#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Mar 4 15:25:33 2022 @author: tanu """ #%% import os, sys import pandas as pd import numpy as np import pprint as pp #from copy import deepcopy from sklearn import linear_model from sklearn.linear_model import LogisticRegression, LinearRegression from sklearn.naive_bayes import BernoulliNB from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier from sklearn.neural_network import MLPClassifier from xgboost import XGBClassifier from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.compose import make_column_transformer from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score from sklearn.metrics import make_scorer from sklearn.metrics import classification_report from sklearn.metrics import average_precision_score from sklearn.model_selection import cross_validate from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedKFold from sklearn.pipeline import Pipeline from sklearn.pipeline import make_pipeline from sklearn.feature_selection import RFE from sklearn.feature_selection import RFECV import itertools import seaborn as sns import matplotlib.pyplot as plt import numpy as np print(np.__version__) print(pd.__version__) from statistics import mean, stdev, median, mode from imblearn.over_sampling import RandomOverSampler from imblearn.over_sampling import SMOTE from imblearn.pipeline import Pipeline #from sklearn.datasets import make_classification from sklearn.model_selection import cross_validate from sklearn.model_selection import RepeatedStratifiedKFold from sklearn.ensemble import AdaBoostClassifier from imblearn.combine import SMOTEENN from imblearn.under_sampling import EditedNearestNeighbours from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn.neural_network import MLPClassifier from sklearn.linear_model import RidgeClassifier, SGDClassifier, PassiveAggressiveClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.svm import SVC from xgboost import XGBClassifier from sklearn.naive_bayes import MultinomialNB from sklearn.linear_model import SGDClassifier from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder #%% rs = {'random_state': 42} njobs = {'n_jobs': 10} scoring_fn = ({ 'fscore' : make_scorer(f1_score) , 'mcc' : make_scorer(matthews_corrcoef) , 'precision' : make_scorer(precision_score) , 'recall' : make_scorer(recall_score) , 'accuracy' : make_scorer(accuracy_score) , 'roc_auc' : make_scorer(roc_auc_score) , 'jaccard' : make_scorer(jaccard_score) }) # Multiple Classification - Model Pipeline def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categorical','mixed']): ''' @ param input_df: input features @ type: df with input features WITHOUT the target variable @param target: target (or output) feature @type: df or np.array or Series @param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass @type: int or StratifiedKfold() @var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder) @type: list returns Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training ''' # determine categorical and numerical features numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns numerical_ix categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns categorical_ix # Determine preprocessing steps ~ var_type if var_type == 'numerical': t = [('num', MinMaxScaler(), numerical_ix)] if var_type == 'categorical': t = [('cat', OneHotEncoder(), categorical_ix)] if var_type == 'mixed': t = [('cat', OneHotEncoder(), categorical_ix) , ('num', MinMaxScaler(), numerical_ix)] col_transform = ColumnTransformer(transformers = t , remainder='passthrough') #%% Specify multiple Classification models log_reg = LogisticRegression(**rs) nb = BernoulliNB() knn = KNeighborsClassifier() svm = SVC(**rs) mlp = MLPClassifier(max_iter = 500, **rs) dt = DecisionTreeClassifier(**rs) et = ExtraTreesClassifier(**rs) rf = RandomForestClassifier(**rs, n_estimators = 1000 ) rf2 = RandomForestClassifier( min_samples_leaf = 5 , n_estimators = 100 #10 , bootstrap = True , oob_score = True , **njobs , **rs , max_features = 'auto') xgb = XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) lda = LinearDiscriminantAnalysis() mnb = MultinomialNB() pa = PassiveAggressiveClassifier(**rs, **njobs) sgd = SGDClassifier(**rs, **njobs) models = [('Logistic Regression', log_reg) , ('Naive Bayes' , nb) , ('K-Nearest Neighbors', knn) , ('SVM' , svm) , ('MLP' , mlp) # , ('Decision Tree' , dt) # , ('Extra Trees' , et) # , ('Random Forest' , rf) # , ('Naive Bayes' , nb) # , ('Random Forest2' , rf2) # , ('XGBoost' , xgb) # , ('LDA' , lda) # , ('MultinomialNB' , mnb) # , ('PassiveAggresive' , pa) # , ('StochasticGDescent' , sgd) ] mm_skf_scoresD = {} for model_name, model_fn in models: print('\nModel_name:', model_name , '\nModel func:' , model_fn , '\nList of models:', models) model_pipeline = Pipeline([ ('prep' , col_transform) , ('model' , model_fn)]) print('Running model pipeline:', model_pipeline) skf_cv_mod = cross_validate(model_pipeline , input_df , target , cv = skf_cv , scoring = scoring_fn , return_train_score = True) mm_skf_scoresD[model_name] = {} for key, value in skf_cv_mod.items(): print('\nkey:', key, '\nvalue:', value) print('\nmean value:', mean(value)) mm_skf_scoresD[model_name][key] = round(mean(value),2) #pp.pprint(mm_skf_scoresD) #return(mm_skf_scoresD) #%% #========================= # Blind test: BTS results #========================= # Build the final results with all scores for a feature selected model #bts_predict = gscv_fs.predict(X_bts) model_pipeline.fit(input_df, target) bts_predict = model_pipeline.predict(X_bts) print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2)) print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2)) bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2) # Diff b/w train and bts test scores # train_test_diff = train_bscore - bts_mcc_score # print('\nDiff b/w train and blind test score (MCC):', train_test_diff) # create a dict with all scores lr_btsD = { 'model_name': model_name , 'bts_mcc':None , 'bts_fscore':None , 'bts_precision':None , 'bts_recall':None , 'bts_accuracy':None , 'bts_roc_auc':None , 'bts_jaccard':None} lr_btsD lr_btsD['bts_mcc'] = bts_mcc_score lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2) lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2) lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2) lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2) lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2) lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2) lr_btsD return(lr_btsD)