115 lines
6.2 KiB
Python
Executable file
115 lines
6.2 KiB
Python
Executable file
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from UQ_yc_RunAllClfs import run_all_ML
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#%% CALL function
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#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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# Baseline_data
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YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
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CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
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BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# from FUNC
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YC_resD2 = run_all_ML(input_pd=df2['X'], target_label=df2['y'], blind_test_input_df=df2['X'], blind_test_target=df2['y'], preprocess = True, var_type = 'mixed')
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CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
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BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
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YC_resD_ros = run_all_ML(input_pd=df2['X_ros'], target_label=df2['y_ros'], blind_test_input_df=df2['X'], blind_test_target=df2['y'], preprocess = True, var_type = 'mixed')
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CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
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BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
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# from sklearn.utils import all_estimators
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# for name, algorithm in all_estimators(type_filter="classifier"):
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# clf = algorithm()
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# print('Name:', name, '\nAlgo:', clf)
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# Random Oversampling
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YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
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CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
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BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
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# Random Undersampling
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YC_resD_rus = run_all_ML(input_pd=X_rus, target_label=y_rus, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
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CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
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BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
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# Random Oversampling+Undersampling
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YC_resD_rouC = run_all_ML(input_pd=X_rouC, target_label=y_rouC, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
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CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
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BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
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# SMOTE NC
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YC_resD_smnc = run_all_ML(input_pd=X_smnc, target_label=y_smnc, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
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CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
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BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
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##############################################################################
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#============================================
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# BASELINE models with dissected featues
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#============================================
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# Genomics
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yC_gf = run_all_ML(input_pd=X[X_genomicFN], target_label=y, blind_test_input_df=X_bts[X_genomicFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_gfCT_baseline= yC_gf['CrossValResultsDF']
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yc_gfCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_gfBT_baseline = yC_gf['BlindTestResultsDF']
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yc_gfBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# Evolutionary
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yC_ev = run_all_ML(input_pd=X[X_evolFN], target_label=y, blind_test_input_df=X_bts[X_evolFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_evCT_baseline= yC_ev['CrossValResultsDF']
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yc_evCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_evBT_baseline = yC_ev['BlindTestResultsDF']
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yc_evBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# strucF:All
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yC_sfall = run_all_ML(input_pd=X[X_strFN], target_label=y, blind_test_input_df=X_bts[X_strFN], blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_sfallCT_baseline= yC_sfall['CrossValResultsDF']
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yc_sfallCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_sfallBT_baseline = yC_sfall['BlindTestResultsDF']
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yc_sfallBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# strucF:Common ONLY
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c = [x for x in X_ssFN if x not in X_foldX_cols]
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yC_sfco= run_all_ML(input_pd=X[X_stabilityN], target_label=y
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, blind_test_input_df=X_bts[x_stabilityN]
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, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_sfcoCT_baseline= yC_sfco['CrossValResultsDF']
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yc_sfcoCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_sfcoBT_baseline = yC_sfco['BlindTestResultsDF']
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yc_sfcoBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# strucF:common_stability + foldX_cols i.e interaction
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yC_fxss= run_all_ML(input_pd=X[common_cols_stabiltyN+foldX_cols], target_label=y
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, blind_test_input_df=X_bts[common_cols_stabiltyN+foldX_cols]
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, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_fxssCT_baseline= yC_fxss['CrossValResultsDF']
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yc_fxssCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_fxssBT_baseline = yC_fxss['BlindTestResultsDF']
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yc_fxssBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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# categorical
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yC_cat= run_all_ML(input_pd=X[categorical_FN], target_label=y
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, blind_test_input_df=X_bts[categorical_FN]
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, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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yc_catCT_baseline= yC_cat['CrossValResultsDF']
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yc_catCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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yc_catBT_baseline = yC_cat['BlindTestResultsDF']
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yc_catBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
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#=================================================
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# Dissected features with Over and Undersampling
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#=================================================
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