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added combined_model_iterator.py that has oversampling

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Tanushree Tunstall 2022-09-02 09:50:51 +01:00
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#!/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/'
outdir = homedir + '/git/LSHTM_ML/output/test/'
#====================
# Import ML functions
#====================
from ml_data_combined import *
from MultClfs import *
#from GetMLData import *
#from SplitTTS import *
skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
#logo = LeaveOneGroupOut()
#random_state = 42
#njobs = os.cpu_count()
sampling_names = {"": "none", "_ros": "Oversampling", "_rus": "Undersampling", "_rouC": "Over+Under", "_smnc": "SMOTE"}
########################################################################
# COMPLETE data: No tts_split
########################################################################
#%%
def CMLogoSkf(cm_input_df
, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
#, bts_genes = ["embb"]
, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
, target_var = 'dst_mode'
, gene_group = 'gene_name'
, std_gene_omit = []
, output_dir = outdir
, file_suffix = ""
, random_state = 42
, k_smote = 5
, njobs = os.cpu_count()
):
rs = {'random_state': random_state}
njobs = {'n_jobs': njobs }
for bts_gene in bts_genes:
outDict = {}
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
# if len(file_suffix) > 0:
# file_suffix = '_' + file_suffix
# else:
# file_suffix = file_suffix
#outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + ".csv"
#print(outFile)
#-------
# 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')
#---------------
# 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")
# NULL, ros, rus, rouc, smnc
outDict.update({'X_cm' : cm_X
, 'y_cm' : cm_y
, 'X_bts_cm' : cm_bts_X
, 'y_bts_cm' : cm_bts_y
})
#######################################################################
# 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
num_featuresL = list(numerical_ix)
numerical_colind = cm_X.columns.get_indexer(list(numerical_ix) )
categorical_ix = cm_X.select_dtypes(include=['object', 'bool']).columns
categorical_colind = cm_X.columns.get_indexer(list(categorical_ix))
#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)
outDict.update({'X_ros_cm' : X_ros
, 'y_ros_cm' : y_ros
, 'X_rus_cm' : X_rus
, 'y_rus_cm' : y_rus
, 'X_rouC_cm': X_rouC
, 'y_rouC_cm': y_rouC
, 'X_smnc_cm': X_smnc
, 'y_smnc_cm': y_smnc})
#%%:Running Multiple models on LOGO with SKF
# cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X # two func were identical excpet for name
for i in sampling_names.keys():
print("thing:", "X"+i+"_cm", "y"+i+"_cm")
current_X = "X" + i + "_cm"
current_y = "y" + i + "_cm"
current_X_df = outDict[current_X]
current_y_df = outDict[current_y]
cD3_v2 = MultModelsCl(input_df = current_X_df
, target = current_y_df
, sel_cv = skf_cv
, tts_split_type = tts_split_type
, resampling_type = sampling_names[i] # 'none' # default
, add_cm = True
, add_yn = True
, var_type = 'mixed'
, scale_numeric = ['min_max']
, run_blind_test = True
, blind_test_df = cm_bts_X
, blind_test_target = cm_bts_y
, return_formatted_output = True
, random_state = 42
, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
)
outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + i + ".csv"
cD3_v2.to_csv(outFile)
# outDict.update({'X' : cm_X
# , 'y' : cm_y
# , 'X_bts' : cm_bts_X
# , 'y_bts' : cm_bts_y
# })
# return(outDict)
#%% RUN
#===============
# Complete Data
#==============
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")