from UQ_yc_RunAllClfs import run_all_ML

#%% CALL function
#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')
# Baseline_data

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')
CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
CVResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']
BTSResultsDF_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# from FUNC

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')
CVResultsDF_baseline = YC_resD2['CrossValResultsDF']
BTSResultsDF_baseline = YC_resD2['BlindTestResultsDF']

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')
CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']





# from sklearn.utils import all_estimators
# for name, algorithm in all_estimators(type_filter="classifier"):
#     clf = algorithm()
#     print('Name:', name, '\nAlgo:', clf)

# Random Oversampling
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')
CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)

# Random Undersampling
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')
CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)

# Random Oversampling+Undersampling
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')
CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)

# SMOTE NC
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')
CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
##############################################################################
#============================================
# BASELINE models with dissected featues
#============================================
# Genomics
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')
yc_gfCT_baseline= yC_gf['CrossValResultsDF']
yc_gfCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_gfBT_baseline = yC_gf['BlindTestResultsDF']
yc_gfBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# Evolutionary
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')
yc_evCT_baseline= yC_ev['CrossValResultsDF']
yc_evCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_evBT_baseline = yC_ev['BlindTestResultsDF']
yc_evBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# strucF:All
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')
yc_sfallCT_baseline= yC_sfall['CrossValResultsDF']
yc_sfallCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_sfallBT_baseline = yC_sfall['BlindTestResultsDF']
yc_sfallBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# strucF:Common ONLY
c = [x for x in X_ssFN if x not in X_foldX_cols]
yC_sfco= run_all_ML(input_pd=X[X_stabilityN], target_label=y
                   , blind_test_input_df=X_bts[x_stabilityN]
                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
yc_sfcoCT_baseline= yC_sfco['CrossValResultsDF']
yc_sfcoCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_sfcoBT_baseline = yC_sfco['BlindTestResultsDF']
yc_sfcoBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# strucF:common_stability + foldX_cols i.e interaction
yC_fxss= run_all_ML(input_pd=X[common_cols_stabiltyN+foldX_cols], target_label=y
                   , blind_test_input_df=X_bts[common_cols_stabiltyN+foldX_cols]
                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
yc_fxssCT_baseline= yC_fxss['CrossValResultsDF']
yc_fxssCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_fxssBT_baseline = yC_fxss['BlindTestResultsDF']
yc_fxssBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

# categorical
yC_cat= run_all_ML(input_pd=X[categorical_FN], target_label=y
                   , blind_test_input_df=X_bts[categorical_FN]
                   , blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
yc_catCT_baseline= yC_cat['CrossValResultsDF']
yc_catCT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)
yc_catBT_baseline = yC_cat['BlindTestResultsDF']
yc_catBT_baseline.sort_values(by=['matthew'], ascending=False, inplace=True)

#=================================================
# Dissected features with Over and Undersampling
#=================================================