renamed UQ_LR_FS.py to UQ_LR_FS_p1.py
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2 changed files with 17 additions and 14 deletions
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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 = rskf_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 = rskf_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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# IMP: nice eg of including it as part of pipeline
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# https://www.tomasbeuzen.com/post/scikit-learn-gridsearch-pipelines/
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