787 lines
32 KiB
Python
Executable file
787 lines
32 KiB
Python
Executable file
#!/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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from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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import argparse
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import re
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% FOR LATER: Combine ED logo data
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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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geneL_basic = ['pnca']
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geneL_na = ['gid']
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geneL_na_ppi2 = ['rpob']
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geneL_ppi2 = ['alr', 'embb', 'katg']
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#num_type = ['int64', 'float64']
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num_type = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
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cat_type = ['object', 'bool']
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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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#---------
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# File 1
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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_features_df = pd.read_csv(infile_ml1, index_col = 0)
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my_features_df = my_features_df .reset_index(drop = True)
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my_features_df.index
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my_features_df.dtypes
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mycols = my_features_df.columns
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#---------
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# File 2
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#---------
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infile_aaindex = outdir + 'aa_index/' + gene.lower() + '_aa.csv'
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aaindex_df = pd.read_csv(infile_aaindex, index_col = 0)
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aaindex_df.dtypes
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#-----------
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# check for non-numerical columns
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#-----------
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if any(aaindex_df.dtypes==object):
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print('\naaindex_df contains non-numerical data')
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aaindex_df_object = aaindex_df.select_dtypes(include = cat_type)
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print('\nTotal no. of non-numerial columns:', len(aaindex_df_object.columns))
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expected_aa_ncols = len(aaindex_df.columns) - len(aaindex_df_object.columns)
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#-----------
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# Extract numerical data only
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#-----------
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print('\nSelecting numerical data only')
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aaindex_df = aaindex_df.select_dtypes(include = num_type)
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#---------------------------
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# aaindex: sanity check 1
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#---------------------------
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if len(aaindex_df.columns) == expected_aa_ncols:
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print('\nPASS: successfully selected numerical columns only for aaindex_df')
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else:
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print('\nFAIL: Numbers mismatch'
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, '\nExpected ncols:', expected_aa_ncols
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, '\nGot:', len(aaindex_df.columns))
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#---------------
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# check for NA
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#---------------
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print('\nNow checking for NA in the remaining aaindex_cols')
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c1 = aaindex_df.isna().sum()
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c2 = c1.sort_values(ascending=False)
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print('\nCounting aaindex_df cols with NA'
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, '\nncols with NA:', sum(c2>0), 'columns'
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, '\nDropping these...'
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, '\nOriginal ncols:', len(aaindex_df.columns)
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)
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aa_df = aaindex_df.dropna(axis=1)
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print('\nRevised df ncols:', len(aa_df.columns))
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c3 = aa_df.isna().sum()
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c4 = c3.sort_values(ascending=False)
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print('\nChecking NA in revised df...')
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if sum(c4>0):
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sys.exit('\nFAIL: aaindex_df still contains cols with NA, please check and drop these before proceeding...')
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else:
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print('\nPASS: cols with NA successfully dropped from aaindex_df'
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, '\nProceeding with combining aa_df with other features_df')
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#---------------------------
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# aaindex: sanity check 2
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#---------------------------
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expected_aa_ncols2 = len(aaindex_df.columns) - sum(c2>0)
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if len(aa_df.columns) == expected_aa_ncols2:
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print('\nPASS: ncols match'
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, '\nExpected ncols:', expected_aa_ncols2
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, '\nGot:', len(aa_df.columns))
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else:
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print('\nFAIL: Numbers mismatch'
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, '\nExpected ncols:', expected_aa_ncols2
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, '\nGot:', len(aa_df.columns))
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# Important: need this to identify aaindex cols
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aa_df_cols = aa_df.columns
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print('\nTotal no. of columns in clean aa_df:', len(aa_df_cols))
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###############################################################################
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#%% Combining my_features_df and aaindex_df
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#===========================
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# Merge my_df + aaindex_df
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#===========================
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if aa_df.columns[aa_df.columns.isin(my_features_df.columns)] == my_features_df.columns[my_features_df.columns.isin(aa_df.columns)]:
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print('\nMerging on column: mutationinformation')
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if len(my_features_df) == len(aa_df):
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expected_nrows = len(my_features_df)
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print('\nProceeding to merge, expected nrows in merged_df:', expected_nrows)
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else:
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sys.exit('\nNrows mismatch, cannot merge. Please check'
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, '\nnrows my_df:', len(my_features_df)
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, '\nnrows aa_df:', len(aa_df))
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#-----------------
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# Reset index: mutationinformation
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# Very important for merging
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#-----------------
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aa_df = aa_df.reset_index()
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expected_ncols = len(my_features_df.columns) + len(aa_df.columns) - 1 # for the no. of merging col
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#-----------------
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# Merge: my_features_df + aa_df
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#-----------------
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merged_df = pd.merge(my_features_df
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, aa_df
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, on = 'mutationinformation')
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#---------------------------
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# aaindex: sanity check 3
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#---------------------------
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if len(merged_df.columns) == expected_ncols:
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print('\nPASS: my_features_df and aa_df successfully combined'
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, '\nnrows:', len(merged_df)
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, '\nncols:', len(merged_df.columns))
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else:
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sys.exit('\nFAIL: could not combine my_features_df and aa_df'
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, '\nCheck dims and merging cols!')
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#--------
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# Reassign so downstream code doesn't need to change
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#--------
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my_df = merged_df.copy()
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#%% Data: my_df
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# Check if non structural pos have crept in
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# IDEALLY remove from source! But for rpoB do it here
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# Drop NA where numerical cols have them
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if gene.lower() in geneL_na_ppi2:
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#D1148 get rid of
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na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
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my_df = my_df.drop(index=na_index)
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# FIXED: complete data for all muts inc L114M, F115L, V123L, V125I, V131M
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# if gene.lower() in ['embb']:
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# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
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# my_df = my_df.drop(index=na_index)
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# # Sanity check for non-structural positions
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# print('\nChecking for non-structural postions')
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# na_index = my_df['mutationinformation'].index[my_df['ligand_distance'].apply(np.isnan)]
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# if len(na_index) > 0:
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# print('\nNon-structural positions detected for gene:', gene.lower()
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# , '\nTotal number of these detected:', len(na_index)
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# , '\These are at index:', na_index
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# , '\nOriginal nrows:', len(my_df)
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# , '\nDropping these...')
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# my_df = my_df.drop(index=na_index)
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# print('\nRevised nrows:', len(my_df))
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# else:
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# print('\nNo non-structural positions detected for gene:', gene.lower()
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# , '\nnrows:', len(my_df))
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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_uc = 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_uc
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###########################################################################
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#%% Active site annotation column
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# change from numberic to categorical
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if my_df['active_site'].dtype in num_type:
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my_df['active_site'] = my_df['active_site'].astype(object)
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my_df['active_site'].dtype
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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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#--------------------
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# Impute OR values
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#--------------------
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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=3, 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() # should be 0
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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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if my_df['logorI'].isna().sum() == 0:
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print('\nPASS: OR values imputed, data ready for ML')
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else:
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sys.exit('\nFAIL: something went wrong, Data not ready for ML. Please check upstream!')
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#---------------------------------------
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# TODO: try other imputation like MICE
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#---------------------------------------
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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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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# Build column names to mask for affinity chanhes
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if gene.lower() in geneL_basic:
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#X_stabilityN = common_cols_stabiltyN
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gene_affinity_colnames = []# not needed as its the common ones
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cols_to_mask = ['ligand_affinity_change']
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if gene.lower() in geneL_ppi2:
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gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
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#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
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if gene.lower() in geneL_na:
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gene_affinity_colnames = ['mcsm_na_affinity']
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#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
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if gene.lower() in geneL_na_ppi2:
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gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
|
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
|
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
|
|
|
#=======================
|
|
# Masking columns:
|
|
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
|
#=======================
|
|
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
|
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
|
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
|
|
|
# mask the mcsm affinity related columns where ligand distance > 10
|
|
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask] = 0
|
|
(my_df_ml['ligand_affinity_change'] == 0).sum()
|
|
|
|
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
|
|
|
#===================================================
|
|
# write file for check
|
|
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
|
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
|
#===================================================
|
|
###############################################################################
|
|
#%% Feature groups (FG): Build X for Input ML
|
|
############################################################################
|
|
#===========================
|
|
# FG1: Evolutionary features
|
|
#===========================
|
|
X_evolFN = ['consurf_score'
|
|
, 'snap2_score'
|
|
, 'provean_score']
|
|
|
|
###############################################################################
|
|
#========================
|
|
# FG2: Stability features
|
|
#========================
|
|
#--------
|
|
# common
|
|
#--------
|
|
X_common_stability_Fnum = [
|
|
'duet_stability_change'
|
|
, 'ddg_foldx'
|
|
, 'deepddg'
|
|
, 'ddg_dynamut2'
|
|
, 'contacts']
|
|
#--------
|
|
# FoldX
|
|
#--------
|
|
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
|
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
|
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
|
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
|
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
|
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
|
|
|
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
|
|
|
###############################################################################
|
|
#===================
|
|
# FG3: Affinity features
|
|
#===================
|
|
common_affinity_Fnum = ['ligand_distance'
|
|
, 'ligand_affinity_change'
|
|
, 'mmcsm_lig']
|
|
|
|
# if gene.lower() in geneL_basic:
|
|
# X_affinityFN = common_affinity_Fnum
|
|
# else:
|
|
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
|
|
|
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
|
|
|
###############################################################################
|
|
#============================
|
|
# FG4: Residue level features
|
|
#============================
|
|
#-----------
|
|
# AA index
|
|
#-----------
|
|
X_aaindex_Fnum = list(aa_df_cols)
|
|
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
|
|
|
#-----------------
|
|
# surface area
|
|
# depth
|
|
# hydrophobicity
|
|
#-----------------
|
|
X_str_Fnum = ['rsa'
|
|
#, 'asa'
|
|
, 'kd_values'
|
|
, 'rd_values']
|
|
|
|
#---------------------------
|
|
# Other aa properties
|
|
# active site indication
|
|
#---------------------------
|
|
X_aap_Fcat = ['ss_class'
|
|
# , 'wt_prop_water'
|
|
# , 'mut_prop_water'
|
|
# , 'wt_prop_polarity'
|
|
# , 'mut_prop_polarity'
|
|
# , 'wt_calcprop'
|
|
# , 'mut_calcprop'
|
|
, 'aa_prop_change'
|
|
, 'electrostatics_change'
|
|
, 'polarity_change'
|
|
, 'water_change'
|
|
, 'active_site']
|
|
|
|
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
|
###############################################################################
|
|
#========================
|
|
# FG5: Genomic features
|
|
#========================
|
|
X_gn_mafor_Fnum = ['maf'
|
|
, 'logorI'
|
|
# , 'or_rawI'
|
|
# , 'or_mychisq'
|
|
# , 'or_logistic'
|
|
# , 'or_fisher'
|
|
# , 'pval_fisher'
|
|
]
|
|
|
|
X_gn_linegae_Fnum = ['lineage_proportion'
|
|
, 'dist_lineage_proportion'
|
|
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
|
, 'lineage_count_all'
|
|
, 'lineage_count_unique'
|
|
]
|
|
|
|
X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
|
#, 'gene_name' # will be required for the combined stuff
|
|
]
|
|
|
|
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
|
###############################################################################
|
|
#========================
|
|
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
|
#========================
|
|
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
|
|
|
###############################################################################
|
|
#========================
|
|
# BUILDING all features
|
|
#========================
|
|
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
|
|
|
###############################################################################
|
|
#%% Define training and test data
|
|
#======================================================
|
|
# Training and BLIND test set: actual vs imputed
|
|
# dst with actual values : training set
|
|
# dst with imputed values : blind test
|
|
#======================================================
|
|
my_df_ml[drug].isna().sum() #'na' ones are the blind_test set
|
|
|
|
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
|
|
|
|
# Target 1: dst_mode
|
|
training_df[drug].value_counts()
|
|
training_df['dst_mode'].value_counts()
|
|
|
|
####################################################################
|
|
#=====================================
|
|
# ML data: actual vs imputed
|
|
#=====================================
|
|
#------
|
|
# X: Training and Blind test (BTS)
|
|
#------
|
|
X = training_df[all_featuresN]
|
|
X_bts = blind_test_df[all_featuresN]
|
|
|
|
#------
|
|
# y
|
|
#------
|
|
y = training_df['dst_mode']
|
|
y_bts = blind_test_df['dst_mode']
|
|
|
|
yc1 = Counter(y)
|
|
yc1_ratio = yc1[0]/yc1[1]
|
|
|
|
yc2 = Counter(y_bts)
|
|
yc2_ratio = yc2[0]/yc2[1]
|
|
|
|
###############################################################################
|
|
#======================================================
|
|
# Determine categorical and numerical features
|
|
#======================================================
|
|
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
|
numerical_cols
|
|
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
|
categorical_cols
|
|
|
|
################################################################################
|
|
# IMPORTANT sanity checks
|
|
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
|
print('\nPASS: ML data with input features, training and test generated...'
|
|
, '\n\nTotal no. of input features:' , len(X.columns)
|
|
, '\n--------No. of numerical features:' , len(numerical_cols)
|
|
, '\n--------No. of categorical features:' , len(categorical_cols)
|
|
|
|
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
|
|
|
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
|
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
|
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
|
|
|
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
|
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
|
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
|
|
|
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
|
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
|
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
|
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
|
|
|
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
|
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
|
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
|
, '\n--------Other cols:' , len(X_gn_Fcat)
|
|
)
|
|
else:
|
|
print('\nFAIL: numbers mismatch'
|
|
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
|
, '\nGot:', len(X.columns))
|
|
sys.exit()
|
|
###############################################################################
|
|
print('\n-------------------------------------------------------------'
|
|
, '\nSuccessfully split data: ALL features'
|
|
, '\nactual values: training set'
|
|
, '\nimputed values: blind test set'
|
|
|
|
, '\n\nTotal data size:', len(X) + len(X_bts)
|
|
|
|
, '\n\nTrain data size:', X.shape
|
|
, '\ny_train numbers:', yc1
|
|
|
|
, '\n\nTest data size:', X_bts.shape
|
|
, '\ny_test_numbers:', yc2
|
|
|
|
, '\n\ny_train ratio:',yc1_ratio
|
|
, '\ny_test ratio:', yc2_ratio
|
|
, '\n-------------------------------------------------------------'
|
|
)
|
|
##########################################################################
|
|
# Quick check
|
|
#(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
|
for i in range(len(cols_to_mask)):
|
|
ind = i+1
|
|
print('\nindex:', i, '\nind:', ind)
|
|
print('\nMask count check:'
|
|
, (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
|
)
|
|
|
|
print('Original Data\n', Counter(y)
|
|
, 'Data dim:', X.shape)
|
|
###########################################################################
|
|
#%%
|
|
###########################################################################
|
|
# RESAMPLING
|
|
###########################################################################
|
|
#------------------------------
|
|
# Simple Random oversampling
|
|
# [Numerical + catgeorical]
|
|
#------------------------------
|
|
oversample = RandomOverSampler(sampling_strategy='minority')
|
|
X_ros, y_ros = oversample.fit_resample(X, y)
|
|
print('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('\nSMOTE_NC OverSampling\n', Counter(y_smnc))
|
|
print(X_smnc.shape)
|
|
globals().update(locals()) # TROLOLOLOLOLOLS
|
|
#print("i did a horrible hack :-)")
|
|
###############################################################################
|
|
#%% SMOTE RESAMPLING for NUMERICAL ONLY*
|
|
# #------------------------------
|
|
# # SMOTE: Oversampling
|
|
# # [Numerical ONLY]
|
|
# #------------------------------
|
|
# k_sm = 1
|
|
# sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k_sm, **rs)
|
|
# X_sm, y_sm = sm.fit_resample(X, y)
|
|
# print(X_sm.shape)
|
|
# print('\nSMOTE OverSampling\n', Counter(y_sm))
|
|
# y_sm_df = y_sm.to_frame()
|
|
# y_sm_df.value_counts().plot(kind = 'bar')
|
|
|
|
# #------------------------------
|
|
# # SMOTE: Over + Undersampling COMBINED
|
|
# # [Numerical ONLY]
|
|
# #-----------------------------
|
|
# sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all', **rs, **njobs ))
|
|
# X_enn, y_enn = sm_enn.fit_resample(X, y)
|
|
# print(X_enn.shape)
|
|
# print('\nSMOTE Over+Under Sampling combined\n', Counter(y_enn))
|
|
|
|
###############################################################################
|
|
# TODO: Find over and undersampling JUST for categorical data
|