def predict(ver, predict_ver, alike_metrics):
predictor_rep = PredictorRepository(predict_ver, ver)
training_m = Metrics_Origin(ver, METRICS_DIR)
evaluate_m = Metrics_Origin(predict_ver, METRICS_DIR)
ens_analyzer = AUCAnalyzer(predict_ver, 'ENS', TARGET)
for i in tqdm(range(ITER)):
# NML MODEL
predictor = predictor_rep.get_predictor('ENS', PRED_TYPE)
if predictor is None:
print(' predictor has not found, type: ' + PRED_TYPE)
return
# sm = RandomOverSampler(ratio='auto', random_state=random.randint(1,100))
# X_resampled, y_resampled = sm.fit_sample(training_m.product_df, training_m.fault)
X_resampled, y_resampled = training_m.product_df.as_matrix(
), training_m.fault.as_matrix()
nml_model = predictor.train_model(X_resampled, y_resampled)
ev_data, dv_data = evaluate_m.get_not_modified_df()
nml_value, _ = predictor.predict_ensemble_test_data(
nml_model, ev_data, dv_data, None)
# RFN MODEL
sm = RandomOverSampler(ratio='auto',
random_state=random.randint(1, 100))
X_resampled, y_resampled = sm.fit_sample(training_m.mrg_df,
training_m.fault)
rfn_model = predictor.train_model(X_resampled, y_resampled)
ev_data, dv_data = evaluate_m.get_modified_df()
mrg_value, _ = predictor.predict_ensemble_test_data(
rfn_model, ev_data, dv_data, None)
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
report_df[REPORT_COLUMNS].to_csv('df.csv')
if report_df is not None:
ens_analyzer.set_report_df(report_df[REPORT_COLUMNS])
ens_analyzer.calculate()
ens_analyzer.analyze_predict_result()
# export report
ens_df = ens_analyzer.calculate_average(ITER)
ens_analyzer.export(target_sw=TARGET, df=ens_df, predictor_type=PRED_TYPE)
ens_df = ens_analyzer.calculate_num_report_averge(ITER)
ens_analyzer.export_count_report(target_sw=TARGET,
df=ens_df,
predictor_type=PRED_TYPE)
def predict(ver, predict_ver, alike_metrics):
training_m = Metrics_Origin(ver, METRICS_DIR)
evaluate_m = Metrics_Origin(predict_ver, METRICS_DIR)
ens_analyzer = Analyzer(predict_ver, 'ENS')
predictor_rep = PredictorRepository(predict_ver, ver)
for i in tqdm(range(ITER)):
# NML MODEL
predictor = predictor_rep.get_predictor('ENS', PRED_TYPE)
if predictor is None:
print(' predictor has not found, type: ' + PRED_TYPE)
return
sm = RandomOverSampler(ratio='auto',
random_state=random.randint(1, 100))
X_resampled, y_resampled = sm.fit_sample(training_m.product_df,
training_m.fault)
model = predictor.train_model(X_resampled, y_resampled)
ev_data, dv_data = evaluate_m.get_not_modified_df()
nml_value, _ = predictor.predict_ensemble_proba(
model, ev_data, dv_data, None)
# DST MODEL
predictor2 = predictor_rep.get_predictor('ENS', PRED_TYPE2)
if predictor2 is None:
print(' predictor has not found, type: ' + PRED_TYPE2)
return
sm = RandomOverSampler(ratio='auto',
random_state=random.randint(1, 100))
X_resampled, y_resampled = sm.fit_sample(training_m.mrg_df,
training_m.fault)
model = predictor2.train_model(X_resampled, y_resampled)
ev_data, dv_data = evaluate_m.get_modified_df()
mrg_value, _ = predictor2.predict_ensemble_proba(
model, ev_data, dv_data, None)
predictor2.set_is_new_df(evaluate_m.isNew)
predictor2.set_is_modified_df(evaluate_m.isModified)
report_df = predictor2.export_report(predict_ver)
if report_df is not None:
ens_analyzer.set_report_df(report_df[REPORT_COLUMNS])
ens_analyzer.calculate()
# export report
ens_df = ens_analyzer.calculate_average(ITER)
predictor_type_name = "{0}{1}".format(PRED_TYPE, PRED_TYPE2)
ens_analyzer.export(target_sw=TARGET, df=ens_df, predictor_type=PRED_TYPE)
ens_analyzer.export_accum_df(target_sw=TARGET)