removed MultModelsCl.py and ProcessMultModelsCl.py as these are merged into a single script for convenience
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2 changed files with 0 additions and 506 deletions
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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 import datasets
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from collections import Counter
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
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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, ExtraTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.gaussian_process import GaussianProcessClassifier, kernels
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from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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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.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 make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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# added
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.feature_selection import RFE, 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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from statistics import mean, stdev, median, mode
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from imblearn.over_sampling import RandomOverSampler
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import SMOTE
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from sklearn.datasets import make_classification
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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from imblearn.over_sampling import SMOTENC
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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from sklearn.model_selection import GridSearchCV
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from sklearn.base import BaseEstimator
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from sklearn.impute import KNNImputer as KNN
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import json
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import argparse
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import re
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'fscore' : make_scorer(f1_score)
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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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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#====================
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# Import ProcessFunc
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#====================
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from ProcessMultModelsCl import *
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#%%
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############################
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# MultModelsCl()
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# Run Multiple Classifiers
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############################
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target, skf_cv
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, blind_test_df
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, blind_test_target
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, tts_split_type
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, resampling_type = 'none' # default
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, add_cm = True # adds confusion matrix based on cross_val_predict
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, add_yn = True # adds target var class numbers
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, var_type = ['numerical', 'categorical','mixed']
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, return_formatted_output = True):
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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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#======================================================
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# Determine categorical and numerical features
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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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#======================================================
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# Determine preprocessing steps ~ var_type
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#======================================================
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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 = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix) ]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#======================================================
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# Specify multiple Classification Models
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#======================================================
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models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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# , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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# , ('Decision Tree' , DecisionTreeClassifier(**rs) )
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# , ('Extra Tree' , ExtraTreeClassifier(**rs) )
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# , ('Extra Trees' , ExtraTreesClassifier(**rs) )
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# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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# , ('Gaussian NB' , GaussianNB() )
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# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
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# , ('LDA' , LinearDiscriminantAnalysis() )
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, ('Logistic Regression' , LogisticRegression(**rs) )
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# , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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# , ('Multinomial' , MultinomialNB() )
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# , ('Naive Bayes' , BernoulliNB() )
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# , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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# , ('QDA' , QuadraticDiscriminantAnalysis() )
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# , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
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# , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
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# , n_estimators = 1000
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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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# , ('Ridge Classifier' , RidgeClassifier(**rs) )
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# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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# , ('SVC' , SVC(**rs) )
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# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) )
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]
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mm_skf_scoresD = {}
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print('\n==============================================================\n'
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, '\nRunning several classification models (n):', len(models)
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,'\nList of models:')
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for m in models:
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print(m)
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print('\n================================================================\n')
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index = 1
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for model_name, model_fn in models:
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print('\nRunning classifier:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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index = index+1
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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('\nRunning model pipeline:', model_pipeline)
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skf_cv_modD = 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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#######################################################################
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#======================================================
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# Option: Add confusion matrix from cross_val_predict
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# Understand and USE with caution
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# cross_val_score, cross_val_predict, "Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from cross_validate and cross_val_score unless all tests sets have equal size and the metric decomposes over samples."
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# https://stackoverflow.com/questions/65645125/producing-a-confusion-matrix-with-cross-validate
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#======================================================
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if add_cm:
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#-----------------------------------------------------------
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# Initialise dict of Confusion Matrix (cm)
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#-----------------------------------------------------------
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cmD = {}
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# Calculate cm
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y_pred = cross_val_predict(model_pipeline, input_df, target, cv = skf_cv, **njobs)
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#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
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tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
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# Build dict
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cmD = {'TN' : tn
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, 'FP': fp
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, 'FN': fn
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, 'TP': tp}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(cmD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#=============================================
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# Option: Add targety numbers for data
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#=============================================
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if add_yn:
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#-----------------------------------------------------------
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# Initialise dict of target numbers: training and blind (tbt)
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#-----------------------------------------------------------
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tbtD = {}
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# training y
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tyn = Counter(target)
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tyn_neg = tyn[0]
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tyn_pos = tyn[1]
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# blind test y
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btyn = Counter(blind_test_target)
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btyn_neg = btyn[0]
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btyn_pos = btyn[1]
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# Build dict
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tbtD = {'n_trainingY_neg' : tyn_neg
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, 'n_trainingY_pos' : tyn_pos
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, 'n_blindY_neg' : btyn_neg
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, 'n_blindY_pos' : btyn_pos}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(tbtD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#==============================
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# Extract mean values for CV
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#==============================
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mm_skf_scoresD[model_name] = {}
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for key, value in skf_cv_modD.items():
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print('\nkey:', key, '\nvalue:', value)
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print('\nmean value:', np.mean(value))
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mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
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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 the model
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#bts_predict = gscv_fs.predict(blind_test_df)
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model_pipeline.fit(input_df, target)
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bts_predict = model_pipeline.predict(blind_test_df)
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
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print('\nMCC on Blind test:' , bts_mcc_score)
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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# Diff b/w train and bts test scores
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# train_test_diff_MCC = cvtrain_mcc - bts_mcc_score
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# print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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mm_skf_scoresD[model_name]['bts_mcc'] = bts_mcc_score
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mm_skf_scoresD[model_name]['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
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#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
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#return(mm_skf_scoresD)
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#%%
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# ADD more info: meta data related to input and blind and resampling
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# target numbers: training
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yc1 = Counter(target)
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yc1_ratio = yc1[0]/yc1[1]
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# target numbers: test
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yc2 = Counter(blind_test_target)
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yc2_ratio = yc2[0]/yc2[1]
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mm_skf_scoresD[model_name]['resampling'] = resampling_type
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mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
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mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(yc1_ratio, 2)
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mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df)
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mm_skf_scoresD[model_name]['n_testY_ratio'] = round(yc2_ratio,2)
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mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
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mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
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#return(mm_skf_scoresD)
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#============================
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# Process the dict to have WF
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#============================
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if return_formatted_output:
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CV_BT_metaDF = ProcessMultModelsCl(mm_skf_scoresD)
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return(CV_BT_metaDF)
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else:
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return(mm_skf_scoresD)
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@ -1,146 +0,0 @@
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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 Jun 23 20:39:20 2022
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@author: tanu
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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 re
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##############################################################################
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#%% FUNCTION: Process output dict from MultModelsCl
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############################
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# ProcessMultModelsCl()
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############################
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#Processes the dict from above if use_formatted_output = True
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def ProcessMultModelsCl(inputD = {}):
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scoresDF = pd.DataFrame(inputD)
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#------------------------
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# Extracting split_name
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#-----------------------
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tts_split_nameL = []
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for k,v in inputD.items():
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tts_split_nameL = tts_split_nameL + [v['tts_split']]
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if len(set(tts_split_nameL)) == 1:
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tts_split_name = str(list(set(tts_split_nameL))[0])
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print('\nExtracting tts_split_name:', tts_split_name)
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#------------------------
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# WF: only CV and BTS
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#-----------------------
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scoresDFT = scoresDF.T
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scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns
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# map colnames for consistency to allow concatenting
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scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns
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scoresDF_CV['source_data'] = 'CV'
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scoresDF_BT = scoresDFT.filter(regex='^bts_.*$', axis = 1); scoresDF_BT.columns
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# map colnames for consistency to allow concatenting
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scoresDF_BT.columns = scoresDF_BT.columns.map(scoreBT_mapD); scoresDF_BT.columns
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scoresDF_BT['source_data'] = 'BT'
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# dfs_combine_wf = [baseline_BT, smnc_BT, ros_BT, rus_BT, rouC_BT,
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# baseline_CV, smnc_CV, ros_CV, rus_CV, rouC_CV]
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#baseline_all = baseline_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
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#metaDF = scoresDFT.filter(regex='training_size|blind_test_size|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling', axis = 1); scoresDF_BT.columns
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#metaDF = scoresDFT.filter(regex='n_.*$|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling|tts.*', axis = 1); metaDF.columns
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metaDF = scoresDFT.filter(regex='^(?!test_.*$|bts_.*$|train_.*$).*'); metaDF.columns
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print('\nTotal cols in each df:'
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, '\nCV df:', len(scoresDF_CV.columns)
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, '\nBT_df:', len(scoresDF_BT.columns)
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, '\nmetaDF:', len(metaDF.columns))
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if len(scoresDF_CV.columns) == len(scoresDF_BT.columns):
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print('\nFirst proceeding to rowbind CV and BT dfs:')
|
||||
expected_ncols_out = len(scoresDF_BT.columns) + len(metaDF.columns)
|
||||
print('\nFinal output should have:',expected_ncols_out, 'columns' )
|
||||
|
||||
#-----------------
|
||||
# Combine WF
|
||||
#-----------------
|
||||
dfs_combine_wf = [scoresDF_CV, scoresDF_BT]
|
||||
|
||||
print('\nCombinig', len(dfs_combine_wf), 'using pd.concat by row ~ rowbind'
|
||||
, '\nChecking Dims of df to combine:'
|
||||
, '\nDim of CV:', scoresDF_CV.shape
|
||||
, '\nDim of BT:', scoresDF_BT.shape)
|
||||
#print(scoresDF_CV)
|
||||
#print(scoresDF_BT)
|
||||
|
||||
dfs_nrows_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_nrows_wf = dfs_nrows_wf + [len(df)]
|
||||
dfs_nrows_wf = max(dfs_nrows_wf)
|
||||
|
||||
dfs_ncols_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_ncols_wf = dfs_ncols_wf + [len(df.columns)]
|
||||
dfs_ncols_wf = max(dfs_ncols_wf)
|
||||
print(dfs_ncols_wf)
|
||||
|
||||
expected_nrows_wf = len(dfs_combine_wf) * dfs_nrows_wf
|
||||
expected_ncols_wf = dfs_ncols_wf
|
||||
|
||||
common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf)))
|
||||
print('\nNumber of Common columns:', dfs_ncols_wf
|
||||
, '\nThese are:', common_cols_wf)
|
||||
|
||||
if len(common_cols_wf) == dfs_ncols_wf :
|
||||
combined_baseline_wf = pd.concat([df[common_cols_wf] for df in dfs_combine_wf], ignore_index=False)
|
||||
print('\nConcatenating dfs with different resampling methods [WF]:'
|
||||
, '\nSplit type:', tts_split_name
|
||||
, '\nNo. of dfs combining:', len(dfs_combine_wf))
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
if len(combined_baseline_wf) == expected_nrows_wf and len(combined_baseline_wf.columns) == expected_ncols_wf:
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
|
||||
print('\nPASS:', len(dfs_combine_wf), 'dfs successfully combined'
|
||||
, '\nnrows in combined_df_wf:', len(combined_baseline_wf)
|
||||
, '\nncols in combined_df_wf:', len(combined_baseline_wf.columns))
|
||||
else:
|
||||
print('\nFAIL: concatenating failed'
|
||||
, '\nExpected nrows:', expected_nrows_wf
|
||||
, '\nGot:', len(combined_baseline_wf)
|
||||
, '\nExpected ncols:', expected_ncols_wf
|
||||
, '\nGot:', len(combined_baseline_wf.columns))
|
||||
sys.exit('\nFIRST IF FAILS')
|
||||
else:
|
||||
print('\nConcatenting dfs not possible [WF],check numbers ')
|
||||
|
||||
|
||||
#-------------------------------------
|
||||
# Combine WF+Metadata: Final output
|
||||
#-------------------------------------
|
||||
# checking indices for the dfs to combine:
|
||||
c1 = list(set(combined_baseline_wf.index))
|
||||
c2 = list(metaDF.index)
|
||||
|
||||
if c1 == c2:
|
||||
print('\nPASS: proceeding to merge metadata with CV and BT dfs')
|
||||
combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True)
|
||||
else:
|
||||
sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs')
|
||||
|
||||
if len(combDF.columns) == expected_ncols_out:
|
||||
print('\nPASS: Combined df has expected ncols')
|
||||
else:
|
||||
sys.exit('\nFAIL: Length mismatch for combined_df')
|
||||
|
||||
print('\n========================================================='
|
||||
, '\nSUCCESS: Ran multiple classifiers'
|
||||
, '\n=======================================================')
|
||||
|
||||
#resampling_methods_wf = combined_baseline_wf[['resampling']]
|
||||
#resampling_methods_wf = resampling_methods_wf.drop_duplicates()
|
||||
#, '\n', resampling_methods_wf)
|
||||
|
||||
return combDF
|
||||
Loading…
Add table
Add a link
Reference in a new issue