451 lines
17 KiB
Python
451 lines
17 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Mar 6 13:41:54 2022
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@author: tanu
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"""
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def setvars(gene,drug):
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#https://stackoverflow.com/questions/51695322/compare-multiple-algorithms-with-sklearn-pipeline
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import os, sys
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import pandas as pd
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import numpy as np
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print(np.__version__)
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print(pd.__version__)
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import pprint as pp
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from copy import deepcopy
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from collections import Counter
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from sklearn.impute import KNNImputer as KNN
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from imblearn.over_sampling import RandomOverSampler
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import SMOTE
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from sklearn.datasets import make_classification
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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from imblearn.over_sampling import SMOTENC
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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#%% FOR LATER: Combine ED logo data
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#%% FOR LARER: active aa site annotations
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###########################################################################
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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homedir = os.path.expanduser("~")
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#==============
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# directories
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#==============
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datadir = homedir + '/git/Data/'
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indir = datadir + drug + '/input/'
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outdir = datadir + drug + '/output/'
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#=======
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# input
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#=======
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infile_ml1 = outdir + gene.lower() + '_merged_df3.csv'
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#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
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my_df = pd.read_csv(infile_ml1, index_col = 0)
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my_df.dtypes
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my_df_cols = my_df.columns
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geneL_basic = ['pnca']
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# -- CHECK script -- imports.py
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#%% get cols
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mycols = my_df.columns
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mycols
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# change from numberic to
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num_type = ['int64', 'float64']
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cat_type = ['object', 'bool']
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#TODO:
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# #Treat active site aa pos as category and not numerical: This needs to be part of merged_df3!
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# if my_df['active_aa_pos'].dtype in num_type:
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# my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
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# my_df['active_aa_pos'].dtype
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# -- CHECK script -- imports.py
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###########################################################################
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#%% Add lineage calculation columns
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#FIXME: Check if this can be imported from config?
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total_mtblineage_u = 8
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lineage_colnames = ['lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
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#bar = my_df[lineage_colnames]
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my_df['lineage_proportion'] = my_df['lineage_count_unique']/my_df['lineage_count_all']
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my_df['dist_lineage_proportion'] = my_df['lineage_count_unique']/total_mtblineage_u
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###########################################################################
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#%% AA property change
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#--------------------
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# Water prop change
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#--------------------
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my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water']
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my_df['water_change'].value_counts()
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water_prop_changeD = {
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'hydrophobic_to_neutral' : 'change'
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, 'hydrophobic_to_hydrophobic' : 'no_change'
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, 'neutral_to_neutral' : 'no_change'
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, 'neutral_to_hydrophobic' : 'change'
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, 'hydrophobic_to_hydrophilic' : 'change'
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, 'neutral_to_hydrophilic' : 'change'
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, 'hydrophilic_to_neutral' : 'change'
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, 'hydrophilic_to_hydrophobic' : 'change'
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, 'hydrophilic_to_hydrophilic' : 'no_change'
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}
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my_df['water_change'] = my_df['water_change'].map(water_prop_changeD)
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my_df['water_change'].value_counts()
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#--------------------
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# Polarity change
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#--------------------
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my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity']
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my_df['polarity_change'].value_counts()
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polarity_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_neutral' : 'change'
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, 'neutral_to_non-polar' : 'change'
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, 'neutral_to_neutral' : 'no_change'
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, 'non-polar_to_basic' : 'change'
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, 'acidic_to_neutral' : 'change'
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, 'basic_to_neutral' : 'change'
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, 'non-polar_to_acidic' : 'change'
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, 'neutral_to_basic' : 'change'
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, 'acidic_to_non-polar' : 'change'
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, 'basic_to_non-polar' : 'change'
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, 'neutral_to_acidic' : 'change'
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, 'acidic_to_acidic' : 'no_change'
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, 'basic_to_acidic' : 'change'
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, 'basic_to_basic' : 'no_change'
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, 'acidic_to_basic' : 'change'}
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my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD)
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my_df['polarity_change'].value_counts()
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#--------------------
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# Electrostatics change
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#--------------------
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my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop']
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my_df['electrostatics_change'].value_counts()
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calc_prop_changeD = {
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'non-polar_to_non-polar' : 'no_change'
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, 'non-polar_to_polar' : 'change'
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, 'polar_to_non-polar' : 'change'
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, 'non-polar_to_pos' : 'change'
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, 'neg_to_non-polar' : 'change'
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, 'non-polar_to_neg' : 'change'
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, 'pos_to_polar' : 'change'
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, 'pos_to_non-polar' : 'change'
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, 'polar_to_polar' : 'no_change'
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, 'neg_to_neg' : 'no_change'
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, 'polar_to_neg' : 'change'
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, 'pos_to_neg' : 'change'
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, 'pos_to_pos' : 'no_change'
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, 'polar_to_pos' : 'change'
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, 'neg_to_polar' : 'change'
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, 'neg_to_pos' : 'change'
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}
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my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD)
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my_df['electrostatics_change'].value_counts()
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#--------------------
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# Summary change: Create a combined column summarising these three cols
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#--------------------
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detect_change = 'change'
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check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change']
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#my_df['aa_prop_change'] = (my_df.values == detect_change).any(1).astype(int)
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my_df['aa_prop_change'] = (my_df[check_prop_cols].values == detect_change).any(1).astype(int)
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my_df['aa_prop_change'].value_counts()
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my_df['aa_prop_change'].dtype
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my_df['aa_prop_change'] = my_df['aa_prop_change'].map({1:'change'
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, 0: 'no_change'})
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my_df['aa_prop_change'].value_counts()
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my_df['aa_prop_change'].dtype
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#%% IMPUTE values for OR [check script for exploration: UQ_or_imputer]
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#or_cols = ['or_mychisq', 'log10_or_mychisq', 'or_fisher']
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sel_cols = ['mutationinformation', 'or_mychisq', 'log10_or_mychisq']
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or_cols = ['or_mychisq', 'log10_or_mychisq']
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print("count of NULL values before imputation\n")
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print(my_df[or_cols].isnull().sum())
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my_dfI = pd.DataFrame(index = my_df['mutationinformation'] )
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my_dfI = pd.DataFrame(KNN(n_neighbors= 5, weights="uniform").fit_transform(my_df[or_cols])
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, index = my_df['mutationinformation']
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, columns = or_cols )
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my_dfI.columns = ['or_rawI', 'logorI']
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my_dfI.columns
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my_dfI = my_dfI.reset_index(drop = False) # prevents old index from being added as a column
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my_dfI.head()
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print("count of NULL values AFTER imputation\n")
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print(my_dfI.isnull().sum())
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#-------------------------------------------
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# OR df Merge: with original based on index
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#-------------------------------------------
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my_df['index_bm'] = my_df.index
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mydf_imputed = pd.merge(my_df
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, my_dfI
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, on = 'mutationinformation')
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mydf_imputed = mydf_imputed.set_index(['index_bm'])
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my_df['log10_or_mychisq'].isna().sum()
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mydf_imputed['log10_or_mychisq'].isna().sum()
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mydf_imputed['logorI'].isna().sum()
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len(my_df.columns)
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len(mydf_imputed.columns)
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#-----------------------------------------
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# REASSIGN my_df after imputing OR values
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#-----------------------------------------
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my_df = mydf_imputed.copy()
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#%%########################################################################
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#==========================
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# Data for ML
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#==========================
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my_df_ml = my_df.copy()
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#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
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my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
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my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.groupby(['mutationinformation'])['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
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(my_df_ml['ligand_affinity_change'] == 0).sum()
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#%%########################################################################
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#==========================
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# BLIND test set
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#==========================
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# Separate blind test set
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my_df_ml[drug].isna().sum()
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blind_test_df = my_df_ml[my_df_ml[drug].isna()]
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blind_test_df.shape
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training_df = my_df_ml[my_df_ml[drug].notna()]
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training_df.shape
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# Target1: dst
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training_df[drug].value_counts()
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training_df['dst_mode'].value_counts()
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#%% Build X: input for ML
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common_cols_stabiltyN = ['ligand_distance'
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, 'ligand_affinity_change'
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, 'duet_stability_change'
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, 'ddg_foldx'
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, 'deepddg'
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, 'ddg_dynamut2'
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, 'mmcsm_lig'
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, 'contacts']
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foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
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, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
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, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
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, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
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, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
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]
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X_strFN = ['rsa'
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#, 'asa'
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, 'kd_values'
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, 'rd_values']
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X_evolFN = ['consurf_score'
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, 'snap2_score'
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, 'provean_score']
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X_genomic_mafor = ['maf'
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, 'logorI'
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# , 'or_rawI'
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# , 'or_mychisq'
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# , 'or_logistic'
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# , 'or_fisher'
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# , 'pval_fisher'
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]
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X_genomic_linegae = ['lineage_proportion'
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, 'dist_lineage_proportion'
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#, 'lineage' # could be included as a category but it has L2;L4 formatting
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, 'lineage_count_all'
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, 'lineage_count_unique'
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]
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X_genomicFN = X_genomic_mafor+X_genomic_linegae
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#%% Construct numerical and categorical column names
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# numerical feature names
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numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN
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#categorical feature names
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categorical_FN = ['ss_class'
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# , 'wt_prop_water'
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# , 'mut_prop_water'
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# , 'wt_prop_polarity'
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# , 'mut_prop_polarity'
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# , 'wt_calcprop'
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# , 'mut_calcprop'
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, 'aa_prop_change'
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, 'electrostatics_change'
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, 'polarity_change'
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, 'water_change'
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, 'drtype_mode_labels' # beware then you can use it to predict
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# , 'active_aa_pos' # TODO?
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]
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#%% extracting dfs based on numerical, categorical column names
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#----------------------------------
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# WITHOUT the target var included
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#----------------------------------
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num_df = training_df[numerical_FN]
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num_df.shape
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cat_df = training_df[categorical_FN]
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cat_df.shape
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all_df = training_df[numerical_FN + categorical_FN]
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all_df.shape
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#------------------------------
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# WITH the target var included:
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#'wtgt': with target
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#------------------------------
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# drug and dst_mode should be the same thing
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num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
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num_df_wtgt.shape
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cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
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cat_df_wtgt.shape
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all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
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all_df_wtgt.shape
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#%%================================================================
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#%% Apply ML
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#%% Data
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#------
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# X
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#------
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X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
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X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
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#X = all_df_wtgt[numerical_FN] # training numerical only
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#X_bts = blind_test_df[numerical_FN] # blind test data numerical
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#------
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# y
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#------
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y = all_df_wtgt['dst_mode'] # training data y
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y_bts = blind_test_df['dst_mode'] # blind data test y
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#Blind test data {same format}
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#X_bts = blind_test_df[numerical_FN]
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#X_bts = blind_test_df[numerical_FN + categorical_FN]
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#y_bts = blind_test_df['dst_mode']
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X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
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# Quick check
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(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
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##############################################################################
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print('Original Data\n', Counter(y)
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, 'Data dim:', X.shape)
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###############################################################################
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#%%
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############################################################################
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# RESAMPLING
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###############################################################################
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#------------------------------
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# Simple Random oversampling
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# [Numerical + catgeorical]
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#------------------------------
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(X, y)
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print('Simple Random OverSampling\n', Counter(y_ros))
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print(X_ros.shape)
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#------------------------------
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# Simple Random Undersampling
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# [Numerical + catgeorical]
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#------------------------------
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rus, y_rus = undersample.fit_resample(X, y)
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print('Simple Random UnderSampling\n', Counter(y_rus))
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print(X_rus.shape)
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#------------------------------
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# Simple combine ROS and RUS
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# [Numerical + catgeorical]
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#------------------------------
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(X, y)
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros)
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print('Simple Combined Over and UnderSampling\n', Counter(y_rouC))
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print(X_rouC.shape)
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#------------------------------
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# SMOTE_NC: oversampling
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# [numerical + categorical]
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#https://stackoverflow.com/questions/47655813/oversampling-smote-for-binary-and-categorical-data-in-python
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#------------------------------
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# Determine categorical and numerical features
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numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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num_featuresL = list(numerical_ix)
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numerical_colind = X.columns.get_indexer(list(numerical_ix) )
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numerical_colind
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categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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categorical_colind = X.columns.get_indexer(list(categorical_ix))
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categorical_colind
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k_sm = 5 # 5 is deafult
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sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm, **rs, **njobs)
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X_smnc, y_smnc = sm_nc.fit_resample(X, y)
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print('SMOTE_NC OverSampling\n', Counter(y_smnc))
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print(X_smnc.shape)
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globals().update(locals()) # TROLOLOLOLOLOLS
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#print("i did a horrible hack :-)")
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###############################################################################
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#%% SMOTE RESAMPLING for NUMERICAL ONLY*
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# #------------------------------
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# # SMOTE: Oversampling
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# # [Numerical ONLY]
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# #------------------------------
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# k_sm = 1
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# sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k_sm, **rs)
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# X_sm, y_sm = sm.fit_resample(X, y)
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# print(X_sm.shape)
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# print('SMOTE OverSampling\n', Counter(y_sm))
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# y_sm_df = y_sm.to_frame()
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# y_sm_df.value_counts().plot(kind = 'bar')
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# #------------------------------
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# # SMOTE: Over + Undersampling COMBINED
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# # [Numerical ONLY]
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# #-----------------------------
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# sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all', **rs, **njobs ))
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# X_enn, y_enn = sm_enn.fit_resample(X, y)
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# print(X_enn.shape)
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# print('SMOTE Over+Under Sampling combined\n', Counter(y_enn))
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###############################################################################
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# TODO: Find over and undersampling JUST for categorical data
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