Update README.md

README.md

@@ -3,9 +3,9 @@ tags:
 - chemistry
 - chemical information
 pretty_name: Hematotoxicity Dataset
-dataset_summary: >-
-  The hematotoxicity dataset which contains a training set of 1330 molecules, a
-  test set of 442 molecules, and a validation set of 610 new molecules.
+dataset_summary: >- 
+ The hematotoxicity dataset consists of a training set with 1788 molecules and a test set with 594 molecules. 
+ The train and test datasets were created after sanitizing and splitting the original dataset in the paper below.
 citation: |-
   @article{,
     author = {Teng-Zhi Long, Shao-Hua Shi, Shao Liu, Ai-Ping Lu, Zhao-Qian Liu, Min Li, Ting-Jun Hou*, and Dong-Sheng Cao},
@@ -29,29 +29,20 @@ configs:
     path: HematoxLong2023/test.csv
   - split: train
     path: HematoxLong2023/train.csv
-  - split: validation 
-    path: HematoxLong2023/validation.csv
 dataset_info:
 - config_name: HematoxLong2023
   features:
-  - name: canonical SMILES
-    dtype: string
-  - name: Label
-    dtype:
-      class_label:
-        names:
-          '0': 0
-          '1': 1
+    - name: "new SMILES"
+      dtype: string
+    - name: "Label"
+      dtype: int64
   splits:
-    - name: test
-      num_examples: 442
-      num_bytes: 7200 
     - name: train
-      num_examples: 1330
-      num_bytes: 21408 
-    - name: validation
-      num_examples: 610
-      num_bytes: 9888 
+      num_bytes: 28736
+      num_examples: 1788
+    - name: test
+      num_bytes: 9632
+      num_examples: 594 
 ---
 
 # Hematotoxicity Dataset (HematoxLong2023)
@@ -80,10 +71,8 @@ and load one of the `HematoxLong2023` datasets, e.g.,
     Downloading readme: 100%|██████████| 5.23k/5.23k [00:00<00:00, 35.1kkB/s]
     Downloading data: 100%|██████████| 24.0k/24.0k [00:00<00:00, 155kB/s]
     Downloading data: 100%|██████████| 71.9k/71.9k [00:00<00:00, 587kB/s]
-    Downloading data: 100%|██████████| 33.0k/33.0k [00:00<00:00, 469kB/s]
-    Generating test split: 100%|██████████| 442/442 [00:00<00:00, 12705.92 examples/s]
-    Generating train split: 100%|██████████| 1.33k/1.33k [00:00<00:00, 43895.91 examples/s]
-    Generating validation split: 100%|██████████| 610/610 [00:00<00:00, 20229.02 examples/s]
+    Generating test split: 100%|██████████| 594/594 [00:00<00:00, 12705.92 examples/s]
+    Generating train split: 100%|██████████| 1788/1788 [00:00<00:00, 43895.91 examples/s]
 
 and inspecting the loaded dataset
 
@@ -91,17 +80,13 @@ and inspecting the loaded dataset
     HematoxLong2023
     DatasetDict({
       test: Dataset({
-         features: ['canonical SMILES', 'label'],
-         num_rows: 442
+         features: ['new SMILES', 'label'],
+         num_rows: 594
       })
       train: Dataset({
-          features: ['canonical SMILES', 'label'],
-          num_rows: 1330
+          features: ['new SMILES', 'label'],
+          num_rows: 1788
       })    
-      validation: Dataset({
-          features: ['canonical SMILES', 'Label'],
-          num_rows: 610    
-      })
     })
 
 ### Use a dataset to train a model
@@ -126,13 +111,13 @@ Split and evaluate the catboost model
     
     split_featurised_dataset = featurise_dataset(
       split_dataset,
-      column = "canonical SMILES",
+      column = "new SMILES",
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
         "name": "cat_boost_classifier",
         "config": {
-            "x_features": ['canonical SMILES::morgan', 'canonical SMILES::maccs_rdkit'],
+            "x_features": ['new SMILES::morgan', 'new SMILES::maccs_rdkit'],
             "y_features": ['Label']}})
     
     model.train(split_featurised_dataset["train"])