added metadata output for running multiple models
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7 changed files with 325 additions and 88 deletions
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@ -108,6 +108,7 @@ def fsgs(input_df
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = ['numerical', 'categorical' , 'mixed']
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, verbose = 3
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):
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'''
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returns
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@ -98,14 +98,25 @@ rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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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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#FIXME
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#====================
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# Import ProcessFunc
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#====================
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#from ProcessMultModelCl import *
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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_df
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, blind_test_target
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, tts_split_type
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, resampling_type = 'none' # default
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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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, var_type = ['numerical', 'categorical','mixed']
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, return_formatted_output = True):
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'''
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@ param input_df: input features
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@ -151,37 +162,37 @@ def MultModelsCl(input_df, target, skf_cv
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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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models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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# , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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# , ('Decision Tree' , DecisionTreeClassifier(**rs) )
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# , ('Extra Tree' , ExtraTreeClassifier(**rs) )
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# , ('Extra Trees' , ExtraTreesClassifier(**rs) )
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# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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# , ('Gaussian NB' , GaussianNB() )
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# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
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# , ('LDA' , LinearDiscriminantAnalysis() )
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, ('Logistic Regression' , LogisticRegression(**rs) )
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# , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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# , ('Multinomial' , MultinomialNB() )
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# , ('Naive Bayes' , BernoulliNB() )
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# , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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# , ('QDA' , QuadraticDiscriminantAnalysis() )
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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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# , ('Ridge Classifier' , RidgeClassifier(**rs) )
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# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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# , ('SVC' , SVC(**rs) )
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# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) )
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]
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mm_skf_scoresD = {}
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@ -314,5 +325,34 @@ def MultModelsCl(input_df, target, skf_cv
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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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#return(mm_skf_scoresD)
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#%%
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# ADD more info: meta data related to input and blind and resampling
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# target numbers: training
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yc1 = Counter(target)
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yc1_ratio = yc1[0]/yc1[1]
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# target numbers: test
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yc2 = Counter(blind_test_target)
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yc2_ratio = yc2[0]/yc2[1]
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mm_skf_scoresD[model_name]['resampling'] = resampling_type
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mm_skf_scoresD[model_name]['training_size'] = len(input_df)
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mm_skf_scoresD[model_name]['trainingY_ratio'] = round(yc1_ratio, 2)
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mm_skf_scoresD[model_name]['testSize'] = len(blind_test_df)
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mm_skf_scoresD[model_name]['testY_ratio'] = round(yc2_ratio,2)
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mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
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mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
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#return(mm_skf_scoresD)
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#============================
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# Process the dict to have WF
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#============================
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if return_formatted_output:
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CV_BT_metaDF = ProcessMultModelCl(mm_skf_scoresD)
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return(CV_BT_metaDF)
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else:
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return(mm_skf_scoresD)
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@ -37,6 +37,8 @@ def setvars(gene,drug):
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import argparse
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import re
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#%% GLOBALS
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tts_split = "70/30"
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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@ -59,11 +61,9 @@ def setvars(gene,drug):
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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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@ -689,7 +689,8 @@ def setvars(gene,drug):
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print('\n-------------------------------------------------------------'
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, '\nSuccessfully split data: ALL features'
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, '\nactual values: training set'
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, '\nimputed values: blind test set'
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, '\nSplit:', tts_split
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#, '\nimputed values: blind test set'
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, '\n\nTotal data size:', len(X) + len(X_bts)
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@ -44,12 +44,6 @@ from ml_data_7030 import *
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# TT run all ML clfs: baseline model
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from MultModelsCl import MultModelsCl
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############################################################################
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print('\n#####################################################################\n'
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, '\nRunning ML analysis: feature groups '
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, '\nGene name:', gene
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, '\nDrug name:', drug)
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#==================
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# Specify outdir
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#==================
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@ -101,7 +95,13 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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bts_size = len(X_bts)
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yc2 = Counter(y_bts)
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yc2_ratio = yc2[0]/yc2[1]
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###############################################################################
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print('\n#####################################################################\n'
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, '\nRunning ML analysis: feature groups '
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, '\nGene name:', gene
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, '\nDrug name:', drug)
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#%% Basic: No Oversampling
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#================
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# Baseline
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@ -5,45 +5,234 @@ Created on Tue May 24 08:11:05 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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###############################################################################
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#gene = 'pncA'
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#drug = 'pyrazinamide'
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#total_mtblineage_uc = 8
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#%% command line args: case sensitive
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
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arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '')
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args = arg_parser.parse_args()
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drug = args.drug
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gene = args.gene
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###############################################################################
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#==================
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# other vars
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#==================
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tts_split = '70/30'
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OutFile_suffix = '7030_FS'
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###############################################################################
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#==================
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# Import data
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#==================
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from ml_data_7030 import *
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setvars(gene,drug)
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from ml_data_7030 import *
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# from YC run_all_ML: run locally
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#from UQ_yc_RunAllClfs import run_all_ML
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#==========================================
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# Import ML function: Feature selection
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#==========================================
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# TT run all ML clfs: feature selection
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from FS import fsgs
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#==================
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# Specify outdir
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#==================
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outdir_ml = outdir + 'ml/tts_7030/fs/'
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print('\nOutput directory:', outdir_ml)
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OutFileFS = outdir_ml + gene.lower() + '_FS_' + OutFile_suffix + '.json'
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############################################################################
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###############################################################################
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#====================
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# single model CALL
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#====================
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a_fs0 = fsgs(input_df = X
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, target = y
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, param_gridLd = [{'fs__min_features_to_select' : [1]}]
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = LogisticRegression(**rs)
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, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = 'mixed'
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)
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# aFS = fsgs(input_df = X
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# , target = y
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# , param_gridLd = [{'fs__min_features_to_select': [1]}]
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# , blind_test_df = X_bts
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# , blind_test_target = y_bts
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# , estimator = LogisticRegression(**rs)
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# , use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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# , custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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# , cv_method = skf_cv
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# , var_type = 'mixed'
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# )
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#############
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# Loop
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############
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# models_all = [
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# ('XGBoost' , XGBClassifier(**rs, **njobs
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# , n_estimators = 100 # wasn't there
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# , max_depyth = 3 # wasn't there
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# , verbosity = 3
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# #, use_label_encoder = False)
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# ) )
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# ]
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models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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##, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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, ('Decision Tree' , DecisionTreeClassifier(**rs) )
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, ('Extra Tree' , ExtraTreeClassifier(**rs) )
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, ('Extra Trees' , ExtraTreesClassifier(**rs) )
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, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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#, ('Gaussian NB' , GaussianNB() )
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#, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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#, ('K-Nearest Neighbors' , KNeighborsClassifier() )
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, ('LDA' , LinearDiscriminantAnalysis() )
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, ('Logistic Regression' , LogisticRegression(**rs) )
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, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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#, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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#, ('Multinomial' , MultinomialNB() )
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#, ('Naive Bayes' , BernoulliNB() )
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, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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#, ('QDA' , QuadraticDiscriminantAnalysis() )
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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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, ('Ridge Classifier' , RidgeClassifier(**rs) )
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, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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#, ('SVC' , SVC(**rs) )
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, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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# , ('XGBoost' , XGBClassifier(**rs, **njobs, verbosity = 3
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# , use_label_encoder = False) )
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]
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print('\n#####################################################################'
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, '\nRunning Feature Selection using classfication models (n):', len(models)
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, '\nGene:' , gene.lower()
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, '\nDrug:' , drug
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, '\nSplit:' , tts_split
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,'\n####################################################################')
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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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out_fsD = {}
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index = 1
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for model_name, model_fn in models:
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print('\nRunning classifier with FS:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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#, '\nList of models:', models)
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index = index+1
|
||||
|
||||
out_fsD[model_name] = fsgs(input_df = X
|
||||
, target = y
|
||||
, param_gridLd = [{'fs__min_features_to_select': [1]}]
|
||||
, blind_test_df = X_bts
|
||||
, blind_test_target = y_bts
|
||||
, estimator = model_fn
|
||||
, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
|
||||
, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
|
||||
, cv_method = skf_cv
|
||||
, var_type = 'mixed'
|
||||
)
|
||||
out_fsD
|
||||
#%% Checking results dict
|
||||
tot_Ditems = sum(len(v) for v in out_fsD.values())
|
||||
|
||||
checkL = []
|
||||
for k, v in out_fsD.items():
|
||||
l = [len(out_fsD[k])]
|
||||
checkL = checkL + l
|
||||
n_sD = len(checkL) # no. of subDicts
|
||||
l_sD = list(set(checkL)) # length of each subDict
|
||||
|
||||
print('\nTotal no.of subdicts:', n_sD)
|
||||
if len(l_sD) == 1 and tot_Ditems == n_sD*l_sD[0]:
|
||||
print('\nPASS: successful run for all Classifiers'
|
||||
, '\nLength of each subdict:', l_sD)
|
||||
|
||||
print('\nSuccessfully ran Feature selection on', len(models), 'classifiers'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\nSplit type:', tts_split
|
||||
, '\nTotal fs models results:', len(out_fsD)
|
||||
, '\nTotal items in output:', sum(len(v) for v in out_fsD.values()) )
|
||||
|
||||
|
||||
##############################################################################
|
||||
|
|
@ -52,14 +241,15 @@ a_fs0 = fsgs(input_df = X
|
|||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
# #output final dict as a json
|
||||
# outFile = 'LR_FS.json'
|
||||
# with open(outFile, 'w') as f:
|
||||
# f.write(json.dumps(output_modelD,cls=NpEncoder))
|
||||
# Output final dict as a json
|
||||
print('\nWriting Final output file (json):', OutFileFS)
|
||||
with open(OutFileFS, 'w') as f:
|
||||
f.write(json.dumps(out_fsD
|
||||
# , cls = NpEncoder
|
||||
))
|
||||
|
||||
# # read json
|
||||
# file = 'LR_FS.json'
|
||||
# with open(file, 'r') as f:
|
||||
# with open(OutFileFS, 'r') as f:
|
||||
# data = json.load(f)
|
||||
##############################################################################
|
||||
|
||||
|
|
|
|||
|
|
@ -5,12 +5,12 @@
|
|||
# captures error: 2>$1
|
||||
# omitted drtype_labels
|
||||
=================================
|
||||
./run_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_7030.txt
|
||||
./run_7030.py -g embB -d ethambutol 2>&1 | tee log_embb_7030.txt
|
||||
./run_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_7030.txt
|
||||
./run_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_7030.txt
|
||||
./run_7030.py -g gid -d streptomycin 2>&1 | tee log_gid_7030.txt
|
||||
./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030.txt
|
||||
time ./run_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_7030.txt
|
||||
time ./run_7030.py -g embB -d ethambutol 2>&1 | tee log_embb_7030.txt
|
||||
time ./run_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_7030.txt
|
||||
time ./run_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_7030.txt
|
||||
time ./run_7030.py -g gid -d streptomycin 2>&1 | tee log_gid_7030.txt
|
||||
time ./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030.txt
|
||||
# alr: # ERROR, as expected, too few values!
|
||||
# gid: problems
|
||||
########################################################################
|
||||
|
|
@ -69,5 +69,13 @@
|
|||
# Date: 18/05/2022
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
########################################################################
|
||||
########################################################################
|
||||
########################################################################
|
||||
|
||||
|
||||
# running feature selection
|
||||
# Split:70/30
|
||||
time ./run_FS.py -g pncA -d pyrazinamide 2>&1 | tee log_FS_pnca_7030.txt
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -61,9 +61,6 @@ a_fs0 = fsgs(input_df = X
|
|||
, var_type = 'mixed'
|
||||
)
|
||||
###############################################
|
||||
|
||||
|
||||
|
||||
##############################################################################
|
||||
# my function CALL
|
||||
#import fsgs from UQ_FS_fn
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue