renamed hyperparams to gscv

earlier_versions/my_datap9.py

@@ -0,0 +1,144 @@
+#!/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")
+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)
+
+#%%
+numerical_features_df.shape
+categorical_features_df.shape
+all_features_df.shape
+#%%
+target = target1
+#target = target3
+X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+
+X_trainC, X_testC, y_trainC, y_testC = train_test_split(categorical_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+
+X_train, X_test, y_train, y_test = train_test_split(all_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+#%%
+
+#%%
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', MinMaxScaler()  , numerical_features_names)
+        ,('cat', OneHotEncoder(), categorical_features_names)
+        ], remainder = 'passthrough')
+
+f = preprocessor.fit(numerical_features_df)
+f2 = preprocessor.transform(numerical_features_df)
+
+f3 = preprocessor.fit_transform(numerical_features_df)
+(f3==f2).all()
+
+preprocessor.fit_transform(numerical_features_df)
+
+#preprocessor.fit_transform(all_features_df)
+
+#%%
+model_log = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    #,('log_reg', linear_model.LogisticRegression())
+    ,('log_reg', LogisticRegression(
+        class_weight = 'unbalanced'))
+    ])
+model = model_log
+#%%
+seed = 42
+model_rf = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    ,('rf',  RandomForestClassifier(
+                          min_samples_leaf=50,
+                          n_estimators=150,
+                          bootstrap=True,
+                          oob_score=True,
+                          n_jobs=-1,
+                          random_state=seed,
+                          max_features='auto'))
+    ])
+model = model_rf
+#%%
+model.fit(X_trainN, y_trainN)
+y_pred = model.predict(X_testN)
+y_pred
+
+acc  = make_scorer(accuracy_score)
+prec = make_scorer(precision)
+rec  = make_scorer(recall)
+f1   = make_scorer(f1)
+
+output = cross_validate(model, X_trainN, y_trainN
+                        , scoring = {'acc' : acc
+                                  ,'prec': prec
+                                  ,'rec' : rec
+                                  ,'f1'  : f1}
+                        , cv = 10
+                        , return_train_score = False)
+pd.DataFrame(output).mean()
+#%% Run multiple models using MultClassPipeline
+# only good for numerical features as categ features is not supported yet!
+t1_res = MultClassPipeline2(X_trainN, X_testN, y_trainN, y_testN, input_df = all_features_df)
+t1_res
+
+#%%
+# https://machinelearningmastery.com/columntransformer-for-numerical-and-categorical-data/
+#Each transformer is a three-element tuple that defines the name of the transformer, the transform to apply, and the column indices to apply it to. For example:
+# (Name, Object, Columns)
+
+# Determine categorical and numerical features
+numerical_ix = all_features_df.select_dtypes(include=['int64', 'float64']).columns
+numerical_ix
+categorical_ix = all_features_df.select_dtypes(include=['object', 'bool']).columns
+categorical_ix
+
+# Define the data preparation for the columns
+t = [('cat', OneHotEncoder(), categorical_ix)
+     , ('num', MinMaxScaler(), numerical_ix)]
+col_transform = ColumnTransformer(transformers=t
+                                  , remainder='passthrough')
+# create pipeline (unlike example above where the col transfer was a preprocess step and it was fit_transformed)             
+
+pipeline = Pipeline(steps=[('prep', col_transform)
+                                   , ('classifier', LogisticRegression())])
+#%% Added this to the MultClassPipeline
+
+tN_res = MultClassPipeline(X_trainN, X_testN, y_trainN, y_testN)
+tN_res
+
+t2_res = MultClassPipeline2(X_train, X_test, y_train, y_test, input_df = all_features_df)
+t2_res
+
+t3_res = MultClassPipeSKF(input_df = numerical_features_df
+                          , y_targetF = target1
+                          , var_type = 'numerical'
+                          , skf_splits = 10)
+t3_res
+
+
+t4_res = MultClassPipeSKF(input_df = all_features_df
+                          , y_targetF = target1
+                          , var_type = 'mixed'
+                          , skf_splits = 10)
+t4_res