From 93e958ae6abc811ada7e11189e8e5ddc4868cfc6 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Fri, 2 Sep 2022 10:04:27 +0100 Subject: [PATCH] now running for combined gene actual --- scripts/ml/combined_model/cm_logo_skf_v2.py | 341 ------------------ .../combined_model/combined_model_iterator.py | 8 +- 2 files changed, 4 insertions(+), 345 deletions(-) delete mode 100644 scripts/ml/combined_model/cm_logo_skf_v2.py diff --git a/scripts/ml/combined_model/cm_logo_skf_v2.py b/scripts/ml/combined_model/cm_logo_skf_v2.py deleted file mode 100644 index 823dde5..0000000 --- a/scripts/ml/combined_model/cm_logo_skf_v2.py +++ /dev/null @@ -1,341 +0,0 @@ -#!/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 diff --git a/scripts/ml/combined_model/combined_model_iterator.py b/scripts/ml/combined_model/combined_model_iterator.py index 7f8f07f..ce1816f 100644 --- a/scripts/ml/combined_model/combined_model_iterator.py +++ b/scripts/ml/combined_model/combined_model_iterator.py @@ -309,13 +309,13 @@ def CMLogoSkf(cm_input_df #=============== # Complete Data #============== -CMLogoSkf(cm_input_df = combined_df,file_suffix = "complete") -CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], file_suffix = "complete") +#CMLogoSkf(cm_input_df = combined_df,file_suffix = "complete") +#CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], file_suffix = "complete") #=============== # Actual Data #=============== -#CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual") -#CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual") +CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual") +CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")