314 lines
9.9 KiB
Python
314 lines
9.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Mar 6 13:41:54 2022
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@author: tanu
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"""
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#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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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 sklearn.linear_model import LogisticRegression, LinearRegression
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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
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.ensemble import BaggingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.gaussian_process import GaussianProcessClassifier
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from sklearn.gaussian_process import kernels
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from sklearn.gaussian_process.kernels import RBF
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from sklearn.gaussian_process.kernels import DotProduct
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from sklearn.gaussian_process.kernels import Matern
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from sklearn.gaussian_process.kernels import RationalQuadratic
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from sklearn.gaussian_process.kernels import WhiteKernel
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from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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from sklearn.linear_model import RidgeClassifier
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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.linear_model import SGDClassifier
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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 confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score
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from sklearn.metrics import jaccard_score
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from sklearn.metrics import make_scorer
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from sklearn.metrics import classification_report
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from sklearn.metrics import average_precision_score
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from sklearn.model_selection import cross_validate
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import StratifiedKFold
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import make_pipeline
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#from sklearn.feature_selection import RFE
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#from sklearn.feature_selection import 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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import numpy as np
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print(np.__version__)
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print(pd.__version__)
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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.over_sampling import SMOTE
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from imblearn.pipeline import Pipeline
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from sklearn.datasets import make_classification
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from sklearn.model_selection import cross_validate, cross_val_score
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from sklearn.model_selection import RepeatedStratifiedKFold
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from sklearn.ensemble import AdaBoostClassifier
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from imblearn.combine import SMOTEENN
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from imblearn.under_sampling import EditedNearestNeighbours
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from sklearn.model_selection import GridSearchCV
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from sklearn.base import BaseEstimator
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import json
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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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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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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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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homedir = os.path.expanduser("~")
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os.chdir(homedir + "/git/ML_AI_training/")
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# my function
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#from MultClassPipe import MultClassPipeline
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from MultClassPipe2 import MultClassPipeline2
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from loopity_loop import MultClassPipeSKFLoop
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from MultClassPipe3 import MultClassPipeSKFCV
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gene = 'pncA'
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drug = 'pyrazinamide'
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#==============
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# directories
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#==============
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datadir = homedir + '/git/Data/'
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indir = datadir + drug + '/input/'
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outdir = datadir + drug + '/output/'
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#=======
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# input
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#=======
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infile_ml1 = outdir + gene.lower() + '_merged_df3.csv'
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#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
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my_df = pd.read_csv(infile_ml1, index_col = 0)
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my_df.dtypes
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my_df_cols = my_df.columns
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geneL_basic = ['pnca']
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# -- CHECK script -- imports.py
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#%% get cols
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mycols = my_df.columns
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mycols
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# change from numberic to
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num_type = ['int64', 'float64']
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cat_type = ['object', 'bool']
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# TODO:
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# Treat active site aa pos as category and not numerical: This needs to be part of merged_df3!
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#if my_df['active_aa_pos'].dtype in num_type:
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# my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
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# my_df['active_aa_pos'].dtype
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# -- CHECK script -- imports.py
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#%%============================================================================
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#%% IMPUTE values for OR
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#%% Combine mmCSM_lig Data
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#%% Combine PROVEAN data
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#%% Combine ED logo data
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#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
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# get logic from upstream!
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my_df_ml = my_df.copy()
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my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
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my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.groupby(['mutationinformation'])['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
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(my_df_ml['ligand_affinity_change'] == 0).sum()
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#%%============================================================================
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# Separate blind test set
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my_df_ml[drug].isna().sum()
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blind_test_df = my_df_ml[my_df_ml[drug].isna()]
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blind_test_df.shape
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training_df = my_df_ml[my_df_ml[drug].notna()]
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training_df.shape
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# Target1: dst
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training_df[drug].value_counts()
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training_df['dst_mode'].value_counts()
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#%% Build X
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common_cols_stabiltyN = ['ligand_distance'
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, 'ligand_affinity_change'
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, 'duet_stability_change'
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, 'ddg_foldx'
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, 'deepddg'
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, 'ddg_dynamut2']
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foldX_cols = ['contacts'
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#, 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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#, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
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#, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
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#, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
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#, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
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#, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
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]
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X_strFN = ['rsa'
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#, 'asa'
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, 'kd_values'
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, 'rd_values']
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X_evolFN = ['consurf_score'
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, 'snap2_score']
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# quick inspection which lineage to use:
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#foo = my_df_ml[['lineage', 'lineage_count_all', 'lineage_count_unique']]
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X_genomicFN = ['maf'
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# , 'or_mychisq'
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# , 'or_logistic'
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# , 'or_fisher'
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# , 'pval_fisher'
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#, 'lineage'
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#, 'lineage_count_all'
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#, 'lineage_count_unique'
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]
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#%% Construct numerical and categorical column names
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# numerical feature names
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numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN
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#categorical feature names
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categorical_FN = ['ss_class'
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, 'wt_prop_water'
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# , 'lineage_labels' # misleading if using merged_df3
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, 'mut_prop_water'
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, 'wt_prop_polarity'
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, 'mut_prop_polarity'
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, 'wt_calcprop'
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, 'mut_calcprop'
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#, 'active_aa_pos'
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]
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#%% extracting dfs based on numerical, categorical column names
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#----------------------------------
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# WITHOUT the target var included
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#----------------------------------
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num_df = training_df[numerical_FN]
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num_df.shape
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cat_df = training_df[categorical_FN]
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cat_df.shape
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all_df = training_df[numerical_FN + categorical_FN]
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all_df.shape
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#------------------------------
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# WITH the target var included:
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#'wtgt': with target
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#------------------------------
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# drug and dst_mode should be the same thing
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num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
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num_df_wtgt.shape
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cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
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cat_df_wtgt.shape
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all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
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all_df_wtgt.shape
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#%%================================================================
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#%% Apply ML
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#TODO: A
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#%% Data
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#------
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# X
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#------
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X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
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X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
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#X = all_df_wtgt[numerical_FN] # training numerical only
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#X_bts = blind_test_df[numerical_FN] # blind test data numerical
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#------
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# y
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#------
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y = all_df_wtgt['dst_mode'] # training data y
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y_bts = blind_test_df['dst_mode'] # blind data test y
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#Blind test data {same format}
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#X_bts = blind_test_df[numerical_FN]
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#X_bts = blind_test_df[numerical_FN + categorical_FN]
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#y_bts = blind_test_df['dst_mode']
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X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
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# Quick check
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(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
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#%% MultClassPipeSKFCV: function call()
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# mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
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# , target = y
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# , var_type = 'numerical'
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# , skf_cv = skf_cv)
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# mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
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# mm_skf_scores_df_all
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# mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
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# mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
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# print(mm_skf_scores_df_train)
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# print(mm_skf_scores_df_test)
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