def exp(v1, v2):
version1 = Metrics_Origin(v1, METRICS_DIR)
version2 = Metrics_Origin(v2, METRICS_DIR)
print(v1 + '-' + v2)
alike_metrics = st.compare_two_versions(version1, version2)
print(alike_metrics)
predict(v1, v2, alike_metrics)
def exp(model, metrics_dir):
metrics_dir = '/Users/{}/Dropbox/STUDY/{}/Derby/all'\
.format(ENV, model.dir_name)
ex01 = Ex01(model, metrics_dir)
ex01.METRICS_DIR = metrics_dir
v1 = model.curr_version
v2 = model.pre_version
version1 = Metrics_Origin(v1, metrics_dir)
version2 = Metrics_Origin(v2, metrics_dir)
print(v1 + '-' + v2)
alike_metrics = st.compare_two_versions(version1, version2)
print(alike_metrics)
ex01.predict(v1, v2, alike_metrics)
def invest_distribution(self, model1, model2):
# ver, predict_ver = self.model.previous_version, self.model.final_version
# pre_model = mc.retrieve_model(self.model.sw_name, self.model.final_version)
# predictor_rep = PredictorRepository(predict_ver, self.model)
training_m = Metrics(model1.final_version, self.METRICS_DIR, model1)
evaluate_m = Metrics(model2.final_version, self.METRICS_DIR, model2)
alike_metrics = st.compare_two_versions(training_m, evaluate_m)
print(alike_metrics)
print(type(alike_metrics))
print(training_m.mrg_df[alike_metrics])
print(evaluate_m.mrg_df[alike_metrics])
raise Exception
msg = "Sw: {}:{}-{}:{}, alike_metrics: {}"\
.format(model1.sw_name, model1.final_version, model2.sw_name, model2.final_version, alike_metrics)
print(msg)
def predict(self, box_plotting=False, result_exporting=False):
self.model2.set_param_dict(self.PRED_TYPE)
self.__get_logger()
# (sw_name, version)を２つ受け取り、それぞれの全てのメトリクスを取得する。
training_m = Metrics(self.model1.final_version, self.METRICS_DIR,
self.model1)
evaluate_m = Metrics(self.model2.final_version, self.METRICS_DIR,
self.model2)
# alike_metricsを取得する。
self.alike_metrics = st.compare_two_versions(training_m, evaluate_m)
# alike_metricsでフィルターしたメトリクスを作成する。
# フィルターをかけないモデルとかけるモデルで予測精度を比較する。
ver, predict_ver = self.model1.final_version, self.model2.final_version
# pre_model = mc.retrieve_model(self.model.sw_name, self.model.final_version)
predictor_rep = PredictorRepository(predict_ver, self.model1)
# training_m = Metrics(ver, self.METRICS_DIR, pre_model)
# evaluate_m = Metrics(predict_ver, self.METRICS_DIR, self.model)
# self.alike_metrics = st.compare_two_versions(training_m, evaluate_m)
msg = "Sw: {}:{}-{}:{}, alike_metrics: {}"\
.format(self.model1.sw_name, self.model1.final_version, self.model2.sw_name, self.model2.final_version, self.alike_metrics)
self.report_logger.info(msg)
print(msg)
if predict_ver is None or self.TARGET is None:
self.error_logger.error('could not create AUCAnalyzer instance.\
predict_ver: {}, target: {}'.format(predict_ver, self.TARGET))
return
# nml_analyzer = AUCAnalyzer(predict_ver, 'NML', self.TARGET)
rfn_analyzer = AUCAnalyzer(predict_ver, 'RFN', self.TARGET)
dst_analyzer = AUCAnalyzer(predict_ver, 'DST', self.TARGET)
for i in range(self.ITER):
# RFN MODEL
predictor = predictor_rep.get_predictor('RFN', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
# sm = RandomOverSampler(ratio='auto', random_state=random.randint(1,100))
# X_resampled, y_resampled = sm.fit_sample( training_m.mrg_df, training_m.fault )
X_resampled, y_resampled = training_m.mrg_df.as_matrix(),\
training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
rfn_value, importance = predictor.predict_test_data(
model, evaluate_m.mrg_df, evaluate_m.fault,
self.TARGET + "-ex1rfn.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
rfn_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
rfn_analyzer.calculate()
rfn_analyzer.analyze_predict_result()
# DSTRIBUTION MODEL
predictor = predictor_rep.get_predictor('DST', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
alike_df = training_m.get_specific_df(self.alike_metrics)
# sm = RandomOverSampler(ratio='auto', random_state=random.randint(1,100))
# X_resampled, y_resampled = sm.fit_sample(alike_df, training_m.fault)
X_resampled, y_resampled = alike_df.as_matrix(),\
training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
alike_df = evaluate_m.get_specific_df(self.alike_metrics)
itg_value, importance = predictor.predict_test_data(
model, alike_df, evaluate_m.fault, self.TARGET + "-ex1itg.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
dst_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
dst_analyzer.calculate()
dst_analyzer.analyze_predict_result()
# print('feature inportance: {}'.format(importance))
# conducy mann whitneyu test
self.conduct_mh_test(rfn_analyzer, dst_analyzer, index=0)
self.conduct_mh_test(rfn_analyzer, dst_analyzer, index=2)
self.conduct_mh_test(rfn_analyzer, dst_analyzer, index=3)
# draw voxplot graph
if box_plotting:
# self.draw_result_boxplot(rfn_analyzer, itg_analyzer)
pass
# export report
# nml_df = nml_analyzer.calculate_average(self.ITER)
rfn_df = rfn_analyzer.calculate_average(self.ITER)
itg_df = dst_analyzer.calculate_average(self.ITER)
df = pd.concat([rfn_df, itg_df], ignore_index=True)
rfn_analyzer.export(target_sw=self.TARGET,
df=df,
predictor_type=self.PRED_TYPE) # どのanalyzerクラスでも良い
def predict_prob(self, box_plotting=True, result_exporting=True):
self.model.set_param_dict(self.PRED_TYPE)
self.__get_logger()
ver, predict_ver = self.model.previous_version, self.model.final_version
pre_model = mc.retrieve_model(self.model.sw_name,
self.model.final_version)
predictor_rep = PredictorRepository(pre_model, self.model)
training_m = Metrics(ver, self.METRICS_DIR, pre_model)
evaluate_m = Metrics(predict_ver, self.METRICS_DIR, self.model)
self.alike_metrics = st.compare_two_versions(training_m, evaluate_m)
msg = "Sw: {}, version: {}, alike_metrics: {}"\
.format(self.model.sw_name, predict_ver, self.alike_metrics)
self.report_logger.info(msg)
if predict_ver is None or self.TARGET is None:
self.error_logger.error('could not create AUCAnalyzer instance.\
predict_ver: {}, target: {}'.format(predict_ver, self.TARGET))
return
nml_analyzer = Analyzer(predict_ver, 'NML')
rfn_analyzer = Analyzer(predict_ver, 'RFN')
itg_analyzer = Analyzer(predict_ver, 'ITG')
# for i in tqdm(range(self.ITER)):
for i in range(self.ITER):
# NML MODEL
predictor = predictor_rep.get_predictor('NML', self.PRED_TYPE)
if predictor is None:
print(' predictor has not found, type: ' + self.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()
model = predictor.train_model(X_resampled, y_resampled)
nml_value, importance = predictor.predict_proba(
model, evaluate_m.product_df, evaluate_m.fault,
self.TARGET + "-ex1nml.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
nml_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
nml_analyzer.calculate()
# nml_analyzer.analyze_predict_result()
# RFN MODEL
predictor = predictor_rep.get_predictor('RFN', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
sm = RandomOverSampler(ratio='auto',
random_state=random.randint(1, 100))
X_resampled, y_resampled = sm.fit_sample(training_m.mrg_df,
training_m.fault)
# X_resampled, y_resampled = training_m.mrg_df.as_matrix(),\
# training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
rfn_value, importance = predictor.predict_proba(
model, evaluate_m.mrg_df, evaluate_m.fault,
self.TARGET + "-ex1rfn.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
rfn_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
rfn_analyzer.calculate()
# rfn_analyzer.analyze_predict_result()
# INTELLIGENCE MODEL
predictor = predictor_rep.get_predictor('ITG', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
alike_df = training_m.get_specific_df(self.alike_metrics)
sm = RandomOverSampler(ratio='auto',
random_state=random.randint(1, 100))
X_resampled, y_resampled = sm.fit_sample(alike_df,
training_m.fault)
# X_resampled, y_resampled = alike_df.as_matrix(),\
# training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
alike_df = evaluate_m.get_specific_df(self.alike_metrics)
rfn_value, importance = predictor.predict_proba(
model, alike_df, evaluate_m.fault, self.TARGET + "-ex1itg.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
itg_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
itg_analyzer.calculate()
def predict(self, box_plotting=True, result_exporting=True):
self.model.set_param_dict(self.PRED_TYPE)
self.__get_logger()
ver, predict_ver = self.model.previous_version, self.model.final_version
pre_model = mc.retrieve_model(self.model.sw_name,
self.model.final_version)
predictor_rep = PredictorRepository(pre_model, self.model)
training_m = Metrics(ver, self.METRICS_DIR, pre_model)
evaluate_m = Metrics(predict_ver, self.METRICS_DIR, self.model)
self.alike_metrics = st.compare_two_versions(training_m, evaluate_m)
msg = "Sw: {}, version: {}, alike_metrics: {}"\
.format(self.model.sw_name, predict_ver, self.alike_metrics)
self.report_logger.info(msg)
if predict_ver is None or self.TARGET is None:
self.error_logger.error('could not create AUCAnalyzer instance.\
predict_ver: {}, target: {}'.format(predict_ver, self.TARGET))
return
nml_analyzer = AUCAnalyzer(predict_ver, 'NML', self.TARGET)
rfn_analyzer = AUCAnalyzer(predict_ver, 'RFN', self.TARGET)
itg_analyzer = AUCAnalyzer(predict_ver, 'ITG', self.TARGET)
# nml_analyzer = Analyzer(predict_ver, 'NML')
# rfn_analyzer = Analyzer(predict_ver, 'RFN')
# itg_analyzer = Analyzer(predict_ver, 'ITG')
# acum_nml_report= pd.DataFrme([])
# acum_rfn_report= pd.DataFrme([])
# acum_intel_report= pd.DataFrme([])
# for i in tqdm(range(self.ITER)):
for i in range(self.ITER):
# NML MODEL
predictor = predictor_rep.get_predictor('NML', self.PRED_TYPE)
if predictor is None:
print(' predictor has not found, type: ' + self.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()
model = predictor.train_model(X_resampled, y_resampled)
nml_value, importance = predictor.predict_test_data(
model, evaluate_m.product_df, evaluate_m.fault,
self.TARGET + "-ex1nml.csv")
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
nml_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
nml_analyzer.calculate()
nml_analyzer.analyze_predict_result()
# RFN MODEL
predictor = predictor_rep.get_predictor('RFN', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
# sm = RandomOverSampler(ratio='auto', random_state=random.randint(1,100))
# X_resampled, y_resampled = sm.fit_sample( training_m.mrg_df, training_m.fault )
X_resampled, y_resampled = training_m.mrg_df.as_matrix(),\
training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
rfn_value, importance = predictor.predict_test_data(
model,
evaluate_m.mrg_df,
evaluate_m.fault,
self.TARGET + "-ex1rfn.csv",
threshold=0.6)
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
rfn_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
rfn_analyzer.calculate()
rfn_analyzer.analyze_predict_result()
# INTELLIGENCE MODEL
predictor = predictor_rep.get_predictor('ITG', self.PRED_TYPE)
assert predictor is not None,\
print(' predictor has not found, type: ' + self.PRED_TYPE)
alike_df = training_m.get_specific_df(self.alike_metrics)
# sm = RandomOverSampler(ratio='auto', random_state=random.randint(1,100))
# X_resampled, y_resampled = sm.fit_sample(alike_df, training_m.fault)
X_resampled, y_resampled = alike_df.as_matrix(),\
training_m.fault.as_matrix()
model = predictor.train_model(X_resampled, y_resampled)
alike_df = evaluate_m.get_specific_df(self.alike_metrics)
itg_value, importance = predictor.predict_test_data(
model,
alike_df,
evaluate_m.fault,
self.TARGET + "-ex1itg.csv",
threshold=0.6)
predictor.set_is_new_df(evaluate_m.isNew)
predictor.set_is_modified_df(evaluate_m.isModified)
report_df = predictor.export_report(predict_ver)
if report_df is not None:
itg_analyzer.set_report_df(report_df[self.REPORT_COLUMNS])
itg_analyzer.calculate()
itg_analyzer.analyze_predict_result()
print('feature inportance: {}'.format(importance))
# conducy mann whitneyu test
self.conduct_mh_test(rfn_analyzer, itg_analyzer, index=0)
self.conduct_mh_test(rfn_analyzer, itg_analyzer, index=2)
self.conduct_mh_test(rfn_analyzer, itg_analyzer, index=3)
# draw voxplot graph
if box_plotting:
self.draw_result_boxplot(rfn_analyzer, itg_analyzer)
# export report
nml_df = nml_analyzer.calculate_average(self.ITER)
rfn_df = rfn_analyzer.calculate_average(self.ITER)
itg_df = itg_analyzer.calculate_average(self.ITER)
df = pd.concat([nml_df, rfn_df, itg_df], ignore_index=True)
nml_analyzer.export(target_sw=self.TARGET,
df=df,
predictor_type=self.PRED_TYPE) # どのanalyzerクラスでも良い
nml_df = nml_analyzer.calculate_num_report_averge(self.ITER)
rfn_df = rfn_analyzer.calculate_num_report_averge(self.ITER)
itg_df = itg_analyzer.calculate_num_report_averge(self.ITER)
if result_exporting:
nml_analyzer.export_count_report(target_sw=self.TARGET,
df=nml_df,
predictor_type=self.PRED_TYPE)
rfn_analyzer.export_count_report(target_sw=self.TARGET,
df=rfn_df,
predictor_type=self.PRED_TYPE)
itg_analyzer.export_count_report(target_sw=self.TARGET,
df=itg_df,
predictor_type=self.PRED_TYPE)
