trying diff cv thresholds for single gene
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2 changed files with 54 additions and 461 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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import itertools
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from sklearn.model_selection import LeaveOneGroupOut
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from sklearn.decomposition import PCA
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from sklearn.naive_bayes import ComplementNB
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from sklearn.dummy import DummyClassifier
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#%% GLOBALS
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#rs = {'random_state': 42} # INSIDE FUNCTION CALL NOW
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#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
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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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# for sel_cv INSIDE FUNCTION CALL NOW
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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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score_type_ordermapD = { 'mcc' : 1
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, 'fscore' : 2
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, 'jcc' : 3
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, 'precision' : 4
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, 'recall' : 5
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, 'accuracy' : 6
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, 'roc_auc' : 7
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, 'TN' : 8
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, 'FP' : 9
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, 'FN' : 10
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, 'TP' : 11
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, 'trainingY_neg': 12
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, 'trainingY_pos': 13
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, 'blindY_neg' : 14
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, 'blindY_pos' : 15
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, 'fit_time' : 16
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, 'score_time' : 17
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}
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scoreCV_mapD = {'test_mcc' : 'MCC'
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, 'test_fscore' : 'F1'
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, 'test_precision' : 'Precision'
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, 'test_recall' : 'Recall'
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, 'test_accuracy' : 'Accuracy'
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, 'test_roc_auc' : 'ROC_AUC'
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, 'test_jcc' : 'JCC'
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}
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scoreBT_mapD = {'bts_mcc' : 'MCC'
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, 'bts_fscore' : 'F1'
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, 'bts_precision' : 'Precision'
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, 'bts_recall' : 'Recall'
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, 'bts_accuracy' : 'Accuracy'
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, 'bts_roc_auc' : 'ROC_AUC'
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, 'bts_jcc' : 'JCC'
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}
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#gene_group = 'gene_name'
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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_noBTS(input_df
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, target
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, tts_split_type
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, resampling_type
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#, group = None
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, skf_cv_threshold = 10 #[None, 3, 5, 10]
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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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, scale_numeric = ['min_max', 'std', 'min_max_neg', 'none']
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
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):
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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-hot 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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#%% Func globals
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rs = {'random_state': random_state}
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njobs = {'n_jobs': n_jobs}
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skf_cv = StratifiedKFold(n_splits = skf_cv_threshold
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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 = skf_cv_threshold
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# , n_repeats = 3
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# , **rs)
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# logo = LeaveOneGroupOut()
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# select CV type:
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# if group == None:
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# sel_cv = skf_cv
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# else:
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# sel_cv = logo
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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 type(var_type) == list:
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var_type = str(var_type[0])
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else:
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var_type = var_type
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if var_type in ['numerical','mixed']:
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if scale_numeric == ['none']:
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if scale_numeric != ['none']:
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if scale_numeric == ['min_max']:
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scaler = MinMaxScaler()
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if scale_numeric == ['min_max_neg']:
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scaler = MinMaxScaler(feature_range=(-1, 1))
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if scale_numeric == ['std']:
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scaler = StandardScaler()
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t = [('num', scaler, numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'categorical':
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t = [('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, verbose = 3, n_estimators = 100) )
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# #, ('Bernoulli NB' , BernoulliNB() ) # pks Naive Bayes, CAUTION
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# , ('Complement NB' , ComplementNB() )
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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 NB' , MultinomialNB() )
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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, **njobs ) )
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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, **njobs) )
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, ('Dummy Classifier' , DummyClassifier(strategy = 'most_frequent') )
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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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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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#, groups = group
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, scoring = scoring_fn
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, return_train_score = True)
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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 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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# ADD more info: meta data related to input df
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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(Counter(target)[0]/Counter(target)[1], 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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#######################################################################
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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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#======================================================
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if add_cm:
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cmD = {}
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# Calculate cm
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y_pred = cross_val_predict(model_pipeline
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, input_df
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, target
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, cv = skf_cv
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#, groups = group
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, **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 cm 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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# Update cv dict cmD
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mm_skf_scoresD[model_name].update(cmD)
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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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tnD = {}
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# Build tn numbers dict
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tnD = {'n_trainingY_neg' : Counter(target)[0]
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, 'n_trainingY_pos' : Counter(target)[1] }
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# Update cv dict with cmD and tnD
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mm_skf_scoresD[model_name].update(tnD)
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#%%
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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, cv_threshold_suffix = skf_cv_threshold)
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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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#%% Process output function ###################################################
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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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, cv_threshold_suffix = 10
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#, blind_test_data = True
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):
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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: CV results
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#----------------------
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scoresDFT = scoresDF.T
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scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns
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# map colnames for consistency to allow concatenting
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scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns
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#scoresDF_CV['source_data'] = 'CV'
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scoresDF_CV['source_data'] = 'CV_' + str(cv_threshold_suffix)
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#----------------------
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# WF: Meta data
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#----------------------
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metaDF = scoresDFT.filter(regex='^(?!test_.*$|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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, '\nmetaDF:', len(metaDF.columns))
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#-------------------------------------
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# Combine WF: CV + Metadata
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#-------------------------------------
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combDF = pd.merge(scoresDF_CV, metaDF, left_index = True, right_index = True)
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print('\nAdding column: Model_name')
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combDF['Model_name'] = combDF.index
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#-------------------------------------
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# Combine WF+Metadata: Final output
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#-------------------------------------
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# if len(combDF.columns) == expected_ncols_out:
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# print('\nPASS: Combined df has expected ncols')
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# else:
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# sys.exit('\nFAIL: Length mismatch for combined_df')
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# print('\nAdding column: Model_name')
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# combDF['Model_name'] = combDF.index
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print('\n========================================================='
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, '\nSUCCESS: Ran multiple classifiers'
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, '\n=======================================================')
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#resampling_methods_wf = combined_baseline_wf[['resampling']]
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#resampling_methods_wf = resampling_methods_wf.drop_duplicates()
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#, '\n', resampling_methods_wf)
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return combDF
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###############################################################################
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@ -15,8 +15,7 @@ sys.path
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from GetMLData import *
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from SplitTTS import *
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from MultClfs import *
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from MultClfs_noBTS import *
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from MultClfs_CVs import *
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#%%
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rs = {'random_state': 42}
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@ -27,6 +26,7 @@ skf_cv = StratifiedKFold(n_splits = 10
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# , n_repeats = 3
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# , **rs)
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# param dict for getmldata()
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#%% READ data
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gene_model_paramD = {'data_combined_model' : False
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, 'use_or' : False
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, 'omit_all_genomic_features': False
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@ -40,7 +40,7 @@ df = getmldata('embB', 'ethambutol' , **gene_model_paramD)
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#df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD)
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#df = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
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#df = getmldata('alr' , 'cycloserine' , **gene_model_paramD)
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#%% SPLIT, Data and Resampling types
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all(df.columns.isin(['gene_name'])) # should be False
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spl_type = '70_30'
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#spl_type = '80_20'
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|
@ -143,11 +143,13 @@ from sklearn.utils import all_estimators
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all_clfs = all_estimators(type_filter="classifier")
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df = pd.DataFrame (all_clfs, columns = ['classifier_name', 'classifier_fn'])
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df.to_csv("Model_names_ALL.csv")
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################################################################
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#%% TEST different CV Thresholds for split_type = NONE
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||||
|
||||
################################################################
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||||
Counter(df2['y'])
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Counter(df2['y_bts'])
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|
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# READ Data
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||||
spl_type = 'none'
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data_type = "complete"
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||||
|
||||
|
|
@ -160,13 +162,13 @@ df2 = split_tts(df
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, include_gene_name = True
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, random_state = 42 # default
|
||||
)
|
||||
|
||||
fooD = MultModelsCl_noBTS(input_df = df2['X']
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||||
#%% Trying different CV thresholds for resampling 'none' ONLY
|
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fooD = MultModelsCl_CVs(input_df = df2['X']
|
||||
, target = df2['y']
|
||||
, skf_cv_threshold = 10 # IMP to change
|
||||
|
||||
, tts_split_type = spl_type
|
||||
, resampling_type = 'XXXX' # default
|
||||
, resampling_type = 'NONE' # default
|
||||
|
||||
, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
, add_yn = True # adds target var class numbers
|
||||
|
|
@ -185,7 +187,7 @@ for k, v in fooD.items():
|
|||
)
|
||||
|
||||
# formatted df
|
||||
foo_df3 = MultModelsCl_noBTS(input_df = df2['X']
|
||||
foo_df3 = MultModelsCl_CVs(input_df = df2['X']
|
||||
, target = df2['y']
|
||||
, skf_cv_threshold = 5 # IMP to change
|
||||
|
||||
|
|
@ -203,6 +205,7 @@ foo_df3 = MultModelsCl_noBTS(input_df = df2['X']
|
|||
|
||||
)
|
||||
|
||||
|
||||
dfs_combine_wf = [foo_df, foo_df2, foo_df3]
|
||||
|
||||
common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf)))
|
||||
|
|
@ -246,3 +249,46 @@ if len(common_cols_wf) == dfs_ncols_wf :
|
|||
, '\nGot:', len(combined_baseline_wf.columns))
|
||||
sys.exit('\nFIRST IF FAILS')
|
||||
|
||||
#%% TRY with dict containing different Resampling types
|
||||
paramD = {
|
||||
'baseline_paramD': { 'input_df' : df2['X']
|
||||
, 'target' : df2['y']
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type': 'none'}
|
||||
|
||||
, 'smnc_paramD' : { 'input_df' : df2['X_smnc']
|
||||
, 'target' : df2['y_smnc']
|
||||
, 'var_type' : 'mixed'
|
||||
, 'resampling_type' : 'smnc'}
|
||||
}
|
||||
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
print(k)
|
||||
all_scoresDF = pd.DataFrame()
|
||||
for skf_cv_threshold in [3,5]:
|
||||
print('\nRunning CV threhhold:', skf_cv_threshold)
|
||||
current_scoreDF = MultModelsCl_CVs(**paramD[k]
|
||||
, skf_cv_threshold = skf_cv_threshold # IMP to change
|
||||
, tts_split_type = spl_type
|
||||
#, resampling_type = 'XXXX' # default
|
||||
|
||||
, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
, add_yn = True # adds target var class numbers
|
||||
|
||||
#, var_type = ['mixed']
|
||||
, scale_numeric = ['min_max']
|
||||
, random_state = 42
|
||||
, n_jobs = os.cpu_count()
|
||||
, return_formatted_output = True
|
||||
)
|
||||
|
||||
all_scoresDF = pd.concat([all_scoresDF, current_scoreDF])
|
||||
mmDD[k] = all_scoresDF
|
||||
|
||||
for k, v in mmDD.items():
|
||||
print(k, v)
|
||||
out_wf= pd.concat(mmDD, ignore_index = True)
|
||||
out_wf2= pd.concat(mmDD)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue