trying Stratified Kfold split on running multiple pipelines
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7 changed files with 287 additions and 72 deletions
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@ -20,7 +20,8 @@ from xgboost import XGBClassifier
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef
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#%%
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rs = {'random_state': 42}
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# TODO: add preprocessing step with one hot encoder
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@ -63,7 +64,7 @@ def MultClassPipeline(X_train, X_test, y_train, y_test):
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pipelines = []
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scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
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scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'MCC', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
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for clf_name, clf in clfs:
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@ -83,24 +84,26 @@ def MultClassPipeline(X_train, X_test, y_train, y_test):
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# Precision
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pres = precision_score(y_test, y_pred)
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# Recall
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rcall = recall_score(y_test, y_pred)
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recall = recall_score(y_test, y_pred)
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# Accuracy
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accu = accuracy_score(y_test, y_pred)
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# ROC_AUC
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roc_auc = roc_auc_score(y_test, y_pred)
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# Matthews correlation coefficient
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mcc = matthews_corrcoef(y_test, y_pred)
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pipelines.append(pipeline)
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scores_df = scores_df.append({
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'Model' : clf_name,
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'F1_Score' : fscore,
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'Precision' : pres,
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'Recall' : rcall,
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'Accuracy' : accu,
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'ROC_AUC' : roc_auc
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},
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ignore_index = True)
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'Model' : clf_name
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, 'F1_Score' : fscore
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, 'MCC' : mcc
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, 'Precision' : pres
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, 'Recall' : recall
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, 'Accuracy' : accu
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, 'ROC_AUC' : roc_auc
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}
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, ignore_index = True)
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return pipelines, scores_df
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@ -21,7 +21,8 @@ from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef
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#%%
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rs = {'random_state': 42}
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# Done: add preprocessing step with one hot encoder
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@ -70,10 +71,9 @@ def MultClassPipeline2(X_train, X_test, y_train, y_test, input_df):
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('XGBoost', xgb)
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]
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pipelines = []
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scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
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scores_df = pd.DataFrame(columns=['Model', 'F1_Score', 'MCC', 'Precision', 'Recall', 'Accuracy', 'ROC_AUC'])
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for clf_name, clf in clfs:
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#%%
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@ -101,10 +101,12 @@ def MultClassPipeline2(X_train, X_test, y_train, y_test, input_df):
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# F1-Score
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fscore = f1_score(y_test, y_pred)
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# Matthews correlation coefficient
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mcc = matthews_corrcoef(y_test, y_pred)
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# Precision
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pres = precision_score(y_test, y_pred)
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# Recall
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rcall = recall_score(y_test, y_pred)
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recall = recall_score(y_test, y_pred)
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# Accuracy
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accu = accuracy_score(y_test, y_pred)
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# ROC_AUC
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@ -113,15 +115,15 @@ def MultClassPipeline2(X_train, X_test, y_train, y_test, input_df):
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pipelines.append(pipeline)
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scores_df = scores_df.append({
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'Model' : clf_name,
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'F1_Score' : fscore,
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'Precision' : pres,
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'Recall' : rcall,
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'Accuracy' : accu,
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'ROC_AUC' : roc_auc
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},
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ignore_index = True)
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'Model' : clf_name
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, 'F1_Score' : fscore
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, 'MCC' : mcc
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, 'Precision' : pres
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, 'Recall' : recall
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, 'Accuracy' : accu
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, 'ROC_AUC' : roc_auc
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}
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, ignore_index = True)
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return pipelines, scores_df
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@ -21,12 +21,15 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.compose import make_column_transformer
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from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef
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from sklearn.metrics import make_scorer
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from sklearn.metrics import classification_report
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from sklearn.model_selection import cross_validate
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import StratifiedKFold
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import make_pipeline
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@ -39,13 +42,15 @@ import matplotlib.pyplot as plt
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import numpy as np
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print(np.__version__)
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print(pd.__version__)
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from statistics import mean, stdev
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from statistics import mean, stdev, median, mode
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#%%
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homedir = os.path.expanduser("~")
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os.chdir(homedir + "/git/ML_AI_training/")
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# my function
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from MultClassPipe import MultClassPipeline
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from MultClassPipe2 import MultClassPipeline2
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from MultClassPipe3 import MultClassPipeSKF
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gene = 'pncA'
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drug = 'pyrazinamide'
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249
my_data10.py
249
my_data10.py
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@ -6,14 +6,11 @@ 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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#%%
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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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# my function
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from MultClassPipe2 import MultClassPipeline2
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#%% try combinations
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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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@ -23,13 +20,12 @@ def recall(y_true,y_pred):
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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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#%%
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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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#%%
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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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@ -46,44 +42,231 @@ 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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#%%
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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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#%% with feature selection
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X_array = np.array(numerical_features_df)
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Y = target1
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# Determine categorical and numerical features
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input_df = numerical_features_df.copy()
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#input_df = categorical_features_df
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#input_df = all_features_df
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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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numerical_ix = all_features_df.select_dtypes(include=['int64', 'float64']).columns
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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 = all_features_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# prepare data
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), 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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# model pipeline
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model = Pipeline(steps=[('prep', col_transform)
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, ('classifier', LogisticRegression())])
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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y_pred
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rs = {'random_state': 42}
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selector_log = RFECV(estimator = model
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, cv = 10
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, step = 1)
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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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selector_log_x = selector_log.fit_transform(X_train, y_train)
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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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print(selector_log_x.get_support())
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X_trainN.columns
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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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print(selector_logistic_x.ranking_)
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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
|
||||
, var_type = 'mixed'
|
||||
, skf_splits = 10)
|
||||
t4_res
|
||||
22
my_data9.py
22
my_data9.py
|
|
@ -7,12 +7,6 @@ Created on Sat Mar 5 12:57:32 2022
|
|||
"""
|
||||
#%%
|
||||
# data, etc for now comes from my_data6.py and/or my_data5.py
|
||||
#%%
|
||||
homedir = os.path.expanduser("~")
|
||||
os.chdir(homedir + "/git/ML_AI_training/")
|
||||
|
||||
# my function
|
||||
from MultClassPipe2 import MultClassPipeline2
|
||||
#%% try combinations
|
||||
#import sys, os
|
||||
#os.system("imports.py")
|
||||
|
|
@ -130,5 +124,21 @@ 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
|
||||
|
|
@ -85,3 +85,15 @@ all_features: numerical_features + ['ss_class', 'wt_prop_water', 'mut_prop_water
|
|||
9All XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)
|
||||
|
||||
|
||||
#%%
|
||||
Model F1_Score Precision Recall Accuracy ROC_AUC
|
||||
0 Logistic Regression 0.757764 0.701149 0.824324 0.721429 0.715192
|
||||
1 Naive Bayes 0.628571 0.666667 0.594595 0.628571 0.630631
|
||||
2 K-Nearest Neighbors 0.666667 0.623529 0.716216 0.621429 0.615684
|
||||
3 SVM 0.766467 0.688172 0.864865 0.721429 0.712735
|
||||
4 MLP 0.726115 0.686747 0.770270 0.692857 0.688165
|
||||
5 Decision Tree 0.647482 0.692308 0.608108 0.650000 0.652539
|
||||
6 Extra Trees 0.760736 0.696629 0.837838 0.721429 0.714373
|
||||
7 Random Forest 0.736196 0.674157 0.810811 0.692857 0.685708
|
||||
8 Random Forest2 0.736196 0.674157 0.810811 0.692857 0.685708
|
||||
9 XGBoost 0.710526 0.692308 0.729730 0.685714 0.683047)
|
||||
Loading…
Add table
Add a link
Reference in a new issue