From 6be17e3e3ec86528fa85cc89f6b6acaba6e75120 Mon Sep 17 00:00:00 2001 From: Tanushree Tunstall Date: Sat, 16 Jul 2022 15:36:15 +0100 Subject: [PATCH] added test data for runALLclfs --- UQ_ML_data2.py | 714 ----------------------------------- UQ_yc_RunAllClfs.py | 4 + UQ_yc_RunAllClfs_CALL.py | 14 + alr_config.py | 22 +- embb_config.py | 22 +- gid_config.py | 22 +- katg_config.py | 22 +- pnca_config.py | 21 +- rpob_config.py | 11 +- uq_ml_models_FS/fs_UQ_XGB.py | 2 +- 10 files changed, 113 insertions(+), 741 deletions(-) delete mode 100644 UQ_ML_data2.py diff --git a/UQ_ML_data2.py b/UQ_ML_data2.py deleted file mode 100644 index d5fbe11..0000000 --- a/UQ_ML_data2.py +++ /dev/null @@ -1,714 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Sun Mar 6 13:41:54 2022 - -@author: tanu -""" -def setvars(gene,drug): - #https://stackoverflow.com/questions/51695322/compare-multiple-algorithms-with-sklearn-pipeline - import os, sys - import pandas as pd - import numpy as np - print(np.__version__) - print(pd.__version__) - import pprint as pp - from copy import deepcopy - from collections import Counter - from sklearn.impute import KNNImputer as KNN - from imblearn.over_sampling import RandomOverSampler - from imblearn.under_sampling import RandomUnderSampler - from imblearn.over_sampling import SMOTE - from sklearn.datasets import make_classification - from imblearn.combine import SMOTEENN - from imblearn.combine import SMOTETomek - - from imblearn.over_sampling import SMOTENC - from imblearn.under_sampling import EditedNearestNeighbours - from imblearn.under_sampling import RepeatedEditedNearestNeighbours - - from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score - from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report - - from sklearn.model_selection import train_test_split, cross_validate, cross_val_score - from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold - - from sklearn.pipeline import Pipeline, make_pipeline - #%% GLOBALS - rs = {'random_state': 42} - njobs = {'n_jobs': 10} - - scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef) - , 'accuracy' : make_scorer(accuracy_score) - , 'fscore' : make_scorer(f1_score) - , 'precision' : make_scorer(precision_score) - , 'recall' : make_scorer(recall_score) - , 'roc_auc' : make_scorer(roc_auc_score) - , 'jcc' : make_scorer(jaccard_score) - }) - - skf_cv = StratifiedKFold(n_splits = 10 - #, shuffle = False, random_state= None) - , shuffle = True,**rs) - - rskf_cv = RepeatedStratifiedKFold(n_splits = 10 - , n_repeats = 3 - , **rs) - - mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)} - jacc_score_fn = {'jcc': make_scorer(jaccard_score)} - - #%% FOR LATER: Combine ED logo data - #%% DONE: active aa site annotations **DONE on 15/05/2022 as part of generating merged_dfs - ########################################################################### - rs = {'random_state': 42} - njobs = {'n_jobs': 10} - homedir = os.path.expanduser("~") - - geneL_basic = ['pnca'] - geneL_na = ['gid'] - geneL_na_ppi2 = ['rpob'] - geneL_ppi2 = ['alr', 'embb', 'katg'] - - #num_type = ['int64', 'float64'] - 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() - - #========================== - # 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 - - - #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/UQ_yc_RunAllClfs.py b/UQ_yc_RunAllClfs.py index a76199e..5d79add 100755 --- a/UQ_yc_RunAllClfs.py +++ b/UQ_yc_RunAllClfs.py @@ -192,6 +192,9 @@ def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, p _roc_auc = round(roc_auc_score(y_pred, y), 3) _tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() + print('\nMCC on CV:', round(matthews_corrcoef(y_pred, y), 3)) + + # result_pd = result_pd.append(pd.DataFrame(np.column_stack([name # , _tp, _tn # , _fp , _fn @@ -219,6 +222,7 @@ def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, p bts_predict = pipe.predict(blind_test_input_df) bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2) + print('\nMCC on Blind test:' , bts_mcc_score) #print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2)) diff --git a/UQ_yc_RunAllClfs_CALL.py b/UQ_yc_RunAllClfs_CALL.py index 7b46437..6e9ff8a 100755 --- a/UQ_yc_RunAllClfs_CALL.py +++ b/UQ_yc_RunAllClfs_CALL.py @@ -11,6 +11,20 @@ CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True) BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF'] BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True) +# from FUNC + +YC_resD2 = run_all_ML(input_pd=df2['X'], target_label=df2['y'], blind_test_input_df=df2['X'], blind_test_target=df2['y'], preprocess = True, var_type = 'mixed') +CVResultsDF_baseline = YC_resD2['CrossValResultsDF'] +BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF'] + +YC_resD_ros = run_all_ML(input_pd=df2['X_ros'], target_label=df2['y_ros'], blind_test_input_df=df2['X'], blind_test_target=df2['y'], preprocess = True, var_type = 'mixed') +CVResultsDF_ros = YC_resD_ros['CrossValResultsDF'] +BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF'] + + + + + # from sklearn.utils import all_estimators # for name, algorithm in all_estimators(type_filter="classifier"): # clf = algorithm() diff --git a/alr_config.py b/alr_config.py index 593cb71..ab9d3f4 100755 --- a/alr_config.py +++ b/alr_config.py @@ -15,9 +15,16 @@ drug = 'cycloserine' 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 @@ -43,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -62,14 +75,13 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') - ################################################################################ #================== # Baseline models diff --git a/embb_config.py b/embb_config.py index e685568..c9730ce 100755 --- a/embb_config.py +++ b/embb_config.py @@ -15,9 +15,16 @@ drug = 'ethambutol' 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 @@ -43,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -62,14 +75,13 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') - ################################################################################ #================== # Baseline models diff --git a/gid_config.py b/gid_config.py index 11cbc00..30e5be0 100755 --- a/gid_config.py +++ b/gid_config.py @@ -15,9 +15,16 @@ drug = 'streptomycin' 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 @@ -43,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -62,15 +75,14 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') ################################################################################ - #================== # Baseline models #================== diff --git a/katg_config.py b/katg_config.py index 882e8eb..2e1d277 100755 --- a/katg_config.py +++ b/katg_config.py @@ -15,9 +15,16 @@ drug = 'isoniazid' 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 @@ -43,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -62,13 +75,14 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') +################################################################################ #================== # Baseline models #================== diff --git a/pnca_config.py b/pnca_config.py index 71cfbb0..d200adb 100755 --- a/pnca_config.py +++ b/pnca_config.py @@ -15,9 +15,16 @@ drug = 'pyrazinamide' 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 @@ -43,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -62,10 +75,10 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') diff --git a/rpob_config.py b/rpob_config.py index 8a8dc6e..33b77bf 100755 --- a/rpob_config.py +++ b/rpob_config.py @@ -50,6 +50,12 @@ print('Strucutral features (n):' , '\nOther struc columns:', X_str , '\n================================================================\n') +print('AAindex features (n):' + , len(X_aaindexFN) + , '\nThese are:\n' + , X_aaindexFN + , '\n================================================================\n') + print('Evolutionary features (n):' , len(X_evolFN) , '\nThese are:\n' @@ -69,14 +75,13 @@ print('Categorical features (n):' , categorical_FN , '\n================================================================\n') -if ( len(X.columns) == len(X_ssFN) +len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): +if ( len(X.columns) == len(X_ssFN) + len(X_aaindexFN) + len(X_evolFN) + len(X_genomicFN) + len(categorical_FN) ): print('\nPass: No. of features match') else: - print('\nFail: Count of feature mismatch') + sys.exit('\nFail: Count of feature mismatch') print('\n#####################################################################\n') - ################################################################################ #================== # Baseline models diff --git a/uq_ml_models_FS/fs_UQ_XGB.py b/uq_ml_models_FS/fs_UQ_XGB.py index eb9d16c..970bd00 100644 --- a/uq_ml_models_FS/fs_UQ_XGB.py +++ b/uq_ml_models_FS/fs_UQ_XGB.py @@ -193,4 +193,4 @@ output_modelD # json.dump(output_modelD, f) # # # with open(file, 'r') as f: -# data = json.load(f) \ No newline at end of file +# data = json.load(f)