added UQ_ML_data2.py that contains aa_index data now combined with previous features data
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UQ_ML_data2.py
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UQ_ML_data2.py
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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 Sun Mar 6 13:41:54 2022
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@author: tanu
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"""
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#def setvars(gene,drug):
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#https://stackoverflow.com/questions/51695322/compare-multiple-algorithms-with-sklearn-pipeline
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import os, sys
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import pandas as pd
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import numpy as np
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print(np.__version__)
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print(pd.__version__)
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import pprint as pp
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from copy import deepcopy
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from collections import Counter
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from sklearn.impute import KNNImputer as KNN
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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.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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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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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% FOR LATER: Combine ED logo data
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#%% DONE: active aa site annotations **DONE on 15/05/2022 as part of generating merged_dfs
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###########################################################################
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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homedir = os.path.expanduser("~")
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geneL_basic = ['pnca']
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geneL_na = ['gid']
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geneL_na_ppi2 = ['rpob']
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geneL_ppi2 = ['alr', 'embb', 'katg']
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num_type = ['int64', 'float64']
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cat_type = ['object', 'bool']
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#==============
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# directories
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#==============
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datadir = homedir + '/git/Data/'
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indir = datadir + drug + '/input/'
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outdir = datadir + drug + '/output/'
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#=======
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# input
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#=======
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#---------
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# File 1
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#---------
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infile_ml1 = outdir + gene.lower() + '_merged_df3.csv'
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#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
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my_features_df = pd.read_csv(infile_ml1, index_col = 0)
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my_features_df = my_features_df .reset_index(drop = True)
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my_features_df.index
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my_features_df.dtypes
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mycols = my_features_df.columns
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#---------
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# File 2
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#---------
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infile_aaindex = outdir + 'aa_index/' + gene.lower() + '_aa.csv'
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aaindex_df = pd.read_csv(infile_aaindex)
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aaindex_df.dtypes
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c1 = aaindex_df.isna().sum()
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c2 = c1.sort_values(ascending=False)
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print('\nCounting aaindex_df cols with NA'
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, '\nncols with NA:', sum(c2>0), 'columns'
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, '\nDropping these...'
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, '\nOriginal ncols:', len(aaindex_df.columns)
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)
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aa_df = aaindex_df.dropna(axis=1)
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print('\nRevised df ncols:', len(aa_df.columns))
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c3 = aa_df.isna().sum()
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c4 = c3.sort_values(ascending=False)
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print('\nChecking NA in revised df...')
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if sum(c4>0):
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sys.exit('\nFAIL: aaindex_df still contains cols with NA, please check and drop these before proceeding...')
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else:
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print('\nPASS: cols with NA successfully dropped from aaindex_df'
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, '\nProceeding with combining aa_df with other features_df')
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# Important: need this to identify aaindex cols
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aa_df_cols = aa_df.columns
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aa_df_cols = aa_df_cols.drop(['mutationinformation'])
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print('\nTotal no. of columns in clean aa_df:', len(aa_df_cols))
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###############################################################################
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#%% Combining my_features_df and aaindex_df
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#===========================
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# Merge my_df + aaindex_df
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#===========================
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if aa_df.columns[aa_df.columns.isin(my_features_df.columns)] == my_features_df.columns[my_features_df.columns.isin(aa_df.columns)]:
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print('\nMerging on column: mutationinformation')
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if len(my_features_df) == len(aa_df):
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expected_nrows = len(my_features_df)
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print('\nProceeding to merge, expected nrows in merged_df:', expected_nrows)
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else:
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sys.exit('\nNrows mismatch, cannot merge. Please check'
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, '\nnrows my_df:', len(my_features_df)
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, '\nnrows aa_df:', len(aa_df))
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expected_ncols = len(my_features_df.columns) + len(aa_df.columns) - 1 # for the no. of merging col
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merged_df = pd.merge(my_features_df
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, aa_df
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, on = 'mutationinformation')
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if len(merged_df.columns) == expected_ncols:
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print('\nPASS: my_features_df and aa_df successfully combined'
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, '\nnrows:', len(merged_df)
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, '\nncols:', len(merged_df.columns))
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else:
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sys.exit('\nFAIL: could not combine my_features_df and aa_df'
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, '\nCheck dims and merging cols!')
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#--------
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# Reassign so downstream code doesn't need to change
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#--------
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my_df = merged_df.copy()
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#%% Data: my_df
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# Check if non structural pos have crept in
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# IDEALLY remove from source! But for rpoB do it here
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# Drop NA where numerical cols have them
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if gene.lower() in geneL_na_ppi2:
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#D1148 get rid of
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na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
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my_df = my_df.drop(index=na_index)
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# FIXED: complete data for all muts inc L114M, F115L, V123L, V125I, V131M
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# if gene.lower() in ['embb']:
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# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
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# my_df = my_df.drop(index=na_index)
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# # Sanity check for non-structural positions
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# print('\nChecking for non-structural postions')
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# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
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# if len(na_index) > 0:
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# print('\nNon-structural positions detected for gene:', gene.lower()
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# , '\nTotal number of these detected:', len(na_index)
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# , '\These are at index:', na_index
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# , '\nOriginal nrows:', len(my_df)
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# , '\nDropping these...')
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# my_df = my_df.drop(index=na_index)
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# print('\nRevised nrows:', len(my_df))
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# else:
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# print('\nNo non-structural positions detected for gene:', gene.lower()
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# , '\nnrows:', len(my_df))
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###########################################################################
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#%% Add lineage calculation columns
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#FIXME: Check if this can be imported from config?
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total_mtblineage_uc = 8
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lineage_colnames = ['lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
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#bar = my_df[lineage_colnames]
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my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
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my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_uc
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###########################################################################
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#%% Active site annotation column
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# change from numberic to categorical
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num_type = ['int64', 'float64']
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cat_type = ['object', 'bool']
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if my_df['active_site'].dtype in num_type:
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my_df['active_site'] = my_df['active_site'].astype(object)
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my_df['active_site'].dtype
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#%% AA property change
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#--------------------
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# Water prop change
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#--------------------
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my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water']
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my_df['water_change'].value_counts()
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water_prop_changeD = {
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'hydrophobic_to_neutral' : 'change'
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, 'hydrophobic_to_hydrophobic' : 'no_change'
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, 'neutral_to_neutral' : 'no_change'
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, 'neutral_to_hydrophobic' : 'change'
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, 'hydrophobic_to_hydrophilic' : 'change'
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, 'neutral_to_hydrophilic' : 'change'
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, 'hydrophilic_to_neutral' : 'change'
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, 'hydrophilic_to_hydrophobic' : 'change'
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, 'hydrophilic_to_hydrophilic' : 'no_change'
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}
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my_df['water_change'] = my_df['water_change'].map(water_prop_changeD)
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my_df['water_change'].value_counts()
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#--------------------
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# Polarity change
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#--------------------
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my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity']
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my_df['polarity_change'].value_counts()
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polarity_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_neutral' : 'change'
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, 'neutral_to_non-polar' : 'change'
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, 'neutral_to_neutral' : 'no_change'
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, 'non-polar_to_basic' : 'change'
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, 'acidic_to_neutral' : 'change'
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, 'basic_to_neutral' : 'change'
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, 'non-polar_to_acidic' : 'change'
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, 'neutral_to_basic' : 'change'
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, 'acidic_to_non-polar' : 'change'
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, 'basic_to_non-polar' : 'change'
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, 'neutral_to_acidic' : 'change'
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, 'acidic_to_acidic' : 'no_change'
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, 'basic_to_acidic' : 'change'
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, 'basic_to_basic' : 'no_change'
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, 'acidic_to_basic' : 'change'}
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my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD)
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my_df['polarity_change'].value_counts()
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#--------------------
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# Electrostatics change
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#--------------------
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my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop']
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my_df['electrostatics_change'].value_counts()
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calc_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_polar' : 'change'
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, 'polar_to_non-polar' : 'change'
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, 'non-polar_to_pos' : 'change'
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, 'neg_to_non-polar' : 'change'
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, 'non-polar_to_neg' : 'change'
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, 'pos_to_polar' : 'change'
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, 'pos_to_non-polar' : 'change'
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, 'polar_to_polar' : 'no_change'
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, 'neg_to_neg' : 'no_change'
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, 'polar_to_neg' : 'change'
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, 'pos_to_neg' : 'change'
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, 'pos_to_pos' : 'no_change'
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, 'polar_to_pos' : 'change'
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, 'neg_to_polar' : 'change'
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, 'neg_to_pos' : 'change'
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}
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my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD)
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my_df['electrostatics_change'].value_counts()
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#--------------------
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# Summary change: Create a combined column summarising these three cols
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#--------------------
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detect_change = 'change'
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check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change']
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#my_df['aa_prop_change'] = (my_df.values == detect_change).any(1).astype(int)
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my_df['aa_prop_change'] = (my_df[check_prop_cols].values == detect_change).any(1).astype(int)
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my_df['aa_prop_change'].value_counts()
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my_df['aa_prop_change'].dtype
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my_df['aa_prop_change'] = my_df['aa_prop_change'].map({1:'change'
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, 0: 'no_change'})
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my_df['aa_prop_change'].value_counts()
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my_df['aa_prop_change'].dtype
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#%% IMPUTE values for OR [check script for exploration: UQ_or_imputer]
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#--------------------
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# Impute OR values
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#--------------------
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#or_cols = ['or_mychisq', 'log10_or_mychisq', 'or_fisher']
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sel_cols = ['mutationinformation', 'or_mychisq', 'log10_or_mychisq']
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or_cols = ['or_mychisq', 'log10_or_mychisq']
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print("count of NULL values before imputation\n")
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print(my_df[or_cols].isnull().sum())
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my_dfI = pd.DataFrame(index = my_df['mutationinformation'] )
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my_dfI = pd.DataFrame(KNN(n_neighbors=3, weights="uniform").fit_transform(my_df[or_cols])
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, index = my_df['mutationinformation']
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, columns = or_cols )
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my_dfI.columns = ['or_rawI', 'logorI']
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my_dfI.columns
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my_dfI = my_dfI.reset_index(drop = False) # prevents old index from being added as a column
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my_dfI.head()
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print("count of NULL values AFTER imputation\n")
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print(my_dfI.isnull().sum())
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#-------------------------------------------
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# OR df Merge: with original based on index
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#-------------------------------------------
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#my_df['index_bm'] = my_df.index
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mydf_imputed = pd.merge(my_df
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, my_dfI
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, on = 'mutationinformation')
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#mydf_imputed = mydf_imputed.set_index(['index_bm'])
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my_df['log10_or_mychisq'].isna().sum()
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mydf_imputed['log10_or_mychisq'].isna().sum()
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mydf_imputed['logorI'].isna().sum() # should be 0
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len(my_df.columns)
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len(mydf_imputed.columns)
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#-----------------------------------------
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# REASSIGN my_df after imputing OR values
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#-----------------------------------------
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my_df = mydf_imputed.copy()
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if my_df['logorI'].isna().sum() == 0:
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print('\nPASS: OR values imputed, data ready for ML')
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else:
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sys.exit('\nFAIL: something went wrong, Data not ready for ML. Please check upstream!')
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#---------------------------------------
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# TODO: try other imputation like MICE
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#---------------------------------------
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#%%########################################################################
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#==========================
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# Data for ML
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#==========================
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my_df_ml = my_df.copy()
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# # get index for the last column for my_features_df
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# my_features_df_lcolname = my_features_df.columns[-1]
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# my_features_df_lcolname_i = my_features_df.columns.get_loc(my_features_df_lcolname)
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# # get index for the last column for merged_df i.e my_df i.e my_df_ml
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# aa_df_lcolname = aa_df.columns[-1]
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# aa_df = aa_df.columns.get_loc(aa_df_lcolname)
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# aaindex_col_start = my_features_df_lcolname_i + 1
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#==========================
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# BLIND test set
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#==========================
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# Separate blind test set
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my_df_ml[drug].isna().sum()
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blind_test_df = my_df_ml[my_df_ml[drug].isna()]
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blind_test_df.shape
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training_df = my_df_ml[my_df_ml[drug].notna()]
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training_df.shape
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# Target1: dst
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training_df[drug].value_counts()
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training_df['dst_mode'].value_counts()
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#%% Build X: input for ML
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common_cols_stabiltyN = ['ligand_distance'
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, 'ligand_affinity_change'
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, 'duet_stability_change'
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, 'ddg_foldx'
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, 'deepddg'
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, 'ddg_dynamut2'
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, 'mmcsm_lig'
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, 'contacts']
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# Build stability columns ~ gene
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if gene.lower() in geneL_basic:
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X_stabilityN = common_cols_stabiltyN
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cols_to_mask = ['ligand_affinity_change']
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|
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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
|
||||
Loading…
Add table
Add a link
Reference in a new issue