now running for combined gene actual
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2 changed files with 4 additions and 345 deletions
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#!/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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#====================
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# Import ML functions
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#====================
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from ml_data_combined import *
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#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
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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 combined_DF_OS(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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, 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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, oversampling = True
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, k_smote = 5
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, random_state = 42
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, njobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
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):
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outDict = {}
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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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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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#-------
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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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yc1 = Counter(cm_y)
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yc1_ratio = yc1[0]/yc1[1]
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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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yc2 = Counter(cm_bts_y)
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yc2_ratio = yc2[0]/yc2[1]
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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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if oversampling:
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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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numerical_ix
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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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numerical_colind
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categorical_ix = cm_X.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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categorical_colind = cm_X.columns.get_indexer(list(categorical_ix))
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categorical_colind
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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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print('\nSMOTE_NC OverSampling\n', Counter(y_smnc))
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print(X_smnc.shape)
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#======================
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# vars
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#======================
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#======================================================
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# Determine categorical and numerical features
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#======================================================
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numerical_cols = cm_X.select_dtypes(include=['int64', 'float64']).columns
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numerical_cols
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categorical_cols = cm_X.select_dtypes(include=['object', 'bool']).columns
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categorical_cols
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print('\n-------------------------------------------------------------'
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, '\nSuccessfully generated training and test data:'
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#, '\nData used:' , data_type
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#, '\nSplit type:', split_type
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, '\n\nTotal no. of input features:' , len(cm_X.columns)
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, '\n--------No. of numerical features:' , len(numerical_cols)
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, '\n--------No. of categorical features:', len(categorical_cols)
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, '\n==========================='
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, '\n Resampling: NONE'
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, '\n Baseline'
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, '\n==========================='
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, '\ninput data size:' , len(cm_input_df)
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, '\n\nTrain data size:' , cm_X.shape
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, '\ny_train numbers:' , yc1
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, '\n\nTest data size:' , cm_bts_X.shape
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, '\ny_test_numbers:' , yc2
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, '\n\ny_train ratio:' , yc1_ratio
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, '\ny_test ratio:' , yc2_ratio
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, '\n-------------------------------------------------------------')
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print('\nGenerated Resampled data as below:'
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, '\n================================='
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, '\nResampling: Random oversampling'
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, '\n================================'
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, '\n\nTrain data size:', X_ros.shape
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, '\ny_train numbers:', len(y_ros)
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, '\n\ny_train ratio:', Counter(y_ros)[0]/Counter(y_ros)[1]
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, '\ny_test ratio:' , yc2_ratio
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##################################################################
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, '\n================================'
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, '\nResampling: Random underampling'
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, '\n================================'
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, '\n\nTrain data size:', X_rus.shape
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, '\ny_train numbers:', len(y_rus)
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, '\n\ny_train ratio:', Counter(y_rus)[0]/Counter(y_rus)[1]
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, '\ny_test ratio:' , yc2_ratio
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##################################################################
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, '\n================================'
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, '\nResampling:Combined (over+under)'
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, '\n================================'
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, '\n\nTrain data size:', X_rouC.shape
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, '\ny_train numbers:', len(y_rouC)
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, '\n\ny_train ratio:', Counter(y_rouC)[0]/Counter(y_rouC)[1]
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, '\ny_test ratio:' , yc2_ratio
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##################################################################
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, '\n=============================='
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, '\nResampling: Smote NC'
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, '\n=============================='
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, '\n\nTrain data size:', X_smnc.shape
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, '\ny_train numbers:', len(y_smnc)
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, '\n\ny_train ratio:', Counter(y_smnc)[0]/Counter(y_smnc)[1]
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, '\ny_test ratio:' , yc2_ratio
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##################################################################
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, '\n-------------------------------------------------------------')
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outDict.update({'X_ros' : X_ros
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, 'y_ros' : y_ros
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, 'X_rus' : X_rus
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, 'y_rus' : y_rus
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, 'X_rouC': X_rouC
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, 'y_rouC': y_rouC
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, 'X_smnc': X_smnc
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, 'y_smnc': y_smnc})
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return(outDict)
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else:
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return(outDict)
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@ -309,13 +309,13 @@ def CMLogoSkf(cm_input_df
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#===============
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#===============
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# Complete Data
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# Complete Data
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#==============
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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,file_suffix = "complete")
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CMLogoSkf(cm_input_df = combined_df, std_gene_omit=['alr'], 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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#===============
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# Actual Data
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# Actual Data
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#===============
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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, 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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CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")
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