added UQ_MultClassPipe4.py and UQ_imbalance.py
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UQ_MultClassPipe4.py
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UQ_MultClassPipe4.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 Fri Mar 4 15:25:33 2022
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@author: tanu
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"""
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#%%
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import os, sys
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import pandas as pd
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import numpy as np
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import pprint as pp
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#from copy import deepcopy
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from sklearn import linear_model
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from sklearn.linear_model import LogisticRegression, LinearRegression
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.neural_network import MLPClassifier
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from xgboost import XGBClassifier
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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 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, jaccard_score
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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.metrics import average_precision_score
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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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from sklearn.feature_selection import RFE
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from sklearn.feature_selection import RFECV
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import itertools
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import seaborn as sns
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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, median, mode
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from imblearn.over_sampling import RandomOverSampler
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from imblearn.over_sampling import SMOTE
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from imblearn.pipeline import Pipeline
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#from sklearn.datasets import make_classification
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from sklearn.model_selection import cross_validate
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from sklearn.model_selection import RepeatedStratifiedKFold
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from sklearn.ensemble import AdaBoostClassifier
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from imblearn.combine import SMOTEENN
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from imblearn.under_sampling import EditedNearestNeighbours
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from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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from sklearn.linear_model import RidgeClassifier, SGDClassifier, PassiveAggressiveClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.svm import SVC
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from xgboost import XGBClassifier
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.linear_model import SGDClassifier
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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#%%
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'accuracy' : make_scorer(accuracy_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jaccard' : make_scorer(jaccard_score)
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})
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# Multiple Classification - Model Pipeline
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def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categorical','mixed']):
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'''
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@ param input_df: input features
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@ type: df with input features WITHOUT the target variable
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@param target: target (or output) feature
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@type: df or np.array or Series
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@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
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@type: int or StratifiedKfold()
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@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
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@type: list
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returns
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Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
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'''
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# determine categorical and numerical features
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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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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#%% Specify multiple Classification models
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log_reg = LogisticRegression(**rs)
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nb = BernoulliNB()
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knn = KNeighborsClassifier()
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svm = SVC(**rs)
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mlp = MLPClassifier(max_iter = 500, **rs)
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dt = DecisionTreeClassifier(**rs)
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et = ExtraTreesClassifier(**rs)
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rf = RandomForestClassifier(**rs, n_estimators = 1000 )
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rf2 = RandomForestClassifier(
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min_samples_leaf = 5
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, n_estimators = 100 #10
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, bootstrap = True
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, oob_score = True
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, **njobs
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, **rs
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, max_features = 'auto')
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xgb = XGBClassifier(**rs, verbosity = 0, use_label_encoder =False)
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lda = LinearDiscriminantAnalysis()
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mnb = MultinomialNB()
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pa = PassiveAggressiveClassifier(**rs, **njobs)
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sgd = SGDClassifier(**rs, **njobs)
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models = [('Logistic Regression', log_reg)
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, ('Naive Bayes' , nb)
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, ('K-Nearest Neighbors', knn)
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, ('SVM' , svm)
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, ('MLP' , mlp)
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# , ('Decision Tree' , dt)
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# , ('Extra Trees' , et)
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# , ('Random Forest' , rf)
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# , ('Naive Bayes' , nb)
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# , ('Random Forest2' , rf2)
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# , ('XGBoost' , xgb)
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# , ('LDA' , lda)
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# , ('MultinomialNB' , mnb)
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# , ('PassiveAggresive' , pa)
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# , ('StochasticGDescent' , sgd)
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]
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mm_skf_scoresD = {}
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for model_name, model_fn in models:
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print('\nModel_name:', model_name
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, '\nModel func:' , model_fn
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, '\nList of models:', models)
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model_pipeline = Pipeline([
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('prep' , col_transform)
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, ('model' , model_fn)])
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print('Running model pipeline:', model_pipeline)
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skf_cv_mod = cross_validate(model_pipeline
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, input_df
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, target
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, cv = skf_cv
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, scoring = scoring_fn
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, return_train_score = True)
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mm_skf_scoresD[model_name] = {}
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for key, value in skf_cv_mod.items():
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print('\nkey:', key, '\nvalue:', value)
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print('\nmean value:', mean(value))
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mm_skf_scoresD[model_name][key] = round(mean(value),2)
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#pp.pprint(mm_skf_scoresD)
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#return(mm_skf_scoresD)
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#%%
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#=========================
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# Blind test: BTS results
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#=========================
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# Build the final results with all scores for a feature selected model
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#bts_predict = gscv_fs.predict(X_bts)
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model_pipeline.fit(input_df, target)
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bts_predict = model_pipeline.predict(X_bts)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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# Diff b/w train and bts test scores
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# train_test_diff = train_bscore - bts_mcc_score
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# print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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# create a dict with all scores
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lr_btsD = { 'model_name': model_name
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, 'bts_mcc':None
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, 'bts_fscore':None
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, 'bts_precision':None
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, 'bts_recall':None
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, 'bts_accuracy':None
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, 'bts_roc_auc':None
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, 'bts_jaccard':None}
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lr_btsD
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lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
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lr_btsD
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return(lr_btsD)
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68
UQ_imbalance.py
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68
UQ_imbalance.py
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@ -0,0 +1,68 @@
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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 Thu May 26 05:19:25 2022
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@author: tanu
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"""
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#%% https://www.kite.com/blog/python/smote-python-imbalanced-learn-for-oversampling/
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from sklearn.svm import SVC
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from imblearn.over_sampling import SMOTE
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#%%############################################################################
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def train_SVM(df):
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# select the feature columns
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X = df.loc[:, df.columns != 'label']
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# select the label column
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y = df.label
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# train an SVM with linear kernel
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clf = SVC(kernel='linear')
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clf.fit(X, y)
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return clf
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def plot_svm_boundary(clf, df, title):
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fig, ax = plt.subplots()
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X0, X1 = df.iloc[:, 0], df.iloc[:, 1]
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x_min, x_max = X0.min() - 1, X0.max() + 1
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y_min, y_max = X1.min() - 1, X1.max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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out = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
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ax.scatter(X0, X1, c=df.label, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
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ax.set_ylabel('y')
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ax.set_xlabel('x')
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ax.set_title(title)
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plt.show()
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#%%############################################################################
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# SMOTE number of neighbors
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#k = 1 (pnca, extra trees baseline is 0.49,numerical only)
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k = 1
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sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k, **rs)
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X_sm, y_sm = sm.fit_resample(X, y)
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print(len(X_sm)) #228
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print(Counter(y))
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y_sm_df = y_sm.to_frame()
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y_sm_df.value_counts().plot(kind = 'bar')
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(X, y)
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print(len(X_ros)) #228
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rus, y_rus = undersample.fit_resample(X, y)
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print(len(X_rus)) #142
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sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'))
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X_enn, y_enn = sm_enn.fit_resample(X, y)
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print(len(X_enn)) #53
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