116 lines
4 KiB
Python
Executable file
116 lines
4 KiB
Python
Executable file
fs_test = RFECV(DecisionTreeClassifier(**rs)
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, cv = StratifiedKFold(n_splits = 10, shuffle = True,**rs)
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, scoring = 'matthews_corrcoef')
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models = [('Logistic Regression' , LogisticRegression(**rs) )]
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#, ('Logistic RegressionCV' , LogisticRegressionCV(**rs) )]
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for m in models:
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print(m)
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print('\n================================================================\n')
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index = 1
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for model_name, model_fn in models:
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print('\nRunning classifier:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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#, '\nList of models:', models)
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index = index+1
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fs2 = RFECV(model_fn
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, cv = skf_cv
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, scoring = 'matthews_corrcoef')
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from sklearn.datasets import make_friedman1
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from sklearn.datasets import load_iris
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X_eg, y_eg = load_iris(return_X_y=True)
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#X_eg, y_eg = make_friedman1(n_samples=50, n_features=10, random_state=0)
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fs2.fit(X_eg,y_eg)
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fs2.support_
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fs2.ranking_
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###############################################################################
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# LR
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a_fs = fsgs(input_df = X
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, target = y
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#, param_gridLd = [{'fs__min_features_to_select' : []}]
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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#, estimator = RandomForestClassifier(**rs, **njobs, bootstrap = True, oob_score = True)
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, estimator = LogisticRegression(**rs)
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, use_fs = False # set True to use DT as a RFECV estimator
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, var_type = 'mixed')
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a_fs.keys()
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a_fsDF = pd.DataFrame(a_fs.items()) # LR
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a_fsDF2 = pd.DataFrame(a_fs2.items()) # use_FS= True
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a_fsDF3 = pd.DataFrame(a_fs3.items()) # RF
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# this one
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a_fs0 = fsgs(input_df = X
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, target = y
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, param_gridLd = [{'fs__min_features_to_select' : [1]}]
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = LogisticRegression(**rs)
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, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = 'mixed'
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)
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###############################################
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##############################################################################
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# my function CALL
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#import fsgs from UQ_FS_fn
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# RFECV by default uses the estimator provided, custom option to provide fs model using use_fs and
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a_fs = fsgs(input_df = X
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, target = y
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, param_gridLd = [{'fs__min_features_to_select' : [1]}]
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, blind_test_df = X_bts
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, blind_test_target = y_bts
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, estimator = LogisticRegression(**rs)
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#, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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, use_fs = True, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = 'mixed'
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)
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a_fs.keys()
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a_fs2.keys()
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a_fs3.keys()
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a_fsDF = pd.DataFrame(a_fs.items()) # LR
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a_fsDF.columns = ['parameter', 'param_value']
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a_fs2DF2 = pd.DataFrame(a_fs2.items()) # use_FS= True
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a_fs2DF2.columns = ['parameter', 'param_value']
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a_fsDF3 = pd.DataFrame(a_fs3.items()) # RF
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##############
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a_mask = a_fs['fs_res_array']
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a_fsDF.loc[a_fsDF['parameter'] == 'fs_res_array']
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mod_selF = a_fs2DF2.loc[a_fsDF['parameter'] == 'sel_features_names']; mod_selF
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mod_selFT = mod_selF.T
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# subset keys
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#keys_to_extract = ['model_name', 'fs_method', 'sel_features_names', 'all_feature_names', 'fs_res_array']
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keys_to_extract = ['fs_method', 'sel_features_names']
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a_subset = {key: a_fs2[key] for key in keys_to_extract}
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a_subsetDF = pd.DataFrame(a_subset); a_subsetDF
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mod_fs_method = a_fs2['fs_method']
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fs_name = re.search('estimator=(\w+)',mod_fs_method)
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fs_namefN = fs_namef.group(1)
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print('\nFS method:', fs_namefN)
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fsDF = a_subsetDF[['sel_features_names']];fsDF
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fsDF.columns = [fs_namefN+'_FS']
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fsDF.columns; fsDF
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###############################
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