From 89cbeb36106630fe53350bca1f64b7d157318fc7 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Thu, 16 Jun 2022 16:51:24 +0100 Subject: [PATCH] added UQ_ML_data2.py that contains aa_index data now combined with previous features data --- UQ_ML_data2.py | 672 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 672 insertions(+) create mode 100644 UQ_ML_data2.py diff --git a/UQ_ML_data2.py b/UQ_ML_data2.py new file mode 100644 index 0000000..ba1718e --- /dev/null +++ b/UQ_ML_data2.py @@ -0,0 +1,672 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Sun Mar 6 13:41:54 2022 + +@author: tanu +""" +#def setvars(gene,drug): +#https://stackoverflow.com/questions/51695322/compare-multiple-algorithms-with-sklearn-pipeline +import os, sys +import pandas as pd +import numpy as np +print(np.__version__) +print(pd.__version__) +import pprint as pp +from copy import deepcopy +from collections import Counter +from sklearn.impute import KNNImputer as KNN +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.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 + +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 +#%% GLOBALS +rs = {'random_state': 42} +njobs = {'n_jobs': 10} + +scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef) + , 'accuracy' : make_scorer(accuracy_score) + , 'fscore' : make_scorer(f1_score) + , 'precision' : make_scorer(precision_score) + , 'recall' : make_scorer(recall_score) + , 'roc_auc' : make_scorer(roc_auc_score) + , 'jcc' : make_scorer(jaccard_score) + }) + +skf_cv = StratifiedKFold(n_splits = 10 + #, shuffle = False, random_state= None) + , shuffle = True,**rs) + +rskf_cv = RepeatedStratifiedKFold(n_splits = 10 + , n_repeats = 3 + , **rs) + +mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)} +jacc_score_fn = {'jcc': make_scorer(jaccard_score)} + +#%% FOR LATER: Combine ED logo data +#%% DONE: active aa site annotations **DONE on 15/05/2022 as part of generating merged_dfs +########################################################################### +rs = {'random_state': 42} +njobs = {'n_jobs': 10} +homedir = os.path.expanduser("~") + +geneL_basic = ['pnca'] +geneL_na = ['gid'] +geneL_na_ppi2 = ['rpob'] +geneL_ppi2 = ['alr', 'embb', 'katg'] + +num_type = ['int64', 'float64'] +cat_type = ['object', 'bool'] + +#============== +# directories +#============== +datadir = homedir + '/git/Data/' +indir = datadir + drug + '/input/' +outdir = datadir + drug + '/output/' + +#======= +# input +#======= + +#--------- +# File 1 +#--------- +infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' +#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv' + +my_features_df = pd.read_csv(infile_ml1, index_col = 0) +my_features_df = my_features_df .reset_index(drop = True) +my_features_df.index + +my_features_df.dtypes +mycols = my_features_df.columns + +#--------- +# File 2 +#--------- +infile_aaindex = outdir + 'aa_index/' + gene.lower() + '_aa.csv' +aaindex_df = pd.read_csv(infile_aaindex) +aaindex_df.dtypes + +c1 = aaindex_df.isna().sum() +c2 = c1.sort_values(ascending=False) +print('\nCounting aaindex_df cols with NA' + , '\nncols with NA:', sum(c2>0), 'columns' + , '\nDropping these...' + , '\nOriginal ncols:', len(aaindex_df.columns) + ) +aa_df = aaindex_df.dropna(axis=1) + +print('\nRevised df ncols:', len(aa_df.columns)) + +c3 = aa_df.isna().sum() +c4 = c3.sort_values(ascending=False) + +print('\nChecking NA in revised df...') + +if sum(c4>0): + sys.exit('\nFAIL: aaindex_df still contains cols with NA, please check and drop these before proceeding...') +else: + print('\nPASS: cols with NA successfully dropped from aaindex_df' + , '\nProceeding with combining aa_df with other features_df') + +# Important: need this to identify aaindex cols +aa_df_cols = aa_df.columns +aa_df_cols = aa_df_cols.drop(['mutationinformation']) +print('\nTotal no. of columns in clean aa_df:', len(aa_df_cols)) + +############################################################################### +#%% Combining my_features_df and aaindex_df +#=========================== +# Merge my_df + aaindex_df +#=========================== +if aa_df.columns[aa_df.columns.isin(my_features_df.columns)] == my_features_df.columns[my_features_df.columns.isin(aa_df.columns)]: + print('\nMerging on column: mutationinformation') + +if len(my_features_df) == len(aa_df): + expected_nrows = len(my_features_df) + print('\nProceeding to merge, expected nrows in merged_df:', expected_nrows) +else: + sys.exit('\nNrows mismatch, cannot merge. Please check' + , '\nnrows my_df:', len(my_features_df) + , '\nnrows aa_df:', len(aa_df)) + +expected_ncols = len(my_features_df.columns) + len(aa_df.columns) - 1 # for the no. of merging col + +merged_df = pd.merge(my_features_df + , aa_df + , on = 'mutationinformation') + +if len(merged_df.columns) == expected_ncols: + print('\nPASS: my_features_df and aa_df successfully combined' + , '\nnrows:', len(merged_df) + , '\nncols:', len(merged_df.columns)) +else: + sys.exit('\nFAIL: could not combine my_features_df and aa_df' + , '\nCheck dims and merging cols!') + +#-------- +# Reassign so downstream code doesn't need to change +#-------- +my_df = merged_df.copy() + +#%% Data: my_df +# Check if non structural pos have crept in +# IDEALLY remove from source! But for rpoB do it here +# Drop NA where numerical cols have them +if gene.lower() in geneL_na_ppi2: + #D1148 get rid of + na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)] + my_df = my_df.drop(index=na_index) + +# FIXED: complete data for all muts inc L114M, F115L, V123L, V125I, V131M +# if gene.lower() in ['embb']: +# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)] +# my_df = my_df.drop(index=na_index) + +# # Sanity check for non-structural positions +# print('\nChecking for non-structural postions') +# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)] +# if len(na_index) > 0: +# print('\nNon-structural positions detected for gene:', gene.lower() +# , '\nTotal number of these detected:', len(na_index) +# , '\These are at index:', na_index +# , '\nOriginal nrows:', len(my_df) +# , '\nDropping these...') +# my_df = my_df.drop(index=na_index) +# print('\nRevised nrows:', len(my_df)) +# else: +# print('\nNo non-structural positions detected for gene:', gene.lower() +# , '\nnrows:', len(my_df)) + + +########################################################################### +#%% Add lineage calculation columns +#FIXME: Check if this can be imported from config? +total_mtblineage_uc = 8 +lineage_colnames = ['lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode'] +#bar = my_df[lineage_colnames] +my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all'] +my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc +########################################################################### +#%% Active site annotation column +# change from numberic to categorical +num_type = ['int64', 'float64'] +cat_type = ['object', 'bool'] + +if my_df['active_site'].dtype in num_type: + my_df['active_site'] = my_df['active_site'].astype(object) + my_df['active_site'].dtype +#%% AA property change +#-------------------- +# Water prop change +#-------------------- +my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water'] +my_df['water_change'].value_counts() + +water_prop_changeD = { + 'hydrophobic_to_neutral' : 'change' + , 'hydrophobic_to_hydrophobic' : 'no_change' + , 'neutral_to_neutral' : 'no_change' + , 'neutral_to_hydrophobic' : 'change' + , 'hydrophobic_to_hydrophilic' : 'change' + , 'neutral_to_hydrophilic' : 'change' + , 'hydrophilic_to_neutral' : 'change' + , 'hydrophilic_to_hydrophobic' : 'change' + , 'hydrophilic_to_hydrophilic' : 'no_change' +} + +my_df['water_change'] = my_df['water_change'].map(water_prop_changeD) +my_df['water_change'].value_counts() + +#-------------------- +# Polarity change +#-------------------- +my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity'] +my_df['polarity_change'].value_counts() + +polarity_prop_changeD = { + 'non-polar_to_non-polar' : 'no_change' + , 'non-polar_to_neutral' : 'change' + , 'neutral_to_non-polar' : 'change' + , 'neutral_to_neutral' : 'no_change' + , 'non-polar_to_basic' : 'change' + , 'acidic_to_neutral' : 'change' + , 'basic_to_neutral' : 'change' + , 'non-polar_to_acidic' : 'change' + , 'neutral_to_basic' : 'change' + , 'acidic_to_non-polar' : 'change' + , 'basic_to_non-polar' : 'change' + , 'neutral_to_acidic' : 'change' + , 'acidic_to_acidic' : 'no_change' + , 'basic_to_acidic' : 'change' + , 'basic_to_basic' : 'no_change' + , 'acidic_to_basic' : 'change'} + +my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD) +my_df['polarity_change'].value_counts() + +#-------------------- +# Electrostatics change +#-------------------- +my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop'] +my_df['electrostatics_change'].value_counts() + +calc_prop_changeD = { + 'non-polar_to_non-polar' : 'no_change' + , 'non-polar_to_polar' : 'change' + , 'polar_to_non-polar' : 'change' + , 'non-polar_to_pos' : 'change' + , 'neg_to_non-polar' : 'change' + , 'non-polar_to_neg' : 'change' + , 'pos_to_polar' : 'change' + , 'pos_to_non-polar' : 'change' + , 'polar_to_polar' : 'no_change' + , 'neg_to_neg' : 'no_change' + , 'polar_to_neg' : 'change' + , 'pos_to_neg' : 'change' + , 'pos_to_pos' : 'no_change' + , 'polar_to_pos' : 'change' + , 'neg_to_polar' : 'change' + , 'neg_to_pos' : 'change' +} + +my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD) +my_df['electrostatics_change'].value_counts() + +#-------------------- +# Summary change: Create a combined column summarising these three cols +#-------------------- +detect_change = 'change' +check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change'] +#my_df['aa_prop_change'] = (my_df.values == detect_change).any(1).astype(int) +my_df['aa_prop_change'] = (my_df[check_prop_cols].values == detect_change).any(1).astype(int) +my_df['aa_prop_change'].value_counts() +my_df['aa_prop_change'].dtype + +my_df['aa_prop_change'] = my_df['aa_prop_change'].map({1:'change' + , 0: 'no_change'}) + +my_df['aa_prop_change'].value_counts() +my_df['aa_prop_change'].dtype + +#%% IMPUTE values for OR [check script for exploration: UQ_or_imputer] +#-------------------- +# Impute OR values +#-------------------- +#or_cols = ['or_mychisq', 'log10_or_mychisq', 'or_fisher'] +sel_cols = ['mutationinformation', 'or_mychisq', 'log10_or_mychisq'] +or_cols = ['or_mychisq', 'log10_or_mychisq'] + +print("count of NULL values before imputation\n") +print(my_df[or_cols].isnull().sum()) + +my_dfI = pd.DataFrame(index = my_df['mutationinformation'] ) + + +my_dfI = pd.DataFrame(KNN(n_neighbors=3, weights="uniform").fit_transform(my_df[or_cols]) + , index = my_df['mutationinformation'] + , columns = or_cols ) +my_dfI.columns = ['or_rawI', 'logorI'] +my_dfI.columns +my_dfI = my_dfI.reset_index(drop = False) # prevents old index from being added as a column +my_dfI.head() +print("count of NULL values AFTER imputation\n") +print(my_dfI.isnull().sum()) + +#------------------------------------------- +# OR df Merge: with original based on index +#------------------------------------------- +#my_df['index_bm'] = my_df.index +mydf_imputed = pd.merge(my_df + , my_dfI + , on = 'mutationinformation') +#mydf_imputed = mydf_imputed.set_index(['index_bm']) + +my_df['log10_or_mychisq'].isna().sum() +mydf_imputed['log10_or_mychisq'].isna().sum() +mydf_imputed['logorI'].isna().sum() # should be 0 + +len(my_df.columns) +len(mydf_imputed.columns) + +#----------------------------------------- +# REASSIGN my_df after imputing OR values +#----------------------------------------- +my_df = mydf_imputed.copy() + +if my_df['logorI'].isna().sum() == 0: + print('\nPASS: OR values imputed, data ready for ML') +else: + sys.exit('\nFAIL: something went wrong, Data not ready for ML. Please check upstream!') + +#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +#--------------------------------------- +# TODO: try other imputation like MICE +#--------------------------------------- +#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + +#%%######################################################################## +#========================== +# Data for ML +#========================== +my_df_ml = my_df.copy() + + +# # get index for the last column for my_features_df +# my_features_df_lcolname = my_features_df.columns[-1] +# my_features_df_lcolname_i = my_features_df.columns.get_loc(my_features_df_lcolname) + +# # get index for the last column for merged_df i.e my_df i.e my_df_ml +# aa_df_lcolname = aa_df.columns[-1] +# aa_df = aa_df.columns.get_loc(aa_df_lcolname) + + + +# aaindex_col_start = my_features_df_lcolname_i + 1 + + + +#========================== +# BLIND test set +#========================== +# Separate blind test set +my_df_ml[drug].isna().sum() + +blind_test_df = my_df_ml[my_df_ml[drug].isna()] +blind_test_df.shape + +training_df = my_df_ml[my_df_ml[drug].notna()] +training_df.shape + +# Target1: dst +training_df[drug].value_counts() +training_df['dst_mode'].value_counts() + +#%% Build X: input for ML +common_cols_stabiltyN = ['ligand_distance' + , 'ligand_affinity_change' + , 'duet_stability_change' + , 'ddg_foldx' + , 'deepddg' + , 'ddg_dynamut2' + , 'mmcsm_lig' + , 'contacts'] + +# Build stability columns ~ gene +if gene.lower() in geneL_basic: + X_stabilityN = common_cols_stabiltyN + cols_to_mask = ['ligand_affinity_change'] + +if gene.lower() in geneL_ppi2: +# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist'] + geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist'] + X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols + cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity'] + +if gene.lower() in geneL_na: +# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + geneL_na_st_cols = ['mcsm_na_affinity'] + X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols + cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity'] + +if gene.lower() in geneL_na_ppi2: +# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist'] + geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist'] + X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols + cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity'] + + +X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss' +, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss' +, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss' +, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss' +, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss' +, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss' +] + +X_str = ['rsa' + #, 'asa' + , 'kd_values' + , 'rd_values'] + +X_ssFN = X_stabilityN + X_str + X_foldX_cols + +X_evolFN = ['consurf_score' + , 'snap2_score' + , 'provean_score'] + +X_genomic_mafor = ['maf' + , 'logorI' + # , 'or_rawI' + # , 'or_mychisq' + # , 'or_logistic' + # , 'or_fisher' + # , 'pval_fisher' + ] + +X_genomic_linegae = ['lineage_proportion' + , 'dist_lineage_proportion' + #, 'lineage' # could be included as a category but it has L2;L4 formatting + , 'lineage_count_all' + , 'lineage_count_unique' + ] + +X_genomicFN = X_genomic_mafor + X_genomic_linegae + +X_aaindexFN = list(aa_df_cols) + +print('\nTotal no. of features for aaindex:', len(X_aaindexFN)) + +#%% Construct numerical and categorical column names +# numerical feature names +# numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN + +numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN + +#categorical feature names +categorical_FN = ['ss_class' + # , 'wt_prop_water' + # , 'mut_prop_water' + # , 'wt_prop_polarity' + # , 'mut_prop_polarity' + # , 'wt_calcprop' + # , 'mut_calcprop' + , 'aa_prop_change' + , 'electrostatics_change' + , 'polarity_change' + , 'water_change' + #, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1] + , 'active_site' + #, 'gene_name' # will be required for the combined stuff + ] +#---------------------------------------------- +# count numerical and categorical features +#---------------------------------------------- + +print('\nNo. of numerical features:', len(numerical_FN) + , '\nNo. of categorical features:', len(categorical_FN)) + +########################################################################### +#======================= +# Masking columns: +# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10 +#======================= +#%% Masking columns +# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts() +# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts() + +# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0 +# (my_df_ml['ligand_affinity_change'] == 0).sum() + +my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts() +my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts() +my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts() + +# mask the column ligand distance > 10 +my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0 +(my_df_ml['ligand_affinity_change'] == 0).sum() + +mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask] + +# write file for check +mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True) +mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv') + +#%% extracting dfs based on numerical, categorical column names +#---------------------------------- +# WITHOUT the target var included +#---------------------------------- +num_df = training_df[numerical_FN] +num_df.shape + +cat_df = training_df[categorical_FN] +cat_df.shape + +all_df = training_df[numerical_FN + categorical_FN] +all_df.shape + +#------------------------------ +# WITH the target var included: + #'wtgt': with target +#------------------------------ +# drug and dst_mode should be the same thing +num_df_wtgt = training_df[numerical_FN + ['dst_mode']] +num_df_wtgt.shape + +cat_df_wtgt = training_df[categorical_FN + ['dst_mode']] +cat_df_wtgt.shape + +all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']] +all_df_wtgt.shape +#%%######################################################################## +#============ +# ML data +#============ +#------ +# X: Training and Blind test (BTS) +#------ +X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL +X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL +#X = all_df_wtgt[numerical_FN] # training numerical only +#X_bts = blind_test_df[numerical_FN] # blind test data numerical + +#------ +# y +#------ +y = all_df_wtgt['dst_mode'] # training data y +y_bts = blind_test_df['dst_mode'] # blind data test y + +#X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']] + +# Quick check +#(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum() +for i in range(len(cols_to_mask)): + ind = i+1 + print('\nindex:', i, '\nind:', ind) + print('\nMask count check:' + , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum() + ) + +print('Original Data\n', Counter(y) + , 'Data dim:', X.shape) + +############################################################################### +#%% +############################################################################ +# RESAMPLING +############################################################################### +#------------------------------ +# Simple Random oversampling +# [Numerical + catgeorical] +#------------------------------ +oversample = RandomOverSampler(sampling_strategy='minority') +X_ros, y_ros = oversample.fit_resample(X, y) +print('Simple 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(X, y) +print('Simple 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(X, y) +undersample = RandomUnderSampler(sampling_strategy='majority') +X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros) +print('Simple 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 = X.select_dtypes(include=['int64', 'float64']).columns +numerical_ix +num_featuresL = list(numerical_ix) +numerical_colind = X.columns.get_indexer(list(numerical_ix) ) +numerical_colind + +categorical_ix = X.select_dtypes(include=['object', 'bool']).columns +categorical_ix +categorical_colind = X.columns.get_indexer(list(categorical_ix)) +categorical_colind + +k_sm = 5 # 5 is deafult +sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm, **rs, **njobs) +X_smnc, y_smnc = sm_nc.fit_resample(X, y) +print('SMOTE_NC OverSampling\n', Counter(y_smnc)) +print(X_smnc.shape) +globals().update(locals()) # TROLOLOLOLOLOLS +#print("i did a horrible hack :-)") +############################################################################### +#%% SMOTE RESAMPLING for NUMERICAL ONLY* +# #------------------------------ +# # SMOTE: Oversampling +# # [Numerical ONLY] +# #------------------------------ +# k_sm = 1 +# sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k_sm, **rs) +# X_sm, y_sm = sm.fit_resample(X, y) +# print(X_sm.shape) +# print('SMOTE OverSampling\n', Counter(y_sm)) +# y_sm_df = y_sm.to_frame() +# y_sm_df.value_counts().plot(kind = 'bar') + +# #------------------------------ +# # SMOTE: Over + Undersampling COMBINED +# # [Numerical ONLY] +# #----------------------------- +# sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all', **rs, **njobs )) +# X_enn, y_enn = sm_enn.fit_resample(X, y) +# print(X_enn.shape) +# print('SMOTE Over+Under Sampling combined\n', Counter(y_enn)) + +############################################################################### +# TODO: Find over and undersampling JUST for categorical data