#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Mar 6 13:41:54 2022 @author: tanu """ def setvars(gene,drug): #https://stackoverflow.com/questions/51695322/compare-multiple-algorithms-with-sklearn-pipeline import os, sys import pandas as pd import numpy as np print(np.__version__) print(pd.__version__) import pprint as pp from copy import deepcopy from collections import Counter from sklearn.impute import KNNImputer as KNN from imblearn.over_sampling import RandomOverSampler from imblearn.under_sampling import RandomUnderSampler from imblearn.over_sampling import SMOTE from sklearn.datasets import make_classification from imblearn.combine import SMOTEENN from imblearn.combine import SMOTETomek from imblearn.over_sampling import SMOTENC from imblearn.under_sampling import EditedNearestNeighbours from imblearn.under_sampling import RepeatedEditedNearestNeighbours from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report from sklearn.model_selection import train_test_split, cross_validate, cross_val_score from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold from sklearn.pipeline import Pipeline, make_pipeline #%% GLOBALS rs = {'random_state': 42} njobs = {'n_jobs': 10} scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef) , 'accuracy' : make_scorer(accuracy_score) , 'fscore' : make_scorer(f1_score) , 'precision' : make_scorer(precision_score) , 'recall' : make_scorer(recall_score) , 'roc_auc' : make_scorer(roc_auc_score) , 'jcc' : make_scorer(jaccard_score) }) skf_cv = StratifiedKFold(n_splits = 10 #, shuffle = False, random_state= None) , shuffle = True,**rs) rskf_cv = RepeatedStratifiedKFold(n_splits = 10 , n_repeats = 3 , **rs) mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)} jacc_score_fn = {'jcc': make_scorer(jaccard_score)} #%% FOR LATER: Combine ED logo data #%% FOR LATER: 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("~") #============== # directories #============== datadir = homedir + '/git/Data/' indir = datadir + drug + '/input/' outdir = datadir + drug + '/output/' #======= # input #======= infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' #infile_ml2 = outdir + gene.lower() + '_merged_df2.csv' my_df = pd.read_csv(infile_ml1, index_col = 0) my_df.dtypes my_df_cols = my_df.columns geneL_basic = ['pnca'] geneL_na = ['gid'] geneL_na_ppi2 = ['rpob'] geneL_ppi2 = ['alr', 'embb', 'katg'] #%% get cols mycols = my_df.columns # # change from numberic to # num_type = ['int64', 'float64'] # cat_type = ['object', 'bool'] # if my_df['active_aa_pos'].dtype in num_type: # my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object) # my_df['active_aa_pos'].dtype # FIXME: if this is not structural, remove from source.. # 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) # FIXME: either impute or remove! # for embb (L114M, F115L, V123L, V125I, V131M) delete for now 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)# RERUN embb with the 5 values now present ########################################################################### #%% 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 ########################################################################### #%% 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] #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= 5, 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() len(my_df.columns) len(mydf_imputed.columns) #----------------------------------------- # REASSIGN my_df after imputing OR values #----------------------------------------- my_df = mydf_imputed.copy() #%%######################################################################## #========================== # 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 #%% 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 #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 use it to predict #, 'active_aa_pos' # TODO? ] ########################################################################### #======================= # 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