renamed hyperparams to gscv

earlier_versions/my_datap8.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar  5 12:57:32 2022
+
+@author: tanu
+"""
+#%%
+# data, etc for now  comes from my_data6.py and/or my_data5.py
+
+#%% try combinations
+#import sys, os
+#os.system("imports.py")
+
+
+#%%
+seed = 42
+features_to_encode = list(X_train.select_dtypes(include = ['object']).columns) 
+
+col_trans = make_column_transformer(
+                        (OneHotEncoder(),features_to_encode),
+                        remainder = "passthrough"
+                        )
+
+rf_classifier = RandomForestClassifier(
+                      min_samples_leaf=50,
+                      n_estimators=150,
+                      bootstrap=True,
+                      oob_score=True,
+                      n_jobs=-1,
+                      random_state=seed,
+                      max_features='auto')
+
+pipe = make_pipeline(col_trans, rf_classifier)
+pipe.fit(X_train, y_train)
+y_pred = pipe.predict(X_test)
+#%%
+
+all_features_df.shape
+
+
+X_train, X_test, y_train, y_test = train_test_split(all_features_df, 
+                                                    target1, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', MinMaxScaler()  , numerical_features_df)
+        ,('cat', OneHotEncoder(), categorical_features_df)])
+
+seed = 42
+rf_classifier = RandomForestClassifier(
+                      min_samples_leaf=50,
+                      n_estimators=150,
+                      bootstrap=True,
+                      oob_score=True,
+                      n_jobs=-1,
+                      random_state=seed,
+                      max_features='auto')
+
+preprocessor.fit(all_features_df)
+preprocessor.transform(all_features_df)
+
+model = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    ,('regression',linear_model.LogisticRegression())
+    ])
+
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+y_pred
+
+
+def precision(y_true,y_pred):
+    return precision_score(y_true,y_pred,pos_label = 1)
+def recall(y_true,y_pred):
+    return recall_score(y_true, y_pred, pos_label = 1)
+def f1(y_true,y_pred):
+    return f1_score(y_true, y_pred, pos_label = 1)
+
+acc  = make_scorer(accuracy_score)
+prec = make_scorer(precision)
+rec  = make_scorer(recall)
+f1   = make_scorer(f1)
+
+output = cross_validate(model, X_train, y_train
+                        , scoring = {'acc' : acc
+                                  ,'prec': prec
+                                  ,'rec' : rec
+                                  ,'f1'  : f1}
+                        , cv = 10
+                        , return_train_score = False)
+pd.DataFrame(output).mean()
+#%% with feature selection
+preprocessor.fit(numerical_features_df)
+preprocessor.transform(numerical_features_df)
+
+model = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    ,('regression',linear_model.LogisticRegression())
+    ])
+
+
+
+selector_logistic = RFECV(estimator = model
+                       , cv = 10
+                       , step = 1)
+
+X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df 
+                                                    , target1 
+                                                    , test_size = 0.33
+                                                    , random_state = 42)
+
+selector_logistic_xtrain = selector_logistic.fit_transform(X_trainN, y_trainN)
+print(sel_rfe_logistic.get_support())
+X_trainN.columns
+
+print(sel_rfe_logistic.ranking_)