added combined model FS code and run script

scripts/ml/boruta_test_clfs.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep  2 16:10:44 2022
+
+@author: tanu
+"""
+from sklearn.ensemble import VotingClassifier
+from sklearn.ensemble import BaggingClassifier
+from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, ExtraTreesClassifier
+from boruta import BorutaPy
+
+fooD = combined_DF_OS(combined_df)
+
+numerical_ix = fooD['X'].select_dtypes(include=['int64', 'float64']).columns
+numerical_ix
+print("\nNo. of numerical indices:", len(numerical_ix))
+
+categorical_ix = fooD['X'].select_dtypes(include=['object', 'bool']).columns
+categorical_ix  
+print("\nNo. of categorical indices:", len(categorical_ix))
+
+
+var_type = "mixed"
+
+if var_type == 'mixed':
+   
+    t = [('num', MinMaxScaler(), numerical_ix)
+         , ('cat', OneHotEncoder(), categorical_ix)]
+    
+col_transform = ColumnTransformer(transformers = t
+                                   , remainder='passthrough')
+#--------------ALEX help
+# col_transform
+# col_transform.fit(X)
+# test = col_transform.transform(X)
+# print(col_transform.get_feature_names_out())
+
+# foo = col_transform.fit_transform(X)
+Xm_train = col_transform.fit_transform(fooD['X'])
+fooD['X'].shape
+Xm_train.shape
+
+Xm_test = col_transform.fit_transform(fooD['X_bts'])
+fooD['X_bts'].shape
+Xm_test.shape
+
+X_train = Xm_train.copy()
+X_test = Xm_test.copy()
+X_train.shape
+X_test.shape
+
+y_train = fooD['y']
+y_test = fooD['y_bts']
+y_train.shape
+y_test.shape
+
+# perhaps
+#col_transform.fit(fooD['X'])
+#encoded_colnames = pd.Index(col_transform.get_feature_names_out())
+#======================
+#  1 model
+n_jobs = os.cpu_count() 
+njobs = {'n_jobs': n_jobs }
+rs    = {'random_state': 42}
+
+rf_all_features = RandomForestClassifier(n_estimators=1000, max_depth=5
+                                         , **rs, **njobs)
+
+#rf_all_features = VotingClassifier(estimators=1000)
+rf_all_features = BaggingClassifier(random_state=1, n_estimators=100, verbose = 3, **njobs)
+rf_all_features = AdaBoostClassifier(random_state=1, n_estimators=1000)
+rf_all_features = ExtraTreesClassifier(random_state=1, n_estimators=1000, max_depth=5, verbose = 3)
+rf_all_features = DecisionTreeClassifier(random_state=1, max_depth=5)
+
+
+rf_all_features.fit(X_train, np.array(y_train))
+accuracy_score(y_test, rf_all_features.predict(X_test))
+matthews_corrcoef(y_test, rf_all_features.predict(X_test))
+
+# BORUTA
+boruta_selector = BorutaPy(rf_all_features,**rs, verbose = 3)
+boruta_selector.fit(np.array(X_train), np.array(y_train))  
+
+# Tells you how many features: GOOD
+print("Ranking: ", boruta_selector.ranking_)          
+print("No. of significant features: ", boruta_selector.n_features_) 
+
+
+cm_df = combined_df.drop(['gene_name', 'dst', 'dst_mode'], axis = 1)
+col_transform.fit(cm_df)
+col_transform.get_feature_names_out()
+    
+var_type_colnames = col_transform.get_feature_names_out()
+var_type_colnames = pd.Index(var_type_colnames)
+    
+if var_type == 'mixed':
+        print('\nVariable type is:', var_type
+              , '\nNo. of columns in input_df:', len(cm_df.columns)
+              , '\nNo. of columns post one hot encoder:', len(var_type_colnames))
+else:
+        print('\nNo. of columns in input_df:', len(input_df.columns))
+
+
+selected_rf_features = pd.DataFrame({'Feature':list(var_type_colnames),
+                                       'Ranking':boruta_selector.ranking_})
+sel_rf_features_sorted = selected_rf_features.sort_values(by='Ranking')
+
+
+sel_features = var_type_colnames[boruta_selector.support_]
+
+
+# tells you the ranking: GOOD
+#foo2 = selected_rf_features.sort_values(by='Ranking')
+
+X_important_train = boruta_selector.transform(np.array(X_train))
+X_important_test = boruta_selector.transform(np.array(X_test)) 
+
+rf_all_features.fit(X_important_train, y_train) 
+accuracy_score(y_test, rf_all_features.predict(X_important_test))
+matthews_corrcoef(y_test, rf_all_features.predict(X_important_test))