#!/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 import argparse import re #%% GLOBALS tts_split = "sl" 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 ########################################################################### homedir = os.path.expanduser("~") geneL_basic = ['pnca'] geneL_na = ['gid'] geneL_na_ppi2 = ['rpob'] geneL_ppi2 = ['alr', 'embb', 'katg'] #num_type = ['int64', 'float64'] num_type = ['int16', 'int32', 'int64', 'float16', 'float32', '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, index_col = 0) aaindex_df.dtypes #----------- # check for non-numerical columns #----------- if any(aaindex_df.dtypes==object): print('\naaindex_df contains non-numerical data') aaindex_df_object = aaindex_df.select_dtypes(include = cat_type) print('\nTotal no. of non-numerial columns:', len(aaindex_df_object.columns)) expected_aa_ncols = len(aaindex_df.columns) - len(aaindex_df_object.columns) #----------- # Extract numerical data only #----------- print('\nSelecting numerical data only') aaindex_df = aaindex_df.select_dtypes(include = num_type) #--------------------------- # aaindex: sanity check 1 #--------------------------- if len(aaindex_df.columns) == expected_aa_ncols: print('\nPASS: successfully selected numerical columns only for aaindex_df') else: print('\nFAIL: Numbers mismatch' , '\nExpected ncols:', expected_aa_ncols , '\nGot:', len(aaindex_df.columns)) #--------------- # check for NA #--------------- print('\nNow checking for NA in the remaining aaindex_cols') 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') #--------------------------- # aaindex: sanity check 2 #--------------------------- expected_aa_ncols2 = len(aaindex_df.columns) - sum(c2>0) if len(aa_df.columns) == expected_aa_ncols2: print('\nPASS: ncols match' , '\nExpected ncols:', expected_aa_ncols2 , '\nGot:', len(aa_df.columns)) else: print('\nFAIL: Numbers mismatch' , '\nExpected ncols:', expected_aa_ncols2 , '\nGot:', len(aa_df.columns)) # Important: need this to identify aaindex cols aa_df_cols = aa_df.columns 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)) #----------------- # Reset index: mutationinformation # Very important for merging #----------------- aa_df = aa_df.reset_index() expected_ncols = len(my_features_df.columns) + len(aa_df.columns) - 1 # for the no. of merging col #----------------- # Merge: my_features_df + aa_df #----------------- merged_df = pd.merge(my_features_df , aa_df , on = 'mutationinformation') #--------------------------- # aaindex: sanity check 3 #--------------------------- 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 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() # Build column names to mask for affinity chanhes if gene.lower() in geneL_basic: #X_stabilityN = common_cols_stabiltyN gene_affinity_colnames = []# not needed as its the common ones cols_to_mask = ['ligand_affinity_change'] if gene.lower() in geneL_ppi2: gene_affinity_colnames = ['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: gene_affinity_colnames = ['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: gene_affinity_colnames = ['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'] #======================= # Masking columns: # (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10 #======================= 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 mcsm affinity related columns where 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') #=================================================== ############################################################################### #%% Feature groups (FG): Build X for Input ML ############################################################################ #=========================== # FG1: Evolutionary features #=========================== X_evolFN = ['consurf_score' , 'snap2_score' , 'provean_score'] ############################################################################### #======================== # FG2: Stability features #======================== #-------- # common #-------- X_common_stability_Fnum = [ 'duet_stability_change' , 'ddg_foldx' , 'deepddg' , 'ddg_dynamut2' , 'contacts'] #-------- # FoldX #-------- X_foldX_Fnum = [ '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_stability_FN = X_common_stability_Fnum + X_foldX_Fnum ############################################################################### #=================== # FG3: Affinity features #=================== common_affinity_Fnum = ['ligand_distance' , 'ligand_affinity_change' , 'mmcsm_lig'] # if gene.lower() in geneL_basic: # X_affinityFN = common_affinity_Fnum # else: # X_affinityFN = common_affinity_Fnum + gene_affinity_colnames X_affinityFN = common_affinity_Fnum + gene_affinity_colnames ############################################################################### #============================ # FG4: Residue level features #============================ #----------- # AA index #----------- X_aaindex_Fnum = list(aa_df_cols) print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum)) #----------------- # surface area # depth # hydrophobicity #----------------- X_str_Fnum = ['rsa' #, 'asa' , 'kd_values' , 'rd_values'] #--------------------------- # Other aa properties # active site indication #--------------------------- X_aap_Fcat = ['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' , 'active_site'] X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat ############################################################################### #======================== # FG5: Genomic features #======================== X_gn_mafor_Fnum = ['maf' #, 'logorI' # , 'or_rawI' # , 'or_mychisq' # , 'or_logistic' # , 'or_fisher' # , 'pval_fisher' ] X_gn_linegae_Fnum = ['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_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2] # #, 'gene_name' # will be required for the combined stuff # ] X_gn_Fcat = [] X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat ############################################################################### #======================== # FG6 collapsed: Structural : Atability + Affinity + ResidueProp #======================== X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN ############################################################################### #======================== # BUILDING all features #======================== all_featuresN = X_evolFN + X_structural_FN + X_genomicFN ############################################################################### #%% Define training and test data #================================================================ # Training and BLIND test set: scaling law split #https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d # dst with actual values : training set # dst with imputed values : THROW AWAY [unrepresentative] # test data size ~ 1/sqrt(features NOT including target variable) #================================================================ 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 training_df = my_df_ml.copy() # Target 1: dst_mode training_df[drug].value_counts() training_df['dst_mode'].value_counts() #################################################################### #==================================== # ML data: Train test split: SL # with stratification # 1-blind test : training_data for CV # 1/sqrt(columns) : blind test #=========================================== x_features = training_df[all_featuresN] y_target = training_df['dst_mode'] # sanity check if not 'dst_mode' in x_features.columns: print('\nPASS: x_features has no target variable') x_ncols = len(x_features.columns) print('\nNo. of columns for x_features:', x_ncols) # NEED It for scaling law split #https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d else: sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!') #------------------- # train-test split #------------------- sl_test_size = 1/np.sqrt(x_ncols) train = 1 - sl_test_size #x_train, x_test, y_train, y_test # traditional var_names # so my downstream code doesn't need to change X, X_bts, y, y_bts = train_test_split(x_features, y_target , test_size = sl_test_size , **rs , stratify = y_target) yc1 = Counter(y) yc1_ratio = yc1[0]/yc1[1] yc2 = Counter(y_bts) yc2_ratio = yc2[0]/yc2[1] ############################################################################### #====================================================== # Determine categorical and numerical features #====================================================== numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns numerical_cols categorical_cols = X.select_dtypes(include=['object', 'bool']).columns categorical_cols ################################################################################ # IMPORTANT sanity checks if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN): print('\nPASS: ML data with input features, training and test generated...' , '\n\nTotal no. of input features:' , len(X.columns) , '\n--------No. of numerical features:' , len(numerical_cols) , '\n--------No. of categorical features:' , len(categorical_cols) , '\n\nTotal no. of evolutionary features:' , len(X_evolFN) , '\n\nTotal no. of stability features:' , len(X_stability_FN) , '\n--------Common stabilty cols:' , len(X_common_stability_Fnum) , '\n--------Foldx cols:' , len(X_foldX_Fnum) , '\n\nTotal no. of affinity features:' , len(X_affinityFN) , '\n--------Common affinity cols:' , len(common_affinity_Fnum) , '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames) , '\n\nTotal no. of residue level features:', len(X_resprop_FN) , '\n--------AA index cols:' , len(X_aaindex_Fnum) , '\n--------Residue Prop cols:' , len(X_str_Fnum) , '\n--------AA change Prop cols:' , len(X_aap_Fcat) , '\n\nTotal no. of genomic features:' , len(X_genomicFN) , '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum) , '\n--------Lineage cols:' , len(X_gn_linegae_Fnum) , '\n--------Other cols:' , len(X_gn_Fcat) ) else: print('\nFAIL: numbers mismatch' , '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN) , '\nGot:', len(X.columns)) sys.exit() ############################################################################### print('\n-------------------------------------------------------------' , '\nSuccessfully split data: ALL features' , '\nactual values: training set' , '\nSplit:', tts_split #, '\nimputed values: blind test set' , '\n\nTotal data size:', len(X) + len(X_bts) , '\n\nTrain data size:', X.shape , '\ny_train numbers:', yc1 , '\n\nTest data size:', X_bts.shape , '\ny_test_numbers:', yc2 , '\n\ny_train ratio:',yc1_ratio , '\ny_test ratio:', yc2_ratio , '\n-------------------------------------------------------------' ) ########################################################################## # 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('\nSimple Random OverSampling\n', Counter(y_ros)) print(X_ros.shape) #------------------------------ # Simple Random Undersampling # [Numerical + catgeorical] #------------------------------ undersample = RandomUnderSampler(sampling_strategy='majority') X_rus, y_rus = undersample.fit_resample(X, y) print('\nSimple Random UnderSampling\n', Counter(y_rus)) print(X_rus.shape) #------------------------------ # Simple combine ROS and RUS # [Numerical + catgeorical] #------------------------------ oversample = RandomOverSampler(sampling_strategy='minority') X_ros, y_ros = oversample.fit_resample(X, y) undersample = RandomUnderSampler(sampling_strategy='majority') X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros) print('\nSimple Combined Over and UnderSampling\n', Counter(y_rouC)) print(X_rouC.shape) #------------------------------ # SMOTE_NC: oversampling # [numerical + categorical] #https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python #------------------------------ # Determine categorical and numerical features numerical_ix = 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 default sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm, **rs, **njobs) X_smnc, y_smnc = sm_nc.fit_resample(X, y) print('\nSMOTE_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('\nSMOTE 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('\nSMOTE Over+Under Sampling combined\n', Counter(y_enn)) ############################################################################### # TODO: Find over and undersampling JUST for categorical data ########################################################################### print('\n#################################################################' , '\nDim of X for gene:', gene.lower(), '\n', X.shape , '\n###############################################################')