/7927fThd1FgYOZLmKmno3buR+8Zmkp/OAAezvzzNnqq7ixty6dUj9+lFxcarPyl9++YWItqu6jzxx4oQ4JQcOHKj605OTecUKXrpU5Q8RFDevXnHXrnz2LDNnDydkZnJAQIBIijdjxgyVe33oAHNmJu/cyT4+nKc+0Vdn2MOHXuHhAa9f/3L//o7ISFlaGjMHBQXVrl2biDQ1NcULQSqVdu7c+fTp0yoPtWgRX7nC06axqyurWmTP/fv3J6L27durXINfNBTNoMz8+dl3X6NH86FD3Lo1Z2WxqyuHheUZsoqLY09PXr06+xEQPobnz59XrVq1YsXqdetGK5f7QrGSnJzcq1cvc3PzAQMG3Ljx1tLuy5e5efNAHR0da2vrZcuWLV++XEyBkcvlXl5erVsfEXVMxGPOkCFcoQL7+OQ+/vz5zMxJSbxy5Wf5PgW2aBEfPMixsXmfWLOJhLthojRLoQQGZg/1h4Rw/jkszp8/37FjR1E/W1NTUwxQmJubH85b4/edXrx4sXr16vzKDa5ezcx84gSrypzDzDxkyBAi8vLyKtSHwhdNJuOBA7MX3wcEqCzmcvcuT5rE8+ezuzv36MEVKnC3bmsMDAwkROu++UbRurXKZfmftoC0jw83asS9eqmsgS2Xs54ef/ON6rtAZu7YsSMRqTx9fH19xb3mcJXZOhQKdnLKfq18AVAoQUHZ1Z5HjcqvkvPhwzxpEu/YwS4u3KkT163Lffr8T19f30BDI6hMGc7npFPnzGU/P7a15V69OJ/6KdbWTMTPnqneWywNXi0uQm+7JpbK51+jkEeP5tBQjojAKVkc5QmCTJ16QSqVd+uWe8PBgwe/4wbG0ZFnz+bZszm/0N9/mDiR/f353j0eMeK99v+CLHz+XOX7GRkZHh4eUqlUZOQtSL2kRYs4LIz//JM7d+amTfmvv7J/t54944MHuU+fUUQkkUikEknEoEEcrno2+teuyOaU8fGh0qXp+XMiog4dSBSTVbESrXRpcnH5zH0r8tasWSOTyYjo8OGsfErxQBGnp6cnUgJ7e3vnamrcmPbtK2tlxS9evFi4cGFwcLCYzSGVSp2cnLp1o/nzSUODSpSgwEDS16d//qEcsz2yiVImWVkUHv45vk7BvXhBT56QnR0FBuZuYuZly5bFxcXVqFFDJKgvnB9+oP37afZsiol5q77L25o3b968efO7d+8uXLhw+/btJiYmdevW3bBhQ5kyZQr1aWXLlh2W/xrsYcPoxx/p9m26fVv1BmL5UlRUFBE9fPjw5MmTJ0+enDp1asOGDQvVDfiCXLpEXbpQ69ZUowb99RfVqaNyq2rVKDSUQkMpLIxCQsjYOPz169dSqdQ1MbHLtm2mmqrvOtasIVNTCgr6BN0+eJCuXqWrV8nFhfIsZd+2jf73P5owgWbPVn1W6enpEZGXl5efn9/EiRNtbGyUTVWrVq1Vq9aLFy+2bNlSrly5MWPGiI2JiDZupKZNSZmW9c3kbYDCOXmShgyhihVp0CA6e5YqVFC5Vbt2tH8/paRQYCC9fEkJCTdkMplEImmZlRW6c6e2qpNOLqc//iAiio//pF9AlePH6fRpIqJu3ah67swv16+TszP160dHj9LgwSr2LlWqFBH9888/hw4dsrOzEyMQgrm5ebly5Z49e3b37t2YmJi3sqcpFJSVRZmZJE5hnJLFSmoqubpStWr04AF16EAbN97V12914kRcXFy1anXmzbtG9FYKbZG45OXLlyoPpqtL06YREY0e/V6dSUqievWIiBSK99r/C1Iqnwu6lpbW77//fv/+/T179vz888/bt29/V1lGIiKqVYtKlqTRo+nmTTp8mAwNKSuLnJ2pf3+6eJEaN25SosSG1NTUnSYmlps2UVoaeXmRTEaFvLP9whXZX6XOnalPn+wbsLp1KTyc8ikFCx9fjRo1Xr58WbNmzfyqI0NxcOLECc18fq8NdXUdbGwspVJNTU2dtyMuJia0YAFpa9PIkbRqFTGriMgQ0YsX9Mcf2Sk7P7OYmBg3N7eFCxdmZGTkalIoSFOTHBxoyxYiotWrKSkpuyk0NLRVq1aurq5yubxp06YV8rm3/g+//07u7uTpSaoy9eZUs2bNzZs337p16/Dhw8eOHStsRKYgqld/UqfOybi4CJWtZmZmRLRt2zYbG5tq1aqNGTPm4MGDx44d++jdgM+nRAlKTiYiSk4mUfw5HxMn0sWLtGULPXlC3t4DFy1apFAoqlatappPNXciGjaMpk2jT1FI/e/q1ReXKTO5YsXryrMxh6QkevCAHj+m2FgV++7evdvPz+/bb789ePDgihUrKlWq5OTk9OTJEyI6ceJEmzZt7ty5k5KSIpPJpkyZUr58+fnz5wcHBx8bNYoGD6bGjalUKdqzh1avphx5nQAKwcSEoqKIiF69evfP/qRJdPYsnTpF/v7k5eXWr18/Zm7eqpV2PntpaNC0aTRtGr0pr/f53Pz22002NgvKl7+oanAiLIzOnqW0NLp7V8W+kZGRJ0+eNDU1XbNmTefOnX/88cfdu3czMxFlZWX5+vpGRUXp6+s/efKkXLlyLi4uIu19+qNH1Lw5DRtGcnn2k7DIfwzFxOHD1KMHjR1LQ4bQunV06JDBkSOxsbFElJAQbmWVlmtzEZQJCwtTeTBl7F35onCUKbe/kNzbH8DpnfeW4va+b9++/xmRIaK2bcnIiLS0yMeHAgKoenWKjaXnz6laNapalXr16vnq1atDmzf3zMykEiWodWuaN48OHswOjxUZap6p82nkWr505gzHxXGNGihn9pmI4rsmJiZFeOEf/CcHB4dR+U2M/ucfUfhO3ratyvYZM1gm4/PnuVMn1QcQU6/j4ji/ZA4fwtfXt1evXitXrsybS+XkyZPffPONgYEBEVlYWHh4eMTHx4umo0e5Zs3sqqOurtypExNxzZoP3dzcvLy8RD1pc3PzQ4cOffweq0OfPn2IaOnSpXmbQkNDf/jhB2UtbQsLi759+27cuDG8iM44LS6ysnjUKF6yhIcOVbkUiJlnzGA7O75xg6Oj/01kIZPJdHR0NDQ0lPmnczl5Mnsysrf3e3YtODg4v4OLhehE5OjomOuMDgnhFSv49m0ePpxdXfn337MTpjJzRESEyItERBUrVpTmGFc3NjYeNWqUGJ9v2LChGLFv9KYyk6ampoTouYEBE/HWrXzrFmMRL7y3tDQeN45nzeIJE/K7hR04kAcP5ufP+enTf9+8fv06EZUrVy6/A0+blvvFx3XlypX8Ct9OmjRJnCx9+vSRv53vRibjPXv4+HEeMYJdXdnPj3Ou9N28ebN4uhOZRJXq169/+fLl+vXrE5FEIunatWu7NxXgDAwM2rRpU9nUNEtHh4l4/XqeMoXd3BhJ6IsVHx++dImZ+c4dHjyYiWTlymlqaurq6hJRp06dMt8+uTZt2kREzZo1U3kwL6/sFL/5rVL/D3//zRMmsJtbkS8u07JlSyI6efLke+w7fjwnJrKz81upspiZg4L48mUeOvTf7ZTp675+RTMoo1zqrjzLvLy4bFneulVdPSp2RMGz4OBgdXcE1KZOnTrT8rvdu3uX69ThUqV42DCV7XFxvHw5d+rEqgosMHP2+zIZnzhR6I5lZWVNmjSpffv2Pj4+uR7SMjMzPTw8NDQ0RAEpS0tLDw+PhIQEZk5JSXF2dhZPYlWqVKlSpYq45ytduvTEiRN/++23Fi0CifiXX5iZw8L499+5ZUtu1syJ3gwXODg4iAJVRcPMmTPLlCmjpaXVv3//BzmK6fj4+Ijp5RKJRENDZ9++cwjOFikyWX4tCoWidesbRHzuXO4mW1tbyic3JzPv2JGdat/NTfWR/f39586de01V+Zj4+HgnJ6eyZcuWKlXq99/nREX925SSwhMmZJmb/7vMqly5cqtWrUpLS5PL5StWROrqcu/eHBzMvr5cty5LJGxomNCmTftJkyaJhQ+lSpVas2YNMz948GDAgAEaGhpEJAI0Ojo6c+fOzZnj/OLFi3Z2dmJ6YEMbG8WyZSoTrAJ8RDExCZUqPdHQyB0mlcvlYrrio3xigt7e2XGJdzxYpouSGars37/f3t5+4cKFebeJiIjo2rWrpaWljo6Ok9OInLeBGRk8e7bi229biwsEEVWvXn3btm0iNHPqVEq5cjx8eHZBnB9/ZGNj1tVVuLnNvXnzZteuXcVZ3L59+0ePHnl6eirnYmtra4tCkDY2Nn5vHnQDAwN79uypXNw0vkwZLl2aDx7M/98Sii5lxvdhw/iff/jaNXl4uFwuf/z4sVhq3a9fP3GjcuvWraFDh2praxsaGkql0v79+yfnKTA5diyPGMFyeZ54QQF17sza2lyx4tcer8/IyPDz83uVf2UrcZ/87lQy+Rk/npl5xw4eM0ZVM4IyX7WVK9nYOHr48H3q7khx4eDgQESbN29Wd0dAPRQKhaWl5eLFi1U3r1rFM2awiwtfv57fEUaMYCL+6y/Vre3b88GDHBaWnfE3l7CwsM6dO7ds2XLr1q25BkCePXvWvHlzIipRogQRffvtt56enjKZjJn/+eefOnXqEJGGhsbPP/9co0YN5cD4oEGDRGVcLS2tOXPmiCcx8QymHB4vXdpq1arUXPGHq1evOTg41KpVS2VWwq9afHy8vb29eECVSqU9evQ4c+ZMv379xD+Ig4ODm9vRsmUz+/ZVd0fhM4iNvbFvnxidbtiwVd5YhKgePWDAAJV7e3vz0KHs76+iFqZCoVizZo2+vr6xsbFEIrGzs7ue40fD19dXPJhpa2sTUcuWU0uW5N69WaTlFbPVGjSIcnd337RpU/U32SssLS0rVqxYrVodLa2Mfv2y647t28ezZ3OnTvPE6UxE7dq1e/HiRc7OPHnyRASA6tate/fuXZXfReQ1vHz5ciH+9QDeQ3Cw77p1tWrVkkgkv/02KW977969iWjFihUq9x43jkeO5IwM1Q+Wd+7cqVWrlrm5uZ2dXUBAQM6mxMTEoUOHSiQSMV3FxsbG09MzLS2NmRUKxbp160RQvkSJEpqami1ajNfU5O7dedcuZmYHBybi2rXjhw4dunz58vLly4tTsmrVqi1btvzuu/qampkNGmSn+J03j3/5hRs2PCSuvGIIZNOmTcqepKenr1mzpkyZMqVKldLS0ho6dGhSnsezq1evikxPP//8c6qo8QbFU3w8X7jAeSZU+vv7i0G4nj17tmrVShnBL1WqlBhOq1mz5r1798TG0dHs6cnjxvGFC+zllW9Q5vDhw4cOHcqvpnt427ZpEgkT8dvXl69IYmKip6enjY2NtbW1paXloUOq60iIn4hQVXUP/5NyRHb3blXNBw+yhwd7e+dbAfTrVFyCMnFxKSLq6enpeePGjbyBT/i43N3diah27dqXL1+WY7SwWAoJCVF9oqWnZ+ecz8r6N9qdx6RJM83MLH7/fabK1vHjedQofviQ58/PPRq9a9cuMcNZxAvKlSvn6ekperJr1y6xpsbCwmL48OHfffeduPqamJi0aNFChGkqVKhwSUxzzRF20dLSkkgk1apVu5GnfOiVK1c6derk6Oh4//79Av/bFB2PHj0aOnSoMlWzmC6+fv16Zg4NZUtL9vBgZo6JYZksu/hdXJw6OwwfzV9/8axZPG4cr1jBGho3K1USfwOWlpYhISG5tl2/fr1EIjE3N1dOPVO6d483bOCAAHZ0ZGdnnjKFd+zIPqlDQ0PbtGmjnOQizlCJRNKpU6eTJ0+KIixE1KRJk+Dg4GvXrvXpEy2R8K+/srMzy2Q8Zgz36PFvxV+5XH7w4EGxxsHS0tLKyurEiaBc/ZTJZMrV72nvVZPx55/jy5XLePAg3xrbAO8pPZ3HjePZs9nVlUeNYqJ1lSqJUYSffvpJlmcK29ixY4moUqVKf//9d64mmYzHj+dbt/jPP9nFhffsYeW1Oj09ffr06SIIIv5XxNzv3LnDzEeOHClbtqyYKebg4KAcuihXrtwff/whJsQRUceOHV+8eBESEjJmTLSWFg8cyCNHclwcjxnDXbv+u0gwIyPD29u7UqVKYvBDV1d3xYq/cy3SunfvgaGhoThsUFDuE5aZU1JS1q5dm1+hQGbu3HlOgwZz7t1DRAZU8/Pz09TUVP6Zib/8gQMH3r59u2bNmkSkq6s7derU4cOHV6v2kIhFaa8JE/i337hJEz5/Pvs44hLTpEkTEaxs3rz5ixcqJkeLuTlmxsbyrzCnRkhIiIuLi1jCT2+ybjdtuqF//39/Q+RyPnIkolmzZuIHpHu9evzmprrg/rs8XGwsP378Xl/iy1VcgjKxsbEmJibW1tbiz0gqlVaoUMHOzm769Onq7loRFBISYmNjI2LPRGRqatq/f/+DBw++3z0uFDWJidlrFZjzW750f/t2HR0dUU7P29s712yXuDgeP54fP+bhw9nFhStWZC8vWUpKihjBE391tra2CxcurPYmcaiZmZmY6kJEPXr0iI2NZWaFQqG8gorrcc+ePZU5YpQuXbrUu3fvMWPGpOSppwhCRETE5MmTq1Wr1qxZsyc56rYuWcIjRzIzu7rymjXZy7A/Sdlj+MxevmRx9YyO5h49mEiura0hkYjFAqampspnpNevXw8fPjxnhRQTE5OZM2fGx8dnZmYuWRKirc0ODhwYyJcvc9WqLJWyvr6ifv12AwYMEDd8ZmZmIpdnRETEhAkTxJVFRAD19PSWLl2aM+4fHMzjx3NICHt4qJh3w8wKhWLDhg1Lly6Nyyc6eKBv350mJtfr1El7r3H1Jk3YwIBVLbQC+DC7d/PRo8zMp05xmzZMFG5uLoIyRPTDDz8og6FxcXEDBw5UNhFR+/btr1y5wswKhWL16nQTExaz1tzd2d6eibh69aw//1xy7dq12rVri9Dn0KFDg4ODXVxcRDBUKpV+86aCaaNGjcSSBPEUKnYRn2Vqarr17UwBL1+yuzs/ecKTJ6ueWZCRkTFnzpyxY8fmt8xhUteu3fT13apXj32vIffGjZmIL158j12huBATqC0tLQ0MDJydnZ+/qfQsk8kcHR2Vt4gtWozs1InnzWNmjoxkOzsm4hYt7k+ZMmXx4sXlypUTJ4iRkZGhoWH16vUsLNI3b87OpBETw4GBPHasl0QikUgkWlpagYGBeXuSnp5+6tQpcY/6pdmyZYuRkZH4jm3atDl69KhcLl+9+qCODltbc+XKnJrKS5Zw9eqsoZFlY1NBKpVqSiRxhoasocE3bxbqs/r3z647nv/QbRFULIIyGRkZItuQjo6Ovb19rVq1xFRncbeHJTYf1927d0WpF6lU6uDgkLPKTNWqVW+LxzIo5pyc+No13rJF9fKkESNYU9M2R3mXSpUqrV69OjU1lZkXLmRtbf71V2bmWbO4aVMm4pYtZ5YuXVqkgdDT01u5cqVYHixuGRs3bqy8TIoMEblcuHBh8ODBKpvgQyxdyh4efO5cdlBm6VI+cIAdHNTdLfhwt2/zypXZr4cP51q1uFevo1u3BgcHi7WrEolk8uTJFy9eFMPggrOzc7NmzcRrQ0PDb7/91tq6gr7+6yFDWMRCz59nLy/u1u2COGHFkPvLt7NlREdHu7u7iwigynwZYi36/Pk8aNB7fbWffxZpyPnNlPWCe/CAxXq9hQs5KIh9fJiZd+362lMHwJdh7Vr292dmvn2bZ8/mypUzXVwOHDhw8+ZNMevT2Nj4+PHjhw8fVkZPNDQ0nJyclMPaLVu2rF+/fosWrkTcvTszc2Iiu7lx/frcsqUfvVkGWKVKlYs5YhiRkZGTJ08uUaKEqamprq7uvHnzcq3LkMvl27dvb9GiRc+ePSMjI/N23N2dU1J4+fLsnGuF1r599il5505hd01I4OnTed48Pn6cExKyR9afPs1etAggNGjQgIj27NmTd8bZsWPHxAlVq1atnOnzmDkzk2fPlunrmymjn1WqVFm1alVycnJ0dPTAgc+IuE0b9vRkZm7Xjom4UaOXGhoaYsp2iRIlViovo2/WBFlbW1tYWFhZWZ3Lm5uNmZnv3buX34jCp9a8eXNdXd327dvnepQLCOBjx3joUJ49mz09uW9fLl+e1669deHChSvduzMR29jwggU8axaPGfNWQvL8/fdMmaKoWARlhg8fLs6WEiVK3Lp1i5kzMzNPnDhhbm5ORJ07d1Z3B4uOc+fOKWcAWlhYiH/twMBAd3d3IyMjDQ2NiRMnqruP8AVIS+MjR3KPJvv48O+/88SJPG8eE6WZmzu0bz937lzlQ525uXmHDh1Klw52c2Nx45eRwbGxvGcPN2nSUsQBzczMVM5wPnjw4PTp08UfJHw2S5fy06fs5MSjR/OaNbx3L9+9y/37q7tb8OFSU3nAAI6K4i1bsmMPbygUipkzZ4qZLMoJMlKpdMKECSKuqlwVWLZs2TJlypw/nzv2kZmZ6e3t3b9/f09xM1tI+/YxM6el8ZYt7/XVdu5kW1t2cuLCp+W+fZt79uS9e3nqVA4M5EWLOCWFV6x4j2dJgDxCQnjUKH78mJ2d+e0qCgkJCaJYWM4yYZqamu7u7mlpabGxsR4eHspUL5aWlgcO5I5JHD58vH79+jY2Nq6urirnhIaGhnp6el69evU9On78OGdmcmYm79jxHnszz5zJlSpxu3bvsVrh9m2uUYOjo3nqVL59m0W1wDVrcErCW8SivGcim9Hb5s2bJ06oP//8U+W+Z8+etbe3/+mnn3x9fXNO21Qo+NgxHjeOPTz42TMeNowtLHjsWEVIyNPU1FRnZ2dx2C5duvj7+48aNUpZU0xEUTU0NGbMmKGcJy6GGNu0aVOmTJkyZcrklzX/1atXeeNKH0u7dpMtLeveu6diRturVzx/Ps+axaNH8507/9bb4devedw4vnOHf/+dmTkhgceNK8hnKUds88ssWSQV/aDMihUrxF+5RCLJ+UecmJgo5mRqaGhEIPvXx3D48GFRXo6IKlSokHMVw+XLl8X7bfMpgQzFnfKXWqxNGjmSFy0SiSXEpahhw4ZaWlomJiYq89Q8fvxY/IE1adLkM3cc3mHpUn72jG/d4iZN3lq+lJzMO3bw22NO8LUJC+NVqzifapdnzpwxNzc3NDTU0tIqV65crkG/v/76S+QhWrJkyUfv14eOsI0Zw9ev85kzPHVqYXe9fZs3beIRI9jFhQMDuWtXXryYe/bEEyB8JCEhvHUr57i5UlIoFNOnTxc1j4ioZs2a/mJazRuTJk0SE2HeL7DyIZyds7NEvecpuWQJL17Mv//+HnHW27d50SIePz47KNOpE8+ezV274pSEf8nlck1NTYlEojLHgjJ6suO9YorjxnFSEo8eza6u/Ha1T969e7eYMiMm2kgkkvbt2584cSIrK0uUASWiBg0a3L17d+nSpcplB8qlHsOGDct5P/zo0SNnZ2cTExNRv0Jl1cv09PRcJUcLRV+fifjtpHDZRFAmJYWrVuU8OeWY795lkXFcoeDhw9+7A0VeEQ/KPHjwQBQFJCIPkXAyh759+4qmfGvEQGGMGDHC0NCwVKlS33//fa4J52lpaeJ3xMDAIL+E5FCsPX7MCxdmv84n0YxIwBkeHp63KT0pKaZy5QxNTXmtWp+uj1BYCQnZq6mjojgpiUXt1JgYlss5PZ2xXKxo+/HHH4lo7dq1eQfuVq5cKS6+s2fP/uifO2IE79zJO3e+7/Il5e9P4e8dRVAmJIQtLTkw8K1h+QcPvt5SG/B1iI2NJaLSpUtv3Lgx76OXsqS0ytLyn5QpETlQAAAgAElEQVSzM2/dytu2cb9+77W/MquEk1NhdxWn5Jo1bGuLmTKgWnh4OBGZmZmdPXs2b2uPHj3EiXPxvfISidHGXbt44EAVraGhoc2aNStXrpyjo+O9txfMnjt3TszfUUZhKlWqtGLFCplM5u3tLWqKVatW7ebNm8ePH2/Xrp2Yl6qcndq1a9eYHKWmxNooKysrU1PTMWPGvEdoJiiIR4zgzp05T95FZub0dJ48mSdN4gsXWMVMu4wMHjCAg4L4r794z57CfnTx8e9ExyLpyZMnaWlppUqVGjdunIeHR65WkQiNiDZu3PjZu1YE1a1bNykpqU2bNpcuXbKyssrZpKOjI1LBvX79+sGDB2rqIHzBbGzo7l2SyejUKXpTFCkXsaw3Kioqb5O2gYFJWJhWVpY0MvLT9hMKo1Qp0tQkIjIzIwMDErcWJiaUmUlr1lCnTurtHXxaIopavnx55azsXE1EFBcX99E/Vyql2rWpdm16cytbSFlZuV8UWNmy9NNPVL48+fhQ2bIk6qs2a0YixcehQ+/VH4CCMTQ0lEgkSUlJAwcOFHVPchLLl4goMTHxs3eNGjakhg0pR6a4wlCmCc+RL7yANDRIR4ccHSkzkzQ0sjugq0uamrR/P+3b9179gaLl+fPnZcuWTUhIaNWq1YABA2QyWc7Wly9fihe5nmsKqGVLIqKePcnBQUWrjY3NhQsXnj59unbtWmUtM6FFixa3b9/u2LGjrq5unTp1vL29g4KCxCqnAQMGXL16tUaNGkFBQQ0aNGjXrt3x48f19PRGjx796NGjffv2GRsbHzhwoEaNGsePHxf1kr755htXV9fw8PD4+Pjly5e3atUqNDRU+VkKheLUqVMdOnQwMjLq379/SkpK3q527kxDh5KrK50/r+KLaGuTry8tWEClS6s6zbW0aMkSCgyk+vWpe/dC/QMWK0U8KCP+whITEx0cHCR5fs1tbW1tbGykUmloaKi/v79aeliU/PTTT0Tk7++vvPbnbSWiq1evftZuwVdBS4tmz6bVqykujsaMUbmJqCMYmV/YZdkymjSJRowgddxxQqGkpFD16pSQoO5+wKckIi8qwy6fNCgjkVDlylS5MuV5LC0YOztyd6cpU6hfv8LuamxMVaoQEbVpQyYmVLMmEVH16mRsTMxUseJ79QegYDQ1NfX09ORyeXJyct5W9QZlypenChXeN05qbU0bN9KyZVS/fmF3rVGDevcmqZTOn6caNcjJiYhowACqXp0cHCg29r36A0VLgwYNqlevnpmZSUQ+Pj61a9cOCAgQTbdv3y5fvryWlpaZmZmoYVJYx49nvzh1qtD7GhsbHzlyJCYmJiAgYMCAAcp0wvRmfaKjo6O+vr6pqamHh0doaOjy5csrVark4OBw69atpk2bRkZGduzYsXLlysuWLUtOTm7btu2xY8cuXrxYvnz5y5cv16pVa9u2bSkpKStXrqxWrVrbtm39/PxSU1O3bNlSr169O3fu5OxJWhq1b08rV5JCkW9vtbV71a071coqNb8vQ7/88h6ncPGi7qk6n9yUKVOIyNHRUWWrp6cnEX3zzTevXr36zB0rehQKhVgeGRYWlrf11KlT1tbWurq6gwcP/vx9gyJArDdUlktLSEgICgrKzkIfF5dd//bZM0ade4AvwLBhw4ho0aJFSUlJuZpu3Lghhkns7e0/+ucqV7yrXPquLkuX8qFD6u4EFHViMP/+/ft5m6ZPny5u+9etW/eZe6X8AcjzS1Bgjx/zmyrFH8ukSbx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2RZl792jkSIqIoPv3qVq1Ao05OTnCFZW/6yp1aj3j8dCh1KYNbdpE3bvTokViRwNQ8SAHAcSFHAQAAPgYsrKywsPDVdcX0tJo5kwaNIhSU2nLlsLtQtlFoVCoPHJ5LcokJ5NMRgMGkJ8fHThQsDUwsGvTpmlpaZqamgsXLszKyi58gDNnzvj5+QUFBZVGtGWbWhdliMjIiOLjHxoajjp7NjU1VexoACoe5CCAuJCDAGohIYE2biQi+usvysgQOxqACubUqVP16tVr2LDh559/fvbs2YLNt25Rs2ZkaEi9etH58wUa79+/7+bmpqenp1Ao2rRpExYWlr81JiZGV1dXIpHY2NhoaJSrp1ikUhKWDMq/cNCNG3TzJm3YQO3bT3z0iJnlcvm6dUfq19dctYoePCAiunKFdu+mH3/c8PXXXy9evPj777+/fPmyOJdQZqjv6kuvjO/TZ8G2bURUtWrV2rVr29vb29jYmJmZtWnTxtnZWezoANQfchBAXMhBADVw9y717k2//UYHDtCIEWRmJnZAAGrg6lXKzqYvv6RXi1UX9ujRI29v73379hGRpqamXC6XSCRubm5z5851dHTkp08lkyZR06b0+DH5+lJUFK1aRcHBijFjpN26paWlzZw5c8mSJUIvXV3djIwMTU3NoUOH+vr6GhgYLFy4cPbs2enp6Xp6emPGjPHz8yvFi/8Yfv6ZqlalBw+oRQtq04ZmzKAaNSg7myIiaM4cUijGt2hh1Lx5WFivXbuqtWxJurq0fz/1709bt5KhYaq5eQN9fd2wsDBLS8uzZ8/WqFGj4PGfPSNTUyKizEzS1SVmyskhbe3Sv9CSpv5FmQkT5s6fP1FDQyM3Nzf/dkdHx5CQEAsLC7ECg3eXmZmpoaGhqakpdiBQHMhBNYAcLNeQg2oAOQh379KJE3TtGlWuTCNHoigD8MHmzSN7ezI0pDNnaN68wu2pqalz584NCAjIzMw0MDCYPHny1q1b88a5aGtrN2/ePCEq6nxEhE6VKjRiBCUlUXo6GRvTpEknmjf3Skh4+vRpQkKCsL+BgcGOHTsOHjy4bt06hUJhbGysp6cXGxtLRN27d1+4cOHb10gqu548IWNjMjIiIvL0pDVrXr5o3pwiImjcONLRUShowwbKyiI9PcrIoPBwysmhunWpXz+lpqaic+fOR48etbe3P3/+/Ot1u9PTafRoqlePrlyhhQtp6lRatowiI2nHDpowQbSLLTms1kJD2dCQmzc/sXz5ivPnz+/Zs2fJkiUTJkwQvoP6+fmJHSD8h/j4eH9/fxsbGxsbG19f39zcXLEjgveDHCzvkIPlHXKwvEMOguDOHV69ms+c4Zo1OSlJ7GgA1ICHx8sXnp4q2z09PY2MjIioY8eOjx8/Zubnz5///PPP2q9Gatja2hLRlmHD+O7d191yc+W//lrd3t7ExEQikRCRRCJxd3ePiYkR2m/fvv3tt9/KZDJjY2NnZ+ejR4+W6FWWqsGDX74YMqRw4++/8/Xr7OPDvXu/sT09Pb1JkyZEVLt27ejoaGbmjAxesYJPnmRm/ucf/vVX7t2bT57kHTt47tySvQSRqE9RJiuLIyOZmWNj+flzvnSJmTk8nLt1U/FTceLECSKysrLKzMws7UDh3YSGhg4cODDvU0/4UGvZsuXLXIWyBzmoZpCD5Q5yUM0gByG/6GgOCmJm7tuXe/bkwECxAwIo7/IqCHkv3tS1a1cicnJyunnzZv7tT548cXd3b9SoERG1b99eZd/r168TkY2NTevWrc+fP1+gtVmzZsIH+/379z/0KsqU9et5+nSeMoV37CjcuH07h4VxcjK3aVOwKTEx0dbW1sDAYNiwYc+2bOE5c7hLFz53jpn56lVetYr79uULF3jfPhRlyrpHj3j+fGbmDRs4JIQdHfnqVd6/n0+c4JwcFfvXq1ePiDZs2FC6YcJ/UygUnTp1Er59SqXSrl27Llu2LG9sl7m5+f79+8WOEVRADqoN5GA5hRxUG8hBUCkykv/3PyZiIu7ZM0PscADKuYMHefx4njqVi/g92LNnT+FT95xQHXjT2rVriWhwEQWdhw8fEpGjo6PK1iFDllhYDKlZc1tEhNolclYWZ2cX1bhlC1euzOPHF9z+/Plzc3NzIlqxYsXLTYmJPGAABwby4MEcE8MjRjAzP36srkUZtVp96cwZWrSIjh0jIurcmVasIGFZMZWPYHt5eRHRwoULWd1n1Sl3jh8/fvHiRalU2qtXr5s3b+7du3fEiBEbN240MDAgoqSkpC5duowcOVLsMEEF5KB6QA6WX8hB9YAcBJUsLSkmhgwMYnV1B+/ebRQeHi52RADlma4uBQbStm305oRred6+UrVMJiuqKa9vUa0REaMSE9feufODQqFbjMDLNG1t0tIqqlEmo4wMZUpKwbUgZ82alZSU1KhRo2HDhr3cZG5OS5eSqSktXEi2tjRxIhGRrS31719CgYtLrYoyjRvTsGHUsiURkYYG/fgj/f57kTv36tXL1NT0zp07+/fvL60A4Z2kpKQkJCQoFApra+uaNWsKG/v16xcaGtqoUSOJRCKTyQIDA8UNElRCDqoH5GD5hRxUD8hBUElHhzw85qWl2WVm/mZra52ZmSl2RADlQFRU1ObNm9PS0go2xMdTaChFRND9+yo7CmUXIlIoFIVb3152eXtr797k50dPntDx4+90CWrDweFCcrLs3LlGWVlZeRsfPny4ZMkSiUQSEBAgyb8MloEBNWz4cgrhSpWIiDQ1ycqqlGMuHWpVlNHQIF3d1/cDmzYlU1MqnICCJ0+eZGVlGRsb6+jolFqE8C6io6OFF5WE9HvF0dHx9OnTGzduzM3Nzc7OFiM0+A/IQfWAHCy/kIPqATkIRRk0qEfv3r1NTU1TUlI++eQTscP5Lzk5tHs37dtX1EgEgJKWmZnp4uIycODAAQMGFBgWekxb+3MTk7p6eouLWNgub//CtZXdu3dfvHiRiDIyMlT2FYoymZmZPXr0uHz5coHWFy8oLIz09UkNh7sx040b9OiRykZ7e3sbG5vbt28bGRnVq1dvyJAha9asGTx4cFZWVt++fRs2bFi6sZYloj48VbKSk7ltW65UScVzbXK5/KuvviKi7t27ixGaOouLi5s6dWrdunU7d+6cVKzlAby9vYUfzi1bthRuPXfuHBF99dVXHxwplDjkoCiQg5AHOSgK5CCUtJYtWxLRsmXLSvOku3fvbtu2rZubm5ubW79+/Tw8PNzd3VevXv22PiNH8pUr/PffPG5caYWZz717vHPny/nPoaLauXMnvRrzMnny5Lztcrl8yJAhwidt3bp14+NT8vdSKnnr1j+NjY3t7OwqVaq0b9++vKbQ0FAhAaVSaeXKlbW0tGb6+WUoFAXO+/TpU1tbW6E0I5VK+/XrFxUVJTTdvs2LFvE///CECezlVWJXLpbx43nHDg4I4N9+K9CSm5vbpk2batWq1apVK28UEhFpaWnp6uo+efJElHjLCHUuyiiV/Nlnii+/3Ltjx7ECTT4+PkRkb2+fnJwsSmxqKf86ERoaGkRUuXLl4ODg/Pvk5ORs2bLl66+//vbbb4W15Qrr1auXkKKnTp0q3Cp8tnbr1q0kLgE+LuRgKUMOQgHIwVKGHITSsWfPHiJycXFRKpWlc8aIiAh9ff3Co+osLS3z/71aUN5Kw0UsOVyCrl/n8eP5zh0eMoSxYFkF1rlzZyIaOXKklpYWEa1Zs4aZ//rrr1q1agk/w1Kp1M7Owdo6Y8oUvnKFmXnXLm7UiJs0eUBExsbGwm7ffffdnTt3JkyYkL+aYGVlJZFIui9f3v769WXR0WtiYtbFxGS/KtAsWLDAzMwsb2c9Pb3du3dPmpSsocE//MCRkbxsGX/3nYjvTQnIyeHhw1++9vRkPz+eMyf57l1mTkxMtLe3b9y4sdCYlpZ27ty5gICAvn37urq6Ll++XKyQywh1Lsow86xZc4nI1NR01KhRc+bM2bRp019//fX777/LZDINDY3C65NB8Tx58sTV1TVvnYhu3bo1bdo075Nu1KhROTk5qampAQEBDg4OeZ9Npqame/bsKXCokJAQW1tbIqpevfqDBw8KtCYlJc2aNUv4bC2ti4MPghwsHchBKApysHQgB6E0yeXyypUrE9HJkydL54zffvutMKAgf0XG0dGRiGrWrCmXy1V3GzSo4ItSM28eR0QwMwcH89atpX12KBuSk5O1tbVlMllsbOy6deuEcrmrq6vwA+zs7HzgwIHbt28PHXqKiDt04JYtOT2dhw5lc3O2s+Nt245mZGTMmTPHyMiIiDQ1NS0tLYW+xsbGAQEBcrn83LlznjdvNrh0yev+/fhCo1KTk5O9vLyEGr1EItHX169Xb6yGBg8axCkpLJfzunWivDElJjf3dQV20CDW0GCiepaWjo6Oenp69evXL7U6crmj1kWZjIxRrVsLOZD/V4hUKpVIJDNnzhQ7PvXxww8/mJiY6Orqenh43L59m5nDwsJq166d/9e2sGYEEdWqVWvp0qXdu3cX/unu7p6WlqZQKA4cONC6dWtho46Ojo6OzvLly/NSNyIiYtSoUXp6elZWVkQ0e/ZsUa8Y3g1ysLQgB0E15GBpQQ5CMWVk8Pjx7OfHU6ZwoScg3mLGjBlUWgOmhIE5pqamd+/evXTp0tmzZ4OCgoKCgi5duuTs7ExE6wr/ZalQ8K+/8t69/PPP7OPDp0+XQpxv+PVXvniRmXn3bg4MLO2zQ9mwZs0aIvr000+HDRvWuHFjTU1N4Tegnp6er69vZmZm3p7HjvFvv/G2bTxjBnt58YUL/Pz56+MkJiaOGjVKJpOZmJhIJBJ3d/e4uLi8ViXz8eTk9TExi5882fX0aeEw7t275+bmpqOjY21t3aJFi7Cw54X3UR8LFvCcOTxhAs+axUQvXo0VcnJyys3NFTu4skutizLDh8s1Neeam5uamvr5+Y0dO7ZPnz6urq7COLRdu3a9re+JE+znxzt3llas5ZtwP9DQ0DD/SOz09PTBgwcTkUwmE24MNmnS5MCBA3nfL1esWKGrq0tE9vb2ws0WIjIxMfH29h44cKDwz7Zt2169erVLly55i9IRkYWFxdvGykLZgRwsLchBUA05WFqQg1BMv/3GwoC1bdv4xAmVu6SkpMTGxhbYuGfPHi0tLTs7uw88v4/PyxcTJvDatfzLL8zMEye+3iE9PV344VQ5fcz27duJyM7OLi0tLf/27DVrmIj19fnMmQ+MsJjS0nj0aJ45k8eP5+Bgjo8XJwwQVY8ePQrckxDqMitWrCi8886d/PffPHt2kY8UXbhwwcfH5+jRoypbN8bGFh4pk9+JEyfOnj37/hdRDj1/ztnZnJvLwcH/zpplbW1NRC1atBA7rDJNfYsy2dl8+TLr6LBUmvrm74M1azY2bz61Q4e4orry3bs8ZQoz8/r1fORICQeqDqpUqSJ80mVkZBRo2rVrl3AzcMyYMYU73rp1y8XFxcLCgoiqVKni7+//7NkzoWnPnj2mpqZEZGVlpflqUnSpVNq5c+djxwpOjgBlEXKwFCEHQQXkYClCDkIx+fuz8JBacDBv31643c/Pz8HBwcLCIv+fgjExMcLfOV4fPE3oiBH88CE/fMgeHrx0KQ8bxrdv8+jR/M033Lo19+jR59NPPyWihg0bqrzLrVQqhSnDf/jhh127dh0+fDgoKGj37t02pqa7zc0Vmpq8adMHRvhBIiK4Vi0mej3PBVQkSUlJTZs27dmz54IFC06cOJGQkLBs2TIiateuXeGdjx/n0FDOzGR39+Kc62xKSipGgqgSH5/g6nq8SZOneHveQh2LMnI5jxjBc+eyhwc/eMB79xZoT0tjMzO2s+O4or6O7tzJwjO60dGM0d3/RalUCpMampiYqNxBmLBQ5RISzLxlyxYi0tXVXbRoUYGmyMjIZs2a6ejoyGQyLS0td3f3W7dufeTooSQgB0sXchAKQg6WLuQgFN+lSzxtGsfF8U8/8avFWfKcOsXNm+/Im6to+vTpCoVCLpcLk2K0aNHiwx8HGD6ct2zhLVvY3Z2XLuXbt9nDg0eMYCJhpIs+Eenp6dnY2Gzbtq1w92vXrjVq1EjYLT8h5lFdu3IRU0iEhoa6uLhUrlzZycnJycmpQYMGDRo0aNGihcrJrfNkZmbOnTvX399/1apVa9as+e2333bt2rV///4iO1y/zlJprL7+99Wr37t3T9i2ahULBVIvL968+eVDTiNGvNv7BeVcUlKStra2VCotvNbPoUN8/z4zc0CACIGpt6pVmYhv3BA7jjJMg9TPqVPk6kpubnTnDu3dS2PHFmjX16dVq+j8ebK2pgULaNy4fG0KBR04QM7OdOECtWhBoaFUrVppxl4eJSUlZWdnE5Ew51xhMTExRGRnZ6eyNT29vYPDfoXiH4mk4A729vYbN250cnIyNze/e/euubn5Rw0cSgxysHQhB6Eg5GDpQg5C8TVoQNHRNGgQ2dmRXF6gceNGOn2657x5XymVOyZPnjx9+vTg4GAXF5czZ84IVZL8C8EUj0RCffoQEV28SESko0Ndu9Ls2RQURESUnr7tyJHDa9euzcjI+Pnnn7t16ybUHwVKpfKnn366ePEiETk7O9erVy8tLU2Y9Pfvv/9OT0+3rN+a3nx4JM+UKVPCw8Plb16yqalpnz597t+/r6enp7JX7969g4ODk5OT8280NzcfPny4MMlOQXXrbvT27r9gAd2790f16l9/vfXGjd4dO5K5OV269HKXu3dJS4uSkt7l3YJyz8zMrGPHjn/88cf69eunTZuWvykujipVIiIKDxcnNjX29de0fz9ZWNCRI5SQQD/+SET0++/Uv7/IgZUd0v/epdzJzCShYK+vT5mZKnepVImio+nkSYqKovv3KTVV6JeZOGwYdetGs2eTpib5+dH16/RqHj4oSlBQEBHJZDKVX0blcnl0dDQRVRI+5woJDzeLjOwUHT3L3O4E9+EAACAASURBVLxn4da4uDgiEr6PfsygoUQhB0sXchAKQg6WLuQgfIgX2mHR7cMje0RkWqQKW1avpj/+ICKqVYs2biRv7098fHyOHTtmZWV16tSplStXamho7N6928bG5sPPPnjwyxfm5rRvH+XkULt2NHcutW5NrVtT586dVq5cKTzB9Pjx49WrV+fvu379+gsXLhCRmZnZxYsXd+3adeTIkaCgoBMnTmzbdvCrr6JXrPgpLU3FSY8ePbp//35dXd2QkJCIiIjw8PBLly6FhIRUrly5SpUWq1ZlqAz15MmT+/btS3vziFpaWklJSUuWLElMTFTZy7RZM4lEIjwAmJOj/ewZMZOnJ61dS0olEVFyMsXHF/VJCWroxx9/JCJfX98aVapQ27Y0diydPk1Tpnzxx8Qj82/OmkVhYWKHqHaUStqyhdLT6f791/XQvBdAROo4UqZ5cxo9moyNafdu8vQsaq8ffqDt20lTk/r3p4sXqXPnVf/884s8Kemxlpbevn00cyZ9+mlpRl2OHEtOzmG219F5JpcfOnx4Ud++FhYWiYmJhoaGKSkpJiYmwm4PHjxYsmTJzp07hRsaKu8QHjpEjRvT2bO0Zw+9WnHiDcLdxaK+yEIZhRwsYchB+A/IwRKGHISPyLDtpOSaDzQ1Kyslmamp1wMDP4uIoOvXqXVrio5+PZCtVatWf/75p6urq6amZk5Ozp07pq9WXX8/ixfT06c0Zw6NGUOLF9NnnxERJSYm/vvv09OnP712japXp0aNXu8vk8lmz57dpUsXIpo+fXq/fv2EqY6Sk5MnTpwo7LNgwQJhXiRmSkkhU1Nq1arFnDnUuDFlZpKGBikUlPeEU2Zm5vDhw4nIz8/v888/zx/bihUhrq7acXHk5UUFxgDl5OSMGDGCiHx9fTU1NZVKpVwuT0tLMzU1DQ4OPnr06KxZsxYvXlzgYhUKxfTp05nZx8fnl19+ycqizEzaupVkMhowgLy86IsvqFEj+uorOnKkOG8mlEepqWlSqVSpVJrHxNCjRxQSQs+e0fr1l3/N9Qz8yXzy2rg4sUNUO+bmZG9P//xDRJSYSLNnix1QGST281MlIyWFT58u+ll5PneO//iDQ0PZ2ppbtWJdXba2rie8IXUtLeUbNhTukvcU3PXrJRNz+fHLo0dx2dkbYmMbXLrU7soVS2vrDh06CI8TV6tWLSQk5OLFi507d85bJ0Imk9WoUUPloRYs4F69ODWVi5qobsmSJUT0008/leD1QElADpYk5CD8N+RgSUIOwge6d+9eYGDgrVu/P3kyLiHht/v3O2ZnP05NPf7vv9qamty7N9+7xz//zF5er5fJzsrKatCgARHVqFGjfv1JUin36PFyTaGrVzk3l//+++HPP//cs2fPt692v2gRjx/PV6/y6NFcvTo3aMBNmjQTBnn167ewqF7C+mLm5ubz589fs2bN5s2bFy5cKPzMN23aNG9BsdRUnjSJmfnPP/nwYf70U752jc+e5d27Xx9KKOXUrVtXeNCpgOXLecIEZub9+zk5+fX2X375hYiqV6+elZVVoMuNGzeESZfCw8MLNAUEBBDRJ598kn9xqCdPOD2dExL4hx/4hx9ernwsTCYCau/yZa5SRe7g0M3CwmJRgwZMxF98wUOHMvPVq5zRz5MZS6h/TC9e8J49PHYs5+Zyly4cEPB6/ibMvp2fmhZl3ioujrt0YWEescOHmZnlcr58+XLNmjWJaNq0aSp7fegPkFzOO3fy5s1caF2Gcsfz7t21MTGXX7zoefPmjvj4tKwsZr5z5069evWISENDQ3ghkEqlnTp1+uuvv1QeasECvniRJ09mLy/VM8G5u7sTUbt27ZRFTBQH5RFy8AMhB+EDIQc/EHIQPoSXl5eQa9u3N790icLDu2RnP4qK8omPX3rrVj1Pz4wBAzg9nQMCuFs3rluXd+xgZvbw8CAiZ2fnlJSUfftyzczY3Z2FYl3v3mxhwZ9++i8RaWpqSiSSoKCgos6+aBE/eMBDhvDw4S9n8xWmidHV1Z03b15Rvc6dOyes95Sfjo6Onp7ejXyzd6am8vff85497OPDx4/zqFE8ZAgHB/OAAWxqyt98s8HR0VFHR0cqlf7zzz8qT/ToEX/9NR85wnPn8tKl7O/PK1Ycmjp1qrB4fFF5JDyQ0rVr1+Tk5OTkZKEEExsbKwxbO3jwoMoT6ejw//6XdORIUGpyMicnc2YmR0TwjBk8YwZHRxf1VkB5ERPDwhLVDx9ySsrLmxT79rG1NQ8fnsvMyvR0vnSJr1zhQYP46VO+f5/HjRM1ZHWTkJDQrt0TieTl1NqXL/Nff738QGNWudxcxVUBijKFvvxNmnRGKlV061Zwx4EDBxLR2rVrVR5m8GD282M/P/7xx2KFMX48h4RwWBgPG1as/mXI/MhIldtzcnJ8fX2lUqnw3Pu7rBOxYAFHRfGiRdypEzdtyitWsHDX5NEjPnCAf/hhOBFJJBKpRBLbvz/HxJTE5UCJQw5+bMhBeD/IwY8NOQjF9uLFCyMjI5lM9uWXX+7YMSk2dnZKyqHU1MDk5N2xsXOEfc6c4Zwclst5wAAm4mbNVrds2VIogly+fFnYJzWVfX157Vo+coSHD2dtba5eXenhMczY2JiI7OzskvOPM3lFoeBFi/jJEw4OZldXvnOHL13iI0eOWFpaymS67dvfz8wsMvK9e/cOGTLEw8PDw8OjT58+bm5unTp12rp1a/59UlPZ05Nv3+YVK/j4cfb25jNnuH9/7tmTifjrr5cIw20MDQ1TU1NVnuXRIw4IYA8PnjGDW7ViIm7SpA8RVa5c2cHBYf78+Sp7zZgxQ0NDQ3iuKj+JRNK+ffuirmjmzJ3Cbp0cHJiIq1XjgQM5K4tfvGBPzyLfCCgn5s7lx4+ZmUeM4IMHuWVLzs1lLy+OiuKcnDd3TU7mgABevZoLjcOCYouMjKxRo0bVqi4NGiS8WvoMiqSOc8rkycwkLy+qWZNu36b27WnDhlADgxbHjycnJ9esWd/f/x+iNx5XFR7YFmbjK0xHhyZPJiIaMaJYwTx/TsJzs8KsYuWZsYbqHxtNTc3p06ffvHlzz54933zzzfbt283MzN5+qLp1SV+fRoygK1fo0CEyMqLcXBo1itzd6exZaty4ia7u+szMzJ3m5ja//05ZWbR2LaWlka1tCVwWlADkYMlADsK7Qg6WDOQgFNuWLVueP3/erFmzM2fOvNqmjItblJKyR09Sh6xySarRrNnLhvXrqVGjZ+PGTUhPTyei5cuX169fX2gyMiIi6tmTfHxIKqWICKpUSZKdvfjChbOpqakxMTGjRo3avHlzgbPPn0+bN1PNmsLnATk5EREpFN80bjzu+PERR4/qHT1KXbuqjrxr165di2rLx9ycataku3df/rNZM9qwgTp1olWriMjj2bOO/fv3P3v27Lx58/z8/Io6yNix1K4dTZhAn39OJiYdKlfO3blzJxFNmzbthx9+KDDF0h9//OHr60tEubm5Ql0mOzs7IyODiCQSiaWlZVFnqVdPTyaTKRSK9IQEIqLoaNLUJG1tyrfCFJRrmzeTqSlFRhIRtW9PK1cSEamYocvUlEaPLuXY1N6aNWuEabkPHcr9GJOSqzl1XH0pz6FD1L07jRlDgwbRunV08KDh4cNJSUlElJISY2eXVWB34SM+KipK5cHy1uYrYpG+/5KbW/BFuTXkrd8FhUGwffr0+c9vokTUpg2ZmJCmJm3eTJcvk4sLJSVRZCTVrEk1alCPHm5xcXEHN21yk8tJV5datiR/fzpw4OWfBVD2IQdLBnIQ3hVysGQgB6HY1q5dS0TDhg3Lt01qtiXB+ccXDnMVlFEwK4cMMQ0KCurfv//YsWMHDRqUv0lXlyQS8vKipKSXf2dqa2tv2rRJWGloy5Ytu3btyr//48ePjx49eOtWRufOdO/ey4oMEclkMk/PCc7Oet9/T//7H+Xm0pYtxbw6Q0OaMIGI6JtvqFkzmjSJiGj5curcmUxNydRUx8nJacGCBRKJZNGiRU+ePCnQ/cULWryYatSg6tXJz4969yZ/f5o4sfeOHTuEelBmZub06dPzd8nIyPD29hZeT58+XXh8KT09nZnv3LkjlUq3bdt2+/btwqHm5uZOnjxZoVDY2dk1/uYbcnGhNm1ILqecHEpPL2olbyhfOnWiH34gYY71Bg0oJobi48WOqcKoVatWdHR0nTp1Pso6cWpPrYsy2dkvvznq6ZGNDRFZmplpaGhoa2vHxsb27Nkz983vhTo6OkR07949lQdzdqYbN4iouItRDBpE48fTpEnk5las/uWGcItV5RoTb/e//xERjR1LCxeSnh7duUOjR2sYGRl1dHenf/+lEyfo0iWaNYs8PSk7m168+OiRw8eHHBQDchBeQw6KATkIRTl//vzVq1ctLS27deuWf7uW3icaPQdR7dr0+++FezVs2HDdunULFiwosN3Hhxo3pg4daP361xvr1as3ZcoU4fXgwYNNTU3NzMysra2rVq362WefBQd3kkjMPT2Dq1d/41Dt29ONG2RjQ4sWkVJJoaHFvECJhIyNiYh0dUlHh4Rl3PX0Xi+9JFxO9+7dMzMzZ86cWaD70qVRS5bQihVERL17vxwNJPD399fQ0CCiDRs2hOVbstjX1zcyMpKIateuPXLkyPxHq1GjxoABA3Jzc6dOnVo41EWLFt24ccPZ2TkiImLmn3/SrVv05580aRL5+9OiRaSqC5Q7xsZkakp5QxsnTKAbN9TgvkD50KhRIyIKCQlhZrFjKQ/Efn6qJD19ygMH8pkz7OnJt27xP/8oYmIUCkV4eLgwV1nfvn2FafOuXr3q4eGhpaVlZGQklUrd3d2FEnt+Y8bwsGGsUPDo0cUKplMn1tLiqlW5nD9Ul5OTExgYGFf0ih7Vq1cnorc/Ql+UsWOZmXfs4JEjVTV7eLzeT5grH8o45GAJQA7Ce0AOlgDkIBRbnz59iGiSsEBRfnn/s0OGvPvR/vqLly3jRYv43Lk3tsvlcuFEhoaGBb72a2pqdu7c+eHDhyoP6O3N+/fz1q0v1z8qOffu3dPU1JRKpR07dvTw8PD09PTx8Rk4cKBMJmvY0PfKFYXKXkOHDiUimUz2yy+/hIeHR0RE3L9/X1g3SiKRnD59unCXmJgYYYmo8+fP598eGRlpYGBAREeOHCmRK4QyIDf35Yu8Zb7WruXKlfnNeZCgBAk3J+5jbbN3oNZFGWZ+9ozPnOHExAKbQ0JChM9iNze3Fi1a5P2uMjY2FkYd16lTJywsTNg5IYEDAl7OVbZ2bZFfRg8dOnTw4MHcvA+AN8W0aZMlkTARP3ny8S6vVKWmpgYEBDg4ONjb29vY2Bw8eEjlbsIvv8fCzFrv6Y8/Xr7Iv3TiawcOsK8vb9zIPj7FODiIAzn48SAHoTiQgx8PchA+0IMHD3x8fB49elSwoVhFGU9PnjqVmdnLi0NC+MWLl9tPnTolk8mkUumRI0eEx3ni4uIiIiKWLVtGRFZWVs+LqOh5ezMzDxtW4kUZZh4yZIiVlVWBmpG9vf3wold3i42N1dPTE5Zhyk8qlQ4cOLCoXsK4ISMjo9atW3fo0MHNzc3NzU14nqJnz54lc3FQRq1cyWZmCUOH7hU7kIpCeOpw06ZNYgdSDqh7UaZogYGBGhoaRvlGRmpqav7444/Xr1+vU6cOEeno6EyaNGno0KE1a94l4i5dmJnHjeMBA7hJEw4OfnkchUJx4MCBJk2aEJGDg4Orq+uTJwmFTyfck7Q0M1PkVWvLjwcPHowePTrvfoswsX/Tpuvd3TnvTqpCwYcPxzZr1kx4kvn7zz8veOPmHfz3eqtJSRweXqyLgDIHOfjukINQEpCD7w45CCVr+XJesoQDAnj58nfv5O3NS5bwpUs8ciQbGXHVqtm9evU6duyYra0tFbG2fePGjYloxowZhZvWreOZM5mZHzx4XRwsOUqlcseOHb/99tuaNWtWrVrl7+/v7+8/f/58+Vs/HzZu3NikSZMGDRo4OTk5OTlVrlzZ2Nj4f//735OiS72pqakuLi4y2RuTmmtrazs7O0dj3esKJjk5QxgNGhAQcOnSpcIDQuHjEkqi9erVO3/+vEKhegQcCCpuUYaZXV1dicjGxsbQ0HDUqFGRr1a4TEtLGzx4sFBWJ6Kvv/7p22/Z35+ZOT6eO3YUVvW7OXHixIULFzo6Ogqf7yYmJkZGRi4un1tbZ2/a9HKkXGIiX7vGY8aslUgkEolEU1Pz2rVrhSPJzs4+ceJEUlJS6V38O9uyZYuJiYlwja1btz5y5IhCoVi9+oC2Ntvbc7VqnJnJixeziwvLZLkODk5SqVRDIkk2MmKZjK9cea9zubu/XG81744RqDfk4LtADkLJQQ6+C+QglIaYmPdd8tzbm+Vy9vTkdu2YiHV1TwuDTYioZcuWKsesCUs+GRgYbN++XaiDBAQErFmz5sSJs9bWKVIp3737kS6nLElLS9uzZ09QUNDhw4d37dq1a9euhQsXRkVFiR0XlLakpCRzc3MhR4QxVk5OTh07dpwqDDmDj+rBgwcODg7CmFwisrCwcHd3P3DgQBbWHVelQhdlGjZsSER79uxJS0sr0HT06FFhEYq6devevn07f5Nczn5+aQYGlnlF9+rVq69atSo9PT0hIeHHHx8RcevWHBDAzNy2LRPxV19Fy2QyYZU+XV3dlStX5h1NGAttb29vbW1tZ2en8oFYZg4LC0tOTv7I1/9uXF1ddXR02rVrd/369fzbL1/mo0fZw4P9/DgggPv04SpV+Ndfr545c+bi998zETs48Lx5/MsvPHIkF/H0cgH/fYcQ1Aty8F0gB6HkIAffBXIQyiY/P2bms2d58GD+7jtu3Hi7MIzL1tY2Pj6+qF7fffed8AdS/pEjlSpVsra2HT/+fFG9AMq7nJyc5s2bC+Okvvvuu7p162ppaQk//5aWlnjE5uMKDQ0VhuxJpdKuXbs65S32RlSjRo0Cv0yBK3hRRpgbTMWTvcz+/v7Cz82iRYtU9j116tR3333XqFGj/fv35x+OpVTy0aPs7c2+vvzoEXt6srU1jxmjfPDgYWZm5qhRo4TDdu7cOSQkZPjw4fqv5qMXBkXLZLJp06blDd0UxoS3bt3a1tbW1tZ2+/btKoOJi4sr/H36Y2nb1sfGpkFYmIoJC+PieO5c/uUXHjGCb9x4PZ8Wv3jB3t584wZPn87MnJLy8jHl/7JiRcEXoN6Qg+8COQglBzn4LpCDUC4kJiauWrVq7NixJ0+efMtue/bssbS0pDcZGRmZm5u/pZQDUN4JE0ULNwauXr3KzHK5/Pjx48LcRp06dRI7QPVx+vTpvCejra2thXf72rVrU6ZMMTExkclk48ePFzvGMqfiFmUUCoWGhoZEIlE5hirvW+OOHTuKcXBvb37+nEeMYC8vzsl5o2n37t3CrULhBqNEImnXrt3x48dzc3N9fX2FjQ0bNgwNDV2yZEleWTGvlOvp6Zn/Ach79+6NGjXK3Nz8k08+CQgIEBbRKCA7OzunQBDvw8CAiTglRUWT8GU0I4Nr1OAHDwo1h4a+fDRaqeShQ4sdAKgr5OA7Qg5CCUEOviPkIKiTcePGEVHLli3XrFmzfPlyf3//OXPmCH9B3bhRnCXDAMq+5cuXC79BJBJJ/uJ+amqqMHW0TCaLjY0VMUK1cejQIR0dHeHddnJyioiIyGs6f/68sL1NmzYiRlg2VdyiTExMDBFZWlqeOnWqcGv37t2FH5qzZ88W4+DC/bBdu/jHH1W0Pn78uFmzZo6OjoMHD85b20Jw+vRp4b5l3rdPZ2fn5cuXp6Wlbdy4UU9Pj4hq1qx55cqVY8eOtW3bViKRCJ8vws5dunRJzLfEhjAm3M7OzsLCYuTIkcX4SnrnDg8bxp068bNnKlqzs9nHhydM4DNnOCOjUHNODvfrx3fu8IoVvGfP+54a1B5y8F0gB6HkIAffBXIQ1IlCoRDy6++//86/ffLk646OOcK8UQBq5vbt23mjw3x9fQu0Cuv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lpV2Bf4Rdu3ZNmzZNdAs7fq+0tFRXV5fdrqurW1JSQkQu8DQ0NEQnAe3Ro4dY4dra2vLy8sXFxbSct2/f6unp0ViiLzRs2DD6+Jtvvtm9e3dWVpaVlRUhhF5/rlixYvXq1WJDDJiOOnn+oaqqqtbW1vz8fPoWFixY0LVrV8kKE0IKCgru3Lnz9OnTiIgIQkhtbe2zZ8/Kyso0NDTYN96J0NDQ/v3779mzJz4+Pjw8vL6+vqGhobCwkHZYmDNnjuR0cfCJQwyKQQzC32zq1Kk//PCD6BbRGFRUVGS/M3p6eiUlJVwul21oampqNjU1tbS00C06OjqS5XO5XHageFVVVVNTk+hYCTphIfl9iodr165NnTqVEHL+/Pm2tjY6o/ZforS0VEVFhfaUJoRERES0traKNYipc+fONTY2urq60qeysrLbt28vKysT/TnqxIQJE44cOTJt2rTTp0+vXbv2xYsXKioqjx8/pn9dtmxZc3Mz/bj+ux8T+PggBsUgBuFvsGPHjpkzZ4pu6bD9KSMj84ftTyMjI7HCaYgVFxfTb/jbt29FJzyKiYlJS0vz9vaOiop68+ZNfX39rl275s+f37NnT7qDr6+vp6fn+fPn2Sggv49x+S/eKb2/SHMfXbt29ff3V1FRUVFR+ZBj5eTk2tvbCwoKvL29eTyeubl5WVlZa2trWVlZt27d2GRoJyZNmuTg4BAdHR0fH//ZZ5+JtUvnzp3L/r59svGIpMwf4PP5r169oj/cjY2Nr169srCwEN3h1q1bHd4SZ8nJybm7uyckJNDdTp8+zV4BEkKEQmFeXh6NVYFAICcn171795cvXxoaGtK7DcrKyvX19WJl6ujolJWVseVLjhwmhHA4HHYwYU1NjZKSEk1ejhs3TjS2OxQXFzdq1Ch29tDw8PDY2Ng5c+aUlJSwV7advLqlpSWPx4uNjX38+LG3tzeHw+Fyub6+vmLdAcrLyyVvjACIQQwSxCBIlZGRkYyMzMuXLy0tLQkh6enpY8eOFR2IfuvWLT6fz14fdmjYsGEJCQlhYWGEkNOnT1taWoq2TV+/fm1qakoIaWhoKC4u1tPTo9u/++67JUuWyMp20KtXS0urrKyMfoHl5OQ6XL2ew+HQ6BAKhXTGDT6f39zcvGLFim7dunX+rul0Uex8qw4ODufPn+fz+fv37xcKhaJV6vDVAwIC5s+ff+jQIRsbGwMDg7a2tqampmXLlmlpaYnuVlZWRt84QCcQgwQxCH87c3Pzly9ftrS0KCoqNjY2ZmVl/aftT1VVVWdn54SEhMWLFxNCEhMTRdufPB5v3bp1kkexwSgUCnNzc8Vabtra2pWVlWwIdPjlpz8FTU1NqqqqtbW1NM1BfyI2bdr0n3wG/8fBgwdtbW3prNsTJkygb6GqqurNmzei9xc7bJfy+Xw+nx8bG3v//v2EhAS62pSfn5/oB0IIKSsr6zCn/ClAUuYPDBgwoF+/fv7+/qNGjTp9+vTAgQMdHR0JIefPn1dQUHjx4sWJEydu3Ljx5MmTiIiIlJQUExOTH3/8cfjw4aKFrF69esSIERUVFY2NjceOHaPT2k+ePHnixIldunQZMWLE+PHj9fX1Dxw4sGzZMm1t7U2bNtEpmhoaGqKjoyUXxHV0dHz8+PGUKVMIIQ4ODuvWrfvuu+/69Okjuo+rqyudO+r+/fs3b94MCAiQkZGJjIycMmXKwoUL29raHj58ePjwYQsLi+3bt4tNqn/s2DHRrqoTJ06cMGHC+vXrZ86cOWrUqKlTp1ZWVpqYmISGhtrb20dHR2/bto1+LKKHLFmyJCAggP4uREZGzpw5c9GiRQzDpKWlHT58WE5O7tGjR2JHAUhCDBLEIEiVsrLyF198MW7cuNmzZ6enp+fn50+ZMmX37t3p6em7d++urKzcs2fPvn372tvbBw8efPjw4c8//7xPnz6rVq0SLSQ8PLxfv37BwcE8Hi86OvrHH3+UkZHZtGkTh8OZNGmSi4vLoEGDbG1tk5OT7ezsPD09CSEZGRlPnjw5c+ZMh7WiMUg7tdnb28fExCgrK48YMUJ0Hzc3t82bN+fm5p4/f57ejrOwsBg7duyYMWOCg4PLy8ubmprCw8N5PF5+fr5oQ7C+vj45OXnjxo3sFjqKMDY2NioqKjAwcPjw4VlZWUFBQU5OTvb29j/99FN9fX1gYCC7v5qamo+Pz9KlSzdv3kwIMTY2njx58pgxYyZOnFhZWVlTUxMVFUUIefjw4bx58/70vwg+cohBghiEv93AgQP79Onj5+c3atSohIQENzc32qEsOTlZVlY2MzMzISHh5s2bDx8+XLVqVXJysp6e3vHjx+ki8aw1a9YEBgYWFhaWl5cnJyenpaURQgICAiIiIoYNG8amJFJTU0+ePDl//vzW1tZRo0bp6+sPGjTo9u3b7e3tkyZNEi2we/fu+vr6z5496927NyHE3t5+27ZtmZmZos3ILl262NnZLV261M7O7uzZs7Q7TGBg4JYtW0JDQwcNGpSVlWVtba2hobF8+fIXL1584AfC4/EOHjy4f//+Fy9eXL58uXfv3tra2gsXLhwzZszMmTOrq6u5XO60adMcHBy2bdu2detWtv8dNXHixKVLl/r7+yspKRGRdqlQKES7lBAit3btWmnXQcq0tLTs7Oy6d+/ObunSpYulpSWbOA8JCVFSUiosLPTy8lq7dq2srOywYcPU1NQKCwvNzc137NhhYGAgFArl5OQcHBzc3NxsbGzot41lbGw8duzYwsJCHR2dnTt30jxrU1OTlZWVnZ1dcHBwa2trY2NjRETE9OnTCSFeXl4WFhZ5eXkqKirffPPN4MGDxeqsqam5atWq+fPny8rKGhsbOzo6lpaW2tjYWFpa9unTh961sLOzMzQ0zMvL8/HxoYNvDQ0NnZycnJyccnJyOBzOtGnTtLW1XV1dbW1tRTuetbW1ycrKjhs3ju2eZ2hoqKamxufz/f39TUxMioqKdHV1R40apaKi0rNnz169elVVVdna2pqbm9vZ2dHuZ3w+39DQMDQ0lN6R6Nev34ABA3JychQUFKZOnaqrq9vU1DR37tzNmzdjGAUgBhGDIF1qamrW1tbGxsbsFg6HY2ZmZmNjQ5+6u7tbWVnl5ub27Nlz586dXbt27devn4WFRW5urrq6+vr16/v37y8jI9PY2Ojo6Oji4uLk5KSmpib6El27dp08eTK9ZRcZGUkv+VpaWgwNDa2srGbMmMHhcCorK/38/L7++ms6urCgoGD06NF0wV1JTU1Nv/76a3BwMCHE2dm5e/fu7e3ttra2fD7fwcGBtkE9PT2FQmFlZeXs2bN9fHysrKw0NDT8/Py0tLTy8vJ0dXUnT56spaU1fPhwMzMzdkgjIaS8vLx3796is1Px+XxZWVk7O7vJkyfLy8uXlJTY2Ni4ubnJy8sPGjRIRUVFTk7O1tbW2NjYxcWFRq61tXXPnj2DgoLoz9GoUaP09PTy8vK6d+8+ZcqUrl27ZmZm0nklO+yGAJ8UxCBiEP5+HbY/raysaPuTTmZE258+Pj5r1qyRkZHx8PDo0qVLYWEhn8/fuXOnvr6+UChUUFDo16/f4MGDe/fuLdZhjc/njx49uqCgwNDQcPfu3TTGm5qabGxs2KkACSEKCgpmZma2trZycnLTpk3T1NSsqKhwdXXdtm1bly5dxKqdm5ubnZ09ZMgQQoiXl1d7e7uampqtra2FhQWbB/H393/37p28vPyKFSvs7e1tbGxUVFSmTp3a3NxcVFTUu3dvf39/AwMDDw8PsZWeunTp0qtXL2NjYwMDAycnJ7rwNpfLdXBwcHJy4nK5L1++dHV1XbBggZmZGZfLHT58OJ/PLyw6GlmzAAAgAElEQVQs1NLSGj16tKqqqrm5ua2tbUVFhY2NDZ/P79u3L53j5g/bpY2NjeHh4d9+++0fdqP7KMl8sgO3/u28vLzCwsI+fO33f5qff/45MTGRzsEG8G+EGASQosbGRnNz899++63DyUT/FebOncvj8ZYtWybtigD8NxCDAFKRk5MzePBgentP2nX5K8XExJw/f/748ePSroh0IDH8bxUVFZWcnCztWvz3Ll68KNorFeBfBzEIIEXKysrr1q07d+6ctCvyXxIKhZmZmRg3Af9eiEEAqTAzMxs3blxqaqq0K/IXS0lJoQvPfZo+zp4ylZWVly9fZp9qaGjQMXsvX768c+dOly5dvL29JTuDiWpubk5JSSkqKtLX1/f09ORwOCkpKVVVVbKysvr6+o6OjnQxlxs3bhBClJWVe/XqRbu6ZWRk5Ofnjxo1ipZTXFz86NEjb2/vTl7r8ePHDx48UFRUHDJkiKGhoeQOhYWFdnZ2JSUla9eubWtrW79+Pd3OMAw7Qzh16tQp2vmN3ZKYmNijRw87O7va2trz58/TjQYGBg4ODmJruNy8ebO5ubnDpXmp6urqixcvDhgwgJ0Zrq6u7ty5c87OzkZGRrm5uXScZJcuXWxsbDp8I6Lu3buXn5/v7u7OzjP64sWLO3fuKCoqenh40HlM6+vrz58/X1tb6+DgQEdOwqeppKSExhqlo6NDh+w+ePDg8ePHurq6Xl5etIPl++Tn51+/fr2lpcXR0dHW1vbdu3d0FUBVVVUbGxs6P9mFCxdqampkZWW5XK6jo6OCgkJVVVVKSorotzQxMdHNzU10inixGHz58iWdsMbNzc3c3FyyJs+fPx89enR2dvbkyZPd3d3paCkxAoHg/v37hJBu3brZ2tqyEy4KhcLr169nZWVpaGjQBQUljxUKhZcuXXrz5k3Pnj07n05YsvIAot6+ffvbb7+xT7lc7sCBAwkhqampT548oSdHefnOZqZ78+bNtWvXWlpaHBwc+vTpw540VVRUbGxs6EIVly5dqqyslJGRoXGnqKhIzyyurq5sn+rk5GRHR0fJSanZ77BAILhx40Z7e7uzs7O1tbVkTWpqaoyNjd+9excZGamoqLhmzRrJfcrLy+/evSs6LP/Jkye5ubmjR4+mT0tKSh48eMAuQ0ODXUZGxs3NzczMTLLAlpYWumqboqIin8/v1avX++oPHzfRRXMJIePGjSOEvHv3LiUlRU5ObujQoeyPfIfa29uvX7+enZ2tqak5fPhwNTW127dvFxYWEkK4XK69vb2ysnJNTc2FCxcIIRwOh8/n03leXr58mZmZ6e/vT79m1dXVV69e9fPz6+S1nj59mpqaSmfHFx1LxXr37p21tXV5efmaNWuEQmFkZOT7vsPx8fHW1taiX/tff/3VwsKid+/elZWVN2/eHDNmDPun69evm5iYaGhoXL582d/fn91+69YtLpfbYXwRQrKzs2/dusUwzIABA9gFa+gbz8jI8PPzow2D169fFxUViZ4QU1NT1dTULC0tPyQGz5w509zcTP9r8K928+ZN0eWW3dzc6FDuc+fOVVdX9+vXr5NZe69evcrj8UQ7hSUkJAwYMODGjRv+/v7syLvU1NSuXbvy+fyEhIQxY8Z8yHKZ5eXlKSkptbW1VlZW72u2jRkzZsqUKZaWlmPHjn358iV5z+kjNTW1vLxcdK20Bw8evH37ll6W0lYuIURNTc3R0VFs2ezs7OxHjx6NHj1abPi/qDNnzhgaGtrZ2bFbkpOTtbW1HR0dGxoazp49SwjhcDimpqadXLXRi/GuXbt6eXmxF+MPHz589OiRjo6Ol5cX+6GVlpb+9ttvI0eO7LyF//FgPkZpaWmKioqhv1u1ahXDMLt37zY3N1+0aNHIkSMNDQ2Liored/jLly9NTEz69u0bFhbWv39/KyuryspKOzs7Dw+P6dOn9+vXj56QLl68qKamFhYWFhISoquru3z5coZhaB/I06dP06IuXLjQs2fP971Qe3v7xIkTu3fvHhoaGhQUpKGhkZiYKLlbfn6+vLw8LXzx4sXvK62urk5GRmbgwIGib4QQEhwczD4OCwubOnVq3759jYyM0tLS2D1bWlq0tLSUlZUrKireV/6lS5cIIcuWLWO3HDhwgBCyY8cOhmEOHjzI5XLDwsKCgoI0NTW3bt36vnIYhqEDibt163bt2jW65auvvtLS0poxY0ZYWNj69esZhikuLjYyMpowYcLq1asNDAx2797dSYHwcUtJSenSpQsb0Rs3bmQYZtGiRXZ2dhEREQMHDrSzs2toaHjf4du3b1dRUfH39586daq+vv7q1auTk5O7detGv64aGhr062ptbe3l5TV9+vQBAwaYmpoWFhY+evSIjSBKR0dHNHDELFmyRE1NLSgoKDQ0tHv37j/++KPkPk+ePNHS0mIYxt/f//vvv++wnH379tFo8vf3V1dXj42NZRimrKzM0dHRzMxsxowZ3t7eurq6r169EjuwsbHRzc3Nx8cnIiLCzMxs5syZnXyqAJ1LSEhQV1dn4+67775jGGbOnDkODg4REREuLi7Ozs5NTU3vO3zHjh3Kysp+fn7Tpk3T19dfuXIlDeSwsLDx48drampu3ryZYRgHBwd6bnVzczMyMsrLy8vKyiKEeHt7s0XxeDz2ZCFp48aNKioqAQEBU6ZM0dPT++abbyT3qaysJIQ0NjYuWrTo888/77Ccmzdv6uvri26hy76cOnWKPr18+bK5uTl9vHjxYjU1tfHjx9Ng379/v2SB5eXlhJApU6ZMmjSJDqQXCoXvexfwESOE+Pv7s6EkFAovXrxoZGQ0d+7cSZMmqamp3bt3733HlpSU9OvXz9zcfObMmV5eXlwuNzs729/f39nZeebMmW5uboaGhtnZ2ZmZmfLy8mFhYaGhoUZGRpMmTRIKhVu2bCGE7Nu3jxaVnp6urq7+vhcSCoUzZ87U0NCYMGFCSEiIpqbmsWPHJHcrLCyUkZFhGGbp0qURERHvK41e+opGMb3TMGvWLIZh7t27p6GhIbq/q6trTEzM06dPFRUVRbd7eXnt2rWrw5dYtWpVly5dAgMDJ02apK2tvXPnTvZPvr6+srKySUlJ9Gl0dPTw4cNFjw0JCfn666/fV3lR8fHxXC5XTk7uQ3aGfzgPDw9nZ2c2EjMyMoqKiiwtLUNCQubPn6+trU1Pcx1aunTp7Nmz2acZGRna2tp5eXmEkPr6enZ7cHBwZGQkXSe7rKzsD6uUmJjYpUsXT0/PmTNnWlhYBAYGdribu7v70aNHHz9+rKOj00lpXl5esrKyBQUF7BZra2slJaX29naGYaysrLy8vGbNmuXh4dGtWzfRkGEYJiQkRFZW9vjx4+8rvK2tTUFBoW/fvuyWN2/eyMrKjho1imGY/Px8QsjMmTMnTZpkamo6duxY+qJiduzYoa+vP3/+fG9vbxMTk/LycoZhlixZ0rdv34iICFdX1759+9bX1799+zYsLIymjUpLSzt5yx+TjzYpI/ZzzzDM7t272fDo16/fDz/88L7DXV1dg4OD29ra6NMbN24wDGNnZxcXF8cwjFAodHJy2rJly8WLF9n22f379+Xk5Orr65ctW+bg4GBsbFxXV8f8UVJm79693bt3z8/Pp0/T09MrKyvp4/b29pKSEvr4A5MymZmZmpqa3bt3z8vLo1siIyP5fL6zszPDMC9fvqTnUfZPPXr0YNvTSUlJlpaWI0eO7PAyktq/fz+fzzcyMmKblT4+Pnw+n67ncvDgwUGDBtHtiYmJ9LLzfcaMGRMVFcW2s+/du6ekpCR2hRkTE2Nra0sf//zzz9bW1p0UCB+3lJQUHo8nuqWtrW3z5s3Nzc0Mw7S2tmpoaFy5cqXDY58+fSonJ3fhwgX6tLi4+MWLF8nJyXw+n265ePGisrJyW1ubtbU1mxV1c3PbtGnTo0ePtLS0eDze+fPn6fZOkjJnz57lcDhPnjyhT7OysgoLC+nj9vb24uJi+vgDkzLu7u708YEDB+h7nzFjhouLC3vu/+2339jfKFZxcTH7y/b48WM5OblOclUAnUtISOjdu7foltbW1s2bN7e2tjIM09zcrKqq+ttvv3V4LI07NnCKi4ufP3+ekpJibGxMt1y9elVRUbG5udnBwYFtBQ4fPnzt2rVZWVkqKiq9evU6efIk3d5JUubevXtycnI3b96kTwsKCrKzs9m/FhcX0xPWf52Usbe379GjB50blU3KnDlzRklJ6enTp3S3rKws9jZPbW0te+6mSRna6CwtLVVUVHz48GGHLw0fN0LI69evRbckJCSkp6fTx9OmTZszZ877jp08efLAgQPZX/Jbt261tbX5+/vTnCbDMOPGjZs3b15mZqaysjLdUlRUJCMjk5OTs2XLFjs7O21tbfqd7DwpExsbq6amlpOTQ59mZmay10Kip7APTMrcu3ePy+Wqq6uzhSxbtozP53t4eDB/RVLm6tWr8vLy9+/fp08FAgHblq6srFRWVl68eHFISAjd8l8nZaqqqgwNDWNjY5GU+Th4eHjs3btXdMvz58/ZE9DRo0fNzMzed+ydO3d0dXXZdteqVavmzJlDO6z910mZqqoqNTU1NjnS0NAg2sIsLy+nrVzmg5MyPXv25PP527dvp0+fPn1qZGTE4XBoc9TKyopt5aampnI4nNu3b9On9fX1Xbp0iYiI8PX1fV/heXl5ysrKBgYGz58/p1u2bt3K5/PpBRpNytDmQWVlpYKCwuPHjyUL+fbbb9mr1L59++7atau9vV20Pa+pqZmSklJaWpqYmFhWVvZJJWU+oTllwsPD6WzPFRUVBQUFpqam5eXlP/30k9hu1dXVt27dWrlyJdsVzc3NTXQHGRkZPT09urAfq6KiQkVFhU7ePnjw4L59+0ZGRv5hlZKTk6dNm8Yu7W5ra0sn4v7pp5969OgxYsQIHo9H79WLOXjwYElJidhGgUBgZmbm6+sbFxdHt8TFxUVEROTm5kqW8MUXX9TX19+5c4fdMzg4mC43+L7aCgQCHx8fVVVVOjqjrKzst99+mz17tmT5lZWVnYwOi42Nff369eLFi9ktSUlJPj4+ZmZm+fn5DQ0NdCPtCECHRD1+/NjS0vJ9BcInSE5ObtmyZbSLo0AgaGhoMDIyevbsWVJSktie58+fd3JyYlfI1tHREe3kTAjhcrlNTU1NTU3slra2tpqaGvodVlBQiIqKmjdvXmNjY+dVSk5ODggIYHtsmpub6+vrE0J+/vnnHj16jBw50tjYmH6fRVVUVOzfv7+TYrlcbl1dHSHk7NmzS5cupetZEEIGDBggulAF++4+++wz+pgOMPnIJoED6ZKXl1+2bBkdspSdnd3W1mZoaPj06VPJyZUuXLjg4ODg5eVFn+ro6Ij9hnO53JaWFvYHnxAiFAqrqqpo3MnKym7fvn3evHm0aduJc+fODRkyhO3ybWBgQEc6nDlzxsjIaNSoUQYGBikpKWJHtba27t+/v7a29g/fsqurq5OTk9g6lTTY2XFS5ubmXC6XYZhhw4a5uLh4eXn16tWL3kFlaWhoKCkpfcgrwqfA19eXDpRoa2t78eKFqalpe3v7/v37xdqWhJDk5OTPP/+cXZ5v4MCBYr/8+vr6YkdVVVXJycnRMQjW1tb+/v4fMpFtcnJyaGgou+hhr1696JI0hw4dYk9hHc5hERMTQ6+dRAkEAktLy+HDh9OhWwzDxMfHv69F+l9ITk4eNWoUu9CMiYkJ25b+9ddfnZ2dly5dmpSU9CcjLiIiws/Pr3///n+2uvBPZWlpyS6j/uTJE/r9P3v2bGZmptiezs7OHA7n1q1b9Onx48fHjx//J1/91q1bHA5nzpw59KmysrKDgwMh5NWrV3Z2dkOHDjUxMelw7r8bN26wV3AshmHy8vIiIiLYy8Bjx44FBgb26NFDMu4cHR3HjRvHjqxMTEzs1avXypUrL1++TG8nSBIIBCYmJgEBAceOHaNbfvnllw6Durq6WigUqqqqShayZMkSOnKZEEJveMjKyrLt+by8vIaGBmNj4+7du48ePVp0WdJPwUeblKmpqen3u/j4eLqxoKBgyJAh5ubms2bN8vT0LC0t/eGHH8QOFAgEMjIyHa7/d/fu3fj4+DVr1ly+fJkuuVJUVOTp6enq6urn5xcTE8MOw9u6dWt0dPSTJ086r2Rubi5dmldUTk5ORETEzZs309LSwsPDly9fLnngvn373r17J1lzExOTcePG0Wh88uRJRUVFSEhISUmJaMOXokuvZWdnE0KampoSExODgoJ8fX0fPXpEo+vNmzdPfke35Obm8ng8tvyTJ096enr27t2bjcaMjAxPT08XF5fw8PCDBw92WEh5efmSJUv27t0rOj4wPz+/qKjIwsLCx8dHX1+fRvuIESOCgoKcnZ0dHBySkpJiYmI6/zDh41ZYWMhGNDs70t27dwcPHtyvX7/o6GgzM7OMjIyEhASxAwUCgWSUEULq6uri4+MPHz48a9YsX19fevL46quvPDw8eDxe9+7dw8LC6J6BgYFGRkZ/OCduh+Gcl5c3f/78a9eupaWlLV26VLJlXFZWFh0dLVlaSUlJfHz8gQMHIiIiJkyY0NjYWFxcLFm+QCBgQ6ygoIBu/Omnn5ydnRcsWHDq1Cks8wl/RnZ2Nht3169fpxtv3rw5aNAgJyenmJgYIyOj9PT0xMREsQPfF3cNDQ007mbMmDFy5Eh6H+Lrr7+mccfhcNhJNz09PR0cHMSyIZI6fKHq6urJkycfP348NTV1x44dkhN5trS0REdH09H1f+jbb7/dt2/f48eP2S0dBruMjMzJkyefPn368OFDR0fHvXv30u2nT5/+5ZdfQkNDdXV1HR0dP+QV4eMzevRoGkfr1q1jN44YMcLExERHR2fx4sXt7e3R0dFi6ZW6urqysrIOQykjIyM+Pn7Lli0HDx6ki1I3Nzd7enoOGjTI0dFx27Zt9K4AIWTDhg1JSUnXrl3rvIYdhlJ+fv68efOuXr2alpa2bNmyDpM7e/fu7fA2oWiLkc4YOGbMmLy8vPb2dkJIbW3tEBFsg7m1tVV0O72T0dra+kREQ0PD+35hCCHHjh0bP348nf9RsknwPi9fvmTLLy0tJYRcu3bt3LlzX3/99QeWAP8KGzZsoJEoOn3YV199ZWtre+zYMTotw+nTpzMyMsQOlJGRGTt2LF2w8tGjRzU1NezNAB8fH/Ybe+XKlQ+vjEAgMDU1lZyabc6cOUFBQY8fP75///6GDRvEUvyEkKtXr4rO+Ea9ffu2vb19ypQpL1++pNdccXFx48aN4/F4AoFA8tWtrKzoZSD5PWo0NTWHDBlCr5qrqqpEg46IBDW9TBMIBHR6xObmZhoyhBBvb+/Bgwfb2tp+/fXX5ubmlZWVbAliea6amppbt27RVb0JIampqbRPw/fff9/hNfinoLMp+v7VVFVVDx8+TB+zZyZdXd3o6OiMjIz58+cPHz580KBBkjeulZSUGIZpaWmRvMP85MmTvLy8pKSk27dv29nZpaSkaGlpbdq0qamp6ebNm+Hh4TTBSQgxNTVdtmxZeHj4qlWrOqkkh8Npbm4W23j79m05ObkvvviCEFJaWvr06VPJAyVDkfyeNBk2bNjEiRNfvXoVFxcXGBjYrVs3LS2tvLw8yZvqjY2N9K57cnIyh8N5+vTp06dPLSwsjh079sUXX/z000900jhCiIuLy7Zt2wQCwbhx44YOHerp6bl169a4uLiwsDDRUOfxeJs2baJTPU2dOvXRo0eShSxYsMDf33/AgAGiNWlqatLS0rp8+bKqquqJEydmzJjh5+d39erVq1evpqamvnz5MioqKjg4+OzZs5gT8ZOlo6PDRjQ7jbStrW10dPSNGzeWL18+aNCg4OBg2jYVxeFwOsz6NzQ0XLp0KSUlxd7eni15ypQprq6ub968Wbt27aZNm9gJPnfv3u3k5DR58uROathhON+9e1dWVnblypWEkIqKCsmObxYWFg8ePJAsrbKy8tKlSxwOZ+XKlePHj2cYRk5OTrL8bdu2sT9iI0aMWL16NSHE39/f3t7+wIED06dPv3///qcyQRr8D/To0YONDvbuFk2DXrlyZfHixa6urhMnTpw4caLYgUpKSpLXaYSQxsbGS5cuXb582cbG5ujRo3TjxIkTPTw8CgoKIiMjIyMjp02bRrfv3LnT1ta2w8mwRV9IMi7S09MbGhq2bt1KCGloaHj16pXYnQlVVdUO465DJiYmy5cvDw8PZzvAdhjshJDKysro6OiCgoLMzEw2H3r9+nVlZWU7O7vdu3d/avf9gLV161a6SAI7TzwhZOfOnTk5OQsXLty/f/9nn30m+Z1UVFSUlZXt8MuWlZWVlJR06NCh06dPjxw58tmzZwoKCps2bWppaXn06NGaNWvY/h3du3ffsGFDeHg4G8sdet8pTEZGhjZlOzyFEULu3bsnuTE3N9fExGTkyJEzZswoLCykLVI6OUtRUREhREVFZfPmzez+7F0QeXl50e0LFy4khJSXl7M7EEKio6PfF4MlJSVXr1719fWNj4/v0aPHkSNHJk2a1Mm7Zi1fvpydAnb27NmBgYGzZs3asWOHuro6HfwIH4fp06fTOZtFp+CdO3fuyJEjN23atHDhwhMnTvz4448dHhsQEBAcHLx9+/a4uLigoCD2wmrDhg3sNWPn131iOjx/CYXC3377TU5OjoabrKys5D3+Dueqz83NNTY2VlZWHjlyZFxcnIeHR2Njo7OzM4/H67CHGnsZWF1dfeHChcGDB9MZlGJjY2fPnn337l3RDPKdO3foZebAgQNra2sfPXp0/vx5f39/FRUVQ0PD3NxcOlv5xo0b29vbnz59unLlSjc3t/Ly8k2bNtESOBwOmxpub2+nN2ZcXFzolt69e0dHR9+8eXP58uVubm6fZl7mo03KyMnJSS7BoKCgYGlpaWlpeenSpaNHj9IFXMSYmJjIy8s/fPhw8ODBYn+aNWtWYGDggAEDrl69SvtMcjgc+mDgwIGnTp0SXRfw888/P3z4sNiU+2LMzc0lz8FCodDY2JjtIPPhaYjc3FwPDw8FBYUxY8bExcUdP36cDs4yMTERCARiC8GUlZXl5OTQ+fmPHTtmbW1N5/Hl8XixsbFffPHF2rVrxW5R0mi0tbXV1NSMi4u7d+9eQkIC7Y9Nz1hdu3aln4abm1tsbOyNGzfECnn69OnRo0ctLS1p9qqoqCgsLOzIkSP6+vqVlZW0q8KAAQNqampo94GJEyc6ODg4ODgMHTpUX1//1atXYgNP4NOhoKAgGdF04olevXodO3YsISFhyZIlkgfy+fyzZ88KhUKxPiM6Ojp79+69cuVKSEhIU1MT7eZmZmZGv3JNTU3r1q1jkzJWVlbz5s3rvAf4+8LZ0NCww/5unevZsyd7p50yNjZ+8OABm/mldu7cKXmspqYmnWybXnmy5zyA/xSHw5GMO1VVVWtra2tr66NHj545cyY8PFzyQHNz89OnT0vGnZaW1t69e2/duuXr61tfX0+TFKampvb29vb29kKhcNGiRWxShmZDIiIiOqmhubk5m9xhCYVCTU1NNu7WrVv3J1OTS5cuPXToENsn3Nzc/OHDh2L75OTkDBgwYOXKlcHBwUKhkB0RuXXrVrFFLuATxOfzeTye2EZzc3Nzc/P58+cfPHiQHXkqSlFRsUePHg8ePOjTp4/YnwICApYtW9bY2JiSkkKXMZKVlaVtsP79+1++fDkhIaFbt25051mzZsXExEj2DRer4V94ChMIBK6urioqKsOHD4+Liztx4gS9r2ZsbJybm8vhcOTk5EQ7jrFj3mVkZES300UG9fT0xAZr8Pl82moVc+LECdptlhBCp8yQ7FfeIbE+NTt37nz9+nVUVFRUVFRzc7NQKHRwcKBzFcO/GpfLlTyp6enp6enpbd682cLCoqampsOlLQkhAwYMYBjm7t27J06cOHToELu9X79+7NDy/+jX3tzcPDs7W+wV6fQic+bMoTdCli9fzuPxvvvuuz8sjfZkIYSMGzdu7dq15eXl48aNk5GRMTExef36teT+d+/epRN0nDp1ysDA4MWLFy9evCCEZGRk5Obment7i60dnJub27t3b1lZWX9//7i4uPPnz3/zzTfk98tMmpTp16+fvLx8//79b968eeLEie3bt/v6+oq9bn19/fTp0+vr648cOcJuZNvzx48fT0hI+JARlx+fT6hn+4YNG2ifDtoNUk9Pr7q6mi7fJUpJSSkkJGTZsmXsXb6cnBz2rzIyMrt3716/fj3N9LNyc3PFUgYcDmfXrl3soJu6ujrRSSuoqVOnHj169OLFi/RpcXFxXV2di4vL69evtbW1aSOV5k3EnDt3TjJzT+9LEELGjRu3ffv22tpaunypqampWL+16urqsLAwJycnulT22bNnY2Ji9u7du3fv3l9++aWsrEyy215TU9O7d+9o+QEBAQsWLPD09FRTU1NUVNTX1xcrPz09vaSkRDLNaW5unpOTc/bs2ePHjx8/flxbW3vdunW9e/ceM2ZMUlISPXHGxsYaGhpyuVwDA4OXL18yDEPfmoKCAp0SCIAQ0tLSsmrVqoqKCkJITU1NTk6Onp5ebm6u5CBbf3//d+/ebdq0iX6XGhsb3759y/516NChrq6uYvnH9vb2y5cvi3WNXrVq1ePHj2mnm+bmZsmR6qGhoVevXmVnZaqqqiovL3d2ds7Ly1NXV6fhLLkmbk1NjeQ8OB2aNm3axo0b6TJq5P/+LrGKiooiIyNp5/DMzMzW1la6ujzAX6KpqWnlypV0hEVFRQVthwkEgrt374rt6e/vX1paShdKI4Q0NjaKnjRdXV09PT2/+uor0UPoau5icbd06dL8/Hw6g2BLS4vkgKOgoKBXr17t2LGDPq2vry8uLra1taUTo7JxJ5aUaWtrS0pKkhzY+/AerS0AACAASURBVD6Kioq7d++mPdsJIZMmTbp8+fIvv/xCn9Jgv3v3Lp/PX7Bggaurq+TpHkDUgQMH2C7PDx8+1NPTEwqFSUlJdAYxUdOmTVu/fj1dkoxI/PJ/9913hw4dSk9PF91YVlb24MED0VCSlZXds2fPzz//TJ/W19dLzpI2ZcqUU6dOsUMRy8vLq6urnZyc8vPzOzm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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Draw the molecules of the val set for which the model is not confident, i.e., the ones with probability around 0.5\n",
-    "from rdkit.Chem import Draw\n",
-    "from rdkit.Chem import PandasTools\n",
-    "\n",
-    "# Get the indices of the molecules with probability around 0.5\n",
-    "idx = (val_preds > 0.4) & (val_preds < 0.6)\n",
-    "\n",
-    "# Add the actual label and probability to the legends\n",
-    "legends = [f'Prob.: {a:.2f} (true: {\"Active\" if l else \"Inactive\"})\\nE3: {e3}, Cell: {cell}' for a, l, e3, cell in zip(val_preds[idx], val_df[active_col].iloc[idx], val_df['E3 Ligase'].iloc[idx], val_df['Cell Line Identifier'].iloc[idx])]\n",
-    "\n",
-    "# Draw the molecules\n",
-    "PandasTools.AddMoleculeColumnToFrame(val_df)\n",
-    "Draw.MolsToGridImage(\n",
-    "    val_df[idx]['ROMol'],\n",
-    "    legends=legends,\n",
-    "    molsPerRow=5,\n",
-    "    subImgSize=(300, 300),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_25920\\2347175426.py:11: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  test_df['Active'] = [\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_25920\\2347175426.py:13: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  test_df['Active Probability'] = test_preds\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "False    652\n",
-       "True     179\n",
-       "Name: Active, dtype: int64"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the test set predictions which are very confident\n",
-    "def is_active(p, pos_threshold=0.90, neg_threshold=0.005):\n",
-    "    if p > pos_threshold:\n",
-    "        return True\n",
-    "    elif p < neg_threshold:\n",
-    "        return False\n",
-    "    else:\n",
-    "        return np.nan\n",
-    "\n",
-    "\n",
-    "unlabeled_df[active_col] = [\n",
-    "    is_active(p, pos_threshold=0.8, neg_threshold=0.15) for p in test_preds]\n",
-    "unlabeled_df['Active Probability'] = test_preds\n",
-    "unlabeled_df[~unlabeled_df[active_col].isna()][active_col].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:261: UserWarning: Truncating the list of molecules to be displayed to 100. Change the maxMols value to display more.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Draw the molecules of the test set for which the model is confident, i.e., the ones with high probability (p > 0.8)\n",
-    "# and low probability (p < 0.15)\n",
-    "# Get the indices of the confident molecules\n",
-    "confident_mols = unlabeled_df[~unlabeled_df[active_col].isna()].index\n",
-    "# Get the SMILES of the confident molecules\n",
-    "confident_smiles = unlabeled_df.loc[confident_mols, 'Smiles'].tolist()\n",
-    "# Get the active status of the confident molecules\n",
-    "confident_active = unlabeled_df.loc[confident_mols, active_col].tolist()\n",
-    "# Get the active probability of the confident molecules\n",
-    "confident_prob = unlabeled_df.loc[confident_mols, 'Active Probability'].tolist()\n",
-    "# Draw the confident molecules\n",
-    "Draw.MolsToGridImage(\n",
-    "    [Chem.MolFromSmiles(s) for s in confident_smiles],\n",
-    "    molsPerRow=5,\n",
-    "    subImgSize=(200, 200),\n",
-    "    legends=[f'{\"Active\" if a > 0.5 else \"Inactive\"} ({p:.2f})' for a, p in zip(confident_active, confident_prob)],\n",
-    "    maxMols=100,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\rdkit\\Chem\\Draw\\IPythonConsole.py:261: UserWarning: Truncating the list of molecules to be displayed to 50. Change the maxMols value to display more.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 204,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Now let's draw the molecules of the test set for which the model is not confident, i.e., the ones with probability\n",
-    "# between 0.001 and 0.97\n",
-    "# Get the indices of the confident molecules\n",
-    "not_confident_mols = unlabeled_df[unlabeled_df[active_col].isna()].index\n",
-    "# Sort the not confident molecules by their probability\n",
-    "not_confident_mols = sorted(not_confident_mols, key=lambda x: unlabeled_df.loc[x, 'Active Probability'])\n",
-    "# Get the SMILES of the confident molecules\n",
-    "not_confident_smiles = unlabeled_df.loc[not_confident_mols, 'Smiles'].tolist()\n",
-    "# Get the active probability of the confident molecules\n",
-    "not_confident_prob = unlabeled_df.loc[not_confident_mols, 'Active Probability'].tolist()\n",
-    "# Draw the confident molecules\n",
-    "Draw.MolsToGridImage(\n",
-    "    [Chem.MolFromSmiles(s) for s in not_confident_smiles],\n",
-    "    molsPerRow=5,\n",
-    "    subImgSize=(200, 200),\n",
-    "    legends=[f'{p:.2f}' for p in not_confident_prob],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cross-Validation Training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cross validation training with 5 splits. The split operation is done in three different ways:\n",
-    "\n",
-    "* Random split\n",
-    "* POI-wise: some POIs never in both splits\n",
-    "* Least Tanimoto similarity PROTAC-wise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting CV Folds "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visualizations taken from [SKlearn](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cmap_data = plt.cm.Paired\n",
-    "cmap_cv = plt.cm.coolwarm\n",
-    "\n",
-    "def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\n",
-    "    \"\"\"Create a sample plot for indices of a cross-validation object.\"\"\"\n",
-    "\n",
-    "    # Generate the training/testing visualizations for each CV split\n",
-    "    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):\n",
-    "        # Fill in indices with the training/test groups\n",
-    "        indices = np.array([np.nan] * len(X))\n",
-    "        indices[tt] = 1\n",
-    "        indices[tr] = 0\n",
-    "\n",
-    "        # Visualize the results\n",
-    "        ax.scatter(\n",
-    "            range(len(indices)),\n",
-    "            [ii + 0.5] * len(indices),\n",
-    "            c=indices,\n",
-    "            marker=\"_\",\n",
-    "            lw=lw,\n",
-    "            cmap=cmap_cv,\n",
-    "            vmin=-0.2,\n",
-    "            vmax=1.2,\n",
-    "        )\n",
-    "\n",
-    "    # Plot the data classes and groups at the end\n",
-    "    ax.scatter(\n",
-    "        range(len(X)), [ii + 1.5] * len(X), c=y, marker=\"_\", lw=lw, cmap=cmap_data\n",
-    "    )\n",
-    "\n",
-    "    ax.scatter(\n",
-    "        range(len(X)), [ii + 2.5] * len(X), c=group, marker=\"_\", lw=lw, cmap=cmap_data\n",
-    "    )\n",
-    "\n",
-    "    # Formatting\n",
-    "    yticklabels = list(range(n_splits)) + [\"class\", \"group\"]\n",
-    "    ax.set(\n",
-    "        yticks=np.arange(n_splits + 2) + 0.5,\n",
-    "        yticklabels=yticklabels,\n",
-    "        xlabel=\"Sample index\",\n",
-    "        ylabel=\"CV iteration\",\n",
-    "        ylim=[n_splits + 2.2, -0.2],\n",
-    "        xlim=[0, 100],\n",
-    "    )\n",
-    "    ax.set_title(\"{}\".format(type(cv).__name__), fontsize=15)\n",
-    "    return ax"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test Sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the indexes of four test sets, one for each experiment:\n",
-    "* Randomly set aside around 20% of the data\n",
-    "* Isolate all entries with E3 that differs from CRBN and VHL (get at least 15% of the data)\n",
-    "* Tanimoto-based isolate entries\n",
-    "* Target-wise isolate entries\n",
-    "\n",
-    "Perform cross-validation on the non-training data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import OrdinalEncoder\n",
-    "\n",
-    "test_indeces = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Random Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of entries in the test set: 171 (20.0%)\n",
-      "Active/inactive PROTACs in the test set:\n",
-      "True     0.5\n",
-      "False    0.5\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "Number of SMILES leaking in the train_val_df dataset: 76\n",
-      "Number of Uniprot leaking in the train_val_df dataset: 40\n"
-     ]
-    }
-   ],
-   "source": [
-    "active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "\n",
-    "# Randomly select 20% of the active PROTACs as the test set\n",
-    "test_df = active_df.sample(frac=0.2, random_state=42)\n",
-    "test_indeces['random'] = test_df.index\n",
-    "\n",
-    "# Reporting\n",
-    "print(f'Number of entries in the test set: {len(test_df)} ({len(test_df) / len(active_df):.1%})')\n",
-    "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "# Check that the SMILES in test_df are NOT in the train_val_df\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "smiles_leak = set(train_val_df['Smiles']).intersection(set(test_df['Smiles']))\n",
-    "print(f'Number of SMILES leaking in the train_val_df dataset: {len(smiles_leak)}')\n",
-    "# Check that the Uniprot in test_df are NOT in the train_val_df\n",
-    "uniprot_leak = set(train_val_df['Uniprot']).intersection(set(test_df['Uniprot']))\n",
-    "print(f'Number of Uniprot leaking in the train_val_df dataset: {len(uniprot_leak)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### E3-based Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of entries in the test set: 51 (6.0%)\n",
-      "Active/inactive PROTACs in the test set:\n",
-      "False    0.82\n",
-      "True     0.18\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "Number of SMILES leaking in the train_val_df dataset: 0\n",
-      "Number of Uniprot leaking in the train_val_df dataset: 13\n"
-     ]
-    }
-   ],
-   "source": [
-    "encoder = OrdinalEncoder()\n",
-    "protac_df['E3 Group'] = encoder.fit_transform(protac_df[['E3 Ligase']]).astype(int)\n",
-    "\n",
-    "active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "\n",
-    "test_df = active_df[(active_df['E3 Ligase'] != 'VHL') & (active_df['E3 Ligase'] != 'CRBN')]\n",
-    "test_indeces['e3_ligase'] = test_df.index\n",
-    "\n",
-    "# Reporting\n",
-    "print(f'Number of entries in the test set: {len(test_df)} ({len(test_df) / len(active_df):.1%})')\n",
-    "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "# Check that the SMILES in test_df are NOT in the train_val_df\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "smiles_leak = set(train_val_df['Smiles']).intersection(set(test_df['Smiles']))\n",
-    "print(f'Number of SMILES leaking in the train_val_df dataset: {len(smiles_leak)}')\n",
-    "# Check that the Uniprot in test_df are NOT in the train_val_df\n",
-    "uniprot_leak = set(train_val_df['Uniprot']).intersection(set(test_df['Uniprot']))\n",
-    "print(f'Number of Uniprot leaking in the train_val_df dataset: {len(uniprot_leak)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Tanimoto-based Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique groups: 71\n",
-      "Number of entries in the test set: 171 (20.0%)\n",
-      "Active/inactive PROTACs in the test set:\n",
-      "False    0.53\n",
-      "True     0.47\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "Number of SMILES leaking in the train_val_df dataset: 0\n",
-      "Number of Uniprot leaking in the train_val_df dataset: 25\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_bins_tanimoto = 200\n",
-    "tanimoto_groups = pd.cut(protac_df['Avg Tanimoto'], bins=n_bins_tanimoto).copy()\n",
-    "encoder = OrdinalEncoder()\n",
-    "protac_df['Tanimoto Group'] = encoder.fit_transform(tanimoto_groups.values.reshape(-1, 1)).astype(int)\n",
-    "\n",
-    "active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "print(f'Number of unique groups: {active_df[\"Tanimoto Group\"].nunique()}')\n",
-    "\n",
-    "# # Plot the distribution of the Tanimoto Group\n",
-    "# active_df['Tanimoto Group'].value_counts(normalize=True).plot(kind='bar')\n",
-    "# plt.title('Tanimoto Group distribution')\n",
-    "# # y-axis as percentage\n",
-    "# plt.gca().set_yticklabels(['{:,.0%}'.format(x) for x in plt.gca().get_yticks()])\n",
-    "# plt.grid(axis='y', alpha=0.5)\n",
-    "# plt.show()\n",
-    "\n",
-    "# Get the groups ordered by the average tanimoto distance (highest to lowest)\n",
-    "# NOTE: This will put the \"less similar\" PROTACs in the test set\n",
-    "tanimoto_groups = active_df.groupby('Tanimoto Group')['Avg Tanimoto'].mean().sort_values(ascending=False).index\n",
-    "\n",
-    "test_df = []\n",
-    "# For each group, get the number of active and inactive entries. Then, add those\n",
-    "# entries to the test_df if: 1) the test_df lenght + the group entries is less\n",
-    "# 20% of the active_df lenght, and 2) the percentage of True and False entries\n",
-    "# in the active_col in test_df is roughly 50%.\n",
-    "# Start the loop from the groups containing the smallest number of entries.\n",
-    "for group in tanimoto_groups:\n",
-    "    group_df = active_df[active_df['Tanimoto Group'] == group]\n",
-    "    if test_df == []:\n",
-    "        test_df.append(group_df)\n",
-    "        continue\n",
-    "    \n",
-    "    num_entries = len(group_df)\n",
-    "    num_active_group = group_df[active_col].sum()\n",
-    "    num_inactive_group = num_entries - num_active_group\n",
-    "\n",
-    "    tmp_test_df = pd.concat(test_df)\n",
-    "    num_entries_test = len(tmp_test_df)\n",
-    "    num_active_test = tmp_test_df[active_col].sum()\n",
-    "    num_inactive_test = num_entries_test - num_active_test\n",
-    "    \n",
-    "    # Check if the group entries can be added to the test_df\n",
-    "    if num_entries_test + num_entries < 0.2 * len(active_df):\n",
-    "        if num_entries_test + num_entries < 0.1 * len(active_df):\n",
-    "            test_df.append(group_df)\n",
-    "            continue\n",
-    "        if (num_active_group + num_active_test) / (num_entries_test + num_entries) < 0.55:\n",
-    "            if (num_inactive_group + num_inactive_test) / (num_entries_test + num_entries) < 0.55:\n",
-    "                test_df.append(group_df)\n",
-    "test_df = pd.concat(test_df)\n",
-    "# Save to global dictionary of test indeces\n",
-    "test_indeces['tanimoto'] = test_df.index\n",
-    "\n",
-    "# Reporting\n",
-    "print(f'Number of entries in the test set: {len(test_df)} ({len(test_df) / len(active_df):.1%})')\n",
-    "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "# Check that the SMILES in test_df are NOT in the train_val_df\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "smiles_leak = set(train_val_df['Smiles']).intersection(set(test_df['Smiles']))\n",
-    "print(f'Number of SMILES leaking in the train_val_df dataset: {len(smiles_leak)}')\n",
-    "# Check that the Uniprot in test_df are NOT in the train_val_df\n",
-    "uniprot_leak = set(train_val_df['Uniprot']).intersection(set(test_df['Uniprot']))\n",
-    "print(f'Number of Uniprot leaking in the train_val_df dataset: {len(uniprot_leak)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "Number of unique groups: 71\n",
-    "Number of entries in the test set: 170 (19.8%)\n",
-    "Active/inactive PROTACs in the test set:\n",
-    "False    0.56\n",
-    "True     0.44\n",
-    "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-    "Number of SMILES leaking in the train_val_df dataset: 0\n",
-    "Number of Uniprot leaking in the train_val_df dataset: 32\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Target-based Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of entries in the test set: 167 (19.5%)\n",
-      "Active/inactive PROTACs in the test set:\n",
-      "True     0.51\n",
-      "False    0.49\n",
-      "Name: Active (Dmax 0.6, pDC50 6.0), dtype: float64\n",
-      "Number of SMILES leaking in the train_val_df dataset: 17\n",
-      "Number of Uniprot leaking in the train_val_df dataset: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "encoder = OrdinalEncoder()\n",
-    "protac_df['Uniprot Group'] = encoder.fit_transform(protac_df[['Uniprot']]).astype(int)\n",
-    "\n",
-    "active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "\n",
-    "test_df = []\n",
-    "# For each group, get the number of active and inactive entries. Then, add those\n",
-    "# entries to the test_df if: 1) the test_df lenght + the group entries is less\n",
-    "# 20% of the active_df lenght, and 2) the percentage of True and False entries\n",
-    "# in the active_col in test_df is roughly 50%.\n",
-    "# Start the loop from the groups containing the smallest number of entries.\n",
-    "for group in reversed(active_df['Uniprot'].value_counts().index):\n",
-    "    group_df = active_df[active_df['Uniprot'] == group]\n",
-    "    if test_df == []:\n",
-    "        test_df.append(group_df)\n",
-    "        continue\n",
-    "    \n",
-    "    num_entries = len(group_df)\n",
-    "    num_active_group = group_df[active_col].sum()\n",
-    "    num_inactive_group = num_entries - num_active_group\n",
-    "\n",
-    "    tmp_test_df = pd.concat(test_df)\n",
-    "    num_entries_test = len(tmp_test_df)\n",
-    "    num_active_test = tmp_test_df[active_col].sum()\n",
-    "    num_inactive_test = num_entries_test - num_active_test\n",
-    "    \n",
-    "    # Check if the group entries can be added to the test_df\n",
-    "    if num_entries_test + num_entries < 0.2 * len(active_df):\n",
-    "        if num_entries_test + num_entries < 0.1 * len(active_df):\n",
-    "            test_df.append(group_df)\n",
-    "            continue\n",
-    "        if (num_active_group + num_active_test) / (num_entries_test + num_entries) < 0.6:\n",
-    "            if (num_inactive_group + num_inactive_test) / (num_entries_test + num_entries) < 0.6:\n",
-    "                test_df.append(group_df)\n",
-    "test_df = pd.concat(test_df)\n",
-    "# Save to global dictionary of test indeces\n",
-    "test_indeces['uniprot'] = test_df.index\n",
-    "\n",
-    "# Reporting\n",
-    "print(f'Number of entries in the test set: {len(test_df)} ({len(test_df) / len(active_df):.1%})')\n",
-    "print(f'Active/inactive PROTACs in the test set:\\n{test_df[active_col].value_counts(normalize=True).round(2)}')\n",
-    "# Check that the SMILES in test_df are NOT in the train_val_df\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "smiles_leak = set(train_val_df['Smiles']).intersection(set(test_df['Smiles']))\n",
-    "print(f'Number of SMILES leaking in the train_val_df dataset: {len(smiles_leak)}')\n",
-    "# Check that the Uniprot in test_df are NOT in the train_val_df\n",
-    "uniprot_leak = set(train_val_df['Uniprot']).intersection(set(test_df['Uniprot']))\n",
-    "print(f'Number of Uniprot leaking in the train_val_df dataset: {len(uniprot_leak)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 374,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Split type: random\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_25336\\3799429218.py:31: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
-      "  ax.scatter(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_leaking_uniprot (%)</th>\n",
-       "      <th>train_leaking_smiles (%)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>548</td>\n",
-       "      <td>138</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>51.4</td>\n",
-       "      <td>48.6</td>\n",
-       "      <td>36</td>\n",
-       "      <td>45</td>\n",
-       "      <td>85.6</td>\n",
-       "      <td>12.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>549</td>\n",
-       "      <td>137</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.2</td>\n",
-       "      <td>48.8</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>33</td>\n",
-       "      <td>55</td>\n",
-       "      <td>80.7</td>\n",
-       "      <td>13.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>549</td>\n",
-       "      <td>137</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.2</td>\n",
-       "      <td>48.8</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>42</td>\n",
-       "      <td>46</td>\n",
-       "      <td>93.3</td>\n",
-       "      <td>14.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>549</td>\n",
-       "      <td>137</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.2</td>\n",
-       "      <td>48.8</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>39</td>\n",
-       "      <td>45</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>11.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>549</td>\n",
-       "      <td>137</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.2</td>\n",
-       "      <td>48.8</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>48.9</td>\n",
-       "      <td>39</td>\n",
-       "      <td>47</td>\n",
-       "      <td>90.5</td>\n",
-       "      <td>12.4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0        548      138         0.8       0.2              51.1   \n",
-       "1     1        549      137         0.8       0.2              51.2   \n",
-       "2     2        549      137         0.8       0.2              51.2   \n",
-       "3     3        549      137         0.8       0.2              51.2   \n",
-       "4     4        549      137         0.8       0.2              51.2   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                48.9            51.4              48.6                   36   \n",
-       "1                48.8            51.1              48.9                   33   \n",
-       "2                48.8            51.1              48.9                   42   \n",
-       "3                48.8            51.1              48.9                   39   \n",
-       "4                48.8            51.1              48.9                   39   \n",
-       "\n",
-       "   num_leaking_smiles  train_leaking_uniprot (%)  train_leaking_smiles (%)  \n",
-       "0                  45                       85.6                      12.8  \n",
-       "1                  55                       80.7                      13.8  \n",
-       "2                  46                       93.3                      14.2  \n",
-       "3                  45                       86.0                      11.8  \n",
-       "4                  47                       90.5                      12.4  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n",
-      "Split type: e3_ligase\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_leaking_uniprot (%)</th>\n",
-       "      <th>train_leaking_smiles (%)</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>644</td>\n",
-       "      <td>162</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.1</td>\n",
-       "      <td>46.9</td>\n",
-       "      <td>53.1</td>\n",
-       "      <td>46.9</td>\n",
-       "      <td>38</td>\n",
-       "      <td>71</td>\n",
-       "      <td>87.7</td>\n",
-       "      <td>16.9</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>645</td>\n",
-       "      <td>161</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>53.4</td>\n",
-       "      <td>46.6</td>\n",
-       "      <td>40</td>\n",
-       "      <td>78</td>\n",
-       "      <td>93.3</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>645</td>\n",
-       "      <td>161</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>53.4</td>\n",
-       "      <td>46.6</td>\n",
-       "      <td>37</td>\n",
-       "      <td>69</td>\n",
-       "      <td>90.1</td>\n",
-       "      <td>19.1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>645</td>\n",
-       "      <td>161</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.2</td>\n",
-       "      <td>46.8</td>\n",
-       "      <td>52.8</td>\n",
-       "      <td>47.2</td>\n",
-       "      <td>42</td>\n",
-       "      <td>69</td>\n",
-       "      <td>92.1</td>\n",
-       "      <td>16.6</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>645</td>\n",
-       "      <td>161</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.2</td>\n",
-       "      <td>46.8</td>\n",
-       "      <td>52.8</td>\n",
-       "      <td>47.2</td>\n",
-       "      <td>41</td>\n",
-       "      <td>80</td>\n",
-       "      <td>91.9</td>\n",
-       "      <td>20.3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0        644      162         0.8       0.2              53.1   \n",
-       "1     1        645      161         0.8       0.2              53.0   \n",
-       "2     2        645      161         0.8       0.2              53.0   \n",
-       "3     3        645      161         0.8       0.2              53.2   \n",
-       "4     4        645      161         0.8       0.2              53.2   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                46.9            53.1              46.9                   38   \n",
-       "1                47.0            53.4              46.6                   40   \n",
-       "2                47.0            53.4              46.6                   37   \n",
-       "3                46.8            52.8              47.2                   42   \n",
-       "4                46.8            52.8              47.2                   41   \n",
-       "\n",
-       "   num_leaking_smiles  train_leaking_uniprot (%)  train_leaking_smiles (%)  \\\n",
-       "0                  71                       87.7                      16.9   \n",
-       "1                  78                       93.3                      20.0   \n",
-       "2                  69                       90.1                      19.1   \n",
-       "3                  69                       92.1                      16.6   \n",
-       "4                  80                       91.9                      20.3   \n",
-       "\n",
-       "   train_unique_groups  val_unique_groups  \n",
-       "0                    2                  2  \n",
-       "1                    2                  2  \n",
-       "2                    2                  2  \n",
-       "3                    2                  2  \n",
-       "4                    2                  2  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n",
-      "Split type: tanimoto\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_leaking_uniprot (%)</th>\n",
-       "      <th>train_leaking_smiles (%)</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>556</td>\n",
-       "      <td>131</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.2</td>\n",
-       "      <td>46.8</td>\n",
-       "      <td>50.4</td>\n",
-       "      <td>49.6</td>\n",
-       "      <td>36</td>\n",
-       "      <td>0</td>\n",
-       "      <td>89.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>23</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>516</td>\n",
-       "      <td>171</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.9</td>\n",
-       "      <td>48.1</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>34</td>\n",
-       "      <td>0</td>\n",
-       "      <td>85.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>23</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>507</td>\n",
-       "      <td>180</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>51.5</td>\n",
-       "      <td>48.5</td>\n",
-       "      <td>56.1</td>\n",
-       "      <td>43.9</td>\n",
-       "      <td>34</td>\n",
-       "      <td>0</td>\n",
-       "      <td>85.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>584</td>\n",
-       "      <td>103</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>53.9</td>\n",
-       "      <td>46.1</td>\n",
-       "      <td>45.6</td>\n",
-       "      <td>54.4</td>\n",
-       "      <td>27</td>\n",
-       "      <td>0</td>\n",
-       "      <td>74.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>585</td>\n",
-       "      <td>102</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>52.6</td>\n",
-       "      <td>47.4</td>\n",
-       "      <td>52.9</td>\n",
-       "      <td>47.1</td>\n",
-       "      <td>33</td>\n",
-       "      <td>0</td>\n",
-       "      <td>84.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0        556      131         0.8       0.2              53.2   \n",
-       "1     1        516      171         0.8       0.2              51.9   \n",
-       "2     2        507      180         0.7       0.3              51.5   \n",
-       "3     3        584      103         0.9       0.1              53.9   \n",
-       "4     4        585      102         0.9       0.1              52.6   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                46.8            50.4              49.6                   36   \n",
-       "1                48.1            55.0              45.0                   34   \n",
-       "2                48.5            56.1              43.9                   34   \n",
-       "3                46.1            45.6              54.4                   27   \n",
-       "4                47.4            52.9              47.1                   33   \n",
-       "\n",
-       "   num_leaking_smiles  train_leaking_uniprot (%)  train_leaking_smiles (%)  \\\n",
-       "0                   0                       89.4                       0.0   \n",
-       "1                   0                       85.9                       0.0   \n",
-       "2                   0                       85.4                       0.0   \n",
-       "3                   0                       74.1                       0.0   \n",
-       "4                   0                       84.8                       0.0   \n",
-       "\n",
-       "   train_unique_groups  val_unique_groups  \n",
-       "0                   23                  6  \n",
-       "1                   23                  6  \n",
-       "2                   22                  7  \n",
-       "3                   24                  5  \n",
-       "4                   24                  5  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n",
-      "Split type: uniprot\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_leaking_uniprot (%)</th>\n",
-       "      <th>train_leaking_smiles (%)</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>518</td>\n",
-       "      <td>172</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>50.6</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>19</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>574</td>\n",
-       "      <td>116</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>52.4</td>\n",
-       "      <td>47.6</td>\n",
-       "      <td>43.1</td>\n",
-       "      <td>56.9</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>556</td>\n",
-       "      <td>134</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>53.6</td>\n",
-       "      <td>46.4</td>\n",
-       "      <td>39.6</td>\n",
-       "      <td>60.4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>19</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>531</td>\n",
-       "      <td>159</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>57.2</td>\n",
-       "      <td>42.8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>581</td>\n",
-       "      <td>109</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>48.4</td>\n",
-       "      <td>51.6</td>\n",
-       "      <td>64.2</td>\n",
-       "      <td>35.8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>21</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0        518      172         0.8       0.2              51.0   \n",
-       "1     1        574      116         0.8       0.2              52.4   \n",
-       "2     2        556      134         0.8       0.2              53.6   \n",
-       "3     3        531      159         0.8       0.2              49.0   \n",
-       "4     4        581      109         0.8       0.2              48.4   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                49.0            50.6              49.4                    0   \n",
-       "1                47.6            43.1              56.9                    0   \n",
-       "2                46.4            39.6              60.4                    0   \n",
-       "3                51.0            57.2              42.8                    0   \n",
-       "4                51.6            64.2              35.8                    0   \n",
-       "\n",
-       "   num_leaking_smiles  train_leaking_uniprot (%)  train_leaking_smiles (%)  \\\n",
-       "0                  19                        0.0                       3.7   \n",
-       "1                   0                        0.0                       0.0   \n",
-       "2                  19                        0.0                       6.3   \n",
-       "3                   0                        0.0                       0.0   \n",
-       "4                   0                        0.0                       0.0   \n",
-       "\n",
-       "   train_unique_groups  val_unique_groups  \n",
-       "0                   19                  5  \n",
-       "1                   18                  6  \n",
-       "2                   19                  5  \n",
-       "3                   19                  5  \n",
-       "4                   21                  3  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import (\n",
-    "    StratifiedKFold,\n",
-    "    StratifiedGroupKFold,\n",
-    ")\n",
-    "\n",
-    "n_splits = 5\n",
-    "\n",
-    "for split_type, indeces in test_indeces.items():\n",
-    "    active_df = protac_df[protac_df[active_col].notna()].copy()\n",
-    "    test_df = active_df.loc[indeces]\n",
-    "    train_val_df = active_df[~active_df.index.isin(test_df.index)]\n",
-    "    \n",
-    "    if split_type == 'random':\n",
-    "        kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        group = None\n",
-    "    elif split_type == 'e3_ligase':\n",
-    "        kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        group = train_val_df['E3 Group'].to_numpy()\n",
-    "    elif split_type == 'tanimoto':\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        group = train_val_df['Tanimoto Group'].to_numpy()\n",
-    "    elif split_type == 'uniprot':\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        group = train_val_df['Uniprot Group'].to_numpy()\n",
-    "\n",
-    "    X = train_val_df.drop(columns=active_col)\n",
-    "    y = train_val_df[active_col].tolist()\n",
-    "\n",
-    "    print(f'Split type: {split_type}')\n",
-    "    fig, ax = plt.subplots(figsize=(6, 3))\n",
-    "    plot_cv_indices(kf, X=X, y=y, group=group, ax=ax, n_splits=n_splits)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    stats = []\n",
-    "    for k, (train_index, val_index) in enumerate(kf.split(X, y, group)):\n",
-    "        train_df = train_val_df.iloc[train_index]\n",
-    "        val_df = train_val_df.iloc[val_index]\n",
-    "\n",
-    "        leaking_uniprot = list(set(train_df['Uniprot']).intersection(set(val_df['Uniprot'])))\n",
-    "        leaking_smiles = list(set(train_df['Smiles']).intersection(set(val_df['Smiles'])))\n",
-    "\n",
-    "        stat = {\n",
-    "            'fold': k,\n",
-    "            'train_len': len(train_df),\n",
-    "            'val_len': len(val_df),\n",
-    "            'train_perc': len(train_df) / len(train_val_df),\n",
-    "            'val_perc': len(val_df) / len(train_val_df),\n",
-    "            'train_active (%)': train_df[active_col].sum() / len(train_df) * 100,\n",
-    "            'train_inactive (%)': (len(train_df) - train_df[active_col].sum()) / len(train_df) * 100,\n",
-    "            'val_active (%)': val_df[active_col].sum() / len(val_df) * 100,\n",
-    "            'val_inactive (%)': (len(val_df) - val_df[active_col].sum()) / len(val_df) * 100,\n",
-    "            'num_leaking_uniprot': len(leaking_uniprot),\n",
-    "            'num_leaking_smiles': len(leaking_smiles),\n",
-    "            'train_leaking_uniprot (%)': len(train_df[train_df['Uniprot'].isin(leaking_uniprot)]) / len(train_df) * 100,\n",
-    "            'train_leaking_smiles (%)': len(train_df[train_df['Smiles'].isin(leaking_smiles)]) / len(train_df) * 100,\n",
-    "        }\n",
-    "        if split_type != 'random':\n",
-    "            stat['train_unique_groups'] = len(np.unique(group[train_index]))\n",
-    "            stat['val_unique_groups'] = len(np.unique(group[val_index]))\n",
-    "        stats.append(stat)\n",
-    "    display(pd.DataFrame(stats).round(1))\n",
-    "    print('-' * 120)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train/Val Sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Group: random\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\3799429218.py:31: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
-      "  ax.scatter(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
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-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1297</td>\n",
-       "      <td>325</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.7</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>50.8</td>\n",
-       "      <td>49.2</td>\n",
-       "      <td>60</td>\n",
-       "      <td>154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1297</td>\n",
-       "      <td>325</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.7</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>50.8</td>\n",
-       "      <td>49.2</td>\n",
-       "      <td>61</td>\n",
-       "      <td>169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1298</td>\n",
-       "      <td>324</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.7</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>50.6</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>59</td>\n",
-       "      <td>155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1298</td>\n",
-       "      <td>324</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.7</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>50.6</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>62</td>\n",
-       "      <td>150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1298</td>\n",
-       "      <td>324</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.7</td>\n",
-       "      <td>49.3</td>\n",
-       "      <td>50.6</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>56</td>\n",
-       "      <td>149</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0       1297      325         0.8       0.2              50.7   \n",
-       "1     1       1297      325         0.8       0.2              50.7   \n",
-       "2     2       1298      324         0.8       0.2              50.7   \n",
-       "3     3       1298      324         0.8       0.2              50.7   \n",
-       "4     4       1298      324         0.8       0.2              50.7   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                49.3            50.8              49.2                   60   \n",
-       "1                49.3            50.8              49.2                   61   \n",
-       "2                49.3            50.6              49.4                   59   \n",
-       "3                49.3            50.6              49.4                   62   \n",
-       "4                49.3            50.6              49.4                   56   \n",
-       "\n",
-       "   num_leaking_smiles  \n",
-       "0                 154  \n",
-       "1                 169  \n",
-       "2                 155  \n",
-       "3                 150  \n",
-       "4                 149  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n",
-      "Number of unique groups: 87\n",
-      "Group: uniprot\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1037</td>\n",
-       "      <td>585</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>52.6</td>\n",
-       "      <td>47.4</td>\n",
-       "      <td>47.4</td>\n",
-       "      <td>52.6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>104</td>\n",
-       "      <td>66</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1259</td>\n",
-       "      <td>363</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>52.5</td>\n",
-       "      <td>47.5</td>\n",
-       "      <td>44.4</td>\n",
-       "      <td>55.6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>17</td>\n",
-       "      <td>73</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1471</td>\n",
-       "      <td>151</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>49.8</td>\n",
-       "      <td>50.2</td>\n",
-       "      <td>58.9</td>\n",
-       "      <td>41.1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>74</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1304</td>\n",
-       "      <td>318</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>50.2</td>\n",
-       "      <td>49.8</td>\n",
-       "      <td>52.8</td>\n",
-       "      <td>47.2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>68</td>\n",
-       "      <td>66</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1417</td>\n",
-       "      <td>205</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59</td>\n",
-       "      <td>69</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0       1037      585         0.6       0.4              52.6   \n",
-       "1     1       1259      363         0.8       0.2              52.5   \n",
-       "2     2       1471      151         0.9       0.1              49.8   \n",
-       "3     3       1304      318         0.8       0.2              50.2   \n",
-       "4     4       1417      205         0.9       0.1              49.0   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                47.4            47.4              52.6                    0   \n",
-       "1                47.5            44.4              55.6                    0   \n",
-       "2                50.2            58.9              41.1                    0   \n",
-       "3                49.8            52.8              47.2                    0   \n",
-       "4                51.0            62.0              38.0                    0   \n",
-       "\n",
-       "   num_leaking_smiles  train_unique_groups  val_unique_groups  \n",
-       "0                 104                   66                 21  \n",
-       "1                  17                   73                 14  \n",
-       "2                   4                   74                 13  \n",
-       "3                  68                   66                 21  \n",
-       "4                  59                   69                 18  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n",
-      "Number of unique groups: 68\n",
-      "Group: tanimoto\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fold</th>\n",
-       "      <th>train_len</th>\n",
-       "      <th>val_len</th>\n",
-       "      <th>train_perc</th>\n",
-       "      <th>val_perc</th>\n",
-       "      <th>train_active (%)</th>\n",
-       "      <th>train_inactive (%)</th>\n",
-       "      <th>val_active (%)</th>\n",
-       "      <th>val_inactive (%)</th>\n",
-       "      <th>num_leaking_uniprot</th>\n",
-       "      <th>num_leaking_smiles</th>\n",
-       "      <th>train_unique_groups</th>\n",
-       "      <th>val_unique_groups</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1305</td>\n",
-       "      <td>317</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>49.9</td>\n",
-       "      <td>50.1</td>\n",
-       "      <td>53.9</td>\n",
-       "      <td>46.1</td>\n",
-       "      <td>52</td>\n",
-       "      <td>0</td>\n",
-       "      <td>54</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1034</td>\n",
-       "      <td>588</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>49.9</td>\n",
-       "      <td>50.1</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>54</td>\n",
-       "      <td>0</td>\n",
-       "      <td>51</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1412</td>\n",
-       "      <td>210</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>50.6</td>\n",
-       "      <td>49.4</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>58</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1277</td>\n",
-       "      <td>345</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>52.3</td>\n",
-       "      <td>47.7</td>\n",
-       "      <td>44.6</td>\n",
-       "      <td>55.4</td>\n",
-       "      <td>46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>57</td>\n",
-       "      <td>11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>162</td>\n",
-       "      <td>0.9</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>50.5</td>\n",
-       "      <td>49.5</td>\n",
-       "      <td>51.9</td>\n",
-       "      <td>48.1</td>\n",
-       "      <td>39</td>\n",
-       "      <td>0</td>\n",
-       "      <td>52</td>\n",
-       "      <td>16</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   fold  train_len  val_len  train_perc  val_perc  train_active (%)  \\\n",
-       "0     0       1305      317         0.8       0.2              49.9   \n",
-       "1     1       1034      588         0.6       0.4              49.9   \n",
-       "2     2       1412      210         0.9       0.1              50.6   \n",
-       "3     3       1277      345         0.8       0.2              52.3   \n",
-       "4     4       1460      162         0.9       0.1              50.5   \n",
-       "\n",
-       "   train_inactive (%)  val_active (%)  val_inactive (%)  num_leaking_uniprot  \\\n",
-       "0                50.1            53.9              46.1                   52   \n",
-       "1                50.1            52.0              48.0                   54   \n",
-       "2                49.4            51.0              49.0                   46   \n",
-       "3                47.7            44.6              55.4                   46   \n",
-       "4                49.5            51.9              48.1                   39   \n",
-       "\n",
-       "   num_leaking_smiles  train_unique_groups  val_unique_groups  \n",
-       "0                   0                   54                 14  \n",
-       "1                   0                   51                 17  \n",
-       "2                   0                   58                 10  \n",
-       "3                   0                   57                 11  \n",
-       "4                   0                   52                 16  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------------------------------------------------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import (\n",
-    "    StratifiedKFold,\n",
-    "    StratifiedGroupKFold,\n",
-    ")\n",
-    "from sklearn.preprocessing import OrdinalEncoder\n",
-    "\n",
-    "# NOTE: When set to 60, it will result in 29 groups, with nice distributions of\n",
-    "# the number of unique groups in the train and validation sets, together with\n",
-    "# the number of active and inactive PROTACs. \n",
-    "n_bins_tanimoto = 60 if active_col == active_col else 400\n",
-    "n_splits = 5\n",
-    "# The train and validation sets will be created from the active PROTACs only,\n",
-    "# i.e., the ones with active_col column not NaN, and that are NOT in the test set\n",
-    "active_df = protac_df[protac_df[active_col].notna()]\n",
-    "train_val_df = active_df[~active_df.index.isin(test_df.index)].copy()\n",
-    "\n",
-    "# Make three groups for CV:\n",
-    "# * Random split\n",
-    "# * Split by Uniprot (POI)\n",
-    "# * Split by least tanimoto similarity PROTAC-wise\n",
-    "groups = [\n",
-    "    'random',\n",
-    "    'uniprot',\n",
-    "    'tanimoto',\n",
-    "]\n",
-    "for group_type in groups:\n",
-    "    if group_type == 'random':\n",
-    "        kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        groups = None\n",
-    "    elif group_type == 'uniprot':\n",
-    "        # Split by Uniprot\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        encoder = OrdinalEncoder()\n",
-    "        groups = encoder.fit_transform(train_val_df['Uniprot'].values.reshape(-1, 1))\n",
-    "        print(f'Number of unique groups: {len(encoder.categories_[0])}')\n",
-    "    elif group_type == 'tanimoto':\n",
-    "        # Split by tanimoto similarity, i.e., group_type PROTACs with similar Avg Tanimoto\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        tanimoto_groups = pd.cut(train_val_df['Avg Tanimoto'], bins=n_bins_tanimoto).copy()\n",
-    "        encoder = OrdinalEncoder()\n",
-    "        groups = encoder.fit_transform(tanimoto_groups.values.reshape(-1, 1))\n",
-    "        print(f'Number of unique groups: {len(encoder.categories_[0])}')\n",
-    "\n",
-    "    X = train_val_df.drop(columns=active_col)\n",
-    "    y = train_val_df[active_col].tolist()\n",
-    "\n",
-    "    print(f'Group: {group_type}')\n",
-    "    fig, ax = plt.subplots(figsize=(6, 3))\n",
-    "    plot_cv_indices(kf, X=X, y=y, group=groups, ax=ax, n_splits=n_splits)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "    stats = []\n",
-    "    for k, (train_index, val_index) in enumerate(kf.split(X, y, groups)):\n",
-    "        train_df = train_val_df.iloc[train_index]\n",
-    "        val_df = train_val_df.iloc[val_index]\n",
-    "        stat = {\n",
-    "            'fold': k,\n",
-    "            'train_len': len(train_df),\n",
-    "            'val_len': len(val_df),\n",
-    "            'train_perc': len(train_df) / len(train_val_df),\n",
-    "            'val_perc': len(val_df) / len(train_val_df),\n",
-    "            'train_active (%)': train_df[active_col].sum() / len(train_df) * 100,\n",
-    "            'train_inactive (%)': (len(train_df) - train_df[active_col].sum()) / len(train_df) * 100,\n",
-    "            'val_active (%)': val_df[active_col].sum() / len(val_df) * 100,\n",
-    "            'val_inactive (%)': (len(val_df) - val_df[active_col].sum()) / len(val_df) * 100,\n",
-    "            'num_leaking_uniprot': len(set(train_df['Uniprot']).intersection(set(val_df['Uniprot']))),\n",
-    "            'num_leaking_smiles': len(set(train_df['Smiles']).intersection(set(val_df['Smiles']))),\n",
-    "        }\n",
-    "        if group_type != 'random':\n",
-    "            stat['train_unique_groups'] = len(np.unique(groups[train_index]))\n",
-    "            stat['val_unique_groups'] = len(np.unique(groups[val_index]))\n",
-    "        stats.append(stat)\n",
-    "    display(pd.DataFrame(stats).round(1))\n",
-    "    print('-' * 120)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run CV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Global seed set to 42\n"
-     ]
-    }
-   ],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "# Seed everything in pytorch lightning\n",
-    "pl.seed_everything(42)\n",
-    "\n",
-    "\n",
-    "def train_model(\n",
-    "        train_df,\n",
-    "        val_df,\n",
-    "        test_df=None,\n",
-    "        hidden_dim=768,\n",
-    "        batch_size=8,\n",
-    "        learning_rate=2e-5,\n",
-    "        max_epochs=50,\n",
-    "        smiles_emb_dim=1024,\n",
-    "        smote_n_neighbors=5,\n",
-    "        use_ored_activity=False if active_col == active_col else True,\n",
-    "        fast_dev_run=False,\n",
-    "        disabled_embeddings=[],\n",
-    ") -> tuple:\n",
-    "    \"\"\" Train a PROTAC model using the given datasets and hyperparameters.\n",
-    "    \n",
-    "    Args:\n",
-    "        train_df (pd.DataFrame): The training set.\n",
-    "        val_df (pd.DataFrame): The validation set.\n",
-    "        test_df (pd.DataFrame): The test set.\n",
-    "        hidden_dim (int): The hidden dimension of the model.\n",
-    "        batch_size (int): The batch size.\n",
-    "        learning_rate (float): The learning rate.\n",
-    "        max_epochs (int): The maximum number of epochs.\n",
-    "        smiles_emb_dim (int): The dimension of the SMILES embeddings.\n",
-    "        smote_n_neighbors (int): The number of neighbors for the SMOTE oversampler.\n",
-    "        use_ored_activity (bool): Whether to use the ORED activity column.\n",
-    "        fast_dev_run (bool): Whether to run a fast development run.\n",
-    "        disabled_embeddings (list): The list of disabled embeddings.\n",
-    "    \n",
-    "    Returns:\n",
-    "        tuple: The trained model, the trainer, and the metrics.\n",
-    "    \"\"\"\n",
-    "    oversampler = SMOTE(k_neighbors=smote_n_neighbors, random_state=42)\n",
-    "    train_ds = PROTAC_Dataset(\n",
-    "        train_df,\n",
-    "        protein_embeddings,\n",
-    "        cell2embedding,\n",
-    "        smiles2fp,\n",
-    "        use_smote=True,\n",
-    "        oversampler=oversampler,\n",
-    "        use_ored_activity=use_ored_activity,\n",
-    "    )\n",
-    "    val_ds = PROTAC_Dataset(\n",
-    "        val_df,\n",
-    "        protein_embeddings,\n",
-    "        cell2embedding,\n",
-    "        smiles2fp,\n",
-    "        use_ored_activity=use_ored_activity,\n",
-    "    )\n",
-    "    if test_df is not None:\n",
-    "        test_ds = PROTAC_Dataset(\n",
-    "            test_df,\n",
-    "            protein_embeddings,\n",
-    "            cell2embedding,\n",
-    "            smiles2fp,\n",
-    "            use_ored_activity=use_ored_activity,\n",
-    "        )\n",
-    "    logger = pl.loggers.TensorBoardLogger(\n",
-    "        save_dir='../logs',\n",
-    "        name='protac',\n",
-    "    )\n",
-    "    callbacks = [\n",
-    "        pl.callbacks.EarlyStopping(\n",
-    "            monitor='train_loss',\n",
-    "            patience=10,\n",
-    "            mode='max',\n",
-    "            verbose=True,\n",
-    "        ),\n",
-    "        # pl.callbacks.ModelCheckpoint(\n",
-    "        #     monitor='val_acc',\n",
-    "        #     mode='max',\n",
-    "        #     verbose=True,\n",
-    "        #     filename='{epoch}-{val_metrics_opt_score:.4f}',\n",
-    "        # ),\n",
-    "    ]\n",
-    "    # Define Trainer\n",
-    "    trainer = pl.Trainer(\n",
-    "        logger=logger,\n",
-    "        callbacks=callbacks,\n",
-    "        max_epochs=max_epochs,\n",
-    "        fast_dev_run=fast_dev_run,\n",
-    "        enable_model_summary=False,\n",
-    "        enable_checkpointing=False,\n",
-    "    )\n",
-    "    model = PROTAC_Model(\n",
-    "        hidden_dim=hidden_dim,\n",
-    "        smiles_emb_dim=smiles_emb_dim,\n",
-    "        poi_emb_dim=1024,\n",
-    "        e3_emb_dim=1024,\n",
-    "        cell_emb_dim=768,\n",
-    "        batch_size=batch_size,\n",
-    "        learning_rate=learning_rate,\n",
-    "        train_dataset=train_ds,\n",
-    "        val_dataset=val_ds,\n",
-    "        test_dataset=test_ds if test_df is not None else None,\n",
-    "        disabled_embeddings=disabled_embeddings,\n",
-    "    )\n",
-    "    with warnings.catch_warnings():\n",
-    "        warnings.simplefilter(\"ignore\")\n",
-    "        trainer.fit(model)\n",
-    "    metrics = trainer.validate(model, verbose=False)[0]\n",
-    "    if test_df is not None:\n",
-    "        test_metrics = trainer.test(model, verbose=False)[0]\n",
-    "        metrics.update(test_metrics)\n",
-    "    return model, trainer, metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Setup hyperparameter optimization:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import optuna\n",
-    "import pandas as pd\n",
-    "\n",
-    "\n",
-    "def objective(\n",
-    "        trial,\n",
-    "        train_df,\n",
-    "        val_df,\n",
-    "        hidden_dim_options,\n",
-    "        batch_size_options,\n",
-    "        learning_rate_options,\n",
-    "        max_epochs_options,\n",
-    "        fast_dev_run=False,\n",
-    ") -> float:\n",
-    "    # Generate the hyperparameters\n",
-    "    hidden_dim = trial.suggest_categorical('hidden_dim', hidden_dim_options)\n",
-    "    batch_size = trial.suggest_categorical('batch_size', batch_size_options)\n",
-    "    learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-    "    max_epochs = trial.suggest_categorical('max_epochs', max_epochs_options)\n",
-    "\n",
-    "    # Train the model with the current set of hyperparameters\n",
-    "    _, _, metrics = train_model(\n",
-    "        train_df,\n",
-    "        val_df,\n",
-    "        hidden_dim=hidden_dim,\n",
-    "        batch_size=batch_size,\n",
-    "        learning_rate=learning_rate,\n",
-    "        max_epochs=max_epochs,\n",
-    "        fast_dev_run=fast_dev_run,\n",
-    "    )\n",
-    "\n",
-    "    # Metrics is a dictionary containing at least the validation loss\n",
-    "    val_loss = metrics['val_loss']\n",
-    "    val_acc = metrics['val_acc']\n",
-    "    val_roc_auc = metrics['val_roc_auc']\n",
-    "    \n",
-    "    # Optuna aims to minimize the objective\n",
-    "    return val_loss - val_acc - val_roc_auc\n",
-    "\n",
-    "\n",
-    "def hyperparameter_tuning_and_training(\n",
-    "        train_df,\n",
-    "        val_df,\n",
-    "        test_df,\n",
-    "        fast_dev_run=False,\n",
-    "        n_trials=20,\n",
-    ") -> tuple:\n",
-    "    \"\"\" Hyperparameter tuning and training of a PROTAC model.\n",
-    "    \n",
-    "    Args:\n",
-    "        train_df (pd.DataFrame): The training set.\n",
-    "        val_df (pd.DataFrame): The validation set.\n",
-    "        test_df (pd.DataFrame): The test set.\n",
-    "        fast_dev_run (bool): Whether to run a fast development run.\n",
-    "\n",
-    "    Returns:\n",
-    "        tuple: The trained model, the trainer, and the best metrics.\n",
-    "    \"\"\"\n",
-    "    # Define the search space\n",
-    "    hidden_dim_options = [256, 512, 768]\n",
-    "    batch_size_options = [8, 16, 32]\n",
-    "    learning_rate_options = (1e-5, 1e-3) # min and max values for loguniform distribution\n",
-    "    max_epochs_options = [10, 20, 50]\n",
-    "\n",
-    "    # Create an Optuna study object\n",
-    "    study = optuna.create_study(direction='minimize')\n",
-    "    study.optimize(lambda trial: objective(\n",
-    "            trial,\n",
-    "            train_df,\n",
-    "            val_df,\n",
-    "            hidden_dim_options,\n",
-    "            batch_size_options,\n",
-    "            learning_rate_options,\n",
-    "            max_epochs_options,\n",
-    "            fast_dev_run=fast_dev_run,),\n",
-    "        n_trials=n_trials,\n",
-    "    )\n",
-    "\n",
-    "    # Retrieve the best hyperparameters\n",
-    "    best_params = study.best_params\n",
-    "    best_hidden_dim = best_params['hidden_dim']\n",
-    "    best_batch_size = best_params['batch_size']\n",
-    "    best_learning_rate = best_params['learning_rate']\n",
-    "    best_max_epochs = best_params['max_epochs']\n",
-    "\n",
-    "    # Retrain the model with the best hyperparameters\n",
-    "    model, trainer, metrics = train_model(\n",
-    "        train_df,\n",
-    "        val_df,\n",
-    "        test_df,\n",
-    "        hidden_dim=best_hidden_dim,\n",
-    "        batch_size=best_batch_size,\n",
-    "        learning_rate=best_learning_rate,\n",
-    "        max_epochs=best_max_epochs,\n",
-    "        fast_dev_run=fast_dev_run,\n",
-    "    )\n",
-    "\n",
-    "    # Return the best metrics\n",
-    "    return model, trainer, metrics\n",
-    "\n",
-    "# Example usage\n",
-    "# train_df, val_df, test_df = load_your_data()  # You need to load your datasets here\n",
-    "# model, trainer, best_metrics = hyperparameter_tuning_and_training(train_df, val_df, test_df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Loop over the different splits and train the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:16,758] A new study created in memory with name: no-name-5e5f8509-91a1-4cf5-bf2a-2179a65be311\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting CV for group type: random\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ab55d8cf17546eb8133e34b8b4e4238",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e718008d0784ca888b6d8bba5782cfe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "911405ead7364cd7a90e1d15a751c34f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:21,288] Trial 0 finished with value: -0.49837255477905273 and parameters: {'hidden_dim': 256, 'batch_size': 16, 'learning_rate': 0.00020308991149780922, 'max_epochs': 50}. Best is trial 0 with value: -0.49837255477905273.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1f783e5c0ea4750902d7bf1e83464d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c51a0186539948bdb3d128744172f816",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aae91f55e50f49b49a28c4737b510ffa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:22,371] Trial 1 finished with value: -0.35346728563308716 and parameters: {'hidden_dim': 768, 'batch_size': 32, 'learning_rate': 0.00031805066402740723, 'max_epochs': 20}. Best is trial 0 with value: -0.49837255477905273.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1f833f8abff949248a75d41c24f028e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0286d3876764ddb8d59bb2dfc82b837",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec47d14d7dec4444a34487f2b26fbf7b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:23,655] Trial 2 finished with value: -0.2880619168281555 and parameters: {'hidden_dim': 768, 'batch_size': 32, 'learning_rate': 1.176575131420344e-05, 'max_epochs': 20}. Best is trial 0 with value: -0.49837255477905273.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c03e89a9b634d57bc38528e6897041a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1fa21c2ee024b81baff258bdacf8561",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ea38efc9c02d405e96323d8c3f87ca72",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:24,659] Trial 3 finished with value: -0.5414311289787292 and parameters: {'hidden_dim': 768, 'batch_size': 16, 'learning_rate': 0.00013698234841601027, 'max_epochs': 10}. Best is trial 3 with value: -0.5414311289787292.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a0d33a8247d47e6992bd006addeb974",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6ad28ceec1a46ab8e66199cd28738e0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d1d86bbaa9f4b24ac065d96ef1d03b0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:25,674] Trial 4 finished with value: -0.1361178308725357 and parameters: {'hidden_dim': 512, 'batch_size': 16, 'learning_rate': 1.9352863157222062e-05, 'max_epochs': 50}. Best is trial 3 with value: -0.5414311289787292.\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Missing logger folder: ../logs\\protac\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4b53a5ada484ec99466675f2e5439b7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41012eea6cf64742827e8cf16830df0b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7a5c5d53c344cca975e6470a915ab24",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.626\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "442647052642403293f00bb6c46f1545",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "12ab9b01aa774ce49d3ed795dbcc8e0c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f89735636e66403db3ddee8d765ddbdf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db091ba91cc748ba88bc3e3a68693550",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ad2fdbcca1134240b4bfb672c92d25fe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d0e244bac004e6386b9106ca41f1e97",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "089864b2cd074250bd19483f8e7ceed7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1f4ee6bb2fd4209a375966842eccb92",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b62668d532a64825b4d51738129e4648",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=10` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd822a8c95d547998ca6ec6cdc5cf87b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "916e1b36018042aa84b00519ab452ce9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:49,371] A new study created in memory with name: no-name-67599f74-76a3-4f9a-872e-7fc2c9bbfc95\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de881788e89846669071f69cb5766b0c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0d61be46b134a0db9f9630ecade957e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "376c81fa1de740a181705ab6b34d85a9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:50,372] Trial 0 finished with value: -0.3695070147514343 and parameters: {'hidden_dim': 512, 'batch_size': 16, 'learning_rate': 5.4143985566835615e-05, 'max_epochs': 10}. Best is trial 0 with value: -0.3695070147514343.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "23ac678d3eac494aa675c9bbfaee3ba4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bdf99db00edd4cb38407ab30b9ff4905",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2d7b199dd9354f3c83f30524928535c9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:51,384] Trial 1 finished with value: 0.20001351833343506 and parameters: {'hidden_dim': 768, 'batch_size': 8, 'learning_rate': 0.0007220062724631033, 'max_epochs': 20}. Best is trial 0 with value: -0.3695070147514343.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db3169a789ed4885a436c3e8976a2526",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e30d9f9fc0144a8ac4a599785303d50",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f06ef90ad6214a76ad2e319ad2b6bea3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:52,377] Trial 2 finished with value: -0.8092958331108093 and parameters: {'hidden_dim': 256, 'batch_size': 8, 'learning_rate': 9.439937829603916e-05, 'max_epochs': 20}. Best is trial 2 with value: -0.8092958331108093.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "639775c68faf4996843ba43f52a2f30b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd2c604bab2544a4a5eaaac9edaf0265",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "074a0b146f6c4eb8942b5fe3dfeeeb3a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:53,408] Trial 3 finished with value: -0.5887078046798706 and parameters: {'hidden_dim': 512, 'batch_size': 16, 'learning_rate': 8.932618736601939e-05, 'max_epochs': 20}. Best is trial 2 with value: -0.8092958331108093.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "397baad36d304e96bbb58554baa46039",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3254cf4c371c47adbcb66251d6f51da4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "11974d7651b44b818c543c301d60d7e0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:25:54,462] Trial 4 finished with value: -0.40962374210357666 and parameters: {'hidden_dim': 512, 'batch_size': 32, 'learning_rate': 1.2467004427511022e-05, 'max_epochs': 50}. Best is trial 2 with value: -0.8092958331108093.\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57f9e0098bf449788464492a9227c862",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2cca18138e54158aa886cf89a237d96",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58a6d0aa0851473aa3710524a34862f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.675\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5787378c3144ea2badb7aa640d5dd8d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "65991aa8aca7465883e07ce7a6b32f14",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6bfadda7d504dbf94c89cc30e429d71",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "85855c2c1f3745dcba995d29dbdd293d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6d1734877b024e9495f6ffe9e37a6608",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3cf268ef211843e3b03c81636c56c511",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9d3b2dfd2f8a45fbb080598789ab5056",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bfb5161d0fb74895958888e48b6a5f61",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f8b14605cff4f84b561580016d3667b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5ed3b0b5aae478883c86a504c6e5f89",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.675. Signaling Trainer to stop.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "24e70306a126485c9884fa4907dde11f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96171c30664e434090659b763d601979",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:26:53,634] A new study created in memory with name: no-name-9e12d8d9-2324-43cb-90d5-b5b5c0f9a5a6\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bdcaa55c773479ca0f7fc6d84ee30de",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d29c3f01bce84f17abd0d3fb8260baf4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "712d73d389c3490599997d9aa111d313",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:26:55,435] Trial 0 finished with value: -0.3183702230453491 and parameters: {'hidden_dim': 512, 'batch_size': 32, 'learning_rate': 0.00017987995942499933, 'max_epochs': 10}. Best is trial 0 with value: -0.3183702230453491.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb551c95e1d34f02bd52472e59c8b3eb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "844246eb11234733b3891a41050f7246",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "581e772ce6ca478bb1f55b23dea56134",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:26:57,107] Trial 1 finished with value: -0.9303019046783447 and parameters: {'hidden_dim': 768, 'batch_size': 16, 'learning_rate': 2.5785375263063633e-05, 'max_epochs': 50}. Best is trial 1 with value: -0.9303019046783447.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bd538ba481641d79bc225ddac180627",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca995c5f14c2432b9a0a32983dd8c37c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af183cb4a7404b8a906941b85f951de1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:26:58,309] Trial 2 finished with value: -0.4402801990509033 and parameters: {'hidden_dim': 512, 'batch_size': 32, 'learning_rate': 0.0006063649844421645, 'max_epochs': 50}. Best is trial 1 with value: -0.9303019046783447.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cc9f907239f468e9944073c1a405abc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "363ac4e214ad4beda207074b4d6eb09c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d966accc18247358029bbb7201bd867",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:26:59,729] Trial 3 finished with value: -0.44254231452941895 and parameters: {'hidden_dim': 768, 'batch_size': 16, 'learning_rate': 5.679380197917534e-05, 'max_epochs': 10}. Best is trial 1 with value: -0.9303019046783447.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b18fcf281c484a88967c3eaf70d01b02",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "829d20be7725457a9faae5ee8e89f975",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "374504e03d93465082c25b26de8cf01f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:01,015] Trial 4 finished with value: -0.7399877309799194 and parameters: {'hidden_dim': 768, 'batch_size': 8, 'learning_rate': 0.00021518663878199198, 'max_epochs': 20}. Best is trial 1 with value: -0.9303019046783447.\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48cee09c803541769c3efa87b2908dc5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7738c0c77e704692b29499898fe0790e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dbf5eff4eae946e2b4f45e186fe4352c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.689\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9cc0a1366ebd4b48aa4ba03a2a6b92b2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1113f950438d4a209737f5fdc0e59b9a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f38df0289414d1c9c3b483b465babfa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93c1ce820c0a4ccc99bad5c51c83895e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cfdd5412046456585fbf78e3f783bc0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0c8922d49d4480f862665e233e05b13",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "09be53cac3514719b5537e05296acaaa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e3bf41560d44ac5993567d6951a7de4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "26dbc573d27a4a3fbc0697635081e63b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Testing: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:23,770] A new study created in memory with name: no-name-5978f2ae-4e51-4035-aacd-87423edd0942\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d3cc1597aa1f44d591efa47a23d40b45",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2058664a4b184aa5b66e674c0c918b50",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78429b204ce642cc9f5b5533df4955ab",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:25,058] Trial 0 finished with value: -0.6108406186103821 and parameters: {'hidden_dim': 768, 'batch_size': 16, 'learning_rate': 1.9351370333038925e-05, 'max_epochs': 20}. Best is trial 0 with value: -0.6108406186103821.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c2d455a1c7f4b1c9c126023a6f72c2a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "95f4a125e23e4e018bc916c4d025da0e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "54d73a7d56c24b57b369a47968774a06",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:26,310] Trial 1 finished with value: -0.4195728898048401 and parameters: {'hidden_dim': 768, 'batch_size': 32, 'learning_rate': 2.3905094257098406e-05, 'max_epochs': 10}. Best is trial 0 with value: -0.6108406186103821.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e3dd59ea1d04dfc8795b34b3137b92d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69de6910408743e89b767d50b2171a10",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3a07d8bf953e49bb98da1adc738cb733",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:27,552] Trial 2 finished with value: -0.4152451157569885 and parameters: {'hidden_dim': 768, 'batch_size': 32, 'learning_rate': 8.361618937706058e-05, 'max_epochs': 20}. Best is trial 0 with value: -0.6108406186103821.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f5033762ca948a8ae3fc37b634192d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b134cda7cde4916b8dfd6754061ff29",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13efc23cc85f4a6e960981a50817851e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:28,605] Trial 3 finished with value: -0.7726385593414307 and parameters: {'hidden_dim': 256, 'batch_size': 8, 'learning_rate': 1.4712830758755632e-05, 'max_epochs': 50}. Best is trial 3 with value: -0.7726385593414307.\n",
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_21940\\687521909.py:18: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
-      "  learning_rate = trial.suggest_loguniform('learning_rate', *learning_rate_options)\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "Running in `fast_dev_run` mode: will run the requested loop using 1 batch(es). Logging and checkpointing is suppressed.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a28f69021ca4d3a9401a6c0a64f5656",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c53d099417345108c2fcf81f59a33dd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_steps=1` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "108574dc33c6438b825332ae34201e2d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2024-03-11 12:27:29,884] Trial 4 finished with value: -0.5356066226959229 and parameters: {'hidden_dim': 768, 'batch_size': 32, 'learning_rate': 0.0008882131950057027, 'max_epochs': 50}. Best is trial 3 with value: -0.7726385593414307.\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "10311949e7cf4ed79ec42eb01b1801a7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5cf7f0cd972b47948871a1b6731c4096",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0323b68e6a1e48e6a57b84bfce11ef17",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.692\n"
-     ]
-    }
-   ],
-   "source": [
-    "report = []\n",
-    "active_df = protac_df[protac_df[active_col].notna()]\n",
-    "train_val_df = active_df[~active_df.index.isin(unique_samples)]\n",
-    "\n",
-    "# Make directory ../reports if it does not exist\n",
-    "if not os.path.exists('../reports'):\n",
-    "    os.makedirs('../reports')\n",
-    "\n",
-    "for group_type in ['random', 'uniprot', 'tanimoto']:\n",
-    "    print(f'Starting CV for group type: {group_type}')\n",
-    "    # Setup CV iterator and groups\n",
-    "    if group_type == 'random':\n",
-    "        kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        groups = None\n",
-    "    elif group_type == 'uniprot':\n",
-    "        # Split by Uniprot\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        encoder = OrdinalEncoder()\n",
-    "        groups = encoder.fit_transform(train_val_df['Uniprot'].values.reshape(-1, 1))\n",
-    "    elif group_type == 'tanimoto':\n",
-    "        # Split by tanimoto similarity, i.e., group_type PROTACs with similar Avg Tanimoto\n",
-    "        kf = StratifiedGroupKFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "        tanimoto_groups = pd.cut(train_val_df['Avg Tanimoto'], bins=n_bins_tanimoto).copy()\n",
-    "        encoder = OrdinalEncoder()\n",
-    "        groups = encoder.fit_transform(tanimoto_groups.values.reshape(-1, 1))\n",
-    "    # Start the CV over the folds\n",
-    "    X = train_val_df.drop(columns=active_col)\n",
-    "    y = train_val_df[active_col].tolist()\n",
-    "    for k, (train_index, val_index) in enumerate(kf.split(X, y, groups)):\n",
-    "        train_df = train_val_df.iloc[train_index]\n",
-    "        val_df = train_val_df.iloc[val_index]\n",
-    "        stats = {\n",
-    "            'fold': k,\n",
-    "            'group_type': group_type,\n",
-    "            'train_len': len(train_df),\n",
-    "            'val_len': len(val_df),\n",
-    "            'train_perc': len(train_df) / len(train_val_df),\n",
-    "            'val_perc': len(val_df) / len(train_val_df),\n",
-    "            'train_active_perc': train_df[active_col].sum() / len(train_df),\n",
-    "            'train_inactive_perc': (len(train_df) - train_df[active_col].sum()) / len(train_df),\n",
-    "            'val_active_perc': val_df[active_col].sum() / len(val_df),\n",
-    "            'val_inactive_perc': (len(val_df) - val_df[active_col].sum()) / len(val_df),\n",
-    "            'test_active_perc': test_df[active_col].sum() / len(test_df),\n",
-    "            'test_inactive_perc': (len(test_df) - test_df[active_col].sum()) / len(test_df),\n",
-    "            'num_leaking_uniprot': len(set(train_df['Uniprot']).intersection(set(val_df['Uniprot']))),\n",
-    "            'num_leaking_smiles': len(set(train_df['Smiles']).intersection(set(val_df['Smiles']))),\n",
-    "        }\n",
-    "        if group_type != 'random':\n",
-    "            stats['train_unique_groups'] = len(np.unique(groups[train_index]))\n",
-    "            stats['val_unique_groups'] = len(np.unique(groups[val_index]))\n",
-    "        # Train and evaluate the model\n",
-    "        # model, trainer, metrics = train_model(train_df, val_df, test_df)\n",
-    "        model, trainer, metrics = hyperparameter_tuning_and_training(train_df, val_df, test_df, fast_dev_run=True, n_trials=5)\n",
-    "        stats.update(metrics)\n",
-    "        del model\n",
-    "        del trainer\n",
-    "        report.append(stats)\n",
-    "    display(pd.DataFrame(report).round(1))\n",
-    "report = pd.DataFrame(report)\n",
-    "report.to_csv(\n",
-    "    f'../reports/cv_report_hparam_search_{n_splits}-splits.csv', index=False,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val_inactive_perc</th>\n",
-       "      <th>val_acc</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>test_inactive_perc</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>group_type</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>random</th>\n",
-       "      <td>0.740</td>\n",
-       "      <td>0.834</td>\n",
-       "      <td>0.903</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.846</td>\n",
-       "      <td>0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tanimoto</th>\n",
-       "      <td>0.753</td>\n",
-       "      <td>0.774</td>\n",
-       "      <td>0.784</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.892</td>\n",
-       "      <td>0.117</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uniprot</th>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.658</td>\n",
-       "      <td>0.626</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.862</td>\n",
-       "      <td>0.100</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            val_inactive_perc  val_acc  val_roc_auc  test_inactive_perc  \\\n",
-       "group_type                                                                \n",
-       "random                  0.740    0.834        0.903               0.923   \n",
-       "tanimoto                0.753    0.774        0.784               0.923   \n",
-       "uniprot                 0.705    0.658        0.626               0.923   \n",
-       "\n",
-       "            test_acc  test_roc_auc  \n",
-       "group_type                          \n",
-       "random         0.846         0.050  \n",
-       "tanimoto       0.892         0.117  \n",
-       "uniprot        0.862         0.100  "
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "report.groupby('group_type').mean().round(3)[['val_inactive_perc', 'val_acc', 'val_roc_auc', 'test_inactive_perc', 'test_acc', 'test_roc_auc']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CV Plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>val_active_perc</th>\n",
-       "      <th>val_inactive_perc</th>\n",
-       "      <th>val_acc</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>test_active_perc</th>\n",
-       "      <th>test_inactive_perc</th>\n",
-       "      <th>test_acc</th>\n",
-       "      <th>test_roc_auc</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>group_type</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>random</th>\n",
-       "      <td>0.260</td>\n",
-       "      <td>0.740</td>\n",
-       "      <td>0.818</td>\n",
-       "      <td>0.889</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.877</td>\n",
-       "      <td>0.083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>tanimoto</th>\n",
-       "      <td>0.236</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>0.770</td>\n",
-       "      <td>0.723</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.831</td>\n",
-       "      <td>0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>uniprot</th>\n",
-       "      <td>0.295</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.658</td>\n",
-       "      <td>0.602</td>\n",
-       "      <td>0.077</td>\n",
-       "      <td>0.923</td>\n",
-       "      <td>0.846</td>\n",
-       "      <td>0.017</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            val_active_perc  val_inactive_perc  val_acc  val_roc_auc  \\\n",
-       "group_type                                                             \n",
-       "random                0.260              0.740    0.818        0.889   \n",
-       "tanimoto              0.236              0.764    0.770        0.723   \n",
-       "uniprot               0.295              0.705    0.658        0.602   \n",
-       "\n",
-       "            test_active_perc  test_inactive_perc  test_acc  test_roc_auc  \n",
-       "group_type                                                                \n",
-       "random                 0.077               0.923     0.877         0.083  \n",
-       "tanimoto               0.077               0.923     0.831         0.050  \n",
-       "uniprot                0.077               0.923     0.846         0.017  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols_to_show = [\n",
-    "    'val_active_perc',\n",
-    "    'val_inactive_perc',\n",
-    "    'val_acc',\n",
-    "    'val_roc_auc',\n",
-    "    'test_active_perc',\n",
-    "    'test_inactive_perc',\n",
-    "    'test_acc',\n",
-    "    'test_roc_auc',\n",
-    "]\n",
-    "report = pd.read_csv('../reports/cv_report_hparam_search_5-splits_active-and.csv')\n",
-    "report.groupby('group_type').mean().round(3)[cols_to_show]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            val_acc  val_roc_auc  test_acc  test_roc_auc  dummy_accuracy_val  \\\n",
-      "group_type                                                                     \n",
-      "random        0.784        0.874     0.640         0.671               0.507   \n",
-      "tanimoto      0.719        0.789     0.547         0.618               0.501   \n",
-      "uniprot       0.582        0.614     0.547         0.611               0.531   \n",
-      "\n",
-      "            dummy_accuracy_test  \n",
-      "group_type                       \n",
-      "random                    0.533  \n",
-      "tanimoto                  0.533  \n",
-      "uniprot                   0.533  \n"
-     ]
-    }
-   ],
-   "source": [
-    "report = pd.read_csv(f'../reports/cv_report_hparam_search_5-splits_active-or.csv')\n",
-    "tmp = report.groupby('group_type').mean().round(3)[cols_to_show]\n",
-    "# Add a \"dummy_accuracy\" columns, one for val and one for test, to the dataframe consisting of the majority between active and inactive percentages\n",
-    "tmp['dummy_accuracy_val'] = tmp[['val_active_perc', 'val_inactive_perc']].max(axis=1)\n",
-    "tmp['dummy_accuracy_test'] = tmp[['test_active_perc', 'test_inactive_perc']].max(axis=1)\n",
-    "# Drop percentage columns\n",
-    "tmp = tmp.drop(columns=['val_active_perc', 'val_inactive_perc', 'test_active_perc', 'test_inactive_perc'])\n",
-    "# Print to console\n",
-    "print(tmp)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "n_splits = 5\n",
-    "\n",
-    "tmp = report.copy()[[\n",
-    "    'fold', 'group_type',\n",
-    "    'val_acc', 'val_roc_auc', # 'val_precision', 'val_recall', 'val_f1_score',\n",
-    "    'test_acc', 'test_roc_auc', # 'test_precision', 'test_recall', 'test_f1_score',\n",
-    "]]\n",
-    "# Rename columns\n",
-    "tmp.columns = [\n",
-    "    'Fold', 'CV-Groups',\n",
-    "    'Val Accuracy', 'Val ROC AUC', # 'Val Precision', 'Val Recall', 'Val F1 score',\n",
-    "    'Test Accuracy', 'Test ROC AUC', # 'Test Precision', 'Test Recall', 'Test F1 score',\n",
-    "]\n",
-    "# Plot aggregated mean value of all metrics in one bar plot\n",
-    "tmp = tmp.melt(id_vars=['Fold', 'CV-Groups'], var_name='Metric', value_name='Score')\n",
-    "# Rename elements in 'CV-Groups' column\n",
-    "group2name = {\n",
-    "    'random': 'Standard CV split',\n",
-    "    'uniprot': 'Uniprot-wise CV split',\n",
-    "    'tanimoto': 'Tanimoto-wise CV split',\n",
-    "}\n",
-    "\n",
-    "tmp['CV-Groups'] = tmp['CV-Groups'].map(group2name)\n",
-    "# Draw one horizontal line for each CV-group representing the max value of inactive PROTACs percentage across the folds\n",
-    "for i, group in enumerate(report['group_type'].unique()):\n",
-    "    # Get the majority class in group_df\n",
-    "    group_df = report[report['group_type'] == group]\n",
-    "    major_col = 'inactive' if group_df['val_inactive_perc'].mean() > 0.5 else active_col\n",
-    "    # plt.axhline(group_df[f'val_{major_col}_perc'].max(), color=f'C{i}', linestyle='-.', label=f'Max val {major_col} (%) for {group2name[group]}')\n",
-    "    plt.axhline(group_df[f'val_{major_col}_perc'].mean(), color=f'C{i}', linestyle='--', label=f'Mean val {major_col} (%) for {group2name[group].split(\" (\")[0]}')\n",
-    "plt.axhline(report[f'test_{major_col}_perc'].max(), color='black', linestyle=':', label=f'Max test {major_col} (%)')\n",
-    "\n",
-    "sns.barplot(data=tmp, x='Metric', y='Score', hue='CV-Groups')\n",
-    "\n",
-    "# num_data = len(protac_df[protac_df[active_col].notna()])\n",
-    "# plt.title(f'Activity prediction on {n_splits}-fold cross-validation and separate test set')\n",
-    "plt.title(f'Activity prediction performance on {n_splits}-fold cross-validation')\n",
-    "\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "# Set y-axis to end at 1.0\n",
-    "plt.ylim(0, 1.0)\n",
-    "# Make the y-axis as percentage\n",
-    "plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "# Plot the legend external on the left side of the plot\n",
-    "# plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
-    "\n",
-    "# Plot the legend below the x-axis, outside the plot, and divided in two columns\n",
-    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)\n",
-    "\n",
-    "# Rotate x-axis labels to 90 degrees\n",
-    "# plt.xticks(rotation=90)\n",
-    "# Remove axis labels\n",
-    "plt.xlabel('')\n",
-    "plt.ylabel('')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot aggregated results\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "for n_splits in [5, 10]:\n",
-    "    val_metrics = pd.read_csv(\n",
-    "        f'../data/results/val_metrics_{n_splits}-splits_ablation.csv'\n",
-    "    )\n",
-    "\n",
-    "    tmp = val_metrics.copy()[['fold', 'val_acc', 'val_roc_auc',\n",
-    "                              'val_precision', 'val_recall', 'val_f1_score']]\n",
-    "    # Rename columns\n",
-    "    tmp.columns = ['fold', 'Accuracy', 'ROC AUC',\n",
-    "                   'Precision', 'Recall', 'F1 score']\n",
-    "    # Plot aggregated mean value of all metrics in one bar plot\n",
-    "    tmp = tmp.melt(id_vars='fold', var_name='Metric', value_name='Score')\n",
-    "\n",
-    "    sns.barplot(data=tmp, x='Metric', y='Score')\n",
-    "\n",
-    "    num_data = len(protac_df[protac_df[active_col].notna()])\n",
-    "    plt.title(\n",
-    "        f'Validation metrics ({n_splits}-fold cross-validation on {num_data} samples)')\n",
-    "    plt.grid(axis='y', alpha=0.5)\n",
-    "    # Set y-axis to end at 1.0\n",
-    "    plt.ylim(0, 1.0)\n",
-    "    # Make the y-axis as percentage\n",
-    "    plt.gca().yaxis.set_major_formatter(\n",
-    "        plt.matplotlib.ticker.PercentFormatter(1, decimals=0),\n",
-    "    )\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OTqvc0dERy5cvx6effoomTZqgR48eOHjwIOrUqYMJEyZIXdRCCI3l8HrX9esOHz6M1NRUrYt6nZ2d8dFHH2Hv3r25Trt//34AL+8IfVXXrl1RsmTel0ju2rULxsbGGqcLcvPRRx9J13cABdtGGzRogF27dmHChAk4cOAAUlNTNeq2srJCpUqVMGfOHMybNw8nT55Edna2xjjZ2dkay1R9OqF169ZwcHBAeHi4NO7u3btx584drfni8eF/8nN8UM9n7969NU6TmZmZoXPnzjhy5IjWXWU5fc6LFy8KNZ9ycrsr9NXyGzduoF27drC2tsYvv/yCmJgYzJ49G6tWrcKgQYNyrfvs2bM4evQoevbsCWNj4zzbUr9+fQAv97sNGzbkevfXxo0b4e3tDTMzM2n7W7lyZY7bX7NmzWBqaiq9r1atGoCX2/ur86guf309mpuba62LHj16IDs7GwcPHsx1XvJ7PG/QoAGePHmCgIAA/Prrr3j48GGuddrZ2SE7OxsJCQm5jvM6hqVC6tKlCywtLaUD4s6dO3Hv3j2NC+/mzZuHYcOGwcvLC5s2bcKRI0cQFxeHVq1aaR2c8/Lo0SMAgIODg9aw18uOHTsGPz8/AMCKFSvw119/IS4uDpMnTwaAAn82ACQmJkIIAUdHR61hTk5OGm1Us7a21nivVCrz/fkmJiZaBwUjIyNYWVlpjWtkZIQXL15I7+/du4ft27fD0NBQ41W9enUAkN2JXpfT/L7uwYMHACB70belpSViYmJQu3ZtTJo0CdWrV4eTkxOmTZuGjIwM2fpTU1PzdYAEXl4T0a1bNzx69AiXL18GAERERGgtCznq9Zjbun59Pec07evbZMmSJbW2h5w8ePAATk5O+bpe5PX2FWQbXbhwIb788kts3boVzZo1g5WVFTp06CAtM4VCgb1798Lf3x+zZ89G3bp1YWtri5EjR+Lp06cAgBkzZmgsU/U1bSVLlkTv3r2xZcsW6TEMq1atgqOjI/z9/aU28fhQ8ONDXttmdnY2EhMT3/hzCkJdf077xePHjzWOWRMmTEBycjJ2796Nzp07o2nTphg3bhwWLFiAsLCwXP+QU1/7KBeoXtW0aVNs3boVmZmZ6NOnD8qWLYsaNWpoXJO3efNmdO3aFWXKlMHatWtx+PBhxMXFYcCAARrHU7XXj73q66tyK3+9jpzuDldvm3LHlPwez3v37o2wsDDcvHkTnTt3hp2dHby8vBAdHa1Vp/p4WpBtgHfDFZJKpUJAQABWrFiBu3fvIiwsDObm5vj000+lcdauXQtfX18sWbJEY1r1wbYg1DtkTkn49bKoqCgYGhpix44dGl+yW7duLfDnqpUuXRolSpTA3bt3tYapL8q0sbEpdP26ZGNjAw8PD3z99dc5DlcfvPMjP88QUl8o/u+//8reXVGzZk1ERUVBCIFTp05h1apVmDFjBlQqFSZMmJDrdDY2Nnj8+HG+2yz+/yJddeBo164d4uLi8j29elvLbV3LredXt9NX79zLzMyUPSCq2dra4tChQ8jOzs4zML2+bgqyjZqammL69OmYPn067t27J/UytWvXDhcuXADwsldG/SV16dIlbNiwAcHBwUhPT8fSpUsxZMgQtG3bVvoM9ZcwAPTv3x9z5sxBVFQUunXrhm3btmHUqFEwMDCQxuHxoeDy2jZLlCih0dv4LtSsWRMAcPr0aY1HOWRmZuLChQsICAiQyuLj4+Hu7q7RQwP8ryfozJkz8PHx0RiWnp6ONWvWoF69eqhdu3a+2/XJJ5/gk08+QVpaGo4cOYLQ0FD06NEDFSpUQKNGjbB27Vq4uLhg/fr1GvtSWlpavj+jIHJ67qB625T7Q6ogx/P+/fujf//+ePbsGQ4ePIhp06ahbdu2uHTpEsqXLy+Npz6eFmSbZM/SGxg4cCCysrIwZ84c7Ny5E927d4eJiYk0XKFQaBxAAeDUqVM5Pj8jL1WqVIGjoyN+/vlnjTtWbt68qXGXkPpzS5YsqXFgTk1NxZo1a7TqVSqV+UrXpqam8PLywubNmzXGz87Oxtq1a1G2bFlUrly5wPP1NrRt2xZnzpxBpUqV4OnpqfVS71y6+gvTz88PBgYGWl96uVEoFKhVqxbmz5+PUqVK4cSJE7LjV61aFdeuXctX3RkZGVi/fj1sbGykO2asra21loGcRo0aQaVSYe3atRrl//77L/bt24fmzZvnOq36eVXr1q3TKN+wYUOep/+Al136L168KNTDDgu7jdrb26Nfv34ICAjAxYsXtU7jAEDlypXx1VdfoWbNmtL6cnJy0lim6i9N4OWpCC8vL4SHhyMyMhJpaWlad0vx+FBwVapUQZkyZRAZGakxn8+ePcOmTZukO+TeJS8vLzg6Ompts7/88gtSUlLQqVMnqczJyQlnz57Velilep3n1Du9bds2PHz4sNCPC1AqlfDx8cG3334L4OXdtcDL7UD9IFq1hISEHO+G04WnT59Kl62oRUZGokSJEmjatGmu0+X3eP4qU1NTtG7dGpMnT0Z6err0iBS1a9euwdraukDPQmTP0hvw9PSEh4cHFixYACGE1sbctm1bzJw5E9OmTYOPjw8uXryIGTNmwMXFJV9fHK8qUaIEZs6ciUGDBqFjx44YPHgwnjx5guDgYK1u9jZt2mDevHno0aMHhgwZgkePHuG7777TOjAD/+vtWL9+PSpWrAhjY2ONg/6rQkND0bJlSzRr1gxjx46FkZERfvzxR5w5cwY///xzkTzVOiczZsxAdHQ0GjdujJEjR6JKlSp48eIFbty4gZ07d2Lp0qUoW7YszM3NUb58efz6669o3rw5rKysYGNjU+CnHVeoUAGTJk3CzJkzkZqaKt2mfO7cOTx8+BDTp0/Hjh078OOPP6JDhw6oWLEihBDYvHkznjx5gpYtW8rW7+vri7CwMFy6dEnjCycoKAgZGRnw9vaGg4MDbt++jUWLFiE+Ph7h4eEaX4YFUapUKUyZMgWTJk1Cnz59EBAQgEePHmH69OkwNjbGtGnTcp22WrVq6NWrFxYsWABDQ0O0aNECZ86cwXfffQcLC4s8PzsgIADh4eH47LPPcPHiRTRr1gzZ2dk4evQoqlWrhu7du8tOn99t1MvLC23btoWHhwdKly6N8+fPY82aNdKX7alTpzB8+HB8+umncHNzg5GREfbt24dTp07J9gK+asCAARg6dCju3LkjPYjwVTw+FFyJEiUwe/Zs9OzZE23btsXQoUORlpaGOXPm4MmTJ9LjRXTh+fPn2LlzJ4CXjwcAXl4f8/DhQ+nLGHh5Xdbs2bPRu3dvDB06FAEBAbh8+TLGjx+Pli1bajzAdtSoUejQoQNatmyJ0aNHw8bGRur1cXd3l+p81cqVK6FSqbQePCtn6tSp+Pfff9G8eXOULVsWT548wffffw9DQ0Op56pt27bYvHkzAgMD0aVLF9y+fRszZ86Eo6OjdDpal6ytrTFs2DDcunULlStXxs6dO7FixQoMGzYM5cqVy3W6/B7PBw8eDJVKBW9vbzg6OiIhIQGhoaGwtLSUeu7Ujhw5Ah8fn4Jtk/m+FJxy9P333wsAwt3dXWtYWlqaGDt2rChTpowwNjYWdevWFVu3bhV9+/bVujsF+bg1WAghfvrpJ+Hm5iaMjIxE5cqVRVhYWI71hYWFiSpVqgilUikqVqwoQkNDxcqVKwUAcf36dWm8GzduCD8/P2Fubi7dHipEzne7CCHEn3/+KT766CNhamoqVCqVaNiwodi+fbvGODndYSU3T6/r27evMDU11Sr38fER1atX1yovX768aNOmjUbZgwcPxMiRI4WLi4swNDQUVlZWol69emLy5Mkad0D88ccfok6dOkKpVGrcraW+k+bBgwdan5fT7fJCvLyTq379+sLY2FiYmZmJOnXqSMvvwoULIiAgQFSqVEmoVCphaWkpGjRoIFatWiW7LIQQIikpSZiZmWnd1bNy5UrRoEEDYWVlJUqWLClKly4t/P39xe7du/OsUy23dSXEy23Nw8NDGBkZCUtLS/HJJ59o3R6d07JIS0sTY8aMEXZ2dsLY2Fg0bNhQHD58WJQvXz7Pu+GEECI1NVVMnTpV2s6tra3FRx99JGJjY6VxkMMtx2r52UYnTJggPD09RenSpaV9ZPTo0dJt/ffu3RP9+vUTVatWFaampsLMzEx4eHiI+fPni8zMzDznQYiX602lUuV6dx+PD4U7Pgjx8k4sLy8vYWxsLExNTUXz5s3FX3/9pTFObvuw+vNfnc+cqOcxp1dOdxdGRkZK+4uDg4MYOXKk1uM+hBBi3759ws/PTzg4OAiVSiUqV64sxowZI217r7p165YoUaKE6NOnT57L5FU7duwQrVu3FmXKlBFGRkbCzs5OfPzxxxqPVhBCiG+++UZUqFBBKJVKUa1aNbFixYoc9+mc9jf18pkzZ45GuXo9bty4USpTH7sPHDggPD09hVKpFI6OjmLSpEkiIyND67Nev0M5P8fziIgI0axZM2Fvby+MjIyEk5OT6Nq1q8ZdeUIIceXKFa27rfND8f+NIyI9NmLECOzduxdnz57Vmx48IqL88PX1xcOHD3HmzJmibgqmTJmC1atX4+rVq/m6Q1eN1ywRvQe++uor/Pe//8WmTZuKuilERO+lJ0+e4IcffkBISEiBghLAsET0XrC3t8e6det0drszEVFxc/36dUycOLFA13+p8TQcERERkYwi7Vk6ePAg2rVrBycnJygUCq3nfAghEBwcDCcnJ6hUKvj6+mrdApiWloYRI0bAxsYGpqamaN++vcYPk6alpaF3796wsLBAlSpVsG/fPo3pZ8+ejREjRry1eSQiIqL3W5GGpWfPnqFWrVpYvHhxjsNnz56NefPmYfHixYiLi4ODgwNatmyp8dC2UaNGYcuWLYiKisKhQ4eQkpKCtm3bSj89sHz5chw/fhyHDx/G4MGDERAQID2f4/r16/jpp59yfdgVERERkd6chlMoFNiyZQs6dOgA4GWvkpOTE0aNGoUvv/wSwMteInt7e3z77bcYOnQokpKSYGtrizVr1qBbt24AXj7F1dnZGTt37oS/vz8CAwNhYWGBb775BqmpqTAxMcH9+/dha2uLVq1aYejQoejYsWNRzTYRERHpOb19KOX169eRkJAg/YYR8L8nkcbGxmLo0KE4fvw4MjIyNMZxcnJCjRo1EBsbC39/f9SqVQtr1qxBamoqdu/eDUdHR9jY2GDt2rUwNjbOd1BKS0vTeAx8dnY2Hj9+DGtra97KTURE9J4QQuDp06f5/h1KQI/Dkvo3Y15/HLm9vb30a8YJCQkwMjLS+i0ge3t7afoBAwbg1KlTcHd3h42NDTZs2IDExERMmzYN+/fvx1dffYWoqChUqlQJYWFhGr9n9arQ0FBMnz5d17NJREREReD27duyP4D+Kr0NS2qv99oIIfLsyXl1HENDQ/zwww8aw/v164eRI0ciPj4eW7duxT///IPZs2dj5MiRuT7HZuLEiQgKCpLeJyUloVy5crhx40a+fsaBiIiIil5ycjIqVKgAc3PzfE+jt2FJ/XtGCQkJcHR0lMrv378v9TY5ODggPT0diYmJGr1L9+/fR+PGjXOsd9++fTh37hxWrlyJcePG4eOPP4apqSm6du2a64XmwMtTgDn9dlLp0qUZloiIiN4T6t/NLMglNHr7UEoXFxc4ODggOjpaKktPT0dMTIwUhOrVqwdDQ0ONce7evYszZ87kGJZevHiBzz//HMuWLYOBgQGysrKQkZEB4OWvtavvoCMiIiJSK9KepZSUFFy5ckV6f/36dcTHx8PKygrlypXDqFGjEBISAjc3N7i5uSEkJAQmJibS0zctLS0xcOBAjBkzBtbW1rCyssLYsWNRs2ZNtGjRQuvzZsyYgTZt2qBOnToAAG9vb4wbNw79+/fH4sWL4e3t/W5mnIiIiN4bRRqW/v77bzRr1kx6r74mqG/fvli1ahXGjx+P1NRUBAYGIjExEV5eXtizZ4/Gecb58+ejZMmS6Nq1K1JTU9G8eXOsWrVK6mZTO3PmDDZu3Ij4+HiprEuXLjhw4AD+85//oEqVKoiMjHy7M0xERETvHb15ztL7Jjk5GZaWlkhKSuI1S0RERO+Jwnx/6+01S0RERET6gGGJiIiISAbDEhEREZEMhiUiIiIiGQxLRERERDIYloiIiIhkMCwRERERyWBYIiIiIpLBsEREREQkg2GJiIiISAbDEhEREZEMhiUiIiIiGQxLRERERDIYloiIiIhkMCwRERERyWBYIiIiIpLBsEREREQkg2GJiIiISAbDEhEREZEMhiUiIiIiGQxLRERERDIYloiIiIhkMCwRERERyWBYIiIiIpLBsEREREQkg2GJiIiISEbJom4AEQFffPEFHjx4AACwtbXF999/X8QtIiIiNYYlIj3w4MED3Lt3r6ibQUREOeBpOCIiIiIZDEtEREREMhiWiIiIiGTwmiUqVm7NqFnUTchR5hNrAAb///87etvOclNPF3UTiIjeOfYsEREREclgWCIiIiKSwbBEREREJIPXLBHpAStlVo7/JyKiosewRKQHJtV5UtRNICKiXPA0HBEREZEMhiUiIiIiGQxLRERERDIYloiIiIhkMCwRERERyWBYIiIiIpLBsEREREQkg2GJiIiISAbDEhEREZEMhiUiIiIiGfy5k2Lsiy++wIMHDwAAtra2+P7774u4RURERPqHYakYe/DgAe7du1fUzSAiItJrPA1HREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINP8H4H6o1bXdRNyJFFYoqUlu8mpuhlO4/P6VPUTSCiIsKfZCJ9wbBERER6iT/JRPqCp+GIiIiIZDAsEREREclgWCIiIiKSoddhKTMzE1999RVcXFygUqlQsWJFzJgxA9nZ2dI4QggEBwfDyckJKpUKvr6+OHv2rEY9QUFBsLKyQrly5RAVFaUxbMOGDWjXrt07mR8iIiJ6/+j1Bd7ffvstli5dioiICFSvXh1///03+vfvD0tLS3zxxRcAgNmzZ2PevHlYtWoVKleujFmzZqFly5a4ePEizM3NsX37dkRGRmLPnj24fPky+vfvj5YtW8La2hpPnjzB5MmTsXfv3iKeUyIiItJXet2zdPjwYXzyySdo06YNKlSogC5dusDPzw9///03gJe9SgsWLMDkyZPRqVMn1KhRAxEREXj+/DkiIyMBAOfPn4evry88PT0REBAACwsLXLt2DQAwfvx4BAYGoly5ckU2j0RERKTf9LpnqUmTJli6dCkuXbqEypUr459//sGhQ4ewYMECAMD169eRkJAAPz8/aRqlUgkfHx/ExsZi6NChqFWrFpYvX47ExERcu3YNqampcHV1xaFDh3DixAksWbIkX21JS0tDWlqa9D45ORkAkJWVhaysLNlpDRQFnPEioo/tzGvZFlS2wkCn9RU3ul4fRAXB7Y90oTDbkV6HpS+//BJJSUmoWrUqDAwMkJWVha+//hoBAQEAgISEBACAvb29xnT29va4efMmAMDf3x+9evVC/fr1oVKpEBERAVNTUwwbNgyrVq3CkiVLsGjRItjY2GD58uWoXr16jm0JDQ3F9OnTtcqvXr0KMzMz2flo4qws8Ly/C5evmyEj9WVCMlSZoo4etvPy5cs6rS/Jzken9RU3L3S8PojkZGRkaPxf18cDKp5SUlIKPI1eh6X169dj7dq1iIyMRPXq1REfH49Ro0bByckJffv2lcZTKDS7RIQQGmXBwcEIDg7WeN+iRQsYGhpi1qxZOH36NHbs2IE+ffrg+PHjObZl4sSJCAoKkt4nJyfD2dkZlSpVgoWFhex8HAo7VpDZfndcWmm8vXc7LZcRi84cNzed1nd7Y4xO6ytunN3mFXUTqBgxNDTU+L+bjo8HVDypzwwVhF6HpXHjxmHChAno3r07AKBmzZq4efMmQkND0bdvXzg4OAB42cPk6OgoTXf//n2t3ia1CxcuYN26dTh58iTCwsLQtGlT2NraomvXrhgwYACSk5NzDD9KpRJKpXbPi4GBAQwM5E/tZIl8zzK9Jq9lW1AlBLvx34Su1wdRQXD7I10ozHak1xd4P3/+HCVKaDbRwMBAenSAi4sLHBwcEB0dLQ1PT09HTEwMGjdurFWfEAJDhgzB3LlzYWZmhqysLKmbV/3vq48lICIiItLrnqV27drh66+/Rrly5VC9enWcPHkS8+bNw4ABAwC8PP02atQohISEwM3NDW5ubggJCYGJiQl69OihVd+KFStgZ2eH9u3bAwC8vb0RHByMI0eOYNeuXXB3d0epUqXe5SwSERGRntPrsLRo0SJMmTIFgYGBuH//PpycnDB06FBMnTpVGmf8+PFITU1FYGAgEhMT4eXlhT179sDc3Fyjrnv37iEkJASxsbFSWYMGDTBmzBi0adMGdnZ2iIiIeGfzRkSkT2Ka6t/NDy9KGgD/f/3pi4QEvWwjAPgc5LWQHzqFEIJX1BRCcnIyLC0tkZSUlOcF3vXGrX5HrfrwHJ/TR6f13ZpRU6f1FTflpp4u6ibQW6KPQeT7kgZI+v+wZCkEvsjUz2sOGZbeLwX5/lbT62uWiIiIiIoawxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhl6/UO6RERUfFkIABCv/J+oaDAsERGRXuqfpZ8/nEvFD0/DEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQy+JwlIiIikvXFF1/gwYMHAABbW1t8//33Rdyid4thiYiIiGQ9ePAA9+7dK+pmFBmehiMiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREcngQymJiIj0yOIx24u6CVqePn6u8X99bCMADJ/b7q3Uy54lIiIiIhnsWSIiekVx/w0sItLGsERE9Iri/htYRKSNp+GIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhl8zhIRERHJMjYyz/H/xQXDEhEREcnyceta1E0oUjwNR0RERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQz+NhwRFQnvRd5F3YQcKZOVUEABAEhITtDbdv414q+ibgJRscGeJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQy9D0v//e9/0atXL1hbW8PExAS1a9fG8ePHpeFCCAQHB8PJyQkqlQq+vr44e/asRh1BQUGwsrJCuXLlEBUVpTFsw4YNaNeu3TuZFyIiInr/6HVYSkxMhLe3NwwNDbFr1y6cO3cOc+fORalSpaRxZs+ejXnz5mHx4sWIi4uDg4MDWrZsiadPnwIAtm/fjsjISOzZswfffvst+vfvj0ePHgEAnjx5gsmTJ+OHH34oitkjIiKi94Be/9zJt99+C2dnZ4SHh0tlFSpUkP4vhMCCBQswefJkdOrUCQAQEREBe3t7REZGYujQoTh//jx8fX3h6ekJT09PjBo1CteuXYO1tTXGjx+PwMBAlCtX7l3PGhEREb0n9Dosbdu2Df7+/vj0008RExODMmXKIDAwEIMHDwYAXL9+HQkJCfDz85OmUSqV8PHxQWxsLIYOHYpatWph+fLlSExMxLVr15CamgpXV1ccOnQIJ06cwJIlS/LVlrS0NKSlpUnvk5OTAQBZWVnIysqSndZAUdA5J7W8lm1BZSsMdFpfcaPL9WGA92Nd6Gs7db1vCAP9nM/3ga7XBRRCt/UVI/lZF4VZX3odlq5du4YlS5YgKCgIkyZNwrFjxzBy5EgolUr06dMHCQkJAAB7e3uN6ezt7XHz5k0AgL+/P3r16oX69etDpVIhIiICpqamGDZsGFatWoUlS5Zg0aJFsLGxwfLly1G9evUc2xIaGorp06drlV+9ehVmZmay89HEWVmY2ScAly9f1ml9SXY+Oq2vuHmhw/XR0LKhzurSpdMlTiMd6QAAZQklPC09i7hFOdP1vvGiQQOd1lec6HpdWJRjWCqs/KyLlJSUAterEELo7VoxMjKCp6cnYmNjpbKRI0ciLi4Ohw8fRmxsLLy9vXHnzh04OjpK4wwePBi3b9/G77//nmO9wcHBSEpKQv/+/eHn54fTp09jx44dWLx4scbF46/KqWfJ2dkZjx8/hoWFhex8NJq4riCzTa84HNpTp/XdDqmn0/qKG+dJOe8fhdHsx2Y6q0uXSu4uCUXqy+5goRLI9M8s4hblbH/gfp3W92eLljqtrzj5zx/ROq1vyZe/6bS+4mTYt23yHCc5ORlWVlZISkrK8/tbTa97lhwdHeHu7q5RVq1aNWzatAkA4ODgAABISEjQCEv379/X6m1Su3DhAtatW4eTJ08iLCwMTZs2ha2tLbp27YoBAwYgOTk5x4WnVCqhVGr3EBkYGMAgj+7rLL2No/ovr2VbUCWEjrvLixldro8s6Oe6KPnaYVFf26nrfUOh61NJxYiu1wUEr90orPysi8KsL72+G87b2xsXL17UKLt06RLKly8PAHBxcYGDgwOio/+X6tPT0xETE4PGjRtr1SeEwJAhQzB37lyYmZkhKysLGRkZACD9m52d/bZmh4iIiN5Deh2WRo8ejSNHjiAkJARXrlxBZGQkli9fjs8//xwAoFAoMGrUKISEhGDLli04c+YM+vXrBxMTE/To0UOrvhUrVsDOzg7t27cH8DKM7du3D0eOHMH8+fPh7u6u8VgCIiIiIr0+DVe/fn1s2bIFEydOxIwZM+Di4oIFCxagZ8//Xccyfvx4pKamIjAwEImJifDy8sKePXtgbm6uUde9e/cQEhKicf1TgwYNMGbMGLRp0wZ2dnaIiIh4Z/NGRERE7we9DksA0LZtW7Rt2zbX4QqFAsHBwQgODpatx97eHjdu3NAqnzp1KqZOnfqGrSQiIqIPlV6fhiMiIiIqagxLRERERDIYloiIiIhk6P01S0RE75JQiRz/T0TFF8MSEdEr0pumF3UTiEjP8DQcERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIxhuFpfT0dFy8eBGZmZm6ag8RERGRXilUWHr+/DkGDhwIExMTVK9eHbdu3QIAjBw5Et98841OG0hERERUlAoVliZOnIh//vkHBw4cgLGxsVTeokULrF+/XmeNIyIiIipqhfptuK1bt2L9+vVo2LAhFAqFVO7u7o6rV6/qrHFERERERa1QPUsPHjyAnZ2dVvmzZ880whMRERHR+65QYal+/fr47bffpPfqgLRixQo0atRINy0jIiIi0gOFOg0XGhqKVq1a4dy5c8jMzMT333+Ps2fP4vDhw4iJidF1G4mIiIiKTKF6lho3bozY2Fg8f/4clSpVwp49e2Bvb4/Dhw+jXr16um4jERERUZEpcM9SRkYGhgwZgilTpiAiIuJttImIiIhIbxS4Z8nQ0BBbtmx5G20hIiIi0juFOg3XsWNHbN26VcdNISIiItI/hbrA29XVFTNnzkRsbCzq1asHU1NTjeEjR47USeOIiIiIilqhwtJPP/2EUqVK4fjx4zh+/LjGMIVCwbBEREREH4xChaXr16/ruh1EREREeqlQ1yy9SggBIYQu2kJERESkdwodllavXo2aNWtCpVJBpVLBw8MDa9as0WXbiIiIiIpcoU7DzZs3D1OmTMHw4cPh7e0NIQT++usvfPbZZ3j48CFGjx6t63YSERERFYlChaVFixZhyZIl6NOnj1T2ySefoHr16ggODmZYIiIiog9GoU7D3b17F40bN9Yqb9y4Me7evfvGjSIiIiLSF4UKS66urtiwYYNW+fr16+Hm5vbGjSIiIiLSF4U6DTd9+nR069YNBw8ehLe3NxQKBQ4dOoS9e/fmGKKIiIiI3leF6lnq3Lkzjh49ChsbG2zduhWbN2+GjY0Njh07ho4dO+q6jURERERFplA9SwBQr149rF27VpdtISIiItI7hepZ2rlzJ3bv3q1Vvnv3buzateuNG0VERESkLwoVliZMmICsrCytciEEJkyY8MaNIiIiItIXhQpLly9fhru7u1Z51apVceXKlTduFBEREZG+KFRYsrS0xLVr17TKr1y5AlNT0zduFBEREZG+KFRYat++PUaNGoWrV69KZVeuXMGYMWPQvn17nTWOiIiIqKgVKizNmTMHpqamqFq1KlxcXODi4oKqVavC2toa3333na7bSERERFRkCvXoAEtLS8TGxiI6Ohr//PMPVCoVatWqhf/85z+6bh8RERFRkSpQz9LRo0elRwMoFAr4+fnBzs4O3333HTp37owhQ4YgLS3trTSUiIiIqCgUKCwFBwfj1KlT0vvTp09j8ODBaNmyJSZMmIDt27cjNDRU540kIiIiKioFCkvx8fFo3ry59D4qKgoNGjTAihUrEBQUhIULF/K34YiIiOiDUqCwlJiYCHt7e+l9TEwMWrVqJb2vX78+bt++rbvWERERERWxAoUle3t7XL9+HQCQnp6OEydOoFGjRtLwp0+fwtDQULctJCIiIipCBQpLrVq1woQJE/Dnn39i4sSJMDEx0bgD7tSpU6hUqZLOG0lERERUVAr06IBZs2ahU6dO8PHxgZmZGSIiImBkZCQNDwsLg5+fn84bSURERFRUChSWbG1t8eeffyIpKQlmZmYwMDDQGL5x40aYmZnptIFERERERanQD6XMiZWV1Rs1hoiIiEjfFOrnToiIiIiKC4YlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQy3quwFBoaCoVCgVGjRkllQggEBwfDyckJKpUKvr6+OHv2rMZ0QUFBsLKyQrly5RAVFaUxbMOGDWjXrt27aD4RERG9h96bsBQXF4fly5fDw8NDo3z27NmYN28eFi9ejLi4ODg4OKBly5Z4+vQpAGD79u2IjIzEnj178O2336J///549OgRAODJkyeYPHkyfvjhh3c+P0RERPR+eC/CUkpKCnr27IkVK1agdOnSUrkQAgsWLMDkyZPRqVMn1KhRAxEREXj+/DkiIyMBAOfPn4evry88PT0REBAACwsLXLt2DQAwfvx4BAYGoly5ckUyX0RERKT/ShZ1A/Lj888/R5s2bdCiRQvMmjVLKr9+/ToSEhLg5+cnlSmVSvj4+CA2NhZDhw5FrVq1sHz5ciQmJuLatWtITU2Fq6srDh06hBMnTmDJkiX5akNaWhrS0tKk98nJyQCArKwsZGVlyU5roCjI3NKr8lq2BZWtMNBpfcWNLteHAbgu3oSu9w1hwPVRWLpeF1AI3dZXjORnXRRmfel9WIqKisKJEycQFxenNSwhIQEAYG9vr1Fub2+PmzdvAgD8/f3Rq1cv1K9fHyqVChERETA1NcWwYcOwatUqLFmyBIsWLYKNjQ2WL1+O6tWr59iO0NBQTJ8+Xav86tWrMDMzk52HJs7KfM0rabt8+bJO60uy89FpfcXNCx2uj4aWDXVWV3Gk633jRYMGOq2vONH1urAox7BUWPlZFykpKQWuV6/D0u3bt/HFF19gz549MDY2znU8hUKz60YIoVEWHByM4OBgjfctWrSAoaEhZs2ahdOnT2PHjh3o06cPjh8/nuNnTJw4EUFBQdL75ORkODs7o1KlSrCwsJCdj0Nhx2SHU+7muLnptL7bG2N0Wl9x4+w2T2d1HYk+orO6iqNQt1Cd1pdwjMepwnIL+Vqn9f3x0yWd1lecuOXjO0N9Zqgg9DosHT9+HPfv30e9evWksqysLBw8eBCLFy/GxYsXAbzsYXJ0dJTGuX//vlZvk9qFCxewbt06nDx5EmFhYWjatClsbW3RtWtXDBgwAMnJyTmGH6VSCaVSu4fIwMAABnl0X2fxj4RCy2vZFlQJoePu8mJGl+sjC1wXb0LX+4ZC16eSihFdrwsIXrtRWPlZF4VZX3p9gXfz5s1x+vRpxMfHSy9PT0/07NkT8fHxqFixIhwcHBAdHS1Nk56ejpiYGDRu3FirPiEEhgwZgrlz58LMzAxZWVnIyMgAAOnf7OzsdzNzRERE9F7Q654lc3Nz1KhRQ6PM1NQU1tbWUvmoUaMQEhICNzc3uLm5ISQkBCYmJujRo4dWfStWrICdnR3at28PAPD29kZwcDCOHDmCXbt2wd3dHaVKlXrr80VERETvD70OS/kxfvx4pKamIjAwEImJifDy8sKePXtgbm6uMd69e/cQEhKC2NhYqaxBgwYYM2YM2rRpAzs7O0RERLzr5hMREZGee+/C0oEDBzTeKxQKrQu4c2Jvb48bN25olU+dOhVTp07VXQOJiIjog6LX1ywRERERFTWGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCRDr8NSaGgo6tevD3Nzc9jZ2aFDhw64ePGixjhCCAQHB8PJyQkqlQq+vr44e/asxjhBQUGwsrJCuXLlEBUVpTFsw4YNaNeu3VufFyIiIno/6XVYiomJweeff44jR44gOjoamZmZ8PPzw7Nnz6RxZs+ejXnz5mHx4sWIi4uDg4MDWrZsiadPnwIAtm/fjsjISOzZswfffvst+vfvj0ePHgEAnjx5gsmTJ+OHH34okvkjIiIi/afXYen3339Hv379UL16ddSqVQvh4eG4desWjh8/DuBlr9KCBQswefJkdOrUCTVq1EBERASeP3+OyMhIAMD58+fh6+sLT09PBAQEwMLCAteuXQMAjB8/HoGBgShXrlyRzSMRERHpt5JF3YCCSEpKAgBYWVkBAK5fv46EhAT4+flJ4yiVSvj4+CA2NhZDhw5FrVq1sHz5ciQmJuLatWtITU2Fq6srDh06hBMnTmDJkiX5+uy0tDSkpaVJ75OTkwEAWVlZyMrKkp3WQFGg2aRX5LVsCypbYaDT+oobXa4PA3BdvAld7xvCgOujsHS9LqAQuq2vGMnPuijM+npvwpIQAkFBQWjSpAlq1KgBAEhISAAA2Nvba4xrb2+PmzdvAgD8/f3Rq1cv1K9fHyqVChERETA1NcWwYcOwatUqLFmyBIsWLYKNjQ2WL1+O6tWr5/j5oaGhmD59ulb51atXYWZmJtv2Js7KAs8vvXT58mWd1pdk56PT+oqbFzpcHw0tG+qsruJI1/vGiwYNdFpfcaLrdWFRjmGpsPKzLlJSUgpc73sTloYPH45Tp07h0KFDWsMUCs2uGyGERllwcDCCg4M13rdo0QKGhoaYNWsWTp8+jR07dqBPnz7SKb7XTZw4EUFBQdL75ORkODs7o1KlSrCwsJBt+6GwY/mZRcrBHDc3ndZ3e2OMTusrbpzd5umsriPRR3RWV3EU6haq0/oSjvE4VVhuIV/rtL4/frqk0/qKE7d8fGeozwwVxHsRlkaMGIFt27bh4MGDKFu2rFTu4OAA4GUPk6Ojo1R+//59rd4mtQsXLmDdunU4efIkwsLC0LRpU9ja2qJr164YMGAAkpOTcww/SqUSSqV2D5GBgQEM8ui+zuIfCYWW17ItqBJCx93lxYwu10cWuC7ehK73DYWuTyUVI7peFxC8dqOw8rMuCrO+9PoCbyEEhg8fjs2bN2Pfvn1wcXHRGO7i4gIHBwdER0dLZenp6YiJiUHjxo1zrG/IkCGYO3cuzMzMkJWVhYyMDACQ/s3Ozn6Lc0RERETvG73uWfr8888RGRmJX3/9Febm5tI1SpaWllCpVFAoFBg1ahRCQkLg5uYGNzc3hISEwMTEBD169NCqb8WKFbCzs0P79u0BAN7e3ggODsaRI0ewa9cuuLu7o1SpUu9yFomIiEjP6XVYUt+p5uvrq1EeHh6Ofv36AXh5+39qaioCAwORmJgILy8v7NmzB+bm5hrT3Lt3DyEhIYiNjZXKGjRogDFjxqBNmzaws7NDRETEW50fIiIiev/odVgSIu+LfRQKhdYF3Dmxt7fHjRs3tMqnTp2KqVOnFrKFRERE9KHT62uWiIiIiIoawxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREclgWCIiIiKSwbBEREREJINhiYiIiEgGwxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsEREREcn4YMLSjz/+CBcXFxgbG6NevXr4888/pWHfffcd7O3tYW9vj/nz52tMd/ToUdSrVw9ZWVnvuslERET0HihZ1A3QhfXr12PUqFH48ccf4e3tjWXLlqF169Y4d+4ckpKSMHXqVOzYsQNCCLRt2xYtW7ZEjRo1kJGRgc8++wzLly+HgYFBUc8GERER6aEPIizNmzcPAwcOxKBBgwAACxYswO7du7FkyRLUqVMHHh4e+OijjwAAHh4eOH/+PGrUqIE5c+agadOmqF+/flE2n4iIiPTYex+W0tPTcfz4cUyYMEGj3M/PD7GxsejTpw8uXbqEW7duQQiBS5cuoUaNGrhy5QpWrVqF48ePF1HLiYiI6H3w3oelhw8fIisrC/b29hrl9vb2SEhIQLVq1RASEoKWLVsCAEJDQ1GtWjW0aNECs2fPxu7duxEcHAxDQ0N8//33aNq0aY6fk5aWhrS0NOl9UlISACAxMTHv653SU99gDou3xMREndaXlJb3OJQ7Xa4PkSp0VldxpOt9I0VwfRSWrtdFavozndZXnORnXSQnJwMAREG2efGe++9//ysAiNjYWI3yWbNmiSpVquQ4TXh4uOjQoYNISEgQlpaW4tKlS2Lfvn3C0dFRvHjxIsdppk2bJgDwxRdffPHFF18fwOv27dv5zhrvfc+SjY0NDAwMkJCQoFF+//59rd4m4GVP1IwZM3Dw4EEcPXoUlStXhpubG9zc3JCRkYFLly6hZs2aWtNNnDgRQUFB0vvs7Gw8fvwY1tbWUCgUup+xdyQ5ORnOzs64ffs2LCwsiro5xRrXhf7gutAfXBf640NZF0IIPH36FE5OTvme5r0PS0ZGRqhXrx6io6PRsWNHqTw6OhqffPKJ1vijRo3C6NGjUbZsWcTFxSEjI0MalpmZmespNaVSCaVSqVFWqlQp3cyEHrCwsHivN/4PCdeF/uC60B9cF/rjQ1gXlpaWBRr/vQ9LABAUFITevXvD09MTjRo1wvLly3Hr1i189tlnGuNFR0fj8uXLWL16NQCgQYMGuHDhAnbt2oXbt2/DwMAAVapUKYpZICIiIj31QYSlbt264dGjR5gxYwbu3r2LGjVqYOfOnShfvrw0TmpqKoYPH47169ejRImXz+IsU6YMFi1ahP79+0OpVCIiIgIqlaqoZoOIiIj00AcRlgAgMDAQgYGBuQ5XqVS4ePGiVvmgQYOk5zMVR0qlEtOmTdM6xUjvHteF/uC60B9cF/qjOK8LhRC8X5SIiIgoNx/Mb8MRERERvQ0MS0REREQyGJaIiIiIZDAsERG9pkKFCliwYIHOx6V36/V1o1AosHXr1iJrD72/GJb0TGxsLAwMDNCqVauibkqx1a9fPygUCigUCpQsWRLlypXDsGHDcvzNodjYWHz88ccoXbo0jI2NUbNmTcydOzfHh5vu378fH3/8MaytrWFiYgJ3d3eMGTMG//3vf/NsU0hICAwMDPDNN99oDQsODkbt2rW1yp88eQKFQoEDBw5olG/atAm+vr6wtLSEmZkZPDw8MGPGDDx+/DjPdhSFV9eHoaEhKlasiLFjx+LZs7f3+1lxcXEYMmSIzsctTgqyH1HhvbqcX31duXIFAHDw4EG0a9cOTk5ODItvgGFJz4SFhWHEiBE4dOgQbt26VWTtePXJ5sVRq1atcPfuXdy4cQM//fQTtm/frvVoii1btsDHxwdly5bF/v37ceHCBXzxxRf4+uuv0b17d40faVy2bBlatGgBBwcHbNq0CefOncPSpUuRlJSEuXPn5tme8PBwjB8/HmFhYW80X5MnT0a3bt1Qv3597Nq1C2fOnMHcuXPxzz//YM2aNW9U99ukXh/Xrl3DrFmz8OOPP2Ls2LFa4+lqu7W1tYWJiYnOxy1u8rMf0ZtTL+dXXy4uLgCAZ8+eoVatWli8eHERtzJnQghkZmYWdTPylu9fkaO3LiUlRZibm4sLFy6Ibt26ienTp2sM//XXX0W9evWEUqkU1tbWomPHjtKwFy9eiHHjxomyZcsKIyMj4erqKn766SchxMsfDra0tNSoa8uWLeLV1T9t2jRRq1YtsXLlSuHi4iIUCoXIzs4Wu3btEt7e3sLS0lJYWVmJNm3aiCtXrmjUdfv2bdGtWzdRunRpYWJiIurVqyeOHDkirl+/LhQKhYiLi9MYf+HChaJcuXIiOztbF4tN5/r27Ss++eQTjbKgoCBhZWUlvU9JSRHW1taiU6dOWtNv27ZNABBRUVFCiJfLx8jISIwaNSrHz0tMTJRtz4EDB0SZMmVEenq6cHJyEjExMRrD1esup3oBiP379wshhDh69KgAIBYsWFCodhSVnNbHoEGDhIODQ67b7ZMnT8TgwYOFra2tMDc3F82aNRPx8fEadcjtT+XLlxfz58+X3k+bNk04OzsLIyMj4ejoKEaMGJHruDdv3hTt27cXpqamwtzcXHz66aciISFBo65atWqJ1atXi/LlywsLCwvRrVs3kZycrJsFpifysx+FhYWJqlWrCqVSKapUqSJ++OEHjfFzO7YIIcSVK1dE+/bthZ2dnTA1NRWenp4iOjpaY/rX1w0AsWXLFp3OZ1HLaTnnJr/zHx8fL3x9fYWZmZkwNzcXdevW1TiOHzp0SDRt2lSoVCpRqlQp4efnJx4/fiyEePldNGLECGFrayuUSqXw9vYWx44dk6bdv3+/ACB+//13Ua9ePWFoaCj27dsnsrOzxbfffitcXFyEsbGx8PDwEBs3bizQsnib2LOkR9avX48qVaqgSpUq6NWrF8LDw6Xeid9++w2dOnVCmzZtcPLkSezduxeenp7StH369EFUVBQWLlyI8+fPY+nSpTAzMyvQ51+5cgUbNmzApk2bEB8fD+DlXyVBQUGIi4vD3r17UaJECXTs2BHZ2dkAgJSUFPj4+ODOnTvYtm0b/vnnH4wfPx7Z2dmoUKECWrRogfDwcI3PCQ8Pl7qO3wfXrl3D77//DkNDQ6lsz549ePToUY69G+3atUPlypXx888/AwA2btyI9PR0jB8/Psf68/qNwZUrVyIgIACGhoYICAjAypUrCzUf69atg5mZWa5/2b9Pv3WoUqmkXqSctts2bdogISEBO3fuxPHjx1G3bl00b95cOtWY1/70ql9++QXz58/HsmXLcPnyZWzdujXHH9sGXv6V3KFDBzx+/BgxMTGIjo7G1atX0a1bN43xrl69iq1bt2LHjh3YsWMHYmJicjzF+iF5fT9asWIFJk+ejK+//hrnz59HSEgIpkyZgoiICADyxxb18I8//hh//PEHTp48CX9/f7Rr165Ie+Q/FD179pR+P/X48eOYMGGCtN7i4+PRvHlzVK9eHYcPH8ahQ4fQrl076dKD8ePHY9OmTYiIiMCJEyfg6uoKf39/rdP848ePR2hoKM6fPw8PDw989dVXCA8Px5IlS3D27FmMHj0avXr1QkxMzDuf/xwVdVqj/2ncuLH0V39GRoawsbGR/lJq1KiR6NmzZ47TXbx4UQDQ+qtKLb89S4aGhuL+/fuybbx//74AIE6fPi2EEGLZsmXC3NxcPHr0KMfx169fL0qXLi1evHghhHj5F4tCoRDXr1+X/Zyi1LdvX2FgYCBMTU2FsbGxACAAiHnz5knjfPPNNwJArr0x7du3F9WqVRNCCDFs2DBhYWFRqLYkJSUJExMTqVfk5MmTwsTERCQlJUnj5LdnqXXr1sLDw6NQ7ShKr//lfPToUWFtbS26du2a43a7d+9eYWFhIW1zapUqVRLLli0TQsjvT0Jo9kjMnTtXVK5cWaSnp+c57p49e4SBgYG4deuWNPzs2bMCgPTX9bRp04SJiYlGT9K4ceOEl5dX3gvjPZLXfuTs7CwiIyM1ppk5c6Zo1KiRECLvY0tO3N3dxaJFi6T3xaVnSb2c1a8uXbrkOG5+59/c3FysWrUqx2EBAQHC29s7x2EpKSnC0NBQrFu3TipT94jPnj1bCPG/nqWtW7dqTGdsbCxiY2M16hs4cKAICAjIs73vAnuW9MTFixdx7NgxdO/eHQBQsmRJdOvWTbpGRZ3mcxIfHw8DAwP4+Pi8URvKly8PW1tbjbKrV6+iR48eqFixIiwsLKTz4Oq/3uLj41GnTh1YWVnlWGeHDh1QsmRJbNmyBcDLa7KaNWuGChUqvFFb37ZmzZohPj4eR48exYgRI+Dv748RI0ZojSdyeQC+EELqOXv1/wUVGRmJihUrolatWgCA2rVro2LFioiKiipwXW/SjqK2Y8cOmJmZwdjYGI0aNULTpk2xaNEiANrb7fHjx5GSkgJra2uYmZlJr+vXr+Pq1asA5Pen13366adITU1FxYoVMXjwYGzZsiXXayzOnz8PZ2dnODs7S2Xu7u4oVaoUzp8/L5VVqFAB5ubm0ntHR0fcv38//wvkPZHbfvTgwQPcvn0bAwcO1FhHs2bN0lhHcseWZ8+eYfz48dLyNTMzw4ULF4plz5J6OatfCxcufKP6goKCMGjQILRo0QLffPONtE4A+X3n6tWryMjIgLe3t1RmaGiIBg0aaGz/ADR6cs+dO4cXL16gZcuWGtvD6tWrNT67KH0wvw33vlu5ciUyMzNRpkwZqUwIAUNDQyQmJsr+wG9eP/5bokQJrS/1nC6ENTU11Spr164dnJ2dsWLFCjg5OSE7Oxs1atRAenp6vj7byMgIvXv3Rnh4ODp16oTIyMj34jZrU1NTuLq6AgAWLlyIZs2aYfr06Zg5cyYAoHLlygBefjk2btxYa/oLFy7A3d1dGjcpKQl3796Fo6NjgdoRFhaGs2fPomTJ/+2q2dnZWLlypXQHloWFBZKSkrSmffLkCQDA0tJSasehQ4eQkZGhcUrxfdCsWTMsWbIEhoaGcHJy0mj/69ttdnY2HB0dte4CBP53qrEgP5jt7OyMixcvIjo6Gn/88QcCAwMxZ84cxMTEaC3H3ALp6+WvT6dQKKTTSx+S3Paj4cOHA3h5Ks7Ly0tjGgMDAwB5r6Nx48Zh9+7d+O677+Dq6gqVSoUuXbpIx6bi5NXlrAvBwcHo0aMHfvvtN+zatQvTpk1DVFQUOnbsKLte1N8zr+8DOe0Xr+636m3/t99+0/gOBKA3v0PHniU9kJmZidWrV2Pu3Lkafx38888/KF++PNatWwcPDw/s3bs3x+lr1qyJ7OzsXM/t2tra4unTpxq3Wquv7ZDz6NEjnD9/Hl999RWaN2+OatWqad326+Hhgfj4eNnbzgcNGoQ//vgDP/74IzIyMtCpU6c8P1vfTJs2Dd999x3u3LkDAPDz84OVlVWOd7Jt27YNly9fRkBAAACgS5cuMDIywuzZs3OsWx1qXnf69Gn8/fffOHDggMZ2cfDgQcTFxeHMmTMAgKpVq+Lff/9FQkKCxvRxcXEoUaKEdBDt0aMHUlJS8OOPPxaoHfpA/WVQvnz5PINe3bp1kZCQgJIlS8LV1VXjZWNjAwCy+1NOVCoV2rdvj4ULF+LAgQM4fPgwTp8+rTWeu7s7bt26hdu3b0tl586dQ1JSEqpVq5bvz/tQqfejrKwslClTBteuXdNaR+re67yOLX/++Sf69euHjh07ombNmnBwcMCNGzfe4dx82CpXrozRo0djz5496NSpk3Ttqdy+4+rqCiMjIxw6dEgqy8jIwN9//y27/bu7u0OpVOLWrVta28OrvbRFiT1LemDHjh1ITEzEwIEDpV4AtS5dumDlypWYP38+mjdvjkqVKqF79+7IzMzErl27MH78eFSoUAF9+/bFgAEDsHDhQtSqVQs3b97E/fv30bVrV3h5ecHExASTJk3CiBEjcOzYMaxatSrPdpUuXRrW1tZYvnw5HB0dcevWLUyYMEFjnICAAISEhKBDhw4IDQ2Fo6MjTp48CScnJzRq1AgAUK1aNTRs2BBffvklBgwYUKC/6vWFr68vqlevjpCQECxevBimpqZYtmwZunfvjiFDhmD48OGwsLDA3r17MW7cOHTp0gVdu3YF8LJnYv78+Rg+fDiSk5PRp08fVKhQAf/++y9Wr14NMzOzHEPXypUr0aBBAzRt2lRrWKNGjaTtws/PD9WqVUP37t3x9ddfw8nJCadOncLYsWPx2WefSad7vLy8MH78eOnZTh07doSTkxOuXLmCpUuXokmTJvjiiy/e7oJ8B1q0aIFGjRqhQ4cO+Pbbb1GlShXcuXMHO3fuRIcOHeDp6Ylp06bluj+9btWqVcjKypL2ozVr1kClUqF8+fI5fraHhwd69uyJBQsWIDMzE4GBgfDx8cn1AvLi5NX9KDg4GCNHjoSFhQVat26NtLQ0/P3330hMTERQUFCexxZXV1ds3rwZ7dq1g0KhwJQpUz7I3rk3lZKSIj1zCQCuX7+O+Ph4WFlZoVy5clrjp6amSscwFxcX/Pvvv4iLi0Pnzp0BABMnTkTNmjURGBiIzz77DEZGRti/fz8+/fRT2NjYYNiwYRg3bpxU/+zZs/H8+XMMHDgw1zaam5tj7NixGD16NLKzs9GkSRMkJycjNjYWZmZm6Nu3r+4XTEEV1cVS9D9t27YVH3/8cY7Djh8/LgCI48ePi02bNonatWsLIyMjYWNjo3Hbempqqhg9erRwdHSUHh0QFhYmDd+yZYtwdXUVxsbGom3btmL58uU5PjrgddHR0aJatWpCqVQKDw8PceDAAa2LBG/cuCE6d+4sLCwshImJifD09BRHjx7VqGflypUaF7nqs9xuxV23bp0wMjLSuHj34MGDolWrVsLS0lIYGRkJd3d38d1334nMzEyt6aOjo4W/v78oXbq0MDY2FlWrVhVjx44Vd+7c0Ro3LS1NWFtbSxdFvm7u3LnCxsZGpKWlCSGEuHv3rujfv78oX768UKlUomrVqmLGjBlaFzkL8fKi+6ZNmwpzc3NhamoqPDw8xIwZM96rRweo5bbdJicnixEjRggnJydhaGgonJ2dRc+ePTXWndz+9OqFwVu2bBFeXl7CwsJCmJqaioYNG4o//vgjx3GFyP+jA141f/58Ub58+Xwvk/dBfvajdevWSeugdOnSomnTpmLz5s3SuHLHluvXr4tmzZoJlUolnJ2dxeLFi4WPj4/44osvpOmLywXeco8OUF9Q/fqrb9++OY6flpYmunfvLj0qw8nJSQwfPlykpqZK4xw4cEA0btxYKJVKUapUKeHv7y8dP1JTU8WIESOEjY2N7KMDXj/eZGdni++//15UqVJFGBoaCltbW+Hv76/1qJSiohAilytUiXTo66+/RlRUVI6nLoiIiPQZr1mityolJQVxcXFYtGgRRo4cWdTNISIiKjCGJXqrhg8fjiZNmsDHxwcDBgwo6uYQEREVGE/DEREREclgzxIRERGRDIYlIiIiIhkMS0REREQyGJaIiIiIZDAsERG9IYVCga1btxZ1M4joLWFYIqIPQr9+/aBQKPDZZ59pDQsMDIRCoUC/fv3yVdeBAwegUCjy/Xt5d+/eRevWrQvQWiJ6nzAsEdEHw9nZGVFRUUhNTZXKXrx4gZ9//jnH38F6U+pfuHdwcNCbX0cnIt1jWCKiD0bdunVRrlw5bN68WSrbvHkznJ2dUadOHalMCIHZs2ejYsWKUKlUqFWrFn755RcAwI0bN9CsWTMAL39M+tUeKV9fXwwfPhxBQUGwsbFBy5YtAWifhvv333/RvXt3WFlZwdTUFJ6enjh69OhbnnsieltKFnUDiIh0qX///ggPD0fPnj0BAGFhYRgwYAAOHDggjfPVV19h8+bNWLJkCdzc3HDw4EH06tULtra2aNKkCTZt2oTOnTvj4sWLsLCwgEqlkqaNiIjAsGHD8NdffyGnZ/qmpKTAx8cHZcqUwbZt2+Dg4IATJ04gOzv7rc87Eb0dDEtE9EHp3bs3Jk6ciBs3bkChUOCvv/5CVFSUFJaePXuGefPmYd++fWjUqBEAoGLFijh06BCWLVsGHx8fWFlZAQDs7OxQqlQpjfpdXV0xe/bsXD8/MjISDx48QFxcnFSPq6ur7meUiN4ZhiUi+qDY2NigTZs2iIiIgBACbdq0gY2NjTT83LlzePHihXQKTS09PV3jVF1uPD09ZYfHx8ejTp06UlAiovcfwxIRfXAGDBiA4cOHAwB++OEHjWHq02G//fYbypQpozEsPxdpm5qayg5/9ZQdEX0YGJaI6IPTqlUr6U41f39/jWHu7u5QKpW4desWfHx8cpzeyMgIAJCVlVXgz/bw8MBPP/2Ex48fs3eJ6APBu+GI6INjYGCA8+fP4/z58zAwMNAYZm5ujrFjx2L06NGIiIjA1atXcfLkSfzwww+IiIgAAJQvXx4KhQI7duzAgwcPkJKSku/PDggIgIODAzp06IC//voL165dw6ZNm3D48GGdziMRvTsMS0T0QbKwsICFhUWOw2bOnImpU6ciNDQU1apVg7+/P7Zv3w4XFxcAQJkyZTB9+nRMmDAB9vb20im9/DAyMsKePXtgZ2eHjz/+GDVr1sQ333yjFdqI6P2hEDnd+0pEREREANizRERERCSLYYmIiIhIBsMSERERkQyGJSIiIiIZDEtEREREMhiWiIiIiGQwLBERERHJYFgiIiIiksGwRERERCSDYYmIiIhIBsMSERERkQyGJSIiIiIZ/weS+qCO3qwW3gAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot aggregated results\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "n_splits = 5\n",
-    "val_metrics = pd.read_csv(\n",
-    "    f'../data/results/val_metrics_{n_splits}-splits_ablation.csv'\n",
-    ")\n",
-    "\n",
-    "tmp = val_metrics.copy()[['fold', 'val_acc', 'val_roc_auc',\n",
-    "                          'val_precision', 'val_recall', 'val_f1_score']]\n",
-    "# Rename columns\n",
-    "tmp.columns = ['fold', 'Accuracy', 'ROC AUC',\n",
-    "               'Precision', 'Recall', 'F1 score']\n",
-    "# Plot aggregated mean value of all metrics in one bar plot\n",
-    "tmp = tmp.melt(id_vars='fold', var_name='Metric', value_name='Score')\n",
-    "\n",
-    "sns.barplot(data=tmp, x='Metric', y='Score')\n",
-    "\n",
-    "\n",
-    "num_data = len(protac_df[protac_df[active_col].notna()])\n",
-    "plt.title(\n",
-    "    f'Validation metrics ({n_splits}-fold cross-validation on {num_data} samples)')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "# Set y-axis to end at 1.0\n",
-    "plt.ylim(0, 1.0)\n",
-    "# Make the y-axis as percentage\n",
-    "plt.gca().yaxis.set_major_formatter(\n",
-    "    plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ablation Studies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | smiles_emb | Linear     | 787 K \n",
-      "1 | fc1        | Linear     | 590 K \n",
-      "2 | fc2        | Linear     | 590 K \n",
-      "3 | fc3        | Linear     | 769   \n",
-      "4 | dropout    | Dropout    | 0     \n",
-      "5 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "2.0 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "2.0 M     Total params\n",
-      "7.877     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50dbc6fade944436be04b6bcd2a79e91",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
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-     "output_type": "display_data"
-    },
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-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.679\n"
-     ]
-    },
-    {
-     "data": {
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-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.679. Signaling Trainer to stop.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8894008994102478     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8285714387893677     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8894008994102478     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.22978010773658752    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.7179722785949707     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7250000238418579     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9666666388511658     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9700636863708496     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8894008994102478    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8285714387893677    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8894008994102478    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.22978010773658752   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.7179722785949707    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7250000238418579    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9666666388511658    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9700636863708496    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'val_loss': 0.22978010773658752,\n",
-       " 'val_acc': 0.8894008994102478,\n",
-       " 'val_f1_score': 0.8285714387893677,\n",
-       " 'val_hp_metric': 0.8894008994102478,\n",
-       " 'val_opt_score': 1.7179722785949707,\n",
-       " 'val_precision': 0.7250000238418579,\n",
-       " 'val_recall': 0.9666666388511658,\n",
-       " 'val_roc_auc': 0.9700636863708496}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Random split the data into train and test\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "train_df, unlabeled_df = train_test_split(\n",
-    "    protac_df[protac_df[active_col].notna()], test_size=0.2, random_state=42,\n",
-    ")\n",
-    "model, trainer, val_metric = train_model(\n",
-    "    train_df, val_df, disabled_embeddings=['poi', 'e3', 'cell'],\n",
-    ")\n",
-    "val_metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fold 0, disabled embeddings: ['poi', 'e3', 'cell']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | smiles_emb | Linear     | 787 K \n",
-      "1 | fc1        | Linear     | 590 K \n",
-      "2 | fc2        | Linear     | 590 K \n",
-      "3 | fc3        | Linear     | 769   \n",
-      "4 | dropout    | Dropout    | 0     \n",
-      "5 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "2.0 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "2.0 M     Total params\n",
-      "7.877     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6d2003ac853140d4be07094f870d01e7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5dc5bc109154647a91eba389ddfd0d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "717bab699d8e469ebfab4c4d165dbea2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.681\n"
-     ]
-    },
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-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.681. Signaling Trainer to stop.\n",
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-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8256880640983582     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6984127163887024     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8256880640983582     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.43253543972969055    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.5241007804870605     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6666666865348816     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7333333492279053     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8918248414993286     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8256880640983582    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6984127163887024    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8256880640983582    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.43253543972969055   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.5241007804870605    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6666666865348816    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7333333492279053    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8918248414993286    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
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-     "text": [
-      "Fold 1, disabled embeddings: ['poi', 'e3', 'cell']\n"
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-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | smiles_emb | Linear     | 787 K \n",
-      "1 | fc1        | Linear     | 590 K \n",
-      "2 | fc2        | Linear     | 590 K \n",
-      "3 | fc3        | Linear     | 769   \n",
-      "4 | dropout    | Dropout    | 0     \n",
-      "5 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "2.0 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "2.0 M     Total params\n",
-      "7.877     Total estimated model params size (MB)\n"
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-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
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-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n"
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-      "Metric train_loss improved. New best score: 0.681\n"
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-     "text": [
-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.681. Signaling Trainer to stop.\n",
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-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8302752375602722     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.678260862827301     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8302752375602722     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.42626988887786865    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.5085361003875732     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6393442749977112     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7222222089767456     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.878669798374176     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8302752375602722    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.678260862827301    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8302752375602722    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.42626988887786865   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.5085361003875732    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6393442749977112    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7222222089767456    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.878669798374176    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fold 2, disabled embeddings: ['poi', 'e3', 'cell']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | smiles_emb | Linear     | 787 K \n",
-      "1 | fc1        | Linear     | 590 K \n",
-      "2 | fc2        | Linear     | 590 K \n",
-      "3 | fc3        | Linear     | 769   \n",
-      "4 | dropout    | Dropout    | 0     \n",
-      "5 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "2.0 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "2.0 M     Total params\n",
-      "7.877     Total estimated model params size (MB)\n"
-     ]
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-       "model_id": "b34dbf29083d470faaa27389eec33307",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n"
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-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
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-       "Validation: 0it [00:00, ?it/s]"
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-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.681\n"
-     ]
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.681. Signaling Trainer to stop.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8018433451652527     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6814814805984497     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8018433451652527     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.44118818640708923    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.4833247661590576     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.5822784900665283     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8214285969734192     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8986246585845947     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8018433451652527    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6814814805984497    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8018433451652527    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.44118818640708923   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.4833247661590576    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.5822784900665283    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8214285969734192    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8986246585845947    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fold 3, disabled embeddings: ['poi', 'e3', 'cell']\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
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-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
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-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7972350120544434     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6271186470985413     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7972350120544434     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.4882689416408539     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.4243535995483398     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.5441176295280457     </span>│\n",
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-       "└───────────────────────────┴───────────────────────────┘\n",
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-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
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-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7972350120544434    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6271186470985413    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7972350120544434    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4882689416408539    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.4243535995483398    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.5441176295280457    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7400000095367432    \u001b[0m\u001b[35m \u001b[0m│\n",
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-       "└───────────────────────────┴───────────────────────────┘\n"
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-      "Fold 4, disabled embeddings: ['poi', 'e3', 'cell']\n"
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-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\imblearn\\over_sampling\\_smote\\base.py:345: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
-      "  warnings.warn(\n",
-      "GPU available: True (cuda), used: True\n",
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-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | smiles_emb | Linear     | 787 K \n",
-      "1 | fc1        | Linear     | 590 K \n",
-      "2 | fc2        | Linear     | 590 K \n",
-      "3 | fc3        | Linear     | 769   \n",
-      "4 | dropout    | Dropout    | 0     \n",
-      "5 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "2.0 M     Trainable params\n",
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-      "2.0 M     Total params\n",
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-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "c:\\Users\\ste\\Anaconda2\\envs\\env-thesis\\Lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:432: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
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-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4218ae5c129f49c7a168c26bd92df42d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Monitored metric train_loss did not improve in the last 10 records. Best score: 0.679. Signaling Trainer to stop.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a0824ee73ebd4e18bc55bf3923b1ebed",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8018433451652527     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6861313581466675     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8018433451652527     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.46606215834617615    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.4879746437072754     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6103895902633667     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7833333611488342     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8800424337387085     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8018433451652527    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6861313581466675    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8018433451652527    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.46606215834617615   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.4879746437072754    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6103895902633667    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7833333611488342    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8800424337387085    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\ste\\AppData\\Local\\Temp\\ipykernel_9048\\4021510138.py:42: FutureWarning: ['Uniprot', 'Smiles', 'E3 Ligase', 'InChI', 'InChI Key', 'Cell Type', 'Article DOI', 'Comments', 'Database', 'Molecular Formula', 'Target', 'PDB', 'Name', 'Assay (DC50/Dmax)', 'Target (Parsed)', 'POI Sequence', 'E3 Ligase Uniprot', 'E3 Ligase Sequence', 'Cell Line Identifier', 'disabled_embeddings'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
-      "  val_metrics.aggregate(['mean', 'std'])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Compound ID</th>\n",
-       "      <th>Molecular Weight</th>\n",
-       "      <th>Heavy Atom Count</th>\n",
-       "      <th>Ring Count</th>\n",
-       "      <th>Rotatable Bond Count</th>\n",
-       "      <th>Topological Polar Surface Area</th>\n",
-       "      <th>Hydrogen Bond Acceptor Count</th>\n",
-       "      <th>Hydrogen Bond Donor Count</th>\n",
-       "      <th>Treatment Time (h)</th>\n",
-       "      <th>DC50 (nM)</th>\n",
-       "      <th>...</th>\n",
-       "      <th>XLogP3</th>\n",
-       "      <th>val_loss</th>\n",
-       "      <th>val_acc</th>\n",
-       "      <th>val_f1_score</th>\n",
-       "      <th>val_hp_metric</th>\n",
-       "      <th>val_opt_score</th>\n",
-       "      <th>val_precision</th>\n",
-       "      <th>val_recall</th>\n",
-       "      <th>val_roc_auc</th>\n",
-       "      <th>fold</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>1309.798528</td>\n",
-       "      <td>984.393343</td>\n",
-       "      <td>70.029439</td>\n",
-       "      <td>7.385465</td>\n",
-       "      <td>20.139834</td>\n",
-       "      <td>220.732282</td>\n",
-       "      <td>13.651334</td>\n",
-       "      <td>4.467341</td>\n",
-       "      <td>18.154774</td>\n",
-       "      <td>2396.467627</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.260886</td>\n",
-       "      <td>0.450865</td>\n",
-       "      <td>0.811377</td>\n",
-       "      <td>0.674281</td>\n",
-       "      <td>0.811377</td>\n",
-       "      <td>1.485658</td>\n",
-       "      <td>0.608559</td>\n",
-       "      <td>0.760064</td>\n",
-       "      <td>0.883329</td>\n",
-       "      <td>2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>931.846640</td>\n",
-       "      <td>165.936373</td>\n",
-       "      <td>11.294283</td>\n",
-       "      <td>1.368378</td>\n",
-       "      <td>6.379550</td>\n",
-       "      <td>38.692208</td>\n",
-       "      <td>2.872499</td>\n",
-       "      <td>1.429301</td>\n",
-       "      <td>10.410593</td>\n",
-       "      <td>8579.934090</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.418567</td>\n",
-       "      <td>0.025810</td>\n",
-       "      <td>0.015360</td>\n",
-       "      <td>0.027453</td>\n",
-       "      <td>0.015360</td>\n",
-       "      <td>0.037981</td>\n",
-       "      <td>0.047883</td>\n",
-       "      <td>0.041401</td>\n",
-       "      <td>0.012142</td>\n",
-       "      <td>1.581139</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Compound ID  Molecular Weight  Heavy Atom Count  Ring Count  \\\n",
-       "mean  1309.798528        984.393343         70.029439    7.385465   \n",
-       "std    931.846640        165.936373         11.294283    1.368378   \n",
-       "\n",
-       "      Rotatable Bond Count  Topological Polar Surface Area  \\\n",
-       "mean             20.139834                      220.732282   \n",
-       "std               6.379550                       38.692208   \n",
-       "\n",
-       "      Hydrogen Bond Acceptor Count  Hydrogen Bond Donor Count  \\\n",
-       "mean                     13.651334                   4.467341   \n",
-       "std                       2.872499                   1.429301   \n",
-       "\n",
-       "      Treatment Time (h)    DC50 (nM)  ...    XLogP3  val_loss   val_acc  \\\n",
-       "mean           18.154774  2396.467627  ...  5.260886  0.450865  0.811377   \n",
-       "std            10.410593  8579.934090  ...  2.418567  0.025810  0.015360   \n",
-       "\n",
-       "      val_f1_score  val_hp_metric  val_opt_score  val_precision  val_recall  \\\n",
-       "mean      0.674281       0.811377       1.485658       0.608559    0.760064   \n",
-       "std       0.027453       0.015360       0.037981       0.047883    0.041401   \n",
-       "\n",
-       "      val_roc_auc      fold  \n",
-       "mean     0.883329  2.000000  \n",
-       "std      0.012142  1.581139  \n",
-       "\n",
-       "[2 rows x 24 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# K-fold cross-validation loop\n",
-    "import os\n",
-    "from sklearn.model_selection import KFold\n",
-    "\n",
-    "n_splits = 5\n",
-    "embeddings_to_disable = [\n",
-    "    # ['poi'],\n",
-    "    # ['e3'],\n",
-    "    # ['cell'],\n",
-    "    # ['smiles'],\n",
-    "    ['poi', 'e3', 'cell'],\n",
-    "]\n",
-    "\n",
-    "csv_filepath = f'../data/results/val_metrics_{n_splits}-splits_ablation.csv'\n",
-    "if os.path.exists(csv_filepath):\n",
-    "    tmp = pd.read_csv(csv_filepath)\n",
-    "else:\n",
-    "    tmp = pd.DataFrame()\n",
-    "\n",
-    "kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)\n",
-    "val_metrics = []\n",
-    "tmp = protac_df[protac_df[active_col].notna()]\n",
-    "for k, (train_index, val_index) in enumerate(kf.split(tmp)):\n",
-    "    train_df = tmp.iloc[train_index]\n",
-    "    val_df = tmp.iloc[val_index]\n",
-    "    for disabled_embeddings in embeddings_to_disable:\n",
-    "        print(f'Fold {k}, disabled embeddings: {disabled_embeddings}')\n",
-    "        model, trainer, val_metric = train_model(\n",
-    "            train_df, val_df, disabled_embeddings,\n",
-    "        )\n",
-    "        val_metric['fold'] = k\n",
-    "        val_metric['disabled_embeddings'] = ' '.join(disabled_embeddings)\n",
-    "        val_metrics.append(val_metric)\n",
-    "val_metrics = pd.DataFrame(val_metrics)\n",
-    "\n",
-    "val_metrics = pd.concat([tmp, val_metrics])\n",
-    "\n",
-    "val_metrics.to_csv(\n",
-    "    f'../data/results/val_metrics_{n_splits}-splits_ablation.csv',\n",
-    "    index=False\n",
-    ")\n",
-    "val_metrics.aggregate(['mean', 'std'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot aggregated results\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "val_metrics = pd.read_csv(\n",
-    "    f'../data/results/val_metrics_{n_splits}-splits_ablation.csv'\n",
-    ")\n",
-    "\n",
-    "for disabled_embeddings in embeddings_to_disable:\n",
-    "    tmp = val_metrics[val_metrics['disabled_embeddings']\n",
-    "                      == ' '.join(disabled_embeddings)].copy()\n",
-    "    tmp = tmp[['fold', 'val_acc', 'val_roc_auc',\n",
-    "               'val_precision', 'val_recall', 'val_f1_score']]\n",
-    "    # Rename columns\n",
-    "    tmp.columns = ['fold', 'Accuracy', 'ROC AUC',\n",
-    "                   'Precision', 'Recall', 'F1 score']\n",
-    "    # Plot aggregated mean value of all metrics in one bar plot\n",
-    "    tmp = tmp.melt(id_vars='fold', var_name='Metric', value_name='Score')\n",
-    "    sns.barplot(data=tmp, x='Metric', y='Score')\n",
-    "\n",
-    "    num_data = len(protac_df[protac_df[active_col].notna()])\n",
-    "    plt.title(\n",
-    "        f'Val metrics ({n_splits}-fold cross-validation on {num_data} samples, disabled embeddings: {disabled_embeddings[0].upper()})')\n",
-    "    plt.grid(axis='y', alpha=0.5)\n",
-    "    # Set y-axis to end at 1.0\n",
-    "    plt.ylim(0, 1.0)\n",
-    "    # Make the y-axis as percentage\n",
-    "    plt.gca().yaxis.set_major_formatter(\n",
-    "        plt.matplotlib.ticker.PercentFormatter(1, decimals=0))\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Psuedo-Label Training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of parameters: 5,903,617\n"
-     ]
-    }
-   ],
-   "source": [
-    "from copy import copy\n",
-    "\n",
-    "oracle_model = copy(model)\n",
-    "print(\n",
-    "    f'Number of parameters: {sum(p.numel() for p in oracle_model.parameters()):,}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Oracle iteration 0\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | poi_emb    | Linear     | 787 K \n",
-      "1 | e3_emb     | Linear     | 787 K \n",
-      "2 | cell_emb   | Linear     | 590 K \n",
-      "3 | smiles_emb | Linear     | 787 K \n",
-      "4 | fc1        | Linear     | 2.4 M \n",
-      "5 | fc2        | Linear     | 590 K \n",
-      "6 | fc3        | Linear     | 769   \n",
-      "7 | dropout    | Dropout    | 0     \n",
-      "8 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "5.9 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.9 M     Total params\n",
-      "23.614    Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "461d8a19ef004f79a2e1015175be35bd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a972efecb6e64b28bee1223d9b460f22",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0ed4cf70d424bfba05a38940719985c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.681\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6afd0cc025374a2fad9a03acafe19814",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e490e100850f4388b396e94ff14fad8d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca60d1ae8f924ab9b31ff3e4f8f0cbd1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb3f13aeb0c642e289edded6e5dc8ab9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=5` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ce24886429c4947beb2a29d189b5d55",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8119266033172607     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6611570119857788     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8119266033172607     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.36858928203582764    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.4730836153030396     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6557376980781555     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6666666865348816     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8914557695388794     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8119266033172607    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6611570119857788    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8119266033172607    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.36858928203582764   \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.4730836153030396    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6557376980781555    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6666666865348816    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8914557695388794    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b20cd9147ce2493d876d1704fc8d82bf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'val_loss': 0.36858928203582764,\n",
-       "  'val_acc': 0.8119266033172607,\n",
-       "  'val_f1_score': 0.6611570119857788,\n",
-       "  'val_hp_metric': 0.8119266033172607,\n",
-       "  'val_opt_score': 1.4730836153030396,\n",
-       "  'val_precision': 0.6557376980781555,\n",
-       "  'val_recall': 0.6666666865348816,\n",
-       "  'val_roc_auc': 0.8914557695388794}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Oracle iteration 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | poi_emb    | Linear     | 787 K \n",
-      "1 | e3_emb     | Linear     | 787 K \n",
-      "2 | cell_emb   | Linear     | 590 K \n",
-      "3 | smiles_emb | Linear     | 787 K \n",
-      "4 | fc1        | Linear     | 2.4 M \n",
-      "5 | fc2        | Linear     | 590 K \n",
-      "6 | fc3        | Linear     | 769   \n",
-      "7 | dropout    | Dropout    | 0     \n",
-      "8 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "5.9 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.9 M     Total params\n",
-      "23.614    Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ed1d6be59d14ff6be46e16b266751cf",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sanity Checking: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98660bb9f10f4e4497cf935c2d145869",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Training: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c8b5685f0f2643d7b0403dd210c80048",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.680\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3f6a0cf84f54e6aa11d85dde291108f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae9f8760aa8e449886cd1658283c8de4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49e9339fa3634dd3937a7c00dd79a51b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0e36317bc6fb41c4b9d2ef87e913b95f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=5` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5bc6623e98a94a61b5fe3be9424f637a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Validation: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8119266033172607     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7284768223762512     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8119266033172607     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.4363510012626648     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.5404033660888672     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6043956279754639     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9166666865348816     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8967299461364746     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8119266033172607    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7284768223762512    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8119266033172607    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4363510012626648    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.5404033660888672    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6043956279754639    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9166666865348816    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8967299461364746    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
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-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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-       "Predicting: 0it [00:00, ?it/s]"
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-       "[{'val_loss': 0.4363510012626648,\n",
-       "  'val_acc': 0.8119266033172607,\n",
-       "  'val_f1_score': 0.7284768223762512,\n",
-       "  'val_hp_metric': 0.8119266033172607,\n",
-       "  'val_opt_score': 1.5404033660888672,\n",
-       "  'val_precision': 0.6043956279754639,\n",
-       "  'val_recall': 0.9166666865348816,\n",
-       "  'val_roc_auc': 0.8967299461364746}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Oracle iteration 2\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | poi_emb    | Linear     | 787 K \n",
-      "1 | e3_emb     | Linear     | 787 K \n",
-      "2 | cell_emb   | Linear     | 590 K \n",
-      "3 | smiles_emb | Linear     | 787 K \n",
-      "4 | fc1        | Linear     | 2.4 M \n",
-      "5 | fc2        | Linear     | 590 K \n",
-      "6 | fc3        | Linear     | 769   \n",
-      "7 | dropout    | Dropout    | 0     \n",
-      "8 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "5.9 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.9 M     Total params\n",
-      "23.614    Total estimated model params size (MB)\n"
-     ]
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-       "Sanity Checking: 0it [00:00, ?it/s]"
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-       "Training: 0it [00:00, ?it/s]"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Metric train_loss improved. New best score: 0.678\n"
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=5` reached.\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8486238718032837     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7441860437393188     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8486238718032837     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.3814685344696045     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    1.5928099155426025     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.695652186870575     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.800000011920929     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.9017932415008545     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8486238718032837    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7441860437393188    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8486238718032837    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.3814685344696045    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   1.5928099155426025    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.695652186870575    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.800000011920929    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.9017932415008545    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
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-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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-       "Predicting: 0it [00:00, ?it/s]"
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-     "data": {
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-       "[{'val_loss': 0.3814685344696045,\n",
-       "  'val_acc': 0.8486238718032837,\n",
-       "  'val_f1_score': 0.7441860437393188,\n",
-       "  'val_hp_metric': 0.8486238718032837,\n",
-       "  'val_opt_score': 1.5928099155426025,\n",
-       "  'val_precision': 0.695652186870575,\n",
-       "  'val_recall': 0.800000011920929,\n",
-       "  'val_roc_auc': 0.9017932415008545}]"
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Oracle iteration 3\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | poi_emb    | Linear     | 787 K \n",
-      "1 | e3_emb     | Linear     | 787 K \n",
-      "2 | cell_emb   | Linear     | 590 K \n",
-      "3 | smiles_emb | Linear     | 787 K \n",
-      "4 | fc1        | Linear     | 2.4 M \n",
-      "5 | fc2        | Linear     | 590 K \n",
-      "6 | fc3        | Linear     | 769   \n",
-      "7 | dropout    | Dropout    | 0     \n",
-      "8 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "5.9 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.9 M     Total params\n",
-      "23.614    Total estimated model params size (MB)\n"
-     ]
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-       "Sanity Checking: 0it [00:00, ?it/s]"
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-       "Training: 0it [00:00, ?it/s]"
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-       "Validation: 0it [00:00, ?it/s]"
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-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8486238718032837     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7591241002082825     </span>│\n",
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-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.3775690495967865     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     1.607748031616211     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6753246784210205     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8666666746139526     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8989450931549072     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
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-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
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-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8486238718032837    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7591241002082825    \u001b[0m\u001b[35m \u001b[0m│\n",
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-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.3775690495967865    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_opt_score      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    1.607748031616211    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6753246784210205    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8666666746139526    \u001b[0m\u001b[35m \u001b[0m│\n",
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-       "└───────────────────────────┴───────────────────────────┘\n"
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-       "  'val_acc': 0.8486238718032837,\n",
-       "  'val_f1_score': 0.7591241002082825,\n",
-       "  'val_hp_metric': 0.8486238718032837,\n",
-       "  'val_opt_score': 1.607748031616211,\n",
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-       "  'val_roc_auc': 0.8989450931549072}]"
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-     "name": "stdout",
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-     "text": [
-      "Oracle iteration 4\n"
-     ]
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name       | Type       | Params\n",
-      "------------------------------------------\n",
-      "0 | poi_emb    | Linear     | 787 K \n",
-      "1 | e3_emb     | Linear     | 787 K \n",
-      "2 | cell_emb   | Linear     | 590 K \n",
-      "3 | smiles_emb | Linear     | 787 K \n",
-      "4 | fc1        | Linear     | 2.4 M \n",
-      "5 | fc2        | Linear     | 590 K \n",
-      "6 | fc3        | Linear     | 769   \n",
-      "7 | dropout    | Dropout    | 0     \n",
-      "8 | metrics    | ModuleDict | 0     \n",
-      "------------------------------------------\n",
-      "5.9 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "5.9 M     Total params\n",
-      "23.614    Total estimated model params size (MB)\n"
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-      "Metric train_loss improved. New best score: 0.665\n"
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-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=5` reached.\n",
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-       "┃<span style=\"font-weight: bold\">  Runningstage.validating  </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
-       "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8348624110221863     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_f1_score        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">          0.71875          </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_hp_metric       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8348624110221863     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.38852933049201965    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_opt_score       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     1.553612470626831     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       val_precision       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.6764705777168274     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_recall         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7666666507720947     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">        val_roc_auc        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8900843858718872     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
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-       "┃\u001b[1m \u001b[0m\u001b[1m Runningstage.validating \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
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-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8348624110221863    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_f1_score       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m         0.71875         \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      val_hp_metric      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8348624110221863    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.38852933049201965   \u001b[0m\u001b[35m \u001b[0m│\n",
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-       "│\u001b[36m \u001b[0m\u001b[36m      val_precision      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6764705777168274    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_recall        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7666666507720947    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m       val_roc_auc       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8900843858718872    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2a1d290c9a5940aea897f8c0412646f5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'val_loss': 0.38852933049201965,\n",
-       "  'val_acc': 0.8348624110221863,\n",
-       "  'val_f1_score': 0.71875,\n",
-       "  'val_hp_metric': 0.8348624110221863,\n",
-       "  'val_opt_score': 1.553612470626831,\n",
-       "  'val_precision': 0.6764705777168274,\n",
-       "  'val_recall': 0.7666666507720947,\n",
-       "  'val_roc_auc': 0.8900843858718872}]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def get_oracle_predictions(model, trainer, unlabeled_df):\n",
-    "    test_ds = PROTAC_Dataset(\n",
-    "        unlabeled_df,\n",
-    "        protein_embeddings,\n",
-    "        cell2embedding,\n",
-    "        smiles2fp,\n",
-    "    )\n",
-    "    test_dl = DataLoader(test_ds, batch_size=32, shuffle=False)\n",
-    "    test_preds = trainer.predict(model, test_dl)\n",
-    "    test_preds = torch.vstack(test_preds).numpy().flatten()\n",
-    "    unlabeled_df[active_col] = [\n",
-    "        is_active(p, pos_threshold=0.97, neg_threshold=0.001) for p in test_preds]\n",
-    "    return unlabeled_df\n",
-    "\n",
-    "\n",
-    "train_df, val_df = train_test_split(\n",
-    "    protac_df[protac_df[active_col].notna()], test_size=0.2, random_state=42)\n",
-    "unlabeled_df = protac_df[protac_df[active_col].isna()]\n",
-    "\n",
-    "val_metrics = []\n",
-    "for i in range(5):\n",
-    "    print(f'Oracle iteration {i}')\n",
-    "    model, trainer, metrics = train_model(train_df, val_df)\n",
-    "    val_metrics.append(metrics)\n",
-    "    unlabeled_df = get_oracle_predictions(model, trainer, unlabeled_df)\n",
-    "    # Update the training set\n",
-    "    train_df = pd.concat([\n",
-    "        train_df,\n",
-    "        unlabeled_df[unlabeled_df[active_col].notna()]], axis=0).drop_duplicates()\n",
-    "    display(metrics)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5aede109a90c428e900696220d318335",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Predicting: 0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "val_dl = DataLoader(val_ds, batch_size=32, shuffle=False)\n",
-    "val_preds = trainer.predict(model, val_dl)\n",
-    "val_preds = torch.vstack(val_preds).numpy().flatten()\n",
-    "\n",
-    "# Plot histogram of predictions\n",
-    "plt.hist(val_preds, bins=20)\n",
-    "plt.title('Validation predictions')\n",
-    "plt.grid(axis='y', alpha=0.5)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/protac_degradation_predictor.py b/notebooks/protac_degradation_predictor.py
deleted file mode 100644
index b14c0f45ad1bf1caa29b63c9611537be064f8181..0000000000000000000000000000000000000000
--- a/notebooks/protac_degradation_predictor.py
+++ /dev/null
@@ -1,1176 +0,0 @@
-import os
-import pickle
-import warnings
-import logging
-from collections import defaultdict
-from typing import Literal, List, Tuple, Optional
-import urllib.request
-
-import joblib
-import optuna
-from optuna.samplers import TPESampler
-import h5py
-import pandas as pd
-import numpy as np
-
-from rdkit import Chem
-from rdkit.Chem import AllChem
-from rdkit import DataStructs
-from jsonargparse import CLI
-from tqdm.auto import tqdm
-from imblearn.over_sampling import SMOTE, ADASYN
-
-from sklearn.preprocessing import OrdinalEncoder, StandardScaler, LabelEncoder
-from sklearn.model_selection import (
-    StratifiedKFold,
-    StratifiedGroupKFold,
-)
-from sklearn.base import ClassifierMixin
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import pytorch_lightning as pl
-from torch.utils.data import Dataset, DataLoader
-from torchmetrics import (
-    Accuracy,
-    AUROC,
-    Precision,
-    Recall,
-    F1Score,
-    MetricCollection,
-)
-
-
-# Ignore UserWarning from Matplotlib
-warnings.filterwarnings("ignore", ".*FixedLocator*")
-# Ignore UserWarning from PyTorch Lightning
-warnings.filterwarnings("ignore", ".*does not have many workers.*")
-
-protac_df = pd.read_csv('../data/PROTAC-Degradation-DB.csv')
-
-# Map E3 Ligase Iap to IAP
-protac_df['E3 Ligase'] = protac_df['E3 Ligase'].str.replace('Iap', 'IAP')
-
-def is_active(DC50: float, Dmax: float, oring=False, pDC50_threshold=7.0, Dmax_threshold=0.8) -> bool:
-    """ Check if a PROTAC is active based on DC50 and Dmax.	
-    Args:
-        DC50(float): DC50 in nM
-        Dmax(float): Dmax in %
-    Returns:
-        bool: True if active, False if inactive, np.nan if either DC50 or Dmax is NaN
-    """
-    pDC50 = -np.log10(DC50 * 1e-9) if pd.notnull(DC50) else np.nan
-    Dmax = Dmax / 100
-    if pd.notnull(pDC50):
-        if pDC50 < pDC50_threshold:
-            return False
-    if pd.notnull(Dmax):
-        if Dmax < Dmax_threshold:
-            return False
-    if oring:
-        if pd.notnull(pDC50):
-            return True if pDC50 >= pDC50_threshold else False
-        elif pd.notnull(Dmax):
-            return True if Dmax >= Dmax_threshold else False
-        else:
-            return np.nan
-    else:
-        if pd.notnull(pDC50) and pd.notnull(Dmax):
-            return True if pDC50 >= pDC50_threshold and Dmax >= Dmax_threshold else False
-        else:
-            return np.nan
-
-# ## Load Protein Embeddings
-
-# Protein embeddings downloaded from [Uniprot](https://www.uniprot.org/help/embeddings).
-# 
-# Please note that running the following cell the first time might take a while.
-download_link = "https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/embeddings/UP000005640_9606/per-protein.h5"
-embeddings_path = "../data/uniprot2embedding.h5"
-if not os.path.exists(embeddings_path):
-    # Download the file
-    print(f'Downloading embeddings from {download_link}')
-    urllib.request.urlretrieve(download_link, embeddings_path)
-
-protein_embeddings = {}
-with h5py.File("../data/uniprot2embedding.h5", "r") as file:
-    uniprots = protac_df['Uniprot'].unique().tolist()
-    uniprots += protac_df['E3 Ligase Uniprot'].unique().tolist()
-    for i, sequence_id in tqdm(enumerate(uniprots), desc='Loading protein embeddings'):
-        try:
-            embedding = file[sequence_id][:]
-            protein_embeddings[sequence_id] = np.array(embedding)
-        except KeyError:
-            print(f'KeyError for {sequence_id}')
-            protein_embeddings[sequence_id] = np.zeros((1024,))
-
-## Load Cell Embeddings
-cell2embedding_filepath = '../data/cell2embedding.pkl'
-with open(cell2embedding_filepath, 'rb') as f:
-    cell2embedding = pickle.load(f)
-print(f'Loaded {len(cell2embedding)} cell lines')
-
-emb_shape = cell2embedding[list(cell2embedding.keys())[0]].shape
-# Assign all-zero vectors to cell lines that are not in the embedding file
-for cell_line in protac_df['Cell Line Identifier'].unique():
-    if cell_line not in cell2embedding:
-        cell2embedding[cell_line] = np.zeros(emb_shape)
-
-## Precompute Molecular Fingerprints
-fingerprint_size = 224
-morgan_fpgen = AllChem.GetMorganGenerator(
-    radius=15,
-    fpSize=fingerprint_size,
-    includeChirality=True,
-)
-
-smiles2fp = {}
-for smiles in tqdm(protac_df['Smiles'].unique().tolist(), desc='Precomputing fingerprints'):
-    # Get the fingerprint as a bit vector
-    morgan_fp = morgan_fpgen.GetFingerprint(Chem.MolFromSmiles(smiles))
-    smiles2fp[smiles] = morgan_fp
-
-# Count the number of unique SMILES and the number of unique Morgan fingerprints
-print(f'Number of unique SMILES: {len(smiles2fp)}')
-print(f'Number of unique fingerprints: {len(set([tuple(fp) for fp in smiles2fp.values()]))}')
-# Get the list of SMILES with overlapping fingerprints
-overlapping_smiles = []
-unique_fps = set()
-for smiles, fp in smiles2fp.items():
-    if tuple(fp) in unique_fps:
-        overlapping_smiles.append(smiles)
-    else:
-        unique_fps.add(tuple(fp))
-print(f'Number of SMILES with overlapping fingerprints: {len(overlapping_smiles)}')
-print(f'Number of overlapping SMILES in protac_df: {len(protac_df[protac_df["Smiles"].isin(overlapping_smiles)])}')
-
-# Get the pair-wise tanimoto similarity between the PROTAC fingerprints
-tanimoto_matrix = defaultdict(list)
-for i, smiles1 in enumerate(tqdm(protac_df['Smiles'].unique(), desc='Computing Tanimoto similarity')):
-    fp1 = smiles2fp[smiles1]
-    # TODO: Use BulkTanimotoSimilarity for better performance
-    for j, smiles2 in enumerate(protac_df['Smiles'].unique()):
-        if j < i:
-            continue
-        fp2 = smiles2fp[smiles2]
-        tanimoto_dist = DataStructs.TanimotoSimilarity(fp1, fp2)
-        tanimoto_matrix[smiles1].append(tanimoto_dist)
-avg_tanimoto = {k: np.mean(v) for k, v in tanimoto_matrix.items()}
-protac_df['Avg Tanimoto'] = protac_df['Smiles'].map(avg_tanimoto)
-
-smiles2fp = {s: np.array(fp) for s, fp in smiles2fp.items()}
-
-
-class PROTAC_Dataset(Dataset):
-    def __init__(
-        self,
-        protac_df,
-        protein_embeddings=protein_embeddings,
-        cell2embedding=cell2embedding,
-        smiles2fp=smiles2fp,
-        use_smote=False,
-        oversampler=None,
-        active_label='Active',
-        include_mol_graphs=False,
-    ):
-        """ Initialize the PROTAC dataset
-
-        Args:
-            protac_df (pd.DataFrame): The PROTAC dataframe
-            protein_embeddings (dict): Dictionary of protein embeddings
-            cell2embedding (dict): Dictionary of cell line embeddings
-            smiles2fp (dict): Dictionary of SMILES to fingerprint
-            use_smote (bool): Whether to use SMOTE for oversampling
-            use_ored_activity (bool): Whether to use the 'Active - OR' column
-        """
-        # Filter out examples with NaN in active_col column
-        self.data = protac_df  # [~protac_df[active_col].isna()]
-        self.protein_embeddings = protein_embeddings
-        self.cell2embedding = cell2embedding
-        self.smiles2fp = smiles2fp
-        self.active_label = active_label
-        self.include_mol_graphs = include_mol_graphs
-
-        self.smiles_emb_dim = smiles2fp[list(smiles2fp.keys())[0]].shape[0]
-        self.protein_emb_dim = protein_embeddings[list(
-            protein_embeddings.keys())[0]].shape[0]
-        self.cell_emb_dim = cell2embedding[list(
-            cell2embedding.keys())[0]].shape[0]
-
-        # Look up the embeddings
-        self.data = pd.DataFrame({
-            'Smiles': self.data['Smiles'].apply(lambda x: smiles2fp[x].astype(np.float32)).tolist(),
-            'Uniprot': self.data['Uniprot'].apply(lambda x: protein_embeddings[x].astype(np.float32)).tolist(),
-            'E3 Ligase Uniprot': self.data['E3 Ligase Uniprot'].apply(lambda x: protein_embeddings[x].astype(np.float32)).tolist(),
-            'Cell Line Identifier': self.data['Cell Line Identifier'].apply(lambda x: cell2embedding[x].astype(np.float32)).tolist(),
-            self.active_label: self.data[self.active_label].astype(np.float32).tolist(),
-        })
-
-        # Apply SMOTE
-        self.use_smote = use_smote
-        self.oversampler = oversampler
-        if self.use_smote:
-            self.apply_smote()
-
-    def apply_smote(self):
-        # Prepare the dataset for SMOTE
-        features = []
-        labels = []
-        for _, row in self.data.iterrows():
-            features.append(np.hstack([
-                row['Smiles'],
-                row['Uniprot'],
-                row['E3 Ligase Uniprot'],
-                row['Cell Line Identifier'],
-            ]))
-            labels.append(row[self.active_label])
-
-        # Convert to numpy array
-        features = np.array(features).astype(np.float32)
-        labels = np.array(labels).astype(np.float32)
-
-        # Initialize SMOTE and fit
-        if self.oversampler is None:
-            oversampler = SMOTE(random_state=42)
-        else:
-            oversampler = self.oversampler
-        features_smote, labels_smote = oversampler.fit_resample(features, labels)
-
-        # Separate the features back into their respective embeddings
-        smiles_embs = features_smote[:, :self.smiles_emb_dim]
-        poi_embs = features_smote[:,
-                                  self.smiles_emb_dim:self.smiles_emb_dim+self.protein_emb_dim]
-        e3_embs = features_smote[:, self.smiles_emb_dim +
-                                 self.protein_emb_dim:self.smiles_emb_dim+2*self.protein_emb_dim]
-        cell_embs = features_smote[:, -self.cell_emb_dim:]
-
-        # Reconstruct the dataframe with oversampled data
-        df_smote = pd.DataFrame({
-            'Smiles': list(smiles_embs),
-            'Uniprot': list(poi_embs),
-            'E3 Ligase Uniprot': list(e3_embs),
-            'Cell Line Identifier': list(cell_embs),
-            self.active_label: labels_smote
-        })
-        self.data = df_smote
-
-    def fit_scaling(self, use_single_scaler=False, **scaler_kwargs) -> dict:
-        """ Fit the scalers for the data.
-
-        Returns:
-            dict: The fitted scalers.
-        """
-        if use_single_scaler:
-            scaler = StandardScaler(**scaler_kwargs)
-            embeddings = np.hstack([
-                np.array(self.data['Smiles'].tolist()),
-                np.array(self.data['Uniprot'].tolist()),
-                np.array(self.data['E3 Ligase Uniprot'].tolist()),
-                np.array(self.data['Cell Line Identifier'].tolist()),
-            ])
-            scaler.fit(embeddings)
-            return scaler
-        else:
-            scalers = {}
-            scalers['Smiles'] = StandardScaler(**scaler_kwargs)
-            scalers['Uniprot'] = StandardScaler(**scaler_kwargs)
-            scalers['E3 Ligase Uniprot'] = StandardScaler(**scaler_kwargs)
-            scalers['Cell Line Identifier'] = StandardScaler(**scaler_kwargs)
-
-            scalers['Smiles'].fit(np.stack(self.data['Smiles'].to_numpy()))
-            scalers['Uniprot'].fit(np.stack(self.data['Uniprot'].to_numpy()))
-            scalers['E3 Ligase Uniprot'].fit(np.stack(self.data['E3 Ligase Uniprot'].to_numpy()))
-            scalers['Cell Line Identifier'].fit(np.stack(self.data['Cell Line Identifier'].to_numpy()))
-
-            return scalers
-
-    def apply_scaling(self, scalers: dict, use_single_scaler=False):
-        """ Apply scaling to the data.
-
-        Args:
-            scalers (dict): The scalers for each feature.
-        """
-        if use_single_scaler:
-            embeddings = np.hstack([
-                np.array(self.data['Smiles'].tolist()),
-                np.array(self.data['Uniprot'].tolist()),
-                np.array(self.data['E3 Ligase Uniprot'].tolist()),
-                np.array(self.data['Cell Line Identifier'].tolist()),
-            ])
-            scaled_embeddings = scalers.transform(embeddings)
-            self.data = pd.DataFrame({
-                'Smiles': list(scaled_embeddings[:, :self.smiles_emb_dim]),
-                'Uniprot': list(scaled_embeddings[:, self.smiles_emb_dim:self.smiles_emb_dim+self.protein_emb_dim]),
-                'E3 Ligase Uniprot': list(scaled_embeddings[:, self.smiles_emb_dim+self.protein_emb_dim:self.smiles_emb_dim+2*self.protein_emb_dim]),
-                'Cell Line Identifier': list(scaled_embeddings[:, -self.cell_emb_dim:]),
-                self.active_label: self.data[self.active_label]
-            })
-        else:
-            self.data['Smiles'] = self.data['Smiles'].apply(lambda x: scalers['Smiles'].transform(x[np.newaxis, :])[0])
-            self.data['Uniprot'] = self.data['Uniprot'].apply(lambda x: scalers['Uniprot'].transform(x[np.newaxis, :])[0])
-            self.data['E3 Ligase Uniprot'] = self.data['E3 Ligase Uniprot'].apply(lambda x: scalers['E3 Ligase Uniprot'].transform(x[np.newaxis, :])[0])
-            self.data['Cell Line Identifier'] = self.data['Cell Line Identifier'].apply(lambda x: scalers['Cell Line Identifier'].transform(x[np.newaxis, :])[0])
-
-    def get_numpy_arrays(self):
-        X = np.hstack([
-            np.array(self.data['Smiles'].tolist()),
-            np.array(self.data['Uniprot'].tolist()),
-            np.array(self.data['E3 Ligase Uniprot'].tolist()),
-            np.array(self.data['Cell Line Identifier'].tolist()),
-        ]).copy()
-        y = self.data[self.active_label].values.copy()
-        return X, y
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        elem = {
-            'smiles_emb': self.data['Smiles'].iloc[idx],
-            'poi_emb': self.data['Uniprot'].iloc[idx],
-            'e3_emb': self.data['E3 Ligase Uniprot'].iloc[idx],
-            'cell_emb': self.data['Cell Line Identifier'].iloc[idx],
-            'active': self.data[self.active_label].iloc[idx],
-        }
-        return elem
-
-def train_sklearn_model(
-    clf: ClassifierMixin,
-    train_df: pd.DataFrame,
-    val_df: pd.DataFrame,
-    test_df: Optional[pd.DataFrame] = None,
-    active_label: str = 'Active',
-    use_single_scaler: bool = True,
-) -> Tuple[ClassifierMixin, nn.ModuleDict]:
-    """ Train a classifier model on train and val sets and evaluate it on a test set.
-
-    Args:
-        clf: The classifier model to train and evaluate.
-        train_df (pd.DataFrame): The training set.
-        val_df (pd.DataFrame): The validation set.
-        test_df (Optional[pd.DataFrame]): The test set.
-
-    Returns:
-        Tuple[ClassifierMixin, nn.ModuleDict]: The trained model and the metrics.
-    """
-    # Initialize the datasets
-    train_ds = PROTAC_Dataset(
-        train_df,
-        protein_embeddings,
-        cell2embedding,
-        smiles2fp,
-        active_label=active_label,
-        use_smote=False,
-    )
-    scaler = train_ds.fit_scaling(use_single_scaler=use_single_scaler)
-    train_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)
-    val_ds = PROTAC_Dataset(
-        val_df,
-        protein_embeddings,
-        cell2embedding,
-        smiles2fp,
-        active_label=active_label,
-        use_smote=False,
-    )
-    val_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)
-    if test_df is not None:
-        test_ds = PROTAC_Dataset(
-            test_df,
-            protein_embeddings,
-            cell2embedding,
-            smiles2fp,
-            active_label=active_label,
-            use_smote=False,
-        )
-        test_ds.apply_scaling(scaler, use_single_scaler=use_single_scaler)
-
-    # Get the numpy arrays
-    X_train, y_train = train_ds.get_numpy_arrays()
-    X_val, y_val = val_ds.get_numpy_arrays()
-    if test_df is not None:
-        X_test, y_test = test_ds.get_numpy_arrays()
-
-    # Train the model
-    clf.fit(X_train, y_train)
-    # Define the metrics as a module dict
-    stages = ['train_metrics', 'val_metrics', 'test_metrics']
-    metrics = nn.ModuleDict({s: MetricCollection({
-        'acc': Accuracy(task='binary'),
-        'roc_auc': AUROC(task='binary'),
-        'precision': Precision(task='binary'),
-        'recall': Recall(task='binary'),
-        'f1_score': F1Score(task='binary'),
-        'opt_score': Accuracy(task='binary') + F1Score(task='binary'),
-        'hp_metric': Accuracy(task='binary'),
-    }, prefix=s.replace('metrics', '')) for s in stages})
-
-    # Get the predictions
-    metrics_out = {}
-
-    y_pred = torch.tensor(clf.predict_proba(X_train)[:, 1])
-    y_true = torch.tensor(y_train)
-    metrics['train_metrics'].update(y_pred, y_true)
-    metrics_out.update(metrics['train_metrics'].compute())
-
-    y_pred = torch.tensor(clf.predict_proba(X_val)[:, 1])
-    y_true = torch.tensor(y_val)
-    metrics['val_metrics'].update(y_pred, y_true)
-    metrics_out.update(metrics['val_metrics'].compute())
-
-    if test_df is not None:
-        y_pred = torch.tensor(clf.predict_proba(X_test)[:, 1])
-        y_true = torch.tensor(y_test)
-        metrics['test_metrics'].update(y_pred, y_true)
-        metrics_out.update(metrics['test_metrics'].compute())
-
-    return clf, metrics_out
-
-
-class PROTAC_Model(pl.LightningModule):
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        smiles_emb_dim: int = fingerprint_size,
-        poi_emb_dim: int = 1024,
-        e3_emb_dim: int = 1024,
-        cell_emb_dim: int = 768,
-        batch_size: int = 32,
-        learning_rate: float = 1e-3,
-        dropout: float = 0.2,
-        join_embeddings: Literal['beginning', 'concat', 'sum'] = 'concat',
-        train_dataset: PROTAC_Dataset = None,
-        val_dataset: PROTAC_Dataset = None,
-        test_dataset: PROTAC_Dataset = None,
-        disabled_embeddings: list = [],
-        apply_scaling: bool = False,
-    ):
-        super().__init__()
-        self.poi_emb_dim = poi_emb_dim
-        self.e3_emb_dim = e3_emb_dim
-        self.cell_emb_dim = cell_emb_dim
-        self.smiles_emb_dim = smiles_emb_dim
-        self.hidden_dim = hidden_dim
-        self.batch_size = batch_size
-        self.learning_rate = learning_rate
-        self.join_embeddings = join_embeddings
-        self.train_dataset = train_dataset
-        self.val_dataset = val_dataset
-        self.test_dataset = test_dataset
-        self.disabled_embeddings = disabled_embeddings
-        self.apply_scaling = apply_scaling
-        # Set our init args as class attributes
-        self.__dict__.update(locals())  # Add arguments as attributes
-        # Save the arguments passed to init
-        ignore_args_as_hyperparams = [
-            'train_dataset',
-            'test_dataset',
-            'val_dataset',
-        ]
-        self.save_hyperparameters(ignore=ignore_args_as_hyperparams)
-
-        # Define "surrogate models" branches
-        if self.join_embeddings != 'beginning':
-            if 'poi' not in self.disabled_embeddings:
-                self.poi_emb = nn.Linear(poi_emb_dim, hidden_dim)
-            if 'e3' not in self.disabled_embeddings:
-                self.e3_emb = nn.Linear(e3_emb_dim, hidden_dim)
-            if 'cell' not in self.disabled_embeddings:
-                self.cell_emb = nn.Linear(cell_emb_dim, hidden_dim)
-            if 'smiles' not in self.disabled_embeddings:
-                self.smiles_emb = nn.Linear(smiles_emb_dim, hidden_dim)
-
-        # Define hidden dimension for joining layer
-        if self.join_embeddings == 'beginning':
-            joint_dim = smiles_emb_dim if 'smiles' not in self.disabled_embeddings else 0
-            joint_dim += poi_emb_dim if 'poi' not in self.disabled_embeddings else 0
-            joint_dim += e3_emb_dim if 'e3' not in self.disabled_embeddings else 0
-            joint_dim += cell_emb_dim if 'cell' not in self.disabled_embeddings else 0
-        elif self.join_embeddings == 'concat':
-            joint_dim = hidden_dim * (4 - len(self.disabled_embeddings))
-        elif self.join_embeddings == 'sum':
-            joint_dim = hidden_dim
-
-        self.fc0 = nn.Linear(joint_dim, joint_dim)
-        self.fc1 = nn.Linear(joint_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
-        self.fc3 = nn.Linear(hidden_dim, 1)
-
-        self.dropout = nn.Dropout(p=dropout)
-
-        stages = ['train_metrics', 'val_metrics', 'test_metrics']
-        self.metrics = nn.ModuleDict({s: MetricCollection({
-            'acc': Accuracy(task='binary'),
-            'roc_auc': AUROC(task='binary'),
-            'precision': Precision(task='binary'),
-            'recall': Recall(task='binary'),
-            'f1_score': F1Score(task='binary'),
-            'opt_score': Accuracy(task='binary') + F1Score(task='binary'),
-            'hp_metric': Accuracy(task='binary'),
-        }, prefix=s.replace('metrics', '')) for s in stages})
-
-        # Misc settings
-        self.missing_dataset_error = \
-            '''Class variable `{0}` is None. If the model was loaded from a checkpoint, the dataset must be set manually:
-            
-            model = {1}.load_from_checkpoint('checkpoint.ckpt')
-            model.{0} = my_{0}
-            '''
-        
-        # Apply scaling in datasets
-        if self.apply_scaling:
-            use_single_scaler = True if self.join_embeddings == 'beginning' else False
-            self.scalers = self.train_dataset.fit_scaling(use_single_scaler)
-            self.train_dataset.apply_scaling(self.scalers, use_single_scaler)
-            self.val_dataset.apply_scaling(self.scalers, use_single_scaler)
-            if self.test_dataset:
-                self.test_dataset.apply_scaling(self.scalers, use_single_scaler)
-
-    def forward(self, poi_emb, e3_emb, cell_emb, smiles_emb):
-        embeddings = []
-        if self.join_embeddings == 'beginning':
-            if 'poi' not in self.disabled_embeddings:
-                embeddings.append(poi_emb)
-            if 'e3' not in self.disabled_embeddings:
-                embeddings.append(e3_emb)
-            if 'cell' not in self.disabled_embeddings:
-                embeddings.append(cell_emb)
-            if 'smiles' not in self.disabled_embeddings:
-                embeddings.append(smiles_emb)
-            x = torch.cat(embeddings, dim=1)
-            x = self.dropout(F.relu(self.fc0(x)))
-        else:
-            if 'poi' not in self.disabled_embeddings:
-                embeddings.append(self.poi_emb(poi_emb))
-            if 'e3' not in self.disabled_embeddings:
-                embeddings.append(self.e3_emb(e3_emb))
-            if 'cell' not in self.disabled_embeddings:
-                embeddings.append(self.cell_emb(cell_emb))
-            if 'smiles' not in self.disabled_embeddings:
-                embeddings.append(self.smiles_emb(smiles_emb))
-            if self.join_embeddings == 'concat':
-                x = torch.cat(embeddings, dim=1)
-            elif self.join_embeddings == 'sum':
-                if len(embeddings) > 1:
-                    embeddings = torch.stack(embeddings, dim=1)
-                    x = torch.sum(embeddings, dim=1)
-                else:
-                    x = embeddings[0]
-        x = self.dropout(F.relu(self.fc1(x)))
-        x = self.dropout(F.relu(self.fc2(x)))
-        x = self.fc3(x)
-        return x
-
-    def step(self, batch, batch_idx, stage):
-        poi_emb = batch['poi_emb']
-        e3_emb = batch['e3_emb']
-        cell_emb = batch['cell_emb']
-        smiles_emb = batch['smiles_emb']
-        y = batch['active'].float().unsqueeze(1)
-
-        y_hat = self.forward(poi_emb, e3_emb, cell_emb, smiles_emb)
-        loss = F.binary_cross_entropy_with_logits(y_hat, y)
-
-        self.metrics[f'{stage}_metrics'].update(y_hat, y)
-        self.log(f'{stage}_loss', loss, on_epoch=True, prog_bar=True)
-        self.log_dict(self.metrics[f'{stage}_metrics'], on_epoch=True)
-
-        return loss
-
-    def training_step(self, batch, batch_idx):
-        return self.step(batch, batch_idx, 'train')
-
-    def validation_step(self, batch, batch_idx):
-        return self.step(batch, batch_idx, 'val')
-
-    def test_step(self, batch, batch_idx):
-        return self.step(batch, batch_idx, 'test')
-
-    def configure_optimizers(self):
-        return optim.Adam(self.parameters(), lr=self.learning_rate)
-
-    def predict_step(self, batch, batch_idx):
-        poi_emb = batch['poi_emb']
-        e3_emb = batch['e3_emb']
-        cell_emb = batch['cell_emb']
-        smiles_emb = batch['smiles_emb']
-
-        if self.apply_scaling:
-            if self.join_embeddings == 'beginning':
-                embeddings = np.hstack([
-                    np.array(smiles_emb.tolist()),
-                    np.array(poi_emb.tolist()),
-                    np.array(e3_emb.tolist()),
-                    np.array(cell_emb.tolist()),
-                ])
-                embeddings = self.scalers.transform(embeddings)
-                smiles_emb = embeddings[:, :self.smiles_emb_dim]
-                poi_emb = embeddings[:, self.smiles_emb_dim:self.smiles_emb_dim+self.poi_emb_dim]
-                e3_emb = embeddings[:, self.smiles_emb_dim+self.poi_emb_dim:self.smiles_emb_dim+2*self.poi_emb_dim]
-                cell_emb = embeddings[:, -self.cell_emb_dim:]
-            else:
-                poi_emb = self.scalers['Uniprot'].transform(poi_emb)
-                e3_emb = self.scalers['E3 Ligase Uniprot'].transform(e3_emb)
-                cell_emb = self.scalers['Cell Line Identifier'].transform(cell_emb)
-                smiles_emb = self.scalers['Smiles'].transform(smiles_emb)
-
-        y_hat = self.forward(poi_emb, e3_emb, cell_emb, smiles_emb)
-        return torch.sigmoid(y_hat)
-
-    def train_dataloader(self):
-        if self.train_dataset is None:
-            format = 'train_dataset', self.__class__.__name__
-            raise ValueError(self.missing_dataset_error.format(*format))
-        
-        return DataLoader(
-            self.train_dataset,
-            batch_size=self.batch_size,
-            shuffle=True,
-            # drop_last=True,
-        )
-
-    def val_dataloader(self):
-        if self.val_dataset is None:
-            format = 'val_dataset', self.__class__.__name__
-            raise ValueError(self.missing_dataset_error.format(*format))
-        return DataLoader(
-            self.val_dataset,
-            batch_size=self.batch_size,
-            shuffle=False,
-        )
-
-    def test_dataloader(self):
-        if self.test_dataset is None:
-            format = 'test_dataset', self.__class__.__name__
-            raise ValueError(self.missing_dataset_error.format(*format))
-        return DataLoader(
-            self.test_dataset,
-            batch_size=self.batch_size,
-            shuffle=False,
-        )
-
-def train_model(
-        train_df: pd.DataFrame,
-        val_df: pd.DataFrame,
-        test_df: Optional[pd.DataFrame] = None,
-        hidden_dim: int = 768,
-        batch_size: int = 8,
-        learning_rate: float = 2e-5,
-        dropout: float = 0.2,
-        max_epochs: int = 50,
-        smiles_emb_dim: int = fingerprint_size,
-        join_embeddings: Literal['beginning', 'concat', 'sum'] = 'concat',
-        smote_k_neighbors:int = 5,
-        use_smote: bool = True,
-        apply_scaling: bool = False,
-        active_label:str = 'Active',
-        fast_dev_run: bool = False,
-        use_logger: bool = True,
-        logger_name: str = 'protac',
-        disabled_embeddings: List[str] = [],
-) -> tuple:
-    """ Train a PROTAC model using the given datasets and hyperparameters.
-    
-    Args:
-        train_df (pd.DataFrame): The training set.
-        val_df (pd.DataFrame): The validation set.
-        test_df (pd.DataFrame): The test set. If provided, the returned metrics will include test performance.
-        hidden_dim (int): The hidden dimension of the model.
-        batch_size (int): The batch size.
-        learning_rate (float): The learning rate.
-        max_epochs (int): Th    e maximum number of epochs.
-        smiles_emb_dim (int): The dimension of the SMILES embeddings.
-        smote_k_neighbors (int): The number of neighbors for the SMOTE oversampler.
-        fast_dev_run (bool): Whether to run a fast development run.
-        disabled_embeddings (list): The list of disabled embeddings.
-    
-    Returns:
-        tuple: The trained model, the trainer, and the metrics.
-    """
-    oversampler = SMOTE(k_neighbors=smote_k_neighbors, random_state=42)
-    train_ds = PROTAC_Dataset(
-        train_df,
-        protein_embeddings,
-        cell2embedding,
-        smiles2fp,
-        use_smote=use_smote,
-        oversampler=oversampler if use_smote else None,
-        active_label=active_label,
-    )
-    val_ds = PROTAC_Dataset(
-        val_df,
-        protein_embeddings,
-        cell2embedding,
-        smiles2fp,
-        active_label=active_label,
-    )
-    if test_df is not None:
-        test_ds = PROTAC_Dataset(
-            test_df,
-            protein_embeddings,
-            cell2embedding,
-            smiles2fp,
-            active_label=active_label,
-        )
-    logger = pl.loggers.TensorBoardLogger(
-        save_dir='../logs',
-        name=logger_name,
-    )
-    callbacks = [
-        pl.callbacks.EarlyStopping(
-            monitor='train_loss',
-            patience=10,
-            mode='min',
-            verbose=False,
-        ),
-        pl.callbacks.EarlyStopping(
-            monitor='val_loss',
-            patience=5,
-            mode='min',
-            verbose=False,
-        ),
-        pl.callbacks.EarlyStopping(
-            monitor='val_acc',
-            patience=10,
-            mode='max',
-            verbose=False,
-        ),
-        # pl.callbacks.ModelCheckpoint(
-        #     monitor='val_acc',
-        #     mode='max',
-        #     verbose=True,
-        #     filename='{epoch}-{val_metrics_opt_score:.4f}',
-        # ),
-    ]
-    # Define Trainer
-    trainer = pl.Trainer(
-        logger=logger if use_logger else False,
-        callbacks=callbacks,
-        max_epochs=max_epochs,
-        fast_dev_run=fast_dev_run,
-        enable_model_summary=False,
-        enable_checkpointing=False,
-        enable_progress_bar=False,
-        devices=1,
-        num_nodes=1,
-    )
-    model = PROTAC_Model(
-        hidden_dim=hidden_dim,
-        smiles_emb_dim=smiles_emb_dim,
-        poi_emb_dim=1024,
-        e3_emb_dim=1024,
-        cell_emb_dim=768,
-        batch_size=batch_size,
-        join_embeddings=join_embeddings,
-        dropout=dropout,
-        learning_rate=learning_rate,
-        apply_scaling=apply_scaling,
-        train_dataset=train_ds,
-        val_dataset=val_ds,
-        test_dataset=test_ds if test_df is not None else None,
-        disabled_embeddings=disabled_embeddings,
-    )
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        trainer.fit(model)
-    metrics = trainer.validate(model, verbose=False)[0]
-    if test_df is not None:
-        test_metrics = trainer.test(model, verbose=False)[0]
-        metrics.update(test_metrics)
-    return model, trainer, metrics
-
-# Setup hyperparameter optimization:
-
-def objective(
-        trial: optuna.Trial,
-        train_df: pd.DataFrame,
-        val_df: pd.DataFrame,
-        hidden_dim_options: List[int] = [256, 512, 768],
-        batch_size_options: List[int] = [8, 16, 32],
-        learning_rate_options: Tuple[float, float] = (1e-5, 1e-3),
-        smote_k_neighbors_options: List[int] = list(range(3, 16)),
-        dropout_options: Tuple[float, float] = (0.1, 0.5),
-        fast_dev_run: bool = False,
-        active_label: str = 'Active',
-        disabled_embeddings: List[str] = [],
-) -> float:
-    """ Objective function for hyperparameter optimization.
-    
-    Args:
-        trial (optuna.Trial): The Optuna trial object.
-        train_df (pd.DataFrame): The training set.
-        val_df (pd.DataFrame): The validation set.
-        hidden_dim_options (List[int]): The hidden dimension options.
-        batch_size_options (List[int]): The batch size options.
-        learning_rate_options (Tuple[float, float]): The learning rate options.
-        smote_k_neighbors_options (List[int]): The SMOTE k neighbors options.
-        dropout_options (Tuple[float, float]): The dropout options.
-        fast_dev_run (bool): Whether to run a fast development run.
-        active_label (str): The active label column.
-        disabled_embeddings (List[str]): The list of disabled embeddings.
-    """
-    # Generate the hyperparameters
-    hidden_dim = trial.suggest_categorical('hidden_dim', hidden_dim_options)
-    batch_size = trial.suggest_categorical('batch_size', batch_size_options)
-    learning_rate = trial.suggest_float('learning_rate', *learning_rate_options, log=True)
-    join_embeddings = trial.suggest_categorical('join_embeddings', ['beginning', 'concat', 'sum'])
-    smote_k_neighbors = trial.suggest_categorical('smote_k_neighbors', smote_k_neighbors_options)
-    use_smote = trial.suggest_categorical('use_smote', [True, False])
-    apply_scaling = trial.suggest_categorical('apply_scaling', [True, False])
-    dropout = trial.suggest_float('dropout', *dropout_options)
-
-    # Train the model with the current set of hyperparameters
-    _, _, metrics = train_model(
-        train_df,
-        val_df,
-        hidden_dim=hidden_dim,
-        batch_size=batch_size,
-        join_embeddings=join_embeddings,
-        learning_rate=learning_rate,
-        dropout=dropout,
-        max_epochs=100,
-        smote_k_neighbors=smote_k_neighbors,
-        apply_scaling=apply_scaling,
-        use_smote=use_smote,
-        use_logger=False,
-        fast_dev_run=fast_dev_run,
-        active_label=active_label,
-        disabled_embeddings=disabled_embeddings,
-    )
-
-    # Metrics is a dictionary containing at least the validation loss
-    val_loss = metrics['val_loss']
-    val_acc = metrics['val_acc']
-    val_roc_auc = metrics['val_roc_auc']
-    
-    # Optuna aims to minimize the objective
-    return val_loss - val_acc - val_roc_auc
-
-
-def hyperparameter_tuning_and_training(
-        train_df: pd.DataFrame,
-        val_df: pd.DataFrame,
-        test_df: pd.DataFrame,
-        fast_dev_run: bool = False,
-        n_trials: int = 50,
-        logger_name: str = 'protac_hparam_search',
-        active_label: str = 'Active',
-        disabled_embeddings: List[str] = [],
-        study_filename: Optional[str] = None,
-) -> tuple:
-    """ Hyperparameter tuning and training of a PROTAC model.
-    
-    Args:
-        train_df (pd.DataFrame): The training set.
-        val_df (pd.DataFrame): The validation set.
-        test_df (pd.DataFrame): The test set.
-        fast_dev_run (bool): Whether to run a fast development run.
-        n_trials (int): The number of hyperparameter optimization trials.
-        logger_name (str): The name of the logger.
-        active_label (str): The active label column.
-        disabled_embeddings (List[str]): The list of disabled embeddings.
-
-    Returns:
-        tuple: The trained model, the trainer, and the best metrics.
-    """
-    # Define the search space
-    hidden_dim_options = [256, 512, 768]
-    batch_size_options = [8, 16, 32]
-    learning_rate_options = (1e-5, 1e-3) # min and max values for loguniform distribution
-    smote_k_neighbors_options = list(range(3, 16))
-
-    # Set the verbosity of Optuna
-    optuna.logging.set_verbosity(optuna.logging.WARNING)
-    # Create an Optuna study object
-    sampler = TPESampler(seed=42, multivariate=True)
-    study = optuna.create_study(direction='minimize', sampler=sampler)
-
-    study_loaded = False
-    if study_filename:
-        if os.path.exists(study_filename):
-            study = joblib.load(study_filename)
-            study_loaded = True
-            print(f'Loaded study from {study_filename}')
-
-    if not study_loaded:
-        study.optimize(
-            lambda trial: objective(
-                trial,
-                train_df,
-                val_df,
-                hidden_dim_options=hidden_dim_options,
-                batch_size_options=batch_size_options,
-                learning_rate_options=learning_rate_options,
-                smote_k_neighbors_options=smote_k_neighbors_options,
-                fast_dev_run=fast_dev_run,
-                active_label=active_label,
-                disabled_embeddings=disabled_embeddings,
-            ),
-            n_trials=n_trials,
-        )
-        if study_filename:
-            joblib.dump(study, study_filename)
-
-    # Retrain the model with the best hyperparameters
-    model, trainer, metrics = train_model(
-        train_df,
-        val_df,
-        test_df,
-        use_logger=True,
-        logger_name=logger_name,
-        fast_dev_run=fast_dev_run,
-        active_label=active_label,
-        disabled_embeddings=disabled_embeddings,
-        **study.best_params,
-    )
-
-    # Report the best hyperparameters found
-    metrics.update({f'hparam_{k}': v for k, v in study.best_params.items()})
-
-    # Return the best metrics
-    return model, trainer, metrics
-
-
-def main(
-    active_col: str = 'Active (Dmax 0.6, pDC50 6.0)',
-    n_trials: int = 50,
-    fast_dev_run: bool = False,
-    test_split: float = 0.2,
-    cv_n_splits: int = 5,
-):
-    """ Train a PROTAC model using the given datasets and hyperparameters.
-    
-    Args:
-        use_ored_activity (bool): Whether to use the 'Active - OR' column.
-        n_trials (int): The number of hyperparameter optimization trials.
-        n_splits (int): The number of cross-validation splits.
-        fast_dev_run (bool): Whether to run a fast development run.
-    """
-    ## Set the Column to Predict
-    active_name = active_col.replace(' ', '_').replace('(', '').replace(')', '').replace(',', '')
-
-    # Get Dmax_threshold from the active_col
-    Dmax_threshold = float(active_col.split('Dmax')[1].split(',')[0].strip('(').strip(')').strip())
-    pDC50_threshold = float(active_col.split('pDC50')[1].strip('(').strip(')').strip())
-
-    protac_df[active_col] = protac_df.apply(
-        lambda x: is_active(x['DC50 (nM)'], x['Dmax (%)'], pDC50_threshold=pDC50_threshold, Dmax_threshold=Dmax_threshold), axis=1
-    )
-
-    ## Test Sets
-
-    test_indeces = {}
-
-    ### Random Split
-
-    # Randomly select 20% of the active PROTACs as the test set
-    active_df = protac_df[protac_df[active_col].notna()].copy()
-    test_df = active_df.sample(frac=test_split, random_state=42)
-    test_indeces['random'] = test_df.index
-
-    ### E3-based Split
-
-    encoder = OrdinalEncoder()
-    protac_df['E3 Group'] = encoder.fit_transform(protac_df[['E3 Ligase']]).astype(int)
-    active_df = protac_df[protac_df[active_col].notna()].copy()
-    test_df = active_df[(active_df['E3 Ligase'] != 'VHL') & (active_df['E3 Ligase'] != 'CRBN')]
-    test_indeces['e3_ligase'] = test_df.index
-
-    ### Tanimoto-based Split
-
-    n_bins_tanimoto = 200
-    tanimoto_groups = pd.cut(protac_df['Avg Tanimoto'], bins=n_bins_tanimoto).copy()
-    encoder = OrdinalEncoder()
-    protac_df['Tanimoto Group'] = encoder.fit_transform(tanimoto_groups.values.reshape(-1, 1)).astype(int)
-    active_df = protac_df[protac_df[active_col].notna()].copy()
-    # Sort the groups so that samples with the highest tanimoto similarity,
-    # i.e., the "less similar" ones, are placed in the test set first
-    tanimoto_groups = active_df.groupby('Tanimoto Group')['Avg Tanimoto'].mean().sort_values(ascending=False).index
-
-    test_df = []
-    # For each group, get the number of active and inactive entries. Then, add those
-    # entries to the test_df if: 1) the test_df lenght + the group entries is less
-    # 20% of the active_df lenght, and 2) the percentage of True and False entries
-    # in the active_col in test_df is roughly 50%.
-    for group in tanimoto_groups:
-        group_df = active_df[active_df['Tanimoto Group'] == group]
-        if test_df == []:
-            test_df.append(group_df)
-            continue
-        
-        num_entries = len(group_df)
-        num_active_group = group_df[active_col].sum()
-        num_inactive_group = num_entries - num_active_group
-
-        tmp_test_df = pd.concat(test_df)
-        num_entries_test = len(tmp_test_df)
-        num_active_test = tmp_test_df[active_col].sum()
-        num_inactive_test = num_entries_test - num_active_test
-        
-        # Check if the group entries can be added to the test_df
-        if num_entries_test + num_entries < test_split * len(active_df):
-            # Add anything at the beggining
-            if num_entries_test + num_entries < test_split / 2 * len(active_df):
-                test_df.append(group_df)
-                continue
-            # Be more selective and make sure that the percentage of active and
-            # inactive is balanced
-            if (num_active_group + num_active_test) / (num_entries_test + num_entries) < 0.6:
-                if (num_inactive_group + num_inactive_test) / (num_entries_test + num_entries) < 0.6:
-                    test_df.append(group_df)
-    test_df = pd.concat(test_df)
-    # Save to global dictionary of test indeces
-    test_indeces['tanimoto'] = test_df.index
-
-    ### Target-based Split
-
-    encoder = OrdinalEncoder()
-    protac_df['Uniprot Group'] = encoder.fit_transform(protac_df[['Uniprot']]).astype(int)
-    active_df = protac_df[protac_df[active_col].notna()].copy()
-
-    test_df = []
-    # For each group, get the number of active and inactive entries. Then, add those
-    # entries to the test_df if: 1) the test_df lenght + the group entries is less
-    # 20% of the active_df lenght, and 2) the percentage of True and False entries
-    # in the active_col in test_df is roughly 50%.
-    # Start the loop from the groups containing the smallest number of entries.
-    for group in reversed(active_df['Uniprot'].value_counts().index):
-        group_df = active_df[active_df['Uniprot'] == group]
-        if test_df == []:
-            test_df.append(group_df)
-            continue
-        
-        num_entries = len(group_df)
-        num_active_group = group_df[active_col].sum()
-        num_inactive_group = num_entries - num_active_group
-
-        tmp_test_df = pd.concat(test_df)
-        num_entries_test = len(tmp_test_df)
-        num_active_test = tmp_test_df[active_col].sum()
-        num_inactive_test = num_entries_test - num_active_test
-        
-        # Check if the group entries can be added to the test_df
-        if num_entries_test + num_entries < test_split * len(active_df):
-            # Add anything at the beggining
-            if num_entries_test + num_entries < test_split / 2 * len(active_df):
-                test_df.append(group_df)
-                continue
-            # Be more selective and make sure that the percentage of active and
-            # inactive is balanced
-            if (num_active_group + num_active_test) / (num_entries_test + num_entries) < 0.6:
-                if (num_inactive_group + num_inactive_test) / (num_entries_test + num_entries) < 0.6:
-                    test_df.append(group_df)
-    test_df = pd.concat(test_df)
-    # Save to global dictionary of test indeces
-    test_indeces['uniprot'] = test_df.index
-
-    ## Cross-Validation Training
-    
-    # Make directory ../reports if it does not exist
-    if not os.path.exists('../reports'):
-        os.makedirs('../reports')
-
-    report = []
-    for split_type, indeces in test_indeces.items():
-        if split_type != 'tanimoto':
-            continue
-        active_df = protac_df[protac_df[active_col].notna()].copy()
-        test_df = active_df.loc[indeces]
-        train_val_df = active_df[~active_df.index.isin(test_df.index)]
-        
-        if split_type == 'random':
-            kf = StratifiedKFold(n_splits=cv_n_splits, shuffle=True, random_state=42)
-            group = None
-        elif split_type == 'e3_ligase':
-            kf = StratifiedKFold(n_splits=cv_n_splits, shuffle=True, random_state=42)
-            group = train_val_df['E3 Group'].to_numpy()
-        elif split_type == 'tanimoto':
-            kf = StratifiedGroupKFold(n_splits=cv_n_splits, shuffle=True, random_state=42)
-            group = train_val_df['Tanimoto Group'].to_numpy()
-        elif split_type == 'uniprot':
-            kf = StratifiedGroupKFold(n_splits=cv_n_splits, shuffle=True, random_state=42)
-            group = train_val_df['Uniprot Group'].to_numpy()
-        # Start the CV over the folds
-        X = train_val_df.drop(columns=active_col)
-        y = train_val_df[active_col].tolist()
-        for k, (train_index, val_index) in enumerate(kf.split(X, y, group)):
-            print('-' * 100)
-            print(f'Starting CV for group type: {split_type}, fold: {k}')
-            print('-' * 100)
-            train_df = train_val_df.iloc[train_index]
-            val_df = train_val_df.iloc[val_index]
-
-            leaking_uniprot = list(set(train_df['Uniprot']).intersection(set(val_df['Uniprot'])))
-            leaking_smiles = list(set(train_df['Smiles']).intersection(set(val_df['Smiles'])))
-
-            stats = {
-                'fold': k,
-                'split_type': split_type,
-                'train_len': len(train_df),
-                'val_len': len(val_df),
-                'train_perc': len(train_df) / len(train_val_df),
-                'val_perc': len(val_df) / len(train_val_df),
-                'train_active_perc': train_df[active_col].sum() / len(train_df),
-                'train_inactive_perc': (len(train_df) - train_df[active_col].sum()) / len(train_df),
-                'val_active_perc': val_df[active_col].sum() / len(val_df),
-                'val_inactive_perc': (len(val_df) - val_df[active_col].sum()) / len(val_df),
-                'test_active_perc': test_df[active_col].sum() / len(test_df),
-                'test_inactive_perc': (len(test_df) - test_df[active_col].sum()) / len(test_df),
-                'num_leaking_uniprot': len(leaking_uniprot),
-                'num_leaking_smiles': len(leaking_smiles),
-                'train_leaking_uniprot_perc': len(train_df[train_df['Uniprot'].isin(leaking_uniprot)]) / len(train_df),
-                'train_leaking_smiles_perc': len(train_df[train_df['Smiles'].isin(leaking_smiles)]) / len(train_df),
-            }
-            if split_type != 'random':
-                stats['train_unique_groups'] = len(np.unique(group[train_index]))
-                stats['val_unique_groups'] = len(np.unique(group[val_index]))
-            
-            # Train and evaluate the model
-            model, trainer, metrics = hyperparameter_tuning_and_training(
-                train_df,
-                val_df,
-                test_df,
-                fast_dev_run=fast_dev_run,
-                n_trials=n_trials,
-                logger_name=f'protac_{active_name}_{split_type}_fold_{k}_test_split_{test_split}',
-                active_label=active_col,
-                study_filename=f'../reports/study_{active_name}_{split_type}_fold_{k}_test_split_{test_split}.pkl',
-            )
-            hparams = {p.strip('hparam_'): v for p, v in stats.items() if p.startswith('hparam_')}
-            stats.update(metrics)
-            report.append(stats.copy())
-            del model
-            del trainer
-
-            # Ablation study: disable embeddings at a time
-            for disabled_embeddings in [['e3'], ['poi'], ['cell'], ['smiles'], ['e3', 'cell'], ['poi', 'e3', 'cell']]:
-                print('-' * 100)
-                print(f'Ablation study with disabled embeddings: {disabled_embeddings}')
-                print('-' * 100)
-                stats['disabled_embeddings'] = 'disabled ' + ' '.join(disabled_embeddings)
-                model, trainer, metrics = train_model(
-                    train_df,
-                    val_df,
-                    test_df,
-                    fast_dev_run=fast_dev_run,
-                    logger_name=f'protac_{active_name}_{split_type}_fold_{k}_disabled-{"-".join(disabled_embeddings)}',
-                    active_label=active_col,
-                    disabled_embeddings=disabled_embeddings,
-                    **hparams,
-                )
-                stats.update(metrics)
-                report.append(stats.copy())
-                del model
-                del trainer
-
-        report_df = pd.DataFrame(report)
-        report_df.to_csv(
-            f'../reports/cv_report_hparam_search_{cv_n_splits}-splits_{active_name}_test_split_{test_split}_tanimoto.csv',
-            index=False,
-        )
-
-
-if __name__ == '__main__':
-    cli = CLI(main)
\ No newline at end of file