renamed hyperparams to gscv
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earlier_versions/my_datap10.py
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earlier_versions/my_datap10.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Mar 5 12:57:32 2022
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@author: tanu
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"""
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#%%
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# Data, etc for now comes from my_data6.py and/or my_data5.py
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#%% Specify dir and import functions
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homedir = os.path.expanduser("~")
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os.chdir(homedir + "/git/ML_AI_training/")
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#%% Try combinations
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#import sys, os
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#os.system("imports.py")
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def precision(y_true,y_pred):
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return precision_score(y_true,y_pred,pos_label = 1)
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def recall(y_true,y_pred):
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return recall_score(y_true, y_pred, pos_label = 1)
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def f1(y_true,y_pred):
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return f1_score(y_true, y_pred, pos_label = 1)
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#%% Check df features
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numerical_features_df.shape
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categorical_features_df.shape
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all_features_df.shape
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all_features_df.dtypes
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#%% Simple train and test data splits
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target = target1
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#target = target3
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X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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X_trainC, X_testC, y_trainC, y_testC = train_test_split(categorical_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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X_train, X_test, y_train, y_test = train_test_split(all_features_df,
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target,
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test_size = 0.33,
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random_state = 42)
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#%% Stratified K-fold: Single model
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model1 = Pipeline(steps = [('preprocess', MinMaxScaler())
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, ('log_reg', LogisticRegression(class_weight = 'balanced')) ])
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model1
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rs = {'random_state': 42}
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log_reg = LogisticRegression(**rs)
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nb = BernoulliNB()
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clfs = [('Logistic Regression', log_reg)
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,('Naive Bayes', nb)]
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seed_skf = 42
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skf = StratifiedKFold(n_splits = 10
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, shuffle = True
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, random_state = seed_skf)
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X_array = np.array(numerical_features_df)
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Y = target1
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model_scores_df = pd.DataFrame()
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fscoreL = []
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mccL = []
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presL = []
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recallL = []
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accuL = []
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roc_aucL = []
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for train_index, test_index in skf.split(X_array, Y):
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x_train_fold, x_test_fold = X_array[train_index], X_array[test_index]
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y_train_fold, y_test_fold = Y[train_index], Y[test_index]
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model1.fit(x_train_fold, y_train_fold)
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y_pred_fold = model1.predict(x_test_fold)
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#----------------
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# Model metrics
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#----------------
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# F1-Score
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fscore = f1_score(y_test_fold, y_pred_fold)
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fscoreL.append(fscore)
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fscoreM = mean(fscoreL)
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# Matthews correlation coefficient
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mcc = matthews_corrcoef(y_test_fold, y_pred_fold)
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mccL.append(mcc)
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mccM = mean(mccL)
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# Precision
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pres = precision_score(y_test_fold, y_pred_fold)
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presL.append(pres)
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presM = mean(presL)
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# Recall
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recall = recall_score(y_test_fold, y_pred_fold)
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recallL.append(recall)
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recallM = mean(recallL)
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# Accuracy
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accu = accuracy_score(y_test_fold, y_pred_fold)
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accuL.append(accu)
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accuM = mean(accuL)
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# ROC_AUC
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roc_auc = roc_auc_score(y_test_fold, y_pred_fold)
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roc_aucL.append(roc_auc)
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roc_aucM = mean(roc_aucL)
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model_scores_df = model_scores_df.append({'Model' : model1.steps[1][0]
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,'F1_score' : fscoreM
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, 'MCC' : mccM
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, 'Precision': presM
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, 'Recall' : recallM
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, 'Accuracy' : accuM
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, 'ROC_curve': roc_aucM}
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, ignore_index = True)
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print('\nModel metrics:', model_scores_df)
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#%% stratified KFold: Multiple_models:
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input_df = numerical_features_df
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#X_array = np.array(input_df)
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Y = target1
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var_type = 'numerical'
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input_df = all_features_df
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#X_array = np.array(input_df)
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Y = target1
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var_type = 'mixed'
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input_df = categorical_features_df
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#X_array = np.array(input_df)
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Y = target1
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var_type = 'categorical'
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#=================
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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##############################
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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rs = {'random_state': 42}
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#log_reg = LogisticRegression(**rs)
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log_reg = LogisticRegression(class_weight = 'balanced')
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nb = BernoulliNB()
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rf = RandomForestClassifier(**rs)
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clfs = [('Logistic Regression', log_reg)
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#,('Naive Bayes', nb)
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#, ('Random Forest' , rf)
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]
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#seed_skf = 42
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skf = StratifiedKFold(n_splits = 10
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, shuffle = True
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#, random_state = seed_skf
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, **rs)
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#scores_df = pd.DataFrame()
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fscoreL = []
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mccL = []
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presL = []
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recallL = []
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accuL = []
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roc_aucL = []
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for train_index, test_index in skf.split(input_df, Y):
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print('\nSKF train index:', train_index
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, '\nSKF test index:', test_index)
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x_train_fold, x_test_fold = input_df.iloc[train_index], input_df.iloc[test_index]
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y_train_fold, y_test_fold = Y.iloc[train_index], Y.iloc[test_index]
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# for train_index, test_index in skf.split(X_array, Y):
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# print('\nSKF train index:', train_index
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# , '\nSKF test index:', test_index)
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# x_train_fold, x_test_fold = X_array[train_index], X_array[test_index]
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# y_train_fold, y_test_fold = Y[train_index], Y[test_index]
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clf_scores_df = pd.DataFrame()
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for clf_name, clf in clfs:
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# model2 = Pipeline(steps=[('preprocess', MinMaxScaler())
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# , ('classifier', clf)])
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model2 = Pipeline(steps=[('preprocess', col_transform)
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, ('classifier', clf)])
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model2.fit(x_train_fold, y_train_fold)
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y_pred_fold = model2.predict(x_test_fold)
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#----------------
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# Model metrics
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#----------------
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# F1-Score
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fscore = f1_score(y_test_fold, y_pred_fold)
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fscoreL.append(fscore)
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fscoreM = mean(fscoreL)
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# Matthews correlation coefficient
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mcc = matthews_corrcoef(y_test_fold, y_pred_fold)
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mccL.append(mcc)
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mccM = mean(mccL)
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# Precision
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pres = precision_score(y_test_fold, y_pred_fold)
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presL.append(pres)
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presM = mean(presL)
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# Recall
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recall = recall_score(y_test_fold, y_pred_fold)
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recallL.append(recall)
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recallM = mean(recallL)
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# Accuracy
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accu = accuracy_score(y_test_fold, y_pred_fold)
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accuL.append(accu)
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accuM = mean(accuL)
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# ROC_AUC
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roc_auc = roc_auc_score(y_test_fold, y_pred_fold)
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roc_aucL.append(roc_auc)
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roc_aucM = mean(roc_aucL)
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clf_scores_df = clf_scores_df.append({'Model': clf_name
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,'F1_score' : fscoreM
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, 'MCC' : mccM
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, 'Precision': presM
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, 'Recall' : recallM
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, 'Accuracy' : accuM
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, 'ROC_curve': roc_aucM}
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, ignore_index = True)
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#scores_df = scores_df.append(clf_scores_df)
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#%% Call functions
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tN_res = MultClassPipeline(X_trainN, X_testN, y_trainN, y_testN)
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tN_res
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t2_res = MultClassPipeline2(X_train, X_test, y_train, y_test, input_df = all_features_df)
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t2_res
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#CHECK: numbers are awfully close to each other!
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t3_res = MultClassPipeSKF(input_df = numerical_features_df
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, y_targetF = target1
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, var_type = 'numerical'
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, skf_splits = 10)
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t3_res
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#CHECK: numbers are awfully close to each other!
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t4_res = MultClassPipeSKF(input_df = all_features_df
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, y_targetF = target1
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, var_type = 'mixed'
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, skf_splits = 10)
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t4_res
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