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Tanushree Tunstall 2022-07-01 20:37:41 +01:00
parent d812835713
commit b5777a17c9
3 changed files with 103 additions and 22 deletions
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85
scripts/ml/combined_model/cm_logo_skf.py Normal file → Executable file
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@ -9,6 +9,72 @@ import sys, os
import pandas as pd
import numpy as np
import re
from copy import deepcopy
from sklearn import linear_model
from sklearn import datasets
from collections import Counter
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.gaussian_process import GaussianProcessClassifier, kernels
from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer
from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
# added
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.feature_selection import RFE, RFECV
import itertools
import seaborn as sns
import matplotlib.pyplot as plt
from statistics import mean, stdev, median, mode
from imblearn.over_sampling import RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import SMOTE
from sklearn.datasets import make_classification
from imblearn.combine import SMOTEENN
from imblearn.combine import SMOTETomek
from imblearn.over_sampling import SMOTENC
from imblearn.under_sampling import EditedNearestNeighbours
from imblearn.under_sampling import RepeatedEditedNearestNeighbours
from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator
from sklearn.impute import KNNImputer as KNN
import json
import argparse
import re
import itertools
from sklearn.model_selection import LeaveOneGroupOut
###############################################################################
homedir = os.path.expanduser("~")
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
@ -22,7 +88,7 @@ from MultClfs_logo_skf import *
#from GetMLData import *
#from SplitTTS import *
skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True,**rs)
skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
#logo = LeaveOneGroupOut()
@ -38,13 +104,17 @@ def CMLogoSkf(combined_df
for bts_gene in bts_genes:
print('\n BTS gene:', bts_gene)
if not std_gene_omit:
training_genesL = ['alr']
else:
training_genesL = []
tr_gene_omit = std_gene_omit + [bts_gene]
n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
#n_total_genes = (len(bts_genes) - len(std_gene_omit))
n_total_genes = len(all_genes)
training_genesL = std_gene_omit + list(set(bts_genes) - set(tr_gene_omit))
training_genesL = training_genesL + list(set(bts_genes) - set(tr_gene_omit))
#training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
print('\nTotal genes: ', n_total_genes
@ -53,7 +123,7 @@ def CMLogoSkf(combined_df
, '\nOmitted genes:', tr_gene_omit
, '\nBlind test gene:', bts_gene)
tts_split_type = "logoBT_" + bts_gene
tts_split_type = "logo_skf_BT_" + bts_gene
outFile = "/home/tanu/git/Data/ml_combined/" + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
print(outFile)
@ -67,7 +137,6 @@ def CMLogoSkf(combined_df
#cm_y = cm_training_df.loc[:,'dst_mode']
cm_y = cm_training_df.loc[:, target_var]
gene_group = cm_training_df.loc[:,'gene_name']
print('\nTraining data dim:', cm_X.shape
@ -87,14 +156,14 @@ def CMLogoSkf(combined_df
#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
cm_bts_y = cm_test_df.loc[:, target_var]
print('\nTraining data dim:', cm_bts_X.shape
, '\nTraining Target dim:', cm_bts_y.shape)
print('\nTEST data dim:', cm_bts_X.shape
, '\nTEST Target dim:', cm_bts_y.shape)
#%%:Running Multiple models on LOGO with SKF
cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
, target = cm_y
, group = 'none'
#, group = 'none'
, sel_cv = skf_cv
, blind_test_df = cm_bts_X
@ -116,5 +185,5 @@ def CMLogoSkf(combined_df
cD3_v2.to_csv(outFile)
#%%
CMLogoSkf(combined_df)
#CMLogoSkf(combined_df)
CMLogoSkf(combined_df, std_gene_omit=['alr'])

16
scripts/ml/ml_functions/FS.py
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@ -78,6 +78,7 @@ import re
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
, 'fscore' : make_scorer(f1_score)
, 'precision' : make_scorer(precision_score)
@ -87,6 +88,9 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
, 'jcc' : make_scorer(jaccard_score)
})
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
skf_cv = StratifiedKFold(n_splits = 10
#, shuffle = False, random_state= None)
, shuffle = True,**rs)
@ -95,9 +99,6 @@ rskf_cv = RepeatedStratifiedKFold(n_splits = 10
, n_repeats = 3
, **rs)
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
###############################################################################
def fsgs_rfecv(input_df
, target
@ -109,7 +110,10 @@ def fsgs_rfecv(input_df
, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
, cv_method = skf_cv
, var_type = ['numerical', 'categorical' , 'mixed']
, resampling_type = 'none'
, verbose = 3
, random_state = 42
, n_jobs = 10
):
'''
returns
@ -120,6 +124,10 @@ def fsgs_rfecv(input_df
optimised/selected based on mcc
'''
rs = {'random_state': random_state}
njobs = {'n_jobs': n_jobs}
###########################################################################
#================================================
# Determine categorical and numerical features
@ -375,6 +383,8 @@ def fsgs_rfecv(input_df
output_modelD['train_score (MCC)'] = train_bscore
output_modelD['bts_mcc'] = bts_mcc_score
output_modelD['train_bts_diff'] = round(train_test_diff,2)
output_modelD['resampling'] = resampling_type
print(output_modelD)
nlen = len(output_modelD)

24
scripts/ml/ml_functions/MultClfs_logo_skf.py
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@ -77,9 +77,6 @@ import re
import itertools
from sklearn.model_selection import LeaveOneGroupOut
#%% GLOBALS
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
, 'fscore' : make_scorer(f1_score)
, 'precision' : make_scorer(precision_score)
@ -146,7 +143,7 @@ def MultModelsCl_logo_skf(input_df
, blind_test_df = pd.DataFrame()
, blind_test_target = pd.Series(dtype = int)
, tts_split_type = "none"
, group = 'none'
#, group = 'none'
, resampling_type = 'none' # default
, add_cm = True # adds confusion matrix based on cross_val_predict
@ -188,11 +185,11 @@ def MultModelsCl_logo_skf(input_df
, **rs)
logo = LeaveOneGroupOut()
# select CV type:
if group == 'none':
sel_cv = skf_cv
else:
sel_cv = logo
# # select CV type:
# if group == 'none':
# sel_cv = skf_cv
# else:
# sel_cv = logo
#======================================================
# Determine categorical and numerical features
#======================================================
@ -277,7 +274,7 @@ def MultModelsCl_logo_skf(input_df
, input_df
, target
, cv = sel_cv
, groups = group
#, groups = group
, scoring = scoring_fn
, return_train_score = True)
#==============================
@ -306,7 +303,12 @@ def MultModelsCl_logo_skf(input_df
cmD = {}
# Calculate cm
y_pred = cross_val_predict(model_pipeline, input_df, target, cv = sel_cv, groups = group, **njobs)
y_pred = cross_val_predict(model_pipeline
, input_df
, target
, cv = sel_cv
#, groups = group
, **njobs)
#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()