321 lines
12 KiB
Python
321 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Jun 29 19:44:06 2022
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@author: tanu
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"""
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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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sys.path
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###############################################################################
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#outdir = homedir + '/git/LSHTM_ML/output/combined/'
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outdir = homedir + '/git/LSHTM_ML/output/test/'
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#====================
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# Import ML functions
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#====================
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from ml_data_combined import *
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from MultClfs 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, random_state = 42)
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#logo = LeaveOneGroupOut()
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#random_state = 42
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#njobs = os.cpu_count()
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sampling_names = {"": "none", "_ros": "Oversampling", "_rus": "Undersampling", "_rouC": "Over+Under", "_smnc": "SMOTE"}
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########################################################################
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# COMPLETE data: No tts_split
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########################################################################
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#%%
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def CMLogoSkf(cm_input_df
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, all_genes = ["embb", "katg", "rpob", "pnca", "gid", "alr"]
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, bts_genes = ["embb", "katg", "rpob", "pnca", "gid"]
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#, bts_genes = ["embb"]
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, cols_to_drop = ['dst', 'dst_mode', 'gene_name']
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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, output_dir = outdir
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, file_suffix = ""
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, random_state = 42
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, k_smote = 5
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, njobs = os.cpu_count()
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):
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rs = {'random_state': random_state}
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njobs = {'n_jobs': njobs }
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for bts_gene in bts_genes:
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outDict = {}
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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 = 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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,'\nTraining on:', n_tr_genes
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,'\nTraining on genes:', training_genesL
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, '\nOmitted genes:', tr_gene_omit
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, '\nBlind test gene:', bts_gene)
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#print('\nDim of data:', cm_input_df.shape)
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tts_split_type = "logo_skf_BT_" + bts_gene
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# if len(file_suffix) > 0:
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# file_suffix = '_' + file_suffix
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# else:
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# file_suffix = file_suffix
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#outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + ".csv"
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#print(outFile)
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#-------
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# training
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#------
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cm_training_df = cm_input_df[~cm_input_df['gene_name'].isin(tr_gene_omit)]
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cm_X = cm_training_df.drop(cols_to_drop, axis=1, inplace=False)
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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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# , '\nTraining Target dim:', cm_y.shape)
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if all(cm_X.columns.isin(cols_to_drop) == False):
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print('\nChecked training df does NOT have Target var')
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else:
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sys.exit('\nFAIL: training data contains Target var')
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#---------------
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# BTS: genes
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#---------------
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cm_test_df = cm_input_df[cm_input_df['gene_name'].isin([bts_gene])]
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cm_bts_X = cm_test_df.drop(cols_to_drop, axis = 1, inplace = False)
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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('\nTEST data dim:' , cm_bts_X.shape
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# , '\nTEST Target dim:' , cm_bts_y.shape)
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#print("Running Multiple models on LOGO with SKF")
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# NULL, ros, rus, rouc, smnc
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outDict.update({'X_cm' : cm_X
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, 'y_cm' : cm_y
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, 'X_bts_cm' : cm_bts_X
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, 'y_bts_cm' : cm_bts_y
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})
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#######################################################################
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# RESAMPLING
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#######################################################################
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#------------------------------
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# Simple Random oversampling
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# [Numerical + catgeorical]
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#------------------------------
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(cm_X, cm_y)
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# print('\nSimple Random OverSampling\n', Counter(y_ros))
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# print(X_ros.shape)
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#------------------------------
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# Simple Random Undersampling
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# [Numerical + catgeorical]
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#------------------------------
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rus, y_rus = undersample.fit_resample(cm_X, cm_y)
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# print('\nSimple Random UnderSampling\n', Counter(y_rus))
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# print(X_rus.shape)
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#------------------------------
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# Simple combine ROS and RUS
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# [Numerical + catgeorical]
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#------------------------------
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(cm_X, cm_y)
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros)
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# print('\nSimple Combined Over and UnderSampling\n', Counter(y_rouC))
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# print(X_rouC.shape)
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#------------------------------
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# SMOTE_NC: oversampling
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# [numerical + categorical]
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#https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python
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#------------------------------
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# Determine categorical and numerical features
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numerical_ix = cm_X.select_dtypes(include=['int64', 'float64']).columns
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num_featuresL = list(numerical_ix)
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numerical_colind = cm_X.columns.get_indexer(list(numerical_ix) )
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categorical_ix = cm_X.select_dtypes(include=['object', 'bool']).columns
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categorical_colind = cm_X.columns.get_indexer(list(categorical_ix))
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#k_sm = 5 # default
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k_sm = k_smote
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sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm
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, **rs
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, **njobs)
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X_smnc, y_smnc = sm_nc.fit_resample(cm_X, cm_y)
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outDict.update({'X_ros_cm' : X_ros
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, 'y_ros_cm' : y_ros
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, 'X_rus_cm' : X_rus
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, 'y_rus_cm' : y_rus
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, 'X_rouC_cm': X_rouC
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, 'y_rouC_cm': y_rouC
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, 'X_smnc_cm': X_smnc
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, 'y_smnc_cm': y_smnc})
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#%%:Running Multiple models on LOGO with SKF
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# cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X # two func were identical excpet for name
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for i in sampling_names.keys():
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print("thing:", "X"+i+"_cm", "y"+i+"_cm")
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current_X = "X" + i + "_cm"
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current_y = "y" + i + "_cm"
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current_X_df = outDict[current_X]
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current_y_df = outDict[current_y]
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cD3_v2 = MultModelsCl(input_df = current_X_df
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, target = current_y_df
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, sel_cv = skf_cv
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, tts_split_type = tts_split_type
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, resampling_type = sampling_names[i] # 'none' # default
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, add_cm = True
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, add_yn = True
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, var_type = 'mixed'
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, scale_numeric = ['min_max']
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, run_blind_test = True
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, blind_test_df = cm_bts_X
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, blind_test_target = cm_bts_y
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
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)
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outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + i + ".csv"
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cD3_v2.to_csv(outFile)
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# outDict.update({'X' : cm_X
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# , 'y' : cm_y
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# , 'X_bts' : cm_bts_X
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# , 'y_bts' : cm_bts_y
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# })
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# return(outDict)
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#%% RUN
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#===============
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# Complete Data
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#==============
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CMLogoSkf(cm_input_df = combined_df,file_suffix = "complete")
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CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], file_suffix = "complete")
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#===============
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# Actual Data
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#===============
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#CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual")
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#CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")
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