diff --git a/scripts/ml/combined_model/cm_logo_skf_v2.py b/scripts/ml/combined_model/cm_logo_skf_v2.py new file mode 100644 index 0000000..823dde5 --- /dev/null +++ b/scripts/ml/combined_model/cm_logo_skf_v2.py @@ -0,0 +1,341 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 29 19:44:06 2022 + +@author: tanu +""" +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') +sys.path +############################################################################### +outdir = homedir + '/git/LSHTM_ML/output/combined/' + +#==================== +# Import ML functions +#==================== +from ml_data_combined import * + +#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores + +######################################################################## +# COMPLETE data: No tts_split +######################################################################## +#%% +def combined_DF_OS(cm_input_df + , all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"] + , bts_genes = ["embb", "katg", "rpob", "pnca", "gid"] + , cols_to_drop = ['dst', 'dst_mode', 'gene_name'] + , target_var = 'dst_mode' + , gene_group = 'gene_name' + , std_gene_omit = [] + #, output_dir = outdir + #, file_suffix = "" + , oversampling = True + , k_smote = 5 + , random_state = 42 + , njobs = os.cpu_count() # the number of jobs should equal the number of CPU cores + ): + + outDict = {} + rs = {'random_state': random_state} + njobs = {'n_jobs': njobs } + + 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 = 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 + ,'\nTraining on:', n_tr_genes + ,'\nTraining on genes:', training_genesL + , '\nOmitted genes:', tr_gene_omit + , '\nBlind test gene:', bts_gene) + + print('\nDim of data:', cm_input_df.shape) + + tts_split_type = "logo_skf_BT_" + bts_gene + + #------- + # training + #------ + cm_training_df = cm_input_df[~cm_input_df['gene_name'].isin(tr_gene_omit)] + + cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False) + #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 + , '\nTraining Target dim:', cm_y.shape) + + if all(cm_X.columns.isin(cols_to_drop) == False): + print('\nChecked training df does NOT have Target var') + else: + sys.exit('\nFAIL: training data contains Target var') + + yc1 = Counter(cm_y) + yc1_ratio = yc1[0]/yc1[1] + + #--------------- + # BTS: genes + #--------------- + cm_test_df = cm_input_df[cm_input_df['gene_name'].isin([bts_gene])] + + cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False) + #cm_bts_y = cm_test_df.loc[:, 'dst_mode'] + cm_bts_y = cm_test_df.loc[:, target_var] + + print('\nTEST data dim:' , cm_bts_X.shape + , '\nTEST Target dim:' , cm_bts_y.shape) + + print("Running Multiple models on LOGO with SKF") + + yc2 = Counter(cm_bts_y) + yc2_ratio = yc2[0]/yc2[1] + + + outDict.update({'X' : cm_X + , 'y' : cm_y + , 'X_bts' : cm_bts_X + , 'y_bts' : cm_bts_y + }) + + if oversampling: + ####################################################################### + # RESAMPLING + ####################################################################### + #------------------------------ + # Simple Random oversampling + # [Numerical + catgeorical] + #------------------------------ + oversample = RandomOverSampler(sampling_strategy='minority') + X_ros, y_ros = oversample.fit_resample(cm_X, cm_y) + print('\nSimple Random OverSampling\n', Counter(y_ros)) + print(X_ros.shape) + + #------------------------------ + # Simple Random Undersampling + # [Numerical + catgeorical] + #------------------------------ + undersample = RandomUnderSampler(sampling_strategy='majority') + X_rus, y_rus = undersample.fit_resample(cm_X, cm_y) + print('\nSimple Random UnderSampling\n', Counter(y_rus)) + print(X_rus.shape) + + #------------------------------ + # Simple combine ROS and RUS + # [Numerical + catgeorical] + #------------------------------ + oversample = RandomOverSampler(sampling_strategy='minority') + X_ros, y_ros = oversample.fit_resample(cm_X, cm_y) + undersample = RandomUnderSampler(sampling_strategy='majority') + X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros) + print('\nSimple Combined Over and UnderSampling\n', Counter(y_rouC)) + print(X_rouC.shape) + + #------------------------------ + # SMOTE_NC: oversampling + # [numerical + categorical] + #https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python + #------------------------------ + # Determine categorical and numerical features + numerical_ix = cm_X.select_dtypes(include=['int64', 'float64']).columns + numerical_ix + num_featuresL = list(numerical_ix) + numerical_colind = cm_X.columns.get_indexer(list(numerical_ix) ) + numerical_colind + + categorical_ix = cm_X.select_dtypes(include=['object', 'bool']).columns + categorical_ix + categorical_colind = cm_X.columns.get_indexer(list(categorical_ix)) + categorical_colind + + #k_sm = 5 # default + k_sm = k_smote + sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm + , **rs + , **njobs) + + X_smnc, y_smnc = sm_nc.fit_resample(cm_X, cm_y) + print('\nSMOTE_NC OverSampling\n', Counter(y_smnc)) + print(X_smnc.shape) + + #====================== + # vars + #====================== + #====================================================== + # Determine categorical and numerical features + #====================================================== + numerical_cols = cm_X.select_dtypes(include=['int64', 'float64']).columns + numerical_cols + categorical_cols = cm_X.select_dtypes(include=['object', 'bool']).columns + categorical_cols + + print('\n-------------------------------------------------------------' + , '\nSuccessfully generated training and test data:' + #, '\nData used:' , data_type + #, '\nSplit type:', split_type + + , '\n\nTotal no. of input features:' , len(cm_X.columns) + , '\n--------No. of numerical features:' , len(numerical_cols) + , '\n--------No. of categorical features:', len(categorical_cols) + + , '\n===========================' + , '\n Resampling: NONE' + , '\n Baseline' + , '\n===========================' + + , '\ninput data size:' , len(cm_input_df) + + , '\n\nTrain data size:' , cm_X.shape + , '\ny_train numbers:' , yc1 + + , '\n\nTest data size:' , cm_bts_X.shape + , '\ny_test_numbers:' , yc2 + + , '\n\ny_train ratio:' , yc1_ratio + , '\ny_test ratio:' , yc2_ratio + , '\n-------------------------------------------------------------') + + + print('\nGenerated Resampled data as below:' + , '\n=================================' + , '\nResampling: Random oversampling' + , '\n================================' + + , '\n\nTrain data size:', X_ros.shape + , '\ny_train numbers:', len(y_ros) + , '\n\ny_train ratio:', Counter(y_ros)[0]/Counter(y_ros)[1] + + , '\ny_test ratio:' , yc2_ratio + ################################################################## + , '\n================================' + , '\nResampling: Random underampling' + , '\n================================' + + , '\n\nTrain data size:', X_rus.shape + , '\ny_train numbers:', len(y_rus) + , '\n\ny_train ratio:', Counter(y_rus)[0]/Counter(y_rus)[1] + + , '\ny_test ratio:' , yc2_ratio + ################################################################## + , '\n================================' + , '\nResampling:Combined (over+under)' + , '\n================================' + + , '\n\nTrain data size:', X_rouC.shape + , '\ny_train numbers:', len(y_rouC) + , '\n\ny_train ratio:', Counter(y_rouC)[0]/Counter(y_rouC)[1] + + , '\ny_test ratio:' , yc2_ratio + ################################################################## + , '\n==============================' + , '\nResampling: Smote NC' + , '\n==============================' + + , '\n\nTrain data size:', X_smnc.shape + , '\ny_train numbers:', len(y_smnc) + , '\n\ny_train ratio:', Counter(y_smnc)[0]/Counter(y_smnc)[1] + + , '\ny_test ratio:' , yc2_ratio + ################################################################## + , '\n-------------------------------------------------------------') + + outDict.update({'X_ros' : X_ros + , 'y_ros' : y_ros + + , 'X_rus' : X_rus + , 'y_rus' : y_rus + + , 'X_rouC': X_rouC + , 'y_rouC': y_rouC + + , 'X_smnc': X_smnc + , 'y_smnc': y_smnc}) + return(outDict) + else: + return(outDict) + \ No newline at end of file