Update README.md

README.md

@@ -393,29 +393,68 @@ then, from within python load the datasets library
    
 To load one of the `MIP` model datasets, use `datasets.load_dataset(...)`:
 
-    >>> dataset_tag = "single_mutant"
-    >>> dataset = datasets.load_dataset(
+    >>> dataset_tag = "dataset3_single"
+    >>> dataset3_single = datasets.load_dataset(
       path = "RosettaCommons/MegaScale",
       name = dataset_tag,
       data_dir = dataset_tag)
-    Resolving data files: 100%|█████████████████████████████████████████| 54/54 [00:00<00:00, 441.70it/s]
-    Downloading data: 100%|███████████████████████████████████████████| 54/54 [01:34<00:00,  1.74s/files]
-    Generating train split: 100%|███████████████████████| 211069/211069 [01:41<00:00, 2085.54 examples/s]
-    Loading dataset shards: 100%|███████████████████████████████████████| 48/48 [00:00<00:00, 211.74it/s]
+    Downloading readme: 100%|██████████████████████████████| 17.0k/17.0k [00:00<00:00, 290kB/s]
+    Downloading data: 100%|███████████████████████████████| 39.8M/39.8M [00:01<00:00, 36.9MB/s]
+    Downloading data: 100%|███████████████████████████████| 41.2M/41.2M [00:00<00:00, 57.3MB/s]
+    Downloading data: 100%|███████████████████████████████| 40.0M/40.0M [00:00<00:00, 43.9MB/s]
+    Downloading data: 100%|███████████████████████████████| 15.5M/15.5M [00:00<00:00, 26.8MB/s]
+    Downloading data: 100%|███████████████████████████████| 14.9M/14.9M [00:00<00:00, 29.4MB/s]
+    Generating train split: 100%|█████████| 1503063/1503063 [00:05<00:00, 262031.56 examples/s]
+    Generating test split: 100%|████████████| 169032/169032 [00:00<00:00, 264056.98 examples/s]
+    Generating val split: 100%|█████████████| 163968/163968 [00:00<00:00, 251806.22 examples/s]
 
 and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
 
-    >>> dataset_models
-    Dataset({
-        features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'],
-        num_rows: 211069
+    >>> dataset3_single
+    DatasetDict({
+    train: Dataset({
+        features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', '\
+deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfol\
+ded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_typ\
+e', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
+        num_rows: 1503063
     })
+    test: Dataset({
+        features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', '\
+deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfol\
+ded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_typ\
+e', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
+        num_rows: 169032
+    })
+    val: Dataset({
+        features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', '\
+deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfol\
+ded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_typ\
+e', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
+        num_rows: 163968
+    })
+})
+
+which is a column oriented format that can be accessed directly, written to disk as a `parquet` file or converted in to a `pandas.DataFrame`, e.g.
+
+    >>> dataset3_single['train'].data.column('name')
+    >>> dataset3_single['train'].to_parquet("dataset3_single_train.parquet")
+    >>> dataset3_single.to_pandas()[[WT_name', 'mut_type', 'dG_ML', 'ddG_ML']]
+                            WT_name mut_type               dG_ML               ddG_ML
+    0        r10_437_TrROS_Hall.pdb      E1Q  2.9212264903176783   0.2949200736672686
+    1        r10_437_TrROS_Hall.pdb      E1Q  2.9212264903176783   0.2949200736672686
+    2        r10_437_TrROS_Hall.pdb      E1Q  2.9212264903176783   0.2949200736672686
+    3        r10_437_TrROS_Hall.pdb      E1Q  2.9212264903176783   0.2949200736672686
+    4        r10_437_TrROS_Hall.pdb      E1Q  2.9212264903176783   0.2949200736672686
+    ...                         ...      ...                 ...                  ...
+    1503058       HEEH_rd3_0055.pdb     L43C   1.629862324762064  0.07132877903687329
+    1503059       HEEH_rd3_0055.pdb     L43C   1.629862324762064  0.07132877903687329
+    1503060       HEEH_rd3_0055.pdb     L43C   1.629862324762064  0.07132877903687329
+    1503061       HEEH_rd3_0055.pdb     L43C   1.629862324762064  0.07132877903687329
+    1503062       HEEH_rd3_0055.pdb     L43C   1.629862324762064  0.07132877903687329
+
+    
 
-which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
-
-    >>> dataset_models.data.column('pdb')
-    >>> dataset_models.to_pandas()
-    >>> dataset_models.to_parquet("dataset.parquet")
 
 ## Dataset Overview
 The curated a set of 776,298 high-quality folding stabilities covers