added pratice and feature selection scripts for LR and hyperparam for all classification models as separate scripts in uq_ml_models
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UQ_LR_FS.py
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UQ_LR_FS.py
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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 Mon May 16 05:59:12 2022
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@author: tanu
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"""
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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 Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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#%% Import libs
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.svm import SVC
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from sklearn.base import BaseEstimator
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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.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from xgboost import XGBClassifier
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#####################
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from sklearn.feature_selection import RFE
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from sklearn.feature_selection import RFECV
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from sklearn.linear_model import LogisticRegression
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from sklearn.feature_selection import SelectFromModel
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from sklearn.feature_selection import SequentialFeatureSelector
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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#%%
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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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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, 'jaccard' : make_scorer(jaccard_score)
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})
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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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#%% Logistic Regression + hyperparam + FS: BaseEstimator: ClfSwitcher()
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model_lr = LogisticRegression(**rs)
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model_rfecv = RFECV(estimator = model_lr
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, cv = skf_cv
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#, cv = 10
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, scoring = 'matthews_corrcoef'
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)
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model_rfecv = SequentialFeatureSelector(estimator = model_lr
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, n_features_to_select = 'auto'
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, tol = None
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# , cv = 10
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, cv = skf_cv
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# , direction ='backward'
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, direction ='forward'
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, **njobs)
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# param_grid = [
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# { 'C': np.logspace(0, 4, 10),
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# 'penalty': ['l1', 'l2'],
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# 'max_iter': [100],
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# 'solver': ['saga']
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# }#,
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# # { 'C': [1],
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# # 'penalty': ['l1'],
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# # 'max_iter': [100],
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# # 'solver': ['saga']
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# # }
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# ]
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param_grid2 = [
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'max_iter': list(range(100,800,100)),
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'solver': ['saga']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['l2', 'none'],
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'max_iter': list(range(100,800,100)),
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'solver': ['newton-cg', 'lbfgs', 'sag']
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},
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{
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#'clf__estimator': [LogisticRegression(**rs)],
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#'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'C': np.logspace(0, 4, 10),
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'penalty': ['l1', 'l2'],
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'max_iter': list(range(100,800,100)),
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'solver': ['liblinear']
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}
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]
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#-------------------------------------------------------------------------------
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# Grid search CV + FS
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gscv_lr = GridSearchCV(model_lr
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, param_grid2
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, return_train_score = False
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, verbose = 3
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, **njobs)
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#------------------------------------------------------------------------------
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# Create pipeline
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pipeline = Pipeline([('pre', MinMaxScaler())
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#, ('feature_selection', sfs_selector)
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, ('feature_selection', model_rfecv )
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, ('clf', gscv_lr)])
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# Fit
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lr_fs = pipeline.fit(X,y)
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pipeline.predict(X_bts)
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lr_fs.predict(X_bts)
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test_predict = pipeline.predict(X_bts)
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print(test_predict)
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print(np.array(y_bts))
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#y_btsf = np.array(y_bts)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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###############################################################################
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#####################
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# Feature selection: AFTER model selection
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# https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172
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###############################################################################
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######################################
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# Blind test
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######################################
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# See how it does on the BLIND test
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#print('\nBlind test score, mcc:', ))
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#test_predict = gscv_lr_fit.predict(X_bts)
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test_predict = pipeline.predict(X_bts)
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test_predict_fs = sfs_selector.predict(X_bts)
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print(test_predict)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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# create a dict with all scores
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lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
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'bts_fscore':None
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, 'bts_mcc':None
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, 'bts_precision':None
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, 'bts_recall':None
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, 'bts_accuracy':None
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, 'bts_roc_auc':None
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, 'bts_jaccard':None }
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lr_bts_dict
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lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
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lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
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lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
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lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
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lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
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lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
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lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
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lr_bts_dict
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# Create a df from dict with all scores
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lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
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lr_bts_df.columns = ['Logistic_Regression']
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print(lr_bts_df)
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# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
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# d2
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# def Merge(dict1, dict2):
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# res = {**dict1, **dict2}
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# return res
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# d3 = Merge(d2, lr_bts_dict)
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# d3
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# Create df with best model params
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model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
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model_params_df = model_params.to_frame()
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model_params_df
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model_params_df.columns = ['Logistic_Regression']
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model_params_df.columns
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# Combine the df of scores and the best model params
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lr_bts_df.columns
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lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
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lr_output
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# Format the combined df
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# Drop the best_model_params row from lr_output
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lr_df = lr_output.drop([0], axis = 0)
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lr_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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# FIXME: confusion matrix
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print(confusion_matrix(y_bts, test_predict))
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#%% Feature selection
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#####################
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# Feature selection: AFTER model selection?
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# ADD that within the loop
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# https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172
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#####################
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from sklearn.feature_selection import RFE
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from sklearn.linear_model import LogisticRegression
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from sklearn.feature_selection import SelectFromModel
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from sklearn.feature_selection import SequentialFeatureSelector
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# RFE: ~ model coef or feature_importance
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rfe_selector = RFE(estimator = LogisticRegression(**rs
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, penalty='l1'
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, solver='saga'
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, max_iter = 100
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, C= 1.0)
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, n_features_to_select = None # median by default
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, step = 1)
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rfe_selector.fit(X, y)
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rfe_fs = X.columns[rfe_selector.get_support()]
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print('\nFeatures selected from Recursive Feature Elimination:', len(rfe_fs)
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, '\nThese are:', rfe_fs)
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# SFM: ~ model coef or feature_importance
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sfm_selector = SelectFromModel(estimator = LogisticRegression(**rs
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, penalty='l1'
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, solver='saga'
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, max_iter = 100
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, C= 1.0)
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, threshold = "median"
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, max_features = None ) # median by default
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sfm_selector.fit(X, y)
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sfm_fs = X.columns[sfm_selector.get_support()]
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print('\nFeatures selected from Select From Model:', len(sfm_fs)
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, '\nThese are:', sfm_fs)
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# SFS:ML CV
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sfs_selector = SequentialFeatureSelector(estimator = LogisticRegression(**rs
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, penalty='l1'
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, solver='saga'
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, max_iter = 100
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, C = 1.0)
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, n_features_to_select = 'auto'
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, tol = None
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, cv = 10
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#, cv = skf_cv
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# , direction ='backward'
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, direction ='forward'
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, **njobs)
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sfs_selector.fit(X, y)
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sfsb_fs = X.columns[sfs_selector.get_support()]
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print('\nFeatures selected from Sequential Feature Selector (Greedy):', len(sfsb_fs)
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, '\nThese are:', sfsb_fs)
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#Features selected from Sequential Feature Selector (Greedy, Backward): 7 [CV = SKF_CV]
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#These are: Index(['ligand_distance', 'duet_stability_change', 'ddg_foldx', 'deepddg',
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# 'contacts', 'rd_values', 'snap2_score']
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#Features selected from Sequential Feature Selector (Greedy, Backward): 7 [CV=10]
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#These are: Index(['ligand_distance', 'deepddg', 'contacts', 'rsa', 'kd_values',
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# 'rd_values', 'maf']
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#-----
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# Features selected from Sequential Feature Selector (Greedy, Forward): 6 [CV = SKF_CV]
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# These are: Index(['ligand_distance', 'ddg_dynamut2', 'rsa', 'kd_values', 'rd_values', 'maf']
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# Features selected from Sequential Feature Selector (Greedy, Forward): 6 [CV = 10]
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#These are: Index(['duet_stability_change', 'deepddg', 'ddg_dynamut2', 'rsa', 'kd_values', 'maf']
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###############################################################################
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UQ_LR_p1.py
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UQ_LR_p1.py
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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 Mon May 16 05:59:12 2022
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@author: tanu
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"""
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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 Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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#%% Import libs
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import numpy as np
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import pandas as pd
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from sklearn.model_selection import GridSearchCV
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from sklearn import datasets
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.svm import SVC
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from sklearn.base import BaseEstimator
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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.pipeline import Pipeline
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from sklearn.model_selection import GridSearchCV
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from xgboost import XGBClassifier
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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#%% Get train-test split and scoring functions
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# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
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# , num_df_wtgt['mutation_class']
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# , test_size = 0.33
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# , random_state = 2
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# , shuffle = True
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# , stratify = num_df_wtgt['mutation_class'])
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y.to_frame().value_counts().plot(kind = 'bar')
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blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
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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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, 'jaccard' : make_scorer(jaccard_score)
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})
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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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#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
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class ClfSwitcher(BaseEstimator):
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def __init__(
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self,
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estimator = SGDClassifier(),
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):
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"""
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A Custom BaseEstimator that can switch between classifiers.
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:param estimator: sklearn object - The classifier
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"""
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self.estimator = estimator
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def fit(self, X, y=None, **kwargs):
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self.estimator.fit(X, y)
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return self
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def predict(self, X, y=None):
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return self.estimator.predict(X)
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def predict_proba(self, X):
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return self.estimator.predict_proba(X)
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def score(self, X, y):
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return self.estimator.score(X, y)
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parameters = [
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{
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'clf__estimator': [LogisticRegression(**rs)],
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#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__estimator__C': np.logspace(0, 4, 10),
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'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__estimator__max_iter': list(range(100,800,100)),
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'clf__estimator__solver': ['saga']
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},
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# {
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# 'clf__estimator': [LogisticRegression(**rs)],
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# #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__estimator__C': np.logspace(0, 4, 10),
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# 'clf__estimator__penalty': ['l2', 'none'],
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# 'clf__estimator__max_iter': list(range(100,800,100)),
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# 'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
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# },
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# {
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# 'clf__estimator': [LogisticRegression(**rs)],
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# #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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# 'clf__estimator__C': np.logspace(0, 4, 10),
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# 'clf__estimator__penalty': ['l1', 'l2'],
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# 'clf__estimator__max_iter': list(range(100,800,100)),
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# 'clf__estimator__solver': ['liblinear']
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# }
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]
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# Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_lr = GridSearchCV(pipeline
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, parameters
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#, scoring = 'f1', refit = 'f1'
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, **njobs
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, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_lr_fit = gscv_lr.fit(X, y)
|
||||
gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
|
||||
gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_lr_fit_be_mod)
|
||||
print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
|
||||
|
||||
#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', ))
|
||||
|
||||
test_predict = gscv_lr_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_bts, test_predict))
|
||||
print(matthews_corrcoef(y_bts, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
, 'bts_recall':None
|
||||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
lr_bts_dict
|
||||
lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
lr_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
|
||||
lr_bts_df.columns = ['Logistic_Regression']
|
||||
print(lr_bts_df)
|
||||
|
||||
# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
|
||||
# d2
|
||||
# def Merge(dict1, dict2):
|
||||
# res = {**dict1, **dict2}
|
||||
# return res
|
||||
# d3 = Merge(d2, lr_bts_dict)
|
||||
# d3
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
lr_bts_df.columns
|
||||
lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
|
||||
lr_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from lr_output
|
||||
lr_df = lr_output.drop([0], axis = 0)
|
||||
lr_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
# FIXME: confusion matrix
|
||||
print(confusion_matrix(y_bts, test_predict))
|
||||
|
||||
cm = confusion_matrix(y_bts, test_predict)
|
||||
133
uq_ml_models/UQ_ABC.py
Normal file
133
uq_ml_models/UQ_ABC.py
Normal file
|
|
@ -0,0 +1,133 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [AdaBoostClassifier(**rs)]
|
||||
, 'clf__estimator__n_estimators': [none, 1, 2]
|
||||
, 'clf__estimator__base_estiamtor' : ['None', 1*SVC(), 1*KNeighborsClassifier()]
|
||||
#, 'clf__estimator___splitter' : ["best", "random"]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_abc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_abc_fit = gscv_abc.fit(X, y)
|
||||
|
||||
gscv_abc_fit_be_mod = gscv_abc_fit.best_params_
|
||||
gscv_abc_fit_be_res = gscv_abc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_abc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_abc_fit.best_score_, ':' , round(gscv_abc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_abc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_abc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_abc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
abc_bts_dict = {#'best_model': list(gscv_abc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
abc_bts_dict
|
||||
abc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
abc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
abc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(abc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
abc_bts_df = pd.DataFrame.from_dict(abc_bts_dict,orient = 'index')
|
||||
abc_bts_df.columns = ['Logistic_Regression']
|
||||
print(abc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_abc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
abc_bts_df.columns
|
||||
abc_output = pd.concat([model_params_df, abc_bts_df], axis = 0)
|
||||
abc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from abc_output
|
||||
abc_df = abc_output.drop([0], axis = 0)
|
||||
abc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
137
uq_ml_models/UQ_BC.py
Normal file
137
uq_ml_models/UQ_BC.py
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [BaggingClassifier(**rs
|
||||
, **njobs
|
||||
, bootstrap = True
|
||||
, oob_score = True)],
|
||||
, 'clf__estimator__n_estimators' : [10, 100, 1000]
|
||||
# If None, then the base estimator is a DecisionTreeClassifier.
|
||||
, 'clf__estimator__base_estimator' : ['None', 'SVC()', 'KNeighborsClassifier()']# if none, DT is used
|
||||
, 'clf__estimator__gamma': ['scale', 'auto']
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_bc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_bc_fit = gscv_bc.fit(X, y)
|
||||
|
||||
gscv_bc_fit_be_mod = gscv_bc_fit.best_params_
|
||||
gscv_bc_fit_be_res = gscv_bc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_bc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_bc_fit.best_score_, ':' , round(gscv_bc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_bc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_bc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_bc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
bc_bts_dict = {#'best_model': list(gscv_bc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
bc_bts_dict
|
||||
bc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
bc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
bc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(bc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
bc_bts_df = pd.DataFrame.from_dict(bc_bts_dict,orient = 'index')
|
||||
bc_bts_df.columns = ['Logistic_Regression']
|
||||
print(bc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_bc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
bc_bts_df.columns
|
||||
bc_output = pd.concat([model_params_df, bc_bts_df], axis = 0)
|
||||
bc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from bc_output
|
||||
bc_df = bc_output.drop([0], axis = 0)
|
||||
bc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
134
uq_ml_models/UQ_BNB.py
Normal file
134
uq_ml_models/UQ_BNB.py
Normal file
|
|
@ -0,0 +1,134 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [BernoulliNB()]
|
||||
, 'clf__estimator__alpha': [0, 1]
|
||||
, 'clf__estimator__binarize':['None', 0]
|
||||
, 'clf__estimator__fit_prior': [True]
|
||||
, 'clf__estimator__class_prior': ['None']
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_bnb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_bnb_fit = gscv_bnb.fit(X, y)
|
||||
|
||||
gscv_bnb_fit_be_mod = gscv_bnb_fit.best_params_
|
||||
gscv_bnb_fit_be_res = gscv_bnb_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_bnb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_bnb_fit.best_score_, ':' , round(gscv_bnb_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_bnb_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_bnb_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_bnb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
bnb_bts_dict = {#'best_model': list(gscv_bnb_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
bnb_bts_dict
|
||||
bnb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
bnb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(bnb_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
bnb_bts_df = pd.DataFrame.from_dict(bnb_bts_dict,orient = 'index')
|
||||
bnb_bts_df.columns = ['Logistic_Regression']
|
||||
print(bnb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_bnb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
bnb_bts_df.columns
|
||||
bnb_output = pd.concat([model_params_df, bnb_bts_df], axis = 0)
|
||||
bnb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from bnb_output
|
||||
bnb_df = bnb_output.drop([0], axis = 0)
|
||||
bnb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
137
uq_ml_models/UQ_DT.py
Normal file
137
uq_ml_models/UQ_DT.py
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [DecisionTreeClassifier(**rs
|
||||
, **njobs)]
|
||||
, 'clf__estimator__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__estimator__class_weight':['balanced','balanced_subsample']
|
||||
, 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
|
||||
, 'clf__estimator__max_features': [None, 'sqrt', 'log2']
|
||||
, 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
|
||||
, 'clf__estimator__min_samples_split': [2, 5, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_dt = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_dt_fit = gscv_dt.fit(X, y)
|
||||
|
||||
gscv_dt_fit_be_mod = gscv_dt_fit.best_params_
|
||||
gscv_dt_fit_be_res = gscv_dt_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_dt_fit_be_mod)
|
||||
print('Best models score:\n', gscv_dt_fit.best_score_, ':' , round(gscv_dt_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_dt_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_dt_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_dt_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
dt_bts_dict = {#'best_model': list(gscv_dt_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
dt_bts_dict
|
||||
dt_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
dt_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
dt_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(dt_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
dt_bts_df = pd.DataFrame.from_dict(dt_bts_dict,orient = 'index')
|
||||
dt_bts_df.columns = ['Logistic_Regression']
|
||||
print(dt_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_dt_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
dt_bts_df.columns
|
||||
dt_output = pd.concat([model_params_df, dt_bts_df], axis = 0)
|
||||
dt_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from dt_output
|
||||
dt_df = dt_output.drop([0], axis = 0)
|
||||
dt_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
136
uq_ml_models/UQ_GBC.py
Normal file
136
uq_ml_models/UQ_GBC.py
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [GradientBoostingClassifier(**rs)]
|
||||
, 'clf__estimator__n_estimators' : [10, 100, 200, 500, 1000]
|
||||
, 'clf__estimator__n_estimators' : [10, 100, 1000]
|
||||
, 'clf__estimator__learning_rate': [0.001, 0.01, 0.1]
|
||||
, 'clf__estimator__subsample' : [0.5, 0.7, 1.0]
|
||||
, 'clf__estimator__max_depth' : [3, 7, 9]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gbc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_gbc_fit = gscv_gbc.fit(X, y)
|
||||
|
||||
gscv_gbc_fit_be_mod = gscv_gbc_fit.best_params_
|
||||
gscv_gbc_fit_be_res = gscv_gbc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_gbc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gbc_fit.best_score_, ':' , round(gscv_gbc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gbc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gbc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_gbc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
gbc_bts_dict = {#'best_model': list(gscv_gbc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
gbc_bts_dict
|
||||
gbc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gbc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(gbc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
gbc_bts_df = pd.DataFrame.from_dict(gbc_bts_dict,orient = 'index')
|
||||
gbc_bts_df.columns = ['Logistic_Regression']
|
||||
print(gbc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gbc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gbc_bts_df.columns
|
||||
gbc_output = pd.concat([model_params_df, gbc_bts_df], axis = 0)
|
||||
gbc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gbc_output
|
||||
gbc_df = gbc_output.drop([0], axis = 0)
|
||||
gbc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
132
uq_ml_models/UQ_GNB.py
Normal file
132
uq_ml_models/UQ_GNB.py
Normal file
|
|
@ -0,0 +1,132 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [GaussianNB(**rs)]
|
||||
, 'clf__estimator__priors': [None]
|
||||
, 'clf__estimator__var_smoothing': np.logspace(0,-9, num=100)
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gnb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_gnb_fit = gscv_gnb.fit(X, y)
|
||||
|
||||
gscv_gnb_fit_be_mod = gscv_gnb_fit.best_params_
|
||||
gscv_gnb_fit_be_res = gscv_gnb_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_gnb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gnb_fit.best_score_, ':' , round(gscv_gnb_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gnb_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gnb_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_gnb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
gnb_bts_dict = {#'best_model': list(gscv_gnb_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
gnb_bts_dict
|
||||
gnb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gnb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(gnb_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
gnb_bts_df = pd.DataFrame.from_dict(gnb_bts_dict,orient = 'index')
|
||||
gnb_bts_df.columns = ['Logistic_Regression']
|
||||
print(gnb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gnb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gnb_bts_df.columns
|
||||
gnb_output = pd.concat([model_params_df, gnb_bts_df], axis = 0)
|
||||
gnb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gnb_output
|
||||
gnb_df = gnb_output.drop([0], axis = 0)
|
||||
gnb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
132
uq_ml_models/UQ_GPC.py
Normal file
132
uq_ml_models/UQ_GPC.py
Normal file
|
|
@ -0,0 +1,132 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [GaussianProcessClassifier(**rs)]
|
||||
|
||||
, 'clf__estimator__kernel': [1*RBF(), 1*DotProduct(), 1*Matern(), 1*RationalQuadratic(), 1*WhiteKernel()]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_gpc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_gpc_fit = gscv_gpc.fit(X, y)
|
||||
|
||||
gscv_gpc_fit_be_mod = gscv_gpc_fit.best_params_
|
||||
gscv_gpc_fit_be_res = gscv_gpc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_gpc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_gpc_fit.best_score_, ':' , round(gscv_gpc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_gpc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_gpc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_gpc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
gpc_bts_dict = {#'best_model': list(gscv_gpc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
gpc_bts_dict
|
||||
gpc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
gpc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(gpc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
gpc_bts_df = pd.DataFrame.from_dict(gpc_bts_dict,orient = 'index')
|
||||
gpc_bts_df.columns = ['Logistic_Regression']
|
||||
print(gpc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_gpc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
gpc_bts_df.columns
|
||||
gpc_output = pd.concat([model_params_df, gpc_bts_df], axis = 0)
|
||||
gpc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from gpc_output
|
||||
gpc_df = gpc_output.drop([0], axis = 0)
|
||||
gpc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
136
uq_ml_models/UQ_KNN.py
Normal file
136
uq_ml_models/UQ_KNN.py
Normal file
|
|
@ -0,0 +1,136 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [KNeighborsClassifier(**rs
|
||||
, **njobs]
|
||||
#, 'clf__estimator__n_neighbors': range(1, 21, 2)
|
||||
, 'clf__estimator__n_neighbors': [5, 7, 11]
|
||||
, 'clf__estimator__metric' : ['euclidean', 'manhattan', 'minkowski']
|
||||
, 'clf__estimator__weights' : ['uniform', 'distance']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_knn = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_knn_fit = gscv_knn.fit(X, y)
|
||||
|
||||
gscv_knn_fit_be_mod = gscv_knn_fit.best_params_
|
||||
gscv_knn_fit_be_res = gscv_knn_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_knn_fit_be_mod)
|
||||
print('Best models score:\n', gscv_knn_fit.best_score_, ':' , round(gscv_knn_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_knn_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_knn_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_knn_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
knn_bts_dict = {#'best_model': list(gscv_knn_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
knn_bts_dict
|
||||
knn_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
knn_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
knn_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(knn_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
knn_bts_df = pd.DataFrame.from_dict(knn_bts_dict,orient = 'index')
|
||||
knn_bts_df.columns = ['Logistic_Regression']
|
||||
print(knn_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_knn_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
knn_bts_df.columns
|
||||
knn_output = pd.concat([model_params_df, knn_bts_df], axis = 0)
|
||||
knn_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from knn_output
|
||||
knn_df = knn_output.drop([0], axis = 0)
|
||||
knn_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
207
uq_ml_models/UQ_LR.py
Normal file
207
uq_ml_models/UQ_LR.py
Normal file
|
|
@ -0,0 +1,207 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon May 16 05:59:12 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Tue Mar 15 11:09:50 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% Import libs
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn import datasets
|
||||
from sklearn.ensemble import ExtraTreesClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.ensemble import AdaBoostClassifier
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
from sklearn.svm import SVC
|
||||
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.naive_bayes import MultinomialNB
|
||||
from sklearn.linear_model import SGDClassifier
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
|
||||
from xgboost import XGBClassifier
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
#%% Get train-test split and scoring functions
|
||||
# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
|
||||
# , num_df_wtgt['mutation_class']
|
||||
# , test_size = 0.33
|
||||
# , random_state = 2
|
||||
# , shuffle = True
|
||||
# , stratify = num_df_wtgt['mutation_class'])
|
||||
|
||||
y.to_frame().value_counts().plot(kind = 'bar')
|
||||
blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
|
||||
|
||||
scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
|
||||
, 'fscore' : make_scorer(f1_score)
|
||||
, 'mcc' : make_scorer(matthews_corrcoef)
|
||||
, 'precision' : make_scorer(precision_score)
|
||||
, 'recall' : make_scorer(recall_score)
|
||||
, 'roc_auc' : make_scorer(roc_auc_score)
|
||||
, 'jaccard' : make_scorer(jaccard_score)
|
||||
})
|
||||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [LogisticRegression(**rs)],
|
||||
#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
'clf__estimator__C': np.logspace(0, 4, 10),
|
||||
'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
|
||||
'clf__estimator__max_iter': list(range(100,800,100)),
|
||||
'clf__estimator__solver': ['saga']
|
||||
},
|
||||
{
|
||||
'clf__estimator': [LogisticRegression(**rs)],
|
||||
#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
'clf__estimator__C': np.logspace(0, 4, 10),
|
||||
'clf__estimator__penalty': ['l2', 'none'],
|
||||
'clf__estimator__max_iter': list(range(100,800,100)),
|
||||
'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
|
||||
},
|
||||
{
|
||||
'clf__estimator': [LogisticRegression(**rs)],
|
||||
#'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
|
||||
'clf__estimator__C': np.logspace(0, 4, 10),
|
||||
'clf__estimator__penalty': ['l1', 'l2'],
|
||||
'clf__estimator__max_iter': list(range(100,800,100)),
|
||||
'clf__estimator__solver': ['liblinear']
|
||||
}
|
||||
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_lr = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_lr_fit = gscv_lr.fit(X, y)
|
||||
gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
|
||||
gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_lr_fit_be_mod)
|
||||
print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
|
||||
|
||||
#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', ))
|
||||
|
||||
test_predict = gscv_lr_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_bts, test_predict))
|
||||
print(matthews_corrcoef(y_bts, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
'bts_fscore':None
|
||||
, 'bts_mcc':None
|
||||
, 'bts_precision':None
|
||||
, 'bts_recall':None
|
||||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None }
|
||||
lr_bts_dict
|
||||
lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
lr_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(lr_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
|
||||
lr_bts_df.columns = ['Logistic_Regression']
|
||||
print(lr_bts_df)
|
||||
|
||||
# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
|
||||
# d2
|
||||
# def Merge(dict1, dict2):
|
||||
# res = {**dict1, **dict2}
|
||||
# return res
|
||||
# d3 = Merge(d2, lr_bts_dict)
|
||||
# d3
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
lr_bts_df.columns
|
||||
lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
|
||||
lr_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from lr_output
|
||||
lr_df = lr_output.drop([0], axis = 0)
|
||||
lr_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
137
uq_ml_models/UQ_MLP.py
Normal file
137
uq_ml_models/UQ_MLP.py
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [MLPClassifier(**rs
|
||||
, **njobs
|
||||
, max_iter = 500)],
|
||||
, 'clf__estimator__hidden_layer_sizes': [(1), (2), (3)]
|
||||
, 'clf__estimator__max_features': ['auto', 'sqrt']
|
||||
, 'clf__estimator__min_samples_leaf': [2, 4, 8]
|
||||
, 'clf__estimator__min_samples_split': [10, 20]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_mlp = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_mlp_fit = gscv_mlp.fit(X, y)
|
||||
|
||||
gscv_mlp_fit_be_mod = gscv_mlp_fit.best_params_
|
||||
gscv_mlp_fit_be_res = gscv_mlp_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_mlp_fit_be_mod)
|
||||
print('Best models score:\n', gscv_mlp_fit.best_score_, ':' , round(gscv_mlp_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_mlp_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_mlp_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_mlp_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
mlp_bts_dict = {#'best_model': list(gscv_mlp_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
mlp_bts_dict
|
||||
mlp_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
mlp_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(mlp_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
mlp_bts_df = pd.DataFrame.from_dict(mlp_bts_dict,orient = 'index')
|
||||
mlp_bts_df.columns = ['Logistic_Regression']
|
||||
print(mlp_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_mlp_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
mlp_bts_df.columns
|
||||
mlp_output = pd.concat([model_params_df, mlp_bts_df], axis = 0)
|
||||
mlp_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from mlp_output
|
||||
mlp_df = mlp_output.drop([0], axis = 0)
|
||||
mlp_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
131
uq_ml_models/UQ_QDA.py
Normal file
131
uq_ml_models/UQ_QDA.py
Normal file
|
|
@ -0,0 +1,131 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [QuadraticDiscriminantAnalysis()]
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_qda = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_qda_fit = gscv_qda.fit(X, y)
|
||||
|
||||
gscv_qda_fit_be_mod = gscv_qda_fit.best_params_
|
||||
gscv_qda_fit_be_res = gscv_qda_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_qda_fit_be_mod)
|
||||
print('Best models score:\n', gscv_qda_fit.best_score_, ':' , round(gscv_qda_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_qda_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_qda_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_qda_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
qda_bts_dict = {#'best_model': list(gscv_qda_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
qda_bts_dict
|
||||
qda_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
qda_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
qda_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(qda_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
qda_bts_df = pd.DataFrame.from_dict(qda_bts_dict,orient = 'index')
|
||||
qda_bts_df.columns = ['Logistic_Regression']
|
||||
print(qda_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_qda_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
qda_bts_df.columns
|
||||
qda_output = pd.concat([model_params_df, qda_bts_df], axis = 0)
|
||||
qda_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from qda_output
|
||||
qda_df = qda_output.drop([0], axis = 0)
|
||||
qda_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
132
uq_ml_models/UQ_RC.py
Normal file
132
uq_ml_models/UQ_RC.py
Normal file
|
|
@ -0,0 +1,132 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [RidgeClassifier(**rs
|
||||
, **njobs)],
|
||||
, 'clf__estimator__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_rc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_rc_fit = gscv_rc.fit(X, y)
|
||||
|
||||
gscv_rc_fit_be_mod = gscv_rc_fit.best_params_
|
||||
gscv_rc_fit_be_res = gscv_rc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_rc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_rc_fit.best_score_, ':' , round(gscv_rc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_rc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_rc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_rc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
rc_bts_dict = {#'best_model': list(gscv_rc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
rc_bts_dict
|
||||
rc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
rc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
rc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(rc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
rc_bts_df = pd.DataFrame.from_dict(rc_bts_dict,orient = 'index')
|
||||
rc_bts_df.columns = ['Logistic_Regression']
|
||||
print(rc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_rc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
rc_bts_df.columns
|
||||
rc_output = pd.concat([model_params_df, rc_bts_df], axis = 0)
|
||||
rc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from rc_output
|
||||
rc_df = rc_output.drop([0], axis = 0)
|
||||
rc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
140
uq_ml_models/UQ_RF.py
Normal file
140
uq_ml_models/UQ_RF.py
Normal file
|
|
@ -0,0 +1,140 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [RandomForestClassifier(**rs
|
||||
, **njobs
|
||||
, bootstrap = True
|
||||
, oob_score = True)],
|
||||
'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
|
||||
, 'clf__estimator__class_weight':['balanced','balanced_subsample']
|
||||
, 'clf__estimator__n_estimators': [10, 25, 50, 100]
|
||||
, 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
|
||||
, 'clf__estimator__max_features': ['sqrt', 'log2', None] #deafult is sqrt
|
||||
, 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
|
||||
, 'clf__estimator__min_samples_split': [2, 5, 15, 20]
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_rf = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_rf_fit = gscv_rf.fit(X, y)
|
||||
|
||||
gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
|
||||
gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_rf_fit_be_mod)
|
||||
print('Best models score:\n', gscv_rf_fit.best_score_, ':' , round(gscv_rf_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_rf_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
rf_bts_dict
|
||||
rf_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
rf_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
rf_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
|
||||
rf_bts_df.columns = ['Logistic_Regression']
|
||||
print(rf_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_rf_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
rf_bts_df.columns
|
||||
rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
|
||||
rf_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from rf_output
|
||||
rf_df = rf_output.drop([0], axis = 0)
|
||||
rf_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
135
uq_ml_models/UQ_SVC.py
Normal file
135
uq_ml_models/UQ_SVC.py
Normal file
|
|
@ -0,0 +1,135 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [SVC(**rs
|
||||
, **njobs)],
|
||||
, 'clf__estimator__kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}
|
||||
, 'clf__estimator__C' : [50, 10, 1.0, 0.1, 0.01]
|
||||
, 'clf__estimator__gamma': ['scale', 'auto']
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_svc = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_svc_fit = gscv_svc.fit(X, y)
|
||||
|
||||
gscv_svc_fit_be_mod = gscv_svc_fit.best_params_
|
||||
gscv_svc_fit_be_res = gscv_svc_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_svc_fit_be_mod)
|
||||
print('Best models score:\n', gscv_svc_fit.best_score_, ':' , round(gscv_svc_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_svc_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_svc_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_svc_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
svc_bts_dict = {#'best_model': list(gscv_svc_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
svc_bts_dict
|
||||
svc_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
svc_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
svc_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(svc_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
svc_bts_df = pd.DataFrame.from_dict(svc_bts_dict,orient = 'index')
|
||||
svc_bts_df.columns = ['Logistic_Regression']
|
||||
print(svc_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_svc_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
svc_bts_df.columns
|
||||
svc_output = pd.concat([model_params_df, svc_bts_df], axis = 0)
|
||||
svc_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from svc_output
|
||||
svc_df = svc_output.drop([0], axis = 0)
|
||||
svc_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
135
uq_ml_models/UQ_XGB.py
Normal file
135
uq_ml_models/UQ_XGB.py
Normal file
|
|
@ -0,0 +1,135 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed May 18 06:03:24 2022
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
|
||||
class ClfSwitcher(BaseEstimator):
|
||||
def __init__(
|
||||
self,
|
||||
estimator = SGDClassifier(),
|
||||
):
|
||||
"""
|
||||
A Custom BaseEstimator that can switch between classifiers.
|
||||
:param estimator: sklearn object - The classifier
|
||||
"""
|
||||
self.estimator = estimator
|
||||
|
||||
def fit(self, X, y=None, **kwargs):
|
||||
self.estimator.fit(X, y)
|
||||
return self
|
||||
|
||||
def predict(self, X, y=None):
|
||||
return self.estimator.predict(X)
|
||||
|
||||
def predict_proba(self, X):
|
||||
return self.estimator.predict_proba(X)
|
||||
|
||||
def score(self, X, y):
|
||||
return self.estimator.score(X, y)
|
||||
|
||||
parameters = [
|
||||
{
|
||||
'clf__estimator': [XGBClassifier(**rs
|
||||
, **njobs]
|
||||
, 'clf__estimator__learning_rate': [0.01, 0.05, 0.1, 0.2]
|
||||
, 'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20]
|
||||
, 'clf__estimator__min_samples_leaf': [4, 8, 12, 16, 20]
|
||||
, 'clf__estimator__max_features': ['auto', 'sqrt']
|
||||
}
|
||||
]
|
||||
|
||||
# Create pipeline
|
||||
pipeline = Pipeline([
|
||||
('pre', MinMaxScaler()),
|
||||
('clf', ClfSwitcher()),
|
||||
])
|
||||
|
||||
# Grid search i.e hyperparameter tuning and refitting on mcc
|
||||
gscv_xgb = GridSearchCV(pipeline
|
||||
, parameters
|
||||
#, scoring = 'f1', refit = 'f1'
|
||||
, scoring = mcc_score_fn, refit = 'mcc'
|
||||
, cv = skf_cv
|
||||
, **njobs
|
||||
, return_train_score = False
|
||||
, verbose = 3)
|
||||
|
||||
# Fit
|
||||
gscv_xgb_fit = gscv_xgb.fit(X, y)
|
||||
|
||||
gscv_xgb_fit_be_mod = gscv_xgb_fit.best_params_
|
||||
gscv_xgb_fit_be_res = gscv_xgb_fit.cv_results_
|
||||
|
||||
print('Best model:\n', gscv_xgb_fit_be_mod)
|
||||
print('Best models score:\n', gscv_xgb_fit.best_score_, ':' , round(gscv_xgb_fit.best_score_, 2))
|
||||
|
||||
print('\nMean test score from fit results:', round(mean(gscv_xgb_fit_be_re['mean_test_mcc']),2))
|
||||
print('\nMean test score from fit results:', round(np.nanmean(gscv_xgb_fit_be_res['mean_test_mcc']),2))
|
||||
|
||||
######################################
|
||||
# Blind test
|
||||
######################################
|
||||
|
||||
# See how it does on the BLIND test
|
||||
#print('\nBlind test score, mcc:', )
|
||||
|
||||
test_predict = gscv_xgb_fit.predict(X_bts)
|
||||
print(test_predict)
|
||||
print(np.array(y_bts))
|
||||
y_btsf = np.array(y_bts)
|
||||
|
||||
print(accuracy_score(y_btsf, test_predict))
|
||||
print(matthews_corrcoef(y_btsf, test_predict))
|
||||
|
||||
# create a dict with all scores
|
||||
xgb_bts_dict = {#'best_model': list(gscv_xgb_fit_be_mod.items())
|
||||
'bts_fscore' : None
|
||||
, 'bts_mcc' : None
|
||||
, 'bts_precision': None
|
||||
, 'bts_recall' : None
|
||||
, 'bts_accuracy' : None
|
||||
, 'bts_roc_auc' : None
|
||||
, 'bts_jaccard' : None }
|
||||
xgb_bts_dict
|
||||
xgb_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
|
||||
xgb_bts_dict
|
||||
|
||||
# Create a df from dict with all scores
|
||||
pd.DataFrame.from_dict(xgb_bts_dict, orient = 'index', columns = 'best_model')
|
||||
|
||||
xgb_bts_df = pd.DataFrame.from_dict(xgb_bts_dict,orient = 'index')
|
||||
xgb_bts_df.columns = ['Logistic_Regression']
|
||||
print(xgb_bts_df)
|
||||
|
||||
# Create df with best model params
|
||||
model_params = pd.Series(['best_model_params', list(gscv_xgb_fit_be_mod.items() )])
|
||||
model_params_df = model_params.to_frame()
|
||||
model_params_df
|
||||
model_params_df.columns = ['Logistic_Regression']
|
||||
model_params_df.columns
|
||||
|
||||
# Combine the df of scores and the best model params
|
||||
xgb_bts_df.columns
|
||||
xgb_output = pd.concat([model_params_df, xgb_bts_df], axis = 0)
|
||||
xgb_output
|
||||
|
||||
# Format the combined df
|
||||
# Drop the best_model_params row from xgb_output
|
||||
xgb_df = xgb_output.drop([0], axis = 0)
|
||||
xgb_df
|
||||
|
||||
#FIXME: tidy the index of the formatted df
|
||||
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue