Add script to test pipeline

scripts/testing.py

@@ -0,0 +1,73 @@
+import os
+import pandas as pd  
+import sys
+from sklearn.model_selection import train_test_split
+import shap
+import matplotlib.pyplot as plt 
+
+
+root = os.path.dirname(os.path.abspath(__file__))  
+sys.path.append(os.path.join(root, "..", "xai4chem"))
+
+from datamol_desc import DatamolDescriptor
+from regressor import Regressor
+
+if __name__ == "__main__": 
+    # Read data from CSV file into a DataFrame
+    data = pd.read_csv(os.path.join(root, "..", "data", "plasmodium_falciparum_3d7_ic50.csv"))
+
+    # Extract SMILES and target values
+    smiles = data["smiles"]
+    target = data["uM_value"] #uM_value or pchembl_value
+
+    # Split data into training and test sets
+    smiles_train, smiles_valid, y_train, y_valid = train_test_split(smiles, target, test_size=0.2, random_state=42)
+
+
+    # Instantiate the descriptor class 
+    descriptor = DatamolDescriptor(discretize=False)
+
+    descriptor.fit(smiles_train)
+
+    # Transform the data 
+    smiles_train = descriptor.transform(smiles_train)
+
+    # Instantiate the regressor
+    regressor = Regressor(algorithm='xgboost') 
+
+    # Train the model 
+    regressor.train(smiles_train, y_train)
+
+     #Evaluate model
+    # Transform    
+    smiles_valid = descriptor.transform(smiles_valid)
+
+    regressor.evaluate(smiles_valid, y_valid) 
+
+    # Explain the model
+    # Feature names
+    feature_names = descriptor.feature_names
+
+    explanation = regressor.explain(smiles_train) 
+
+    new_explanation = shap.Explanation(
+        values=explanation.values, 
+        base_values=explanation.base_values, 
+        data=explanation.data, 
+        feature_names=feature_names
+    )
+
+    #waterfall plot
+    shap_waterfall_plot = shap.plots.waterfall(new_explanation[0], max_display=15, show=False)
+    shap_waterfall_plot.figure.savefig(os.path.join(root, "..", "results", "pf_3d7_ic50_waterfall_plot_explanation.png"),  bbox_inches='tight') 
+    plt.close(shap_waterfall_plot.figure)
+
+    #summary plot 
+    shap_summary_plot = shap.plots.bar(new_explanation, max_display=20,  show=False)
+    shap_summary_plot.figure.savefig(os.path.join(root, "..", "results", "pf_3d7_ic50_bar_plot_explanation.png"), bbox_inches='tight') 
+    plt.close(shap_summary_plot.figure)
+
+    #beeswarm plot
+    shap_beeswarm_plot = shap.plots.beeswarm(new_explanation,max_display=15, show=False)
+    shap_beeswarm_plot.figure.savefig(os.path.join(root, "..", "results", "pf_3d7_ic50_beeswarm_plot_explanation.png"), bbox_inches='tight') 
+    plt.close(shap_beeswarm_plot.figure)