removed _dissected files and renamed them to _fg
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2 changed files with 0 additions and 1109 deletions
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 4 15:25:33 2022
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@author: tanu
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"""
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#%%
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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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import pprint as pp
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from copy import deepcopy
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from sklearn import linear_model
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from sklearn import datasets
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from collections import Counter
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.gaussian_process import GaussianProcessClassifier, kernels
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from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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from sklearn.svm import SVC
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from xgboost import XGBClassifier
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.compose import make_column_transformer
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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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# added
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
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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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from sklearn.feature_selection import RFE, RFECV
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import itertools
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import seaborn as sns
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import matplotlib.pyplot as plt
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from statistics import mean, stdev, median, mode
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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.model_selection import GridSearchCV
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from sklearn.base import BaseEstimator
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from sklearn.impute import KNNImputer as KNN
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import json
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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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, '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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, 'accuracy' : make_scorer(accuracy_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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#%%
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target, skf_cv
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, blind_test_input_df
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, blind_test_target
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, add_cm = True # adds confusion matrix based on cross_val_predict
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, add_yn = True # adds target var class numbers
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, var_type = ['numerical', 'categorical','mixed']):
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'''
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@ param input_df: input features
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@ type: df with input features WITHOUT the target variable
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@param target: target (or output) feature
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@type: df or np.array or Series
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@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
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@type: int or StratifiedKfold()
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@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
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@type: list
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returns
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Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
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'''
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#======================================================
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# Determine categorical and numerical features
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#======================================================
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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#======================================================
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# Determine preprocessing steps ~ var_type
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#======================================================
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix) ]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#======================================================
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# Specify multiple Classification Models
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#======================================================
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models = [('Logistic Regression' , LogisticRegression(**rs) )
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, ('Logistic RegressionCV' , LogisticRegressionCV(**rs) )
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, ('Gaussian NB' , GaussianNB() )
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, ('Naive Bayes' , BernoulliNB() )
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# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
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# , ('SVC' , SVC(**rs) )
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# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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# , ('Decision Tree' , DecisionTreeClassifier(**rs) )
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# , ('Extra Trees' , ExtraTreesClassifier(**rs) )
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# , ('Extra Tree' , ExtraTreeClassifier(**rs) )
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# , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
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# , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
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# , n_estimators = 1000
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# , bootstrap = True
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# , oob_score = True
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# , **njobs
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# , **rs
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# , max_features = 'auto') )
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# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) )
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# , ('LDA' , LinearDiscriminantAnalysis() )
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# , ('Multinomial' , MultinomialNB() )
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# , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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# , ('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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# , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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# , ('QDA' , QuadraticDiscriminantAnalysis() )
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# , ('Ridge Classifier' , RidgeClassifier(**rs) )
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# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 10) )
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]
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mm_skf_scoresD = {}
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print('\n==============================================================\n'
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, '\nRunning several classification models (n):', len(models)
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,'\nList of models:')
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for m in models:
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print(m)
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print('\n================================================================\n')
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index = 1
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for model_name, model_fn in models:
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print('\nRunning classifier:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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index = index+1
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model_pipeline = Pipeline([
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('prep' , col_transform)
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, ('model' , model_fn)])
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print('\nRunning model pipeline:', model_pipeline)
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skf_cv_modD = cross_validate(model_pipeline
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, input_df
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, target
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, cv = skf_cv
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, scoring = scoring_fn
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, return_train_score = True)
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#######################################################################
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#======================================================
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# Option: Add confusion matrix from cross_val_predict
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# Understand and USE with caution
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# cross_val_score, cross_val_predict, "Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from cross_validate and cross_val_score unless all tests sets have equal size and the metric decomposes over samples."
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# https://stackoverflow.com/questions/65645125/producing-a-confusion-matrix-with-cross-validate
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#======================================================
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if add_cm:
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#-----------------------------------------------------------
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# Initialise dict of Confusion Matrix (cm)
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#-----------------------------------------------------------
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cmD = {}
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# Calculate cm
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y_pred = cross_val_predict(model_pipeline, input_df, target, cv = skf_cv, **njobs)
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#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
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tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
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# Build dict
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cmD = {'TN' : tn
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, 'FP': fp
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, 'FN': fn
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, 'TP': tp}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(cmD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#=============================================
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# Option: Add targety numbers for data
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#=============================================
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if add_yn:
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#-----------------------------------------------------------
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# Initialise dict of target numbers: training and blind (tbt)
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#-----------------------------------------------------------
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tbtD = {}
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# training y
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tyn = Counter(target)
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tyn_neg = tyn[0]
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tyn_pos = tyn[1]
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# blind test y
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btyn = Counter(blind_test_target)
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btyn_neg = btyn[0]
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btyn_pos = btyn[1]
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# Build dict
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tbtD = {'trainingY_neg' : tyn_neg
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, 'trainingY_pos' : tyn_pos
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, 'blindY_neg' : btyn_neg
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, 'blindY_pos' : btyn_pos}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(tbtD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#==============================
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# Extract mean values for CV
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#==============================
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mm_skf_scoresD[model_name] = {}
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for key, value in skf_cv_modD.items():
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print('\nkey:', key, '\nvalue:', value)
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print('\nmean value:', np.mean(value))
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mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
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#return(mm_skf_scoresD)
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#%%
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#=========================
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# Blind test: BTS results
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#=========================
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# Build the final results with all scores for the model
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#bts_predict = gscv_fs.predict(blind_test_input_df)
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model_pipeline.fit(input_df, target)
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bts_predict = model_pipeline.predict(blind_test_input_df)
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
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print('\nMCC on Blind test:' , bts_mcc_score)
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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# Diff b/w train and bts test scores
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# train_test_diff_MCC = cvtrain_mcc - bts_mcc_score
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# print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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mm_skf_scoresD[model_name]['bts_mcc'] = bts_mcc_score
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mm_skf_scoresD[model_name]['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
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#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
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return(mm_skf_scoresD)
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@ -1,791 +0,0 @@
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#!/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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#%% DONE: active aa site annotations **DONE on 15/05/2022 as part of generating merged_dfs
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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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#-----------
|
||||
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
|
||||
#==========================
|
||||
# Data for ML
|
||||
#==========================
|
||||
my_df_ml = my_df.copy()
|
||||
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
gene_affinity_colnames = ['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:
|
||||
gene_affinity_colnames = ['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:
|
||||
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 [UQ]: actual vs imputed
|
||||
# No aa index but active_site included
|
||||
# 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
|
||||
#============
|
||||
#------
|
||||
# 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']
|
||||
|
||||
# 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)
|
||||
|
||||
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-------------------------------------------------------------'
|
||||
)
|
||||
|
||||
###########################################################################
|
||||
#%%
|
||||
###########################################################################
|
||||
# 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
|
||||
Loading…
Add table
Add a link
Reference in a new issue