bdpedigo
/

local_compartment_classifier_bd_boxes

Tabular Classification

Scikit-learn

skops

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bdpedigo commited on Apr 25, 2024

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@@ -2,246 +2,246 @@
 license: mit
 library_name: sklearn
 tags:
-- sklearn
-- skops
-- tabular-classification
 model_format: skops
 model_file: local_compartment_classifier_bd_boxes.skops
 widget:
-- structuredData:
-    area_nm2:
-    - 693824.0
-    - 4852608.0
-    - 17088896.0
-    area_nm2_neighbor_mean:
-    - 10181485.714285716
-    - 9884429.714285716
-    - 9010409.142857144
-    area_nm2_neighbor_std:
-    - 8312409.263207569
-    - 8587259.418816902
-    - 8418630.640116522
-    max_dt_nm:
-    - 69.0
-    - 543.0
-    - 1287.0
-    max_dt_nm_neighbor_mean:
-    - 664.7142857142857
-    - 630.8571428571429
-    - 577.7142857142857
-    max_dt_nm_neighbor_std:
-    - 479.64240342658945
-    - 504.9563358340017
-    - 468.41868657651344
-    mean_dt_nm:
-    - 24.4375
-    - 156.5
-    - 416.0
-    mean_dt_nm_neighbor_mean:
-    - 198.62946428571428
-    - 189.19642857142856
-    - 170.66071428571428
-    mean_dt_nm_neighbor_std:
-    - 150.614304054458
-    - 157.4368957825056
-    - 143.32375093543624
-    pca_ratio_01:
-    - 1.3849340770961909
-    - 1.181656878273399
-    - 1.128046800200765
-    pca_ratio_01_neighbor_mean:
-    - 1.8575624906424115
-    - 1.8760422359899387
-    - 1.880915879451087
-    pca_ratio_01_neighbor_std:
-    - 0.641580757345606
-    - 0.6228187048854344
-    - 0.6165585104590592
-    pca_unwrapped_0:
-    - -0.0046539306640625
-    - -0.497314453125
-    - -0.258544921875
-    pca_unwrapped_0_neighbor_mean:
-    - 0.039224624633789
-    - 0.0840119448575106
-    - 0.0623056238347833
-    pca_unwrapped_0_neighbor_std:
-    - 0.3114910605258688
-    - 0.2573427692683507
-    - 0.296254177168357
-    pca_unwrapped_1:
-    - 0.7392578125
-    - -0.11553955078125
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-    pca_unwrapped_1_neighbor_mean:
-    - 0.0941687497225674
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-    pca_unwrapped_1_neighbor_std:
-    - 0.3179467337379631
-    - 0.3628551035117971
-    - 0.372447324946889
-    pca_unwrapped_2:
-    - -0.673828125
-    - -0.85986328125
-    - 0.94140625
-    pca_unwrapped_2_neighbor_mean:
-    - 0.2258744673295454
-    - 0.2427867542613636
-    - 0.0790349786931818
-    pca_unwrapped_2_neighbor_std:
-    - 0.9134250264562896
-    - 0.8928014788058292
-    - 0.9167197839332804
-    pca_unwrapped_3:
-    - -0.0302886962890625
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-    - -0.2933238636363636
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-    - -0.3480571400035511
-    pca_unwrapped_3_neighbor_std:
-    - 0.6203425764161097
-    - 0.5938304683645145
-    - 0.5600074530240728
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-    - 0.67333984375
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-    pca_unwrapped_4_neighbor_mean:
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-    - 0.2782594507390802
-    pca_unwrapped_4_neighbor_std:
-    - 0.6415192812587974
-    - 0.6430080201673403
-    - 0.6308895861182334
-    pca_unwrapped_5:
-    - 0.73876953125
-    - 0.50048828125
-    - -0.03192138671875
-    pca_unwrapped_5_neighbor_mean:
-    - 0.2028697620738636
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-    - 0.2729325727982954
-    pca_unwrapped_5_neighbor_std:
-    - 0.265173781606759
-    - 0.2994363858938455
-    - 0.2968562365279343
-    pca_unwrapped_6:
-    - 0.99951171875
-    - 0.05828857421875
-    - -0.77880859375
-    pca_unwrapped_6_neighbor_mean:
-    - -0.2386505820534446
-    - -0.1530848416415128
-    - -0.0769850990988991
-    pca_unwrapped_6_neighbor_std:
-    - 0.6776577717043619
-    - 0.7717860533115238
-    - 0.7447135522384378
-    pca_unwrapped_7:
-    - 0.023834228515625
-    - -0.9931640625
-    - 0.52978515625
-    pca_unwrapped_7_neighbor_mean:
-    - -0.4803272594105113
-    - -0.3878728693181818
-    - -0.5263227982954546
-    pca_unwrapped_7_neighbor_std:
-    - 0.4799926318285017
-    - 0.4691567465869561
-    - 0.3891669942534205
-    pca_unwrapped_8:
-    - 0.0192413330078125
-    - 0.0997314453125
-    - -0.3359375
-    pca_unwrapped_8_neighbor_mean:
-    - -0.0384375832297585
-    - -0.0457548661665482
-    - -0.0061485984108664
-    pca_unwrapped_8_neighbor_std:
-    - 0.3037878488292577
-    - 0.3010843368506175
-    - 0.2874409267860334
-    pca_val_unwrapped_0:
-    - 15657.09765625
-    - 40668.40625
-    - 66863.0
-    pca_val_unwrapped_0_neighbor_mean:
-    - 69378.52059659091
-    - 67104.76526988637
-    - 64723.43856534091
-    pca_val_unwrapped_0_neighbor_std:
-    - 20242.245019019712
-    - 24702.906417865197
-    - 25959.16138296664
-    pca_val_unwrapped_1:
-    - 11305.3017578125
-    - 34416.42578125
-    - 59273.25
-    pca_val_unwrapped_1_neighbor_mean:
-    - 41190.40261008523
-    - 39089.39133522727
-    - 36829.68004261364
-    pca_val_unwrapped_1_neighbor_std:
-    - 16625.870141811894
-    - 18875.56976212627
-    - 17666.778281657556
-    pca_val_unwrapped_2:
-    - 1270.4095458984375
-    - 13551.6748046875
-    - 47764.625
-    pca_val_unwrapped_2_neighbor_mean:
-    - 28717.50048828125
-    - 27601.021828391335
-    - 24490.75362881747
-    pca_val_unwrapped_2_neighbor_std:
-    - 14988.204981576571
-    - 16601.48080038032
-    - 15622.078784778376
-    post_synapse_count:
-    - 0.0
-    - 0.0
-    - 0.0
-    post_synapse_count_neighbor_mean:
-    - 0.0
-    - 0.0
-    - 0.0
-    post_synapse_count_neighbor_std:
-    - 0.0
-    - 0.0
-    - 0.0
-    pre_synapse_count:
-    - 0.0
-    - 0.0
-    - 0.0
-    pre_synapse_count_neighbor_mean:
-    - 0.0
-    - 0.0
-    - 0.0
-    pre_synapse_count_neighbor_std:
-    - 0.0
-    - 0.0
-    - 0.0
-    size_nm3:
-    - 12771840.0
-    - 697943040.0
-    - 7550330880.0
-    size_nm3_neighbor_mean:
-    - 3233702034.285714
-    - 3184761234.285714
-    - 2695304960.0
-    size_nm3_neighbor_std:
-    - 3650678969.7909584
-    - 3691650923.5639486
-    - 3518520747.0511127
 ---
 # Model description
-[More Information Needed]
 ## Intended uses & limitations
@@ -256,26 +256,26 @@ widget:
 <details>
 <summary> Click to expand </summary>
-| Hyperparameter                     | Value                                                                                                                     |
-|------------------------------------|---------------------------------------------------------------------------------------------------------------------------|
-| memory                             |                                                                                                                           |
-| steps                              | [('transformer', QuantileTransformer(output_distribution='normal')), ('lda', LinearDiscriminantAnalysis(n_components=3))] |
-| verbose                            | False                                                                                                                     |
-| transformer                        | QuantileTransformer(output_distribution='normal')                                                                         |
-| lda                                | LinearDiscriminantAnalysis(n_components=3)                                                                                |
-| transformer__copy                  | True                                                                                                                      |
-| transformer__ignore_implicit_zeros | False                                                                                                                     |
-| transformer__n_quantiles           | 1000                                                                                                                      |
-| transformer__output_distribution   | normal                                                                                                                    |
-| transformer__random_state          |                                                                                                                           |
-| transformer__subsample             | 10000                                                                                                                     |
-| lda__covariance_estimator          |                                                                                                                           |
-| lda__n_components                  | 3                                                                                                                         |
-| lda__priors                        |                                                                                                                           |
-| lda__shrinkage                     |                                                                                                                           |
-| lda__solver                        | svd                                                                                                                       |
-| lda__store_covariance              | False                                                                                                                     |
-| lda__tol                           | 0.0001                                                                                                                    |
 </details>
@@ -361,53 +361,28 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 ## Evaluation Results
-[More Information Needed]
-# How to Get Started with the Model
-[More Information Needed]
-# Model Card Authors
-This model card is written by following authors:
-[More Information Needed]
-# Model Card Contact
-You can contact the model card authors through following channels:
-[More Information Needed]
-# Citation
-Below you can find information related to citation.
-**BibTeX:**
-```
 [More Information Needed]
-```
-# model_card_authors
-bdpedigo
-# model_description
-This is a model trained to classify pieces of neuron as axon, dendrite, soma, orglia, based only on their local shape and synapse features.The model is a linear discriminant classifier which was trained on compartment labels generated by Bethanny Danskin for 3 6x6x6 um boxes in the Minnie65 Phase3 dataset.
-# Classification Report (overall)
-| type         |   precision |   recall |   f1-score |      support |
-|--------------|-------------|----------|------------|--------------|
-| accuracy     |    0.944357 | 0.944357 |   0.944357 |     0.944357 |
-| macro avg    |    0.854825 | 0.917289 |   0.878753 | 31307        |
-| weighted avg |    0.946879 | 0.944357 |   0.945155 | 31307        |
-# Classification Report (by class)
-| class    |   precision |   recall |   f1-score |   support |
-|----------|-------------|----------|------------|-----------|
-| axon     |    0.956309 | 0.964704 |   0.960488 |     16404 |
-| dendrite |    0.928038 | 0.911341 |   0.919614 |      6948 |
-| glia     |    0.964442 | 0.935279 |   0.949636 |      7540 |
-| soma     |    0.570513 | 0.857831 |   0.685274 |       415 |

 license: mit
 library_name: sklearn
 tags:
+  - sklearn
+  - skops
+  - tabular-classification
 model_format: skops
 model_file: local_compartment_classifier_bd_boxes.skops
 widget:
+  - structuredData:
+      area_nm2:
+        - 693824.0
+        - 4852608.0
+        - 17088896.0
+      area_nm2_neighbor_mean:
+        - 10181485.714285716
+        - 9884429.714285716
+        - 9010409.142857144
+      area_nm2_neighbor_std:
+        - 8312409.263207569
+        - 8587259.418816902
+        - 8418630.640116522
+      max_dt_nm:
+        - 69.0
+        - 543.0
+        - 1287.0
+      max_dt_nm_neighbor_mean:
+        - 664.7142857142857
+        - 630.8571428571429
+        - 577.7142857142857
+      max_dt_nm_neighbor_std:
+        - 479.64240342658945
+        - 504.9563358340017
+        - 468.41868657651344
+      mean_dt_nm:
+        - 24.4375
+        - 156.5
+        - 416.0
+      mean_dt_nm_neighbor_mean:
+        - 198.62946428571428
+        - 189.19642857142856
+        - 170.66071428571428
+      mean_dt_nm_neighbor_std:
+        - 150.614304054458
+        - 157.4368957825056
+        - 143.32375093543624
+      pca_ratio_01:
+        - 1.3849340770961909
+        - 1.181656878273399
+        - 1.128046800200765
+      pca_ratio_01_neighbor_mean:
+        - 1.8575624906424115
+        - 1.8760422359899387
+        - 1.880915879451087
+      pca_ratio_01_neighbor_std:
+        - 0.641580757345606
+        - 0.6228187048854344
+        - 0.6165585104590592
+      pca_unwrapped_0:
+        - -0.0046539306640625
+        - -0.497314453125
+        - -0.258544921875
+      pca_unwrapped_0_neighbor_mean:
+        - 0.039224624633789
+        - 0.0840119448575106
+        - 0.0623056238347833
+      pca_unwrapped_0_neighbor_std:
+        - 0.3114910605258688
+        - 0.2573427692683507
+        - 0.296254177168357
+      pca_unwrapped_1:
+        - 0.7392578125
+        - -0.11553955078125
+        - 0.2169189453125
+      pca_unwrapped_1_neighbor_mean:
+        - 0.0941687497225674
+        - 0.1718776009299538
+        - 0.1416541012850674
+      pca_unwrapped_1_neighbor_std:
+        - 0.3179467337379631
+        - 0.3628551035117971
+        - 0.372447324946889
+      pca_unwrapped_2:
+        - -0.673828125
+        - -0.85986328125
+        - 0.94140625
+      pca_unwrapped_2_neighbor_mean:
+        - 0.2258744673295454
+        - 0.2427867542613636
+        - 0.0790349786931818
+      pca_unwrapped_2_neighbor_std:
+        - 0.9134250264562896
+        - 0.8928014788058292
+        - 0.9167197839332804
+      pca_unwrapped_3:
+        - -0.0302886962890625
+        - -0.86572265625
+        - 0.57177734375
+      pca_unwrapped_3_neighbor_mean:
+        - -0.2933238636363636
+        - -0.2173753218217329
+        - -0.3480571400035511
+      pca_unwrapped_3_neighbor_std:
+        - 0.6203425764161097
+        - 0.5938304683645145
+        - 0.5600074530240728
+      pca_unwrapped_4:
+        - 0.67333984375
+        - -0.0005474090576171
+        - 0.81982421875
+      pca_unwrapped_4_neighbor_mean:
+        - 0.2915762121027166
+        - 0.3528386896306818
+        - 0.2782594507390802
+      pca_unwrapped_4_neighbor_std:
+        - 0.6415192812587974
+        - 0.6430080201673403
+        - 0.6308895861182334
+      pca_unwrapped_5:
+        - 0.73876953125
+        - 0.50048828125
+        - -0.03192138671875
+      pca_unwrapped_5_neighbor_mean:
+        - 0.2028697620738636
+        - 0.2245316938920454
+        - 0.2729325727982954
+      pca_unwrapped_5_neighbor_std:
+        - 0.265173781606759
+        - 0.2994363858938455
+        - 0.2968562365279343
+      pca_unwrapped_6:
+        - 0.99951171875
+        - 0.05828857421875
+        - -0.77880859375
+      pca_unwrapped_6_neighbor_mean:
+        - -0.2386505820534446
+        - -0.1530848416415128
+        - -0.0769850990988991
+      pca_unwrapped_6_neighbor_std:
+        - 0.6776577717043619
+        - 0.7717860533115238
+        - 0.7447135522384378
+      pca_unwrapped_7:
+        - 0.023834228515625
+        - -0.9931640625
+        - 0.52978515625
+      pca_unwrapped_7_neighbor_mean:
+        - -0.4803272594105113
+        - -0.3878728693181818
+        - -0.5263227982954546
+      pca_unwrapped_7_neighbor_std:
+        - 0.4799926318285017
+        - 0.4691567465869561
+        - 0.3891669942534205
+      pca_unwrapped_8:
+        - 0.0192413330078125
+        - 0.0997314453125
+        - -0.3359375
+      pca_unwrapped_8_neighbor_mean:
+        - -0.0384375832297585
+        - -0.0457548661665482
+        - -0.0061485984108664
+      pca_unwrapped_8_neighbor_std:
+        - 0.3037878488292577
+        - 0.3010843368506175
+        - 0.2874409267860334
+      pca_val_unwrapped_0:
+        - 15657.09765625
+        - 40668.40625
+        - 66863.0
+      pca_val_unwrapped_0_neighbor_mean:
+        - 69378.52059659091
+        - 67104.76526988637
+        - 64723.43856534091
+      pca_val_unwrapped_0_neighbor_std:
+        - 20242.245019019712
+        - 24702.906417865197
+        - 25959.16138296664
+      pca_val_unwrapped_1:
+        - 11305.3017578125
+        - 34416.42578125
+        - 59273.25
+      pca_val_unwrapped_1_neighbor_mean:
+        - 41190.40261008523
+        - 39089.39133522727
+        - 36829.68004261364
+      pca_val_unwrapped_1_neighbor_std:
+        - 16625.870141811894
+        - 18875.56976212627
+        - 17666.778281657556
+      pca_val_unwrapped_2:
+        - 1270.4095458984375
+        - 13551.6748046875
+        - 47764.625
+      pca_val_unwrapped_2_neighbor_mean:
+        - 28717.50048828125
+        - 27601.021828391335
+        - 24490.75362881747
+      pca_val_unwrapped_2_neighbor_std:
+        - 14988.204981576571
+        - 16601.48080038032
+        - 15622.078784778376
+      post_synapse_count:
+        - 0.0
+        - 0.0
+        - 0.0
+      post_synapse_count_neighbor_mean:
+        - 0.0
+        - 0.0
+        - 0.0
+      post_synapse_count_neighbor_std:
+        - 0.0
+        - 0.0
+        - 0.0
+      pre_synapse_count:
+        - 0.0
+        - 0.0
+        - 0.0
+      pre_synapse_count_neighbor_mean:
+        - 0.0
+        - 0.0
+        - 0.0
+      pre_synapse_count_neighbor_std:
+        - 0.0
+        - 0.0
+        - 0.0
+      size_nm3:
+        - 12771840.0
+        - 697943040.0
+        - 7550330880.0
+      size_nm3_neighbor_mean:
+        - 3233702034.285714
+        - 3184761234.285714
+        - 2695304960.0
+      size_nm3_neighbor_std:
+        - 3650678969.7909584
+        - 3691650923.5639486
+        - 3518520747.0511127
 ---
 # Model description
+This is a model trained to classify pieces of neuron as axon, dendrite, soma, orglia, based only on their local shape and synapse features.The model is a linear discriminant classifier which was trained on compartment labels generated by Bethanny Danskin for 3 6x6x6 um boxes in the Minnie65 Phase3 dataset.
 ## Intended uses & limitations
 <details>
 <summary> Click to expand </summary>
+| Hyperparameter                       | Value                                                                                                                     |
+| ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
+| memory                               |                                                                                                                           |
+| steps                                | [('transformer', QuantileTransformer(output_distribution='normal')), ('lda', LinearDiscriminantAnalysis(n_components=3))] |
+| verbose                              | False                                                                                                                     |
+| transformer                          | QuantileTransformer(output_distribution='normal')                                                                         |
+| lda                                  | LinearDiscriminantAnalysis(n_components=3)                                                                                |
+| transformer\_\_copy                  | True                                                                                                                      |
+| transformer\_\_ignore_implicit_zeros | False                                                                                                                     |
+| transformer\_\_n_quantiles           | 1000                                                                                                                      |
+| transformer\_\_output_distribution   | normal                                                                                                                    |
+| transformer\_\_random_state          |                                                                                                                           |
+| transformer\_\_subsample             | 10000                                                                                                                     |
+| lda\_\_covariance_estimator          |                                                                                                                           |
+| lda\_\_n_components                  | 3                                                                                                                         |
+| lda\_\_priors                        |                                                                                                                           |
+| lda\_\_shrinkage                     |                                                                                                                           |
+| lda\_\_solver                        | svd                                                                                                                       |
+| lda\_\_store_covariance              | False                                                                                                                     |
+| lda\_\_tol                           | 0.0001                                                                                                                    |
 </details>
 ## Evaluation Results
+### Classification Report (overall)
+| type         | precision | recall   | f1-score | support  |
+| ------------ | --------- | -------- | -------- | -------- |
+| accuracy     | 0.944357  | 0.944357 | 0.944357 | 0.944357 |
+| macro avg    | 0.854825  | 0.917289 | 0.878753 | 31307    |
+| weighted avg | 0.946879  | 0.944357 | 0.945155 | 31307    |
+### Classification Report (by class)
+| class    | precision | recall   | f1-score | support |
+| -------- | --------- | -------- | -------- | ------- |
+| axon     | 0.956309  | 0.964704 | 0.960488 | 16404   |
+| dendrite | 0.928038  | 0.911341 | 0.919614 | 6948    |
+| glia     | 0.964442  | 0.935279 | 0.949636 | 7540    |
+| soma     | 0.570513  | 0.857831 | 0.685274 | 415     |
+# How to Get Started with the Model
 [More Information Needed]
+# Model Card Authors
+Ben Pedigo
+Bethanny Danskin

train.py CHANGED Viewed

@@ -305,6 +305,7 @@ with open(model_pickle_file, mode="bw") as f:
     dump(final_lda, file=f)
 # %%
 from pathlib import Path
 from skops import card, hub_utils
@@ -323,8 +324,8 @@ if not hub_out_path.exists():
 hub_utils.add_files(__file__, dst=hub_out_path, exist_ok=True)
-# if not os.exists(hub_out_path / "README.md"):
-if True:
     model_card = card.Card(model, metadata=card.metadata_from_config(hub_out_path))
     model_card.metadata.license = "mit"
     model_description = (

     dump(final_lda, file=f)
 # %%
+import os
 from pathlib import Path
 from skops import card, hub_utils
 hub_utils.add_files(__file__, dst=hub_out_path, exist_ok=True)
+# if True:
+if not os.path.exists(hub_out_path / "README.md"):
     model_card = card.Card(model, metadata=card.metadata_from_config(hub_out_path))
     model_card.metadata.license = "mit"
     model_description = (