Added testing section after feature selection.

exploration/ml_pipeline.py

@@ -20,6 +20,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import recall_score, f1_score
+
 nb_dir = os.path.split(os.getcwd())[0]
 if nb_dir not in sys.path:
     sys.path.append(nb_dir)
@@ -34,8 +37,8 @@ index_columns = ["local_segment", "local_segment_label", "local_segment_start_da
 df.set_index(index_columns, inplace=True)
 
 # Create binary target 
-# bins = [-1, 0, 4] # bins for stressfulness (0-4) target
-# df['target'], edges = pd.cut(df.target, bins=bins, labels=[0, 1], retbins=True, right=True) #['low', 'medium', 'high']
+bins = [-1, 0, 4] # bins for stressfulness (0-4) target
+df['target'], edges = pd.cut(df.target, bins=bins, labels=[0, 1], retbins=True, right=True) #['low', 'medium', 'high']
 
 
 nan_cols = df.columns[df.isna().any()].tolist()
@@ -53,20 +56,38 @@ for split in cv.get_splits():
     pre.one_hot_encode_train_and_test_sets(categorical_columns)
     train_X, train_y, test_X, test_y = pre.get_train_test_sets()
     
-    # train_X = train_X[train_X.columns[:30]]
+    
+    print(train_X.shape, test_X.shape)
+    # Predict before feature selection
+    rfc = RandomForestClassifier(n_estimators=10)
+    rfc.fit(train_X, train_y)
+    predictions = rfc.predict(test_X)
+    
+    print("Recall:", recall_score(test_y, predictions))
+    print("F1:", f1_score(test_y, predictions))
     
     # Feature selection on train set
-    # Morda se implementira GroupKfold namesto stratifiedKFold? >>
-    # >> Tako se bo posamezen pid pojavil ali v test ali v train setu
     train_groups, test_groups = cv.get_groups_sets(split)
 
     fs = FeatureSelection(train_X, train_y, train_groups) 
-    selected_features = fs.select_features(n_min=20, n_max=50, k=60,
-                                           ml_type="classification_multi", 
-                                           metric="f1", n_tolerance=20)
+    selected_features = fs.select_features(n_min=20, n_max=29, k=40,
+                                           ml_type="classification_bin", 
+                                           metric="recall", n_tolerance=20)
+    
+    train_X = train_X[selected_features]
+    test_X = test_X[selected_features]
+    
     print(selected_features)
     print(len(selected_features))
     
+    # Predict after feature selection    
+    rfc = RandomForestClassifier(n_estimators=500)
+    rfc.fit(train_X, train_y)
+    predictions = rfc.predict(test_X)
+    
+    print("Recall:", recall_score(test_y, predictions))
+    print("F1:", f1_score(test_y, predictions))
+    
     break
 
 # %%