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3 changed files with 103 additions and 22 deletions
85
scripts/ml/combined_model/cm_logo_skf.py
Normal file → Executable file
85
scripts/ml/combined_model/cm_logo_skf.py
Normal file → Executable file
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@ -9,6 +9,72 @@ import sys, os
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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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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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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
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@ -22,7 +88,7 @@ from MultClfs_logo_skf import *
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#from GetMLData import *
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#from SplitTTS import *
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skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True,**rs)
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skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
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#logo = LeaveOneGroupOut()
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@ -38,13 +104,17 @@ def CMLogoSkf(combined_df
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for bts_gene in bts_genes:
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print('\n BTS gene:', bts_gene)
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if not std_gene_omit:
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training_genesL = ['alr']
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else:
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training_genesL = []
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tr_gene_omit = std_gene_omit + [bts_gene]
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n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
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#n_total_genes = (len(bts_genes) - len(std_gene_omit))
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n_total_genes = len(all_genes)
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training_genesL = std_gene_omit + list(set(bts_genes) - set(tr_gene_omit))
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training_genesL = training_genesL + list(set(bts_genes) - set(tr_gene_omit))
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#training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
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print('\nTotal genes: ', n_total_genes
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@ -53,7 +123,7 @@ def CMLogoSkf(combined_df
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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tts_split_type = "logoBT_" + bts_gene
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tts_split_type = "logo_skf_BT_" + bts_gene
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outFile = "/home/tanu/git/Data/ml_combined/" + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
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print(outFile)
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@ -67,7 +137,6 @@ def CMLogoSkf(combined_df
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#cm_y = cm_training_df.loc[:,'dst_mode']
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cm_y = cm_training_df.loc[:, target_var]
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gene_group = cm_training_df.loc[:,'gene_name']
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print('\nTraining data dim:', cm_X.shape
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@ -87,14 +156,14 @@ def CMLogoSkf(combined_df
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#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
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cm_bts_y = cm_test_df.loc[:, target_var]
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print('\nTraining data dim:', cm_bts_X.shape
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, '\nTraining Target dim:', cm_bts_y.shape)
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print('\nTEST data dim:', cm_bts_X.shape
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, '\nTEST Target dim:', cm_bts_y.shape)
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#%%:Running Multiple models on LOGO with SKF
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cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
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, target = cm_y
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, group = 'none'
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#, group = 'none'
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, sel_cv = skf_cv
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, blind_test_df = cm_bts_X
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@ -116,5 +185,5 @@ def CMLogoSkf(combined_df
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cD3_v2.to_csv(outFile)
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#%%
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CMLogoSkf(combined_df)
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#CMLogoSkf(combined_df)
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CMLogoSkf(combined_df, std_gene_omit=['alr'])
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@ -77,6 +77,7 @@ import re
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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 = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'fscore' : make_scorer(f1_score)
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@ -87,6 +88,9 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'jcc' : make_scorer(jaccard_score)
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})
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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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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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@ -95,9 +99,6 @@ 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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def fsgs_rfecv(input_df
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, target
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@ -109,7 +110,10 @@ def fsgs_rfecv(input_df
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = ['numerical', 'categorical' , 'mixed']
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, resampling_type = 'none'
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, verbose = 3
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, random_state = 42
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, n_jobs = 10
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):
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'''
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returns
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@ -120,6 +124,10 @@ def fsgs_rfecv(input_df
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optimised/selected based on mcc
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'''
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rs = {'random_state': random_state}
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njobs = {'n_jobs': n_jobs}
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###########################################################################
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#================================================
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# Determine categorical and numerical features
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@ -375,6 +383,8 @@ def fsgs_rfecv(input_df
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output_modelD['train_score (MCC)'] = train_bscore
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output_modelD['bts_mcc'] = bts_mcc_score
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output_modelD['train_bts_diff'] = round(train_test_diff,2)
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output_modelD['resampling'] = resampling_type
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print(output_modelD)
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nlen = len(output_modelD)
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@ -77,9 +77,6 @@ import re
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import itertools
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from sklearn.model_selection import LeaveOneGroupOut
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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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@ -146,7 +143,7 @@ def MultModelsCl_logo_skf(input_df
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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = int)
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, tts_split_type = "none"
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, group = 'none'
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#, group = 'none'
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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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@ -188,11 +185,11 @@ def MultModelsCl_logo_skf(input_df
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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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# # 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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@ -277,7 +274,7 @@ def MultModelsCl_logo_skf(input_df
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, input_df
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, target
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, cv = sel_cv
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, groups = group
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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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@ -306,7 +303,12 @@ def MultModelsCl_logo_skf(input_df
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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 = sel_cv, groups = group, **njobs)
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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 = sel_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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