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4 changed files with 589 additions and 21 deletions
55
UQ_FS_eg.py
55
UQ_FS_eg.py
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@ -68,7 +68,6 @@ pipe = Pipeline([
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('pre', MinMaxScaler())
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# , ('fs', RFECV(LogisticRegression(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', LogisticRegression(**rs))])
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search_space = [
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@ -204,7 +203,7 @@ print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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# Diff b/w train and bts test scores
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train_test_diff = train_bscore - bts_mcc
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train_test_diff = train_bscore - bts_mcc_score
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print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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@ -232,16 +231,25 @@ lr_btsD
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#===========================
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# Add FS related model info
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#===========================
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output_modelD = {'model_name': model_name
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model_namef = str(model_name)
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# FIXME: doesn't tell you which it has chosen
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fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
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all_featuresL = list(all_features)
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fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
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fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
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sel_featuresf = list(sel_features)
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n_sf = int(n_sf)
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output_modelD = {'model_name': model_namef
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, 'model_refit_param': mod_refit_param
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, 'Best_model_params': b_model_params
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, 'n_all_features': n_all_features
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, 'fs_method': gscv_fs.best_estimator_.named_steps['fs'] # FIXME: doesn't tell you which it has chosen
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, 'fs_res_array': gscv_fs.best_estimator_.named_steps['fs'].get_support()
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, 'fs_res_array_rank': gscv_fs.best_estimator_.named_steps['fs'].ranking_
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, 'all_feature_names': all_features
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, 'fs_method': fs_methodf
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, 'fs_res_array': fs_res_arrayf
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, 'fs_res_array_rank': fs_res_array_rankf
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, 'all_feature_names': all_featuresL
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, 'n_sel_features': n_sf
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, 'sel_features_names': sel_features}
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, 'sel_features_names': sel_featuresf}
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output_modelD
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#========================================
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@ -252,18 +260,33 @@ output_modelD
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output_modelD['train_score (MCC)'] = train_bscore
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output_modelD['bts_mcc'] = bts_mcc_score
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output_modelD['train_bts_diff'] = train_test_diff
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output_modelD['train_bts_diff'] = round(train_test_diff,2)
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output_modelD
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class NpEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, np.integer):
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return int(obj)
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if isinstance(obj, np.floating):
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return float(obj)
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if isinstance(obj, np.ndarray):
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return obj.tolist()
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return super(NpEncoder, self).default(obj)
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json.dumps(output_modelD, cls=NpEncoder)
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#========================================
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# Write final output file
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# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
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#========================================
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# output final dict as a json
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# outFile = 'LR_FS.json'
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# with open(outFile, 'w') as f:
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# json.dump(output_modelD, f)
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# #
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# with open(file, 'r') as f:
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# data = json.load(f)
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#output final dict as a json
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outFile = 'LR_FS.json'
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with open(outFile, 'w') as f:
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f.write(json.dumps(output_modelD,cls=NpEncoder))
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# read json
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file = 'LR_FS.json'
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with open(file, 'r') as f:
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data = json.load(f)
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##############################################################################
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227
UQ_FS_eg_function.py
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227
UQ_FS_eg_function.py
Normal file
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@ -0,0 +1,227 @@
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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 23 23:25:26 2022
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@author: tanu
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"""
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##################################
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#####################################
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def fsgs(input_df
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, target
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, blind_test_df = pd.DataFrame()
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#, y_trueS = pd.Series()
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, estimator = LogisticRegression(**rs)
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, param_gridLd = {}
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#, pipelineO
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, cv_method = 10
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, var_type = ['numerical'
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, 'categorical'
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, 'mixed']
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, fs_estimator = [LogisticRegression(**rs)]
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, fs = RFECV(DecisionTreeClassifier(**rs) , cv = 10, scoring = 'matthews_corrcoef')
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):
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'''
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returns
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Dict containing results from FS and hyperparam tuning
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'''
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# # Determine categorical and numerical features
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# numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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# numerical_ix
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# categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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# categorical_ix
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# # Determine preprocessing steps ~ var_type
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# if var_type == 'numerical':
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# t = [('num', MinMaxScaler(), numerical_ix)]
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# if var_type == 'categorical':
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# t = [('cat', OneHotEncoder(), categorical_ix)]
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# if var_type == 'mixed':
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# t = [('cat', OneHotEncoder(), categorical_ix)
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# , ('num', MinMaxScaler(), numerical_ix)]
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# col_transform = ColumnTransformer(transformers = t
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# , remainder='passthrough')
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# Create Pipeline object
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pipe = Pipeline([
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('pre', MinMaxScaler()),
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#('pre', col_transform),
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('fs', fs),
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#('clf', LogisticRegression(**rs))])
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('clf', estimator)])
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# Define GridSearchCV
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gscv_fs = GridSearchCV(pipe
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, param_gridLd
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, cv = cv_method
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, scoring = mcc_score_fn
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, refit = 'mcc'
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, verbose = 1
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, return_train_score = True
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, **njobs)
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gscv_fs.fit(input_df, target)
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###############################################################
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gscv_fs.best_params_
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gscv_fs.best_score_
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# Training best score corresponds to the max of the mean_test<score>
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train_bscore = round(gscv_fs.best_score_, 2); train_bscore
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print('\nTraining best score (MCC):', train_bscore)
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gscv_fs.cv_results_['mean_test_mcc']
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round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
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check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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, round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
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check_train_score = np.nanmax(check_train_score)
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# Training results
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gscv_tr_resD = gscv_fs.cv_results_
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mod_refit_param = gscv_fs.refit
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# sanity check
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if train_bscore == check_train_score:
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print('\nVerified training score (MCC):', train_bscore )
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else:
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print('\nTraining score could not be internatlly verified. Please check training results dict')
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# Blind test: REAL check!
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#tp = gscv_fs.predict(X_bts)
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tp = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
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############
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# info extraction
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############
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# gives input vals??
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gscv_fs._check_n_features
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# gives gscv params used
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gscv_fs._get_param_names()
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# gives ??
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gscv_fs.best_estimator_
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gscv_fs.best_params_ # gives best estimator params as a dict
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gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
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gscv_fs.best_estimator_.named_steps['fs'].get_support()
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gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
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#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
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###############################################################################
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#============
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# FS results
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#============
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# Now get the features out
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all_features = gscv_fs.feature_names_in_
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n_all_features = gscv_fs.n_features_in_
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#all_features = gsfit.feature_names_in_
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sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
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# get model name
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model_name = gscv_fs.best_estimator_.named_steps['clf']
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b_model_params = gscv_fs.best_params_
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print('\n========================================'
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, '\nRunning model:'
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, '\nModel name:', model_name
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, '\n==============================================='
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, '\nRunning feature selection with RFECV for model'
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, '\nTotal no. of features in model:', len(all_features)
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, '\nThese are:\n', all_features, '\n\n'
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, '\nNo of features for best model: ', n_sf
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, '\nThese are:', sel_features, '\n\n'
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, '\nBest Model hyperparams:', b_model_params
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)
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###############################################################################
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############################## OUTPUT #########################################
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###############################################################################
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#=========================
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# Blind test: BTS results
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#=========================
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# Build the final results with all scores for a feature selected model
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#bts_predict = gscv_fs.predict(X_bts)
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bts_predict = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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# Diff b/w train and bts test scores
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train_test_diff = train_bscore - bts_mcc_score
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print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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# create a dict with all scores
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lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
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#'bts_mcc':None
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'bts_fscore':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_btsD
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#lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
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lr_btsD
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#===========================
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# Add FS related model info
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#===========================
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model_namef = str(model_name)
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# FIXME: doesn't tell you which it has chosen
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fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
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all_featuresL = list(all_features)
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fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
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fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
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sel_featuresf = list(sel_features)
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n_sf = int(n_sf)
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output_modelD = {'model_name': model_namef
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, 'model_refit_param': mod_refit_param
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, 'Best_model_params': b_model_params
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, 'n_all_features': n_all_features
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, 'fs_method': fs_methodf
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, 'fs_res_array': fs_res_arrayf
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, 'fs_res_array_rank': fs_res_array_rankf
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, 'all_feature_names': all_featuresL
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, 'n_sel_features': n_sf
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, 'sel_features_names': sel_featuresf}
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output_modelD
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#========================================
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# Update output_modelD with bts_results
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#========================================
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output_modelD.update(lr_btsD)
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output_modelD
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output_modelD['train_score (MCC)'] = train_bscore
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output_modelD['bts_mcc'] = bts_mcc_score
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output_modelD['train_bts_diff'] = round(train_test_diff,2)
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print(output_modelD)
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return(output_modelD)
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307
UQ_FS_mixed_eg.py
Normal file
307
UQ_FS_mixed_eg.py
Normal file
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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 Sat May 21 02:52:36 2022
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@author: tanu
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"""
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#######################################################
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# determine categorical and numerical features
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numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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# %% begin stupid
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stupid=OneHotEncoder()
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stupid.fit(X[categorical_ix])
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stupid_thing = stupid.get_feature_names()
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horrid = (list(stupid_thing) + list(numerical_ix))
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asdfasdf = pd.Index(horrid)
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asdfasdf[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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col_transform.get_param_names()['transformers']
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len(stupid.get_feature_names())
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len(numerical_ix)
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# cat_trans = Pipeline(steps=[('onehot',OneHotEncoder(), categorical_ix)])
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# num_trans = Pipeline(steps=[('num', MinMaxScaler(), numerical_ix)])
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# pre_p = ColumnTransformer(transformers = [('num', num_trans, numerical_ix),
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# ('cat', cat_trans, categorical_ix)
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# ]
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# annoying = Pipeline([('preprocessor', pre_p),('clf', LogisticRegression())])
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# fukkit = GridSearchCV(annoying
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# , search_space
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# , cv = cv
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# , scoring = mcc_score_fn
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# , refit = 'mcc'
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# , verbose = 1
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# , return_train_score = True
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# , **njobs)
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# fukkit.fit(X, y)
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# fukkit.best_params_
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# fukkit.best_score_
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# end stupid
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#%%
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pipe = Pipeline([
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#('pre', MinMaxScaler())
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('pre', col_transform)
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = 10, scoring = 'matthews_corrcoef'))
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, ('clf', LogisticRegression(**rs))])
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#########################################################
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#cv = rskf_cv
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cv = skf_cv
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# my data: Feature Selelction + GridSearch CV + Pipeline
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search_space = [
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{ 'fs__estimator': [LogisticRegression(**rs)]
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, 'fs__min_features_to_select': [0,1]
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,'fs__cv': [rskf_cv]
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},
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{
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#'clf': [LogisticRegression()],
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#'clf__C': np.logspace(0, 4, 10),
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'clf__C': [1],
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'clf__max_iter': [100],
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'clf__penalty': ['l1', 'l2'],
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'clf__solver': ['saga']
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},
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{
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#'clf': [LogisticRegression()],
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#'clf__C': np.logspace(0, 4, 10),
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'clf__C': [2, 2.5],
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'clf__max_iter': [100],
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'clf__penalty': ['l1', 'l2'],
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'clf__solver': ['saga']
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},
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#{'clf': [RandomForestclf(n_estimators=100)],
|
||||
# 'clf__max_depth': [5, 10, None]},
|
||||
#{'clf': [KNeighborsclf()],
|
||||
# 'clf__n_neighbors': [3, 7, 11],
|
||||
# 'clf__weights': ['uniform', 'distance']
|
||||
#}
|
||||
]
|
||||
|
||||
gscv_fs = GridSearchCV(pipe
|
||||
, search_space
|
||||
, cv = cv
|
||||
, scoring = mcc_score_fn
|
||||
, refit = 'mcc'
|
||||
, verbose = 1
|
||||
, return_train_score = True
|
||||
, **njobs)
|
||||
gscv_fs.fit(X, y)
|
||||
#Fitting 10 folds for each of 8 candidates, totalling 80 fits
|
||||
# QUESTION: HOW??
|
||||
gscv_fs.best_params_
|
||||
gscv_fs.best_score_
|
||||
|
||||
##### CRAP
|
||||
gscv_fs.get_params()['transformers']
|
||||
|
||||
##### END CRAP
|
||||
|
||||
|
||||
# Training best score corresponds to the max of the mean_test<score>
|
||||
train_bscore = round(gscv_fs.best_score_, 2); train_bscore
|
||||
print('\nTraining best score (MCC):', train_bscore)
|
||||
gscv_fs.cv_results_['mean_test_mcc']
|
||||
round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
|
||||
|
||||
check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
|
||||
, round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
|
||||
|
||||
check_train_score = np.nanmax(check_train_score)
|
||||
|
||||
# Training results
|
||||
gscv_tr_resD = gscv_fs.cv_results_
|
||||
mod_refit_param = gscv_fs.refit
|
||||
|
||||
# sanity check
|
||||
if train_bscore == check_train_score:
|
||||
print('\nVerified training score (MCC):', train_bscore )
|
||||
else:
|
||||
print('\nTraining score could not be internatlly verified. Please check training results dict')
|
||||
|
||||
# Blind test: REAL check!
|
||||
tp = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, tp),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, tp),2))
|
||||
|
||||
############
|
||||
# info extraction
|
||||
############
|
||||
# gives input vals??
|
||||
gscv_fs._check_n_features
|
||||
|
||||
# gives gscv params used
|
||||
gscv_fs._get_param_names()
|
||||
|
||||
# gives ??
|
||||
gscv_fs.best_estimator_
|
||||
gscv_fs.best_params_ # gives best estimator params as a dict
|
||||
gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
|
||||
gscv_fs.best_estimator_.named_steps['fs'].get_support()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
|
||||
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
|
||||
gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
|
||||
#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
|
||||
|
||||
###############################################################################
|
||||
#============
|
||||
# FS results
|
||||
#============
|
||||
# Now get the features out
|
||||
all_features = gscv_fs.feature_names_in_
|
||||
n_all_features = gscv_fs.n_features_in_
|
||||
#all_features = gsfit.feature_names_in_
|
||||
|
||||
sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
|
||||
n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
|
||||
|
||||
# get model name
|
||||
model_name = gscv_fs.best_estimator_.named_steps['clf']
|
||||
b_model_params = gscv_fs.best_params_
|
||||
|
||||
print('\n========================================'
|
||||
, '\nRunning model:'
|
||||
, '\nModel name:', model_name
|
||||
, '\n==============================================='
|
||||
, '\nRunning feature selection with RFECV for model'
|
||||
, '\nTotal no. of features in model:', len(all_features)
|
||||
, '\nThese are:\n', all_features, '\n\n'
|
||||
, '\nNo of features for best model: ', n_sf
|
||||
, '\nThese are:', sel_features, '\n\n'
|
||||
, '\nBest Model hyperparams:', b_model_params
|
||||
)
|
||||
|
||||
###############################################################################
|
||||
############################## OUTPUT #########################################
|
||||
###############################################################################
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
bts_predict = gscv_fs.predict(X_bts)
|
||||
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
|
||||
bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
|
||||
|
||||
# Diff b/w train and bts test scores
|
||||
train_test_diff = train_bscore - bts_mcc_score
|
||||
print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
|
||||
|
||||
|
||||
# create a dict with all scores
|
||||
lr_btsD = {#'best_model': list(gscv_lr_fit_be_mod.items())
|
||||
#'bts_mcc':None
|
||||
'bts_fscore':None
|
||||
, 'bts_precision':None
|
||||
, 'bts_recall':None
|
||||
, 'bts_accuracy':None
|
||||
, 'bts_roc_auc':None
|
||||
, 'bts_jaccard':None}
|
||||
|
||||
|
||||
lr_btsD
|
||||
#lr_btsD['bts_mcc'] = bts_mcc_score
|
||||
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
|
||||
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
|
||||
lr_btsD
|
||||
|
||||
#===========================
|
||||
# Add FS related model info
|
||||
#===========================
|
||||
model_namef = str(model_name)
|
||||
# FIXME: doesn't tell you which it has chosen
|
||||
fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
|
||||
all_featuresL = list(all_features)
|
||||
fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
|
||||
fs_res_array_rankf = list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)
|
||||
sel_featuresf = list(sel_features)
|
||||
n_sf = int(n_sf)
|
||||
|
||||
output_modelD = {'model_name': model_namef
|
||||
, 'model_refit_param': mod_refit_param
|
||||
, 'Best_model_params': b_model_params
|
||||
, 'n_all_features': n_all_features
|
||||
, 'fs_method': fs_methodf
|
||||
, 'fs_res_array': fs_res_arrayf
|
||||
, 'fs_res_array_rank': fs_res_array_rankf
|
||||
, 'all_feature_names': all_featuresL
|
||||
, 'n_sel_features': n_sf
|
||||
, 'sel_features_names': sel_featuresf}
|
||||
output_modelD
|
||||
|
||||
#========================================
|
||||
# Update output_modelD with bts_results
|
||||
#========================================
|
||||
output_modelD.update(lr_btsD)
|
||||
output_modelD
|
||||
|
||||
output_modelD['train_score (MCC)'] = train_bscore
|
||||
output_modelD['bts_mcc'] = bts_mcc_score
|
||||
output_modelD['train_bts_diff'] = round(train_test_diff,2)
|
||||
output_modelD
|
||||
|
||||
class NpEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, np.integer):
|
||||
return int(obj)
|
||||
if isinstance(obj, np.floating):
|
||||
return float(obj)
|
||||
if isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
return super(NpEncoder, self).default(obj)
|
||||
|
||||
json.dumps(output_modelD, cls=NpEncoder)
|
||||
|
||||
#========================================
|
||||
# Write final output file
|
||||
# https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file
|
||||
#========================================
|
||||
#output final dict as a json
|
||||
outFile = 'LR_FS.json'
|
||||
with open(outFile, 'w') as f:
|
||||
f.write(json.dumps(output_modelD,cls=NpEncoder))
|
||||
|
||||
# read json
|
||||
file = 'LR_FS.json'
|
||||
with open(file, 'r') as f:
|
||||
data = json.load(f)
|
||||
##############################################################################
|
||||
|
||||
|
|
@ -276,13 +276,24 @@ all_df_wtgt.shape
|
|||
#TODO: A
|
||||
|
||||
#%% Data
|
||||
#X = all_df_wtgt[numerical_FN+categorical_FN]
|
||||
X = all_df_wtgt[numerical_FN]
|
||||
y = all_df_wtgt['dst_mode']
|
||||
#------
|
||||
# X
|
||||
#------
|
||||
X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
#X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
#X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
#------
|
||||
# y
|
||||
#------
|
||||
y = all_df_wtgt['dst_mode'] # training data y
|
||||
y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
#Blind test data {same format}
|
||||
X_bts = blind_test_df[numerical_FN]
|
||||
y_bts = blind_test_df['dst_mode']
|
||||
#X_bts = blind_test_df[numerical_FN]
|
||||
#X_bts = blind_test_df[numerical_FN + categorical_FN]
|
||||
#y_bts = blind_test_df['dst_mode']
|
||||
|
||||
X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue