From c666c426c088a3add4a54c69194ef845383c577b Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Thu, 16 Jun 2022 17:47:00 +0100 Subject: [PATCH] fixed aa_index creeping categorical values in numerical cols --- UQ_ML_data2.py | 1356 +++++++++++++++++++++++++----------------------- rpob_config.py | 11 +- 2 files changed, 708 insertions(+), 659 deletions(-) diff --git a/UQ_ML_data2.py b/UQ_ML_data2.py index ba1718e..d5fbe11 100644 --- a/UQ_ML_data2.py +++ b/UQ_ML_data2.py @@ -5,668 +5,710 @@ 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') +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 -# 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)) + 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'] + 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) -############################################################################### -#%% 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') + #----------- + # Extract numerical data only + #----------- + print('\nSelecting numerical data only') + aaindex_df = aaindex_df.select_dtypes(include = num_type) -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)) + #--------------------------- + # 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)) -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() + #--------------- + # 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 -print('Original Data\n', Counter(y) - , 'Data dim:', X.shape) + #----------------- + # 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 -############################################################################### -#%% -############################################################################ -# 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) + 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() + + #========================== + # 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 + numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN -#------------------------------ -# 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 + + #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 diff --git a/rpob_config.py b/rpob_config.py index 346a049..8a8dc6e 100755 --- a/rpob_config.py +++ b/rpob_config.py @@ -15,9 +15,16 @@ drug = 'rifampicin' homedir = os.path.expanduser("~") os.chdir( homedir + '/git/ML_AI_training/') -from UQ_ML_data import * +#--------------------------- +# Version 1: no AAindex +#from UQ_ML_data import * +#setvars(gene,drug) +#from UQ_ML_data import * +#--------------------------- + +from UQ_ML_data2 import * setvars(gene,drug) -from UQ_ML_data import * +from UQ_ML_data2 import * # from YC run_all_ML: run locally #from UQ_yc_RunAllClfs import run_all_ML