def test_default_template_no_feature_importances(self):
#Load a model
model = joblib.load(
os.path.join(models_path,
'classifier_without_feature_importances_model.pkl'))
#Load y_true
y_true = joblib.load(
os.path.join(models_path,
'classifier_without_feature_importances_y_true.pkl'))
#Load y_pred
y_pred = joblib.load(
os.path.join(models_path,
'classifier_without_feature_importances_y_pred.pkl'))
#Load y_score
y_score = joblib.load(
os.path.join(models_path,
'classifier_without_feature_importances_y_score.pkl'))
#Load X
X = joblib.load(
os.path.join(models_path,
'classifier_without_feature_importances_x.pkl'))
#Generate feature list
feature_list = range(X.shape[1]) #FIND A BETTER WAY TO DO THIS
#Generate target names
target_names = range(len(set(y_true))) #FIND A BETTER WAY
#Report name
name = 'test_report_without_feature_importances'
rg = ReportGenerator()
report = rg.generate_report(model=model,
y_true=y_true,
y_pred=y_pred,
y_score=y_score,
feature_list=feature_list,
target_names=target_names,
name=name)
#Load dummy report
dummy_report_file = open(
os.path.join(
models_path,
'classiffier_without_feature_importances_report.html'))
dummy_report = dummy_report_file.read()
#Compare report
self.assertEqual(report, dummy_report)
def test_default_template_no_feature_importances(self):
#Load a model
model = joblib.load(os.path.join(models_path,'classifier_without_feature_importances_model.pkl'))
#Load y_true
y_true = joblib.load(os.path.join(models_path,'classifier_without_feature_importances_y_true.pkl'))
#Load y_pred
y_pred = joblib.load(os.path.join(models_path,'classifier_without_feature_importances_y_pred.pkl'))
#Load y_score
y_score = joblib.load(os.path.join(models_path,'classifier_without_feature_importances_y_score.pkl'))
#Load X
X = joblib.load(os.path.join(models_path,'classifier_without_feature_importances_x.pkl'))
#Generate feature list
feature_list = range(X.shape[1]) #FIND A BETTER WAY TO DO THIS
#Generate target names
target_names = range(len(set(y_true))) #FIND A BETTER WAY
#Report name
name = 'test_report_without_feature_importances'
rg = ReportGenerator()
report = rg.generate_report(model=model, y_true=y_true, y_pred=y_pred, y_score=y_score, feature_list=feature_list, target_names=target_names, name=name)
#Load dummy report
dummy_report_file = open(os.path.join(models_path, 'classiffier_without_feature_importances_report.html'))
dummy_report = dummy_report_file.read()
#Compare report
self.assertEqual(report, dummy_report)
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.5,
random_state=0)
# Learn to predict each class against the other
classifier = OneVsRestClassifier(
svm.SVC(kernel='linear', probability=True, random_state=random_state))
classifier = classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_score = classifier.decision_function(X_test)
feature_list = range(4)
target_names = ['setosa', 'versicolor', 'virginica']
#Create a trained model instance
tm = ClassificationModelResults(classifier,
y_test,
y_pred,
y_score,
feature_list,
target_names,
model_name='sample_model_report')
#Generate report
report_gen = ReportGenerator(savepath='/Users/Edu/Desktop')
report_gen(tm)
svc.fit(X_train, y_train) y_true = y_test y_pred = svc.predict(X_test) y_score = svc.predict_proba(X_test) #Pickle model joblib.dump(svc, os.path.join(models_path, 'classifier_without_feature_importances_model.pkl')) #Pickle y_true joblib.dump(y_true, os.path.join(models_path, 'classifier_without_feature_importances_y_true.pkl')) #Pickle y_pred joblib.dump(y_pred, os.path.join(models_path, 'classifier_without_feature_importances_y_pred.pkl')) #Pickle y_score joblib.dump(y_score, os.path.join(models_path, 'classifier_without_feature_importances_y_score.pkl')) #Pickle X joblib.dump(X, os.path.join(models_path, 'classifier_without_feature_importances_x.pkl')) #Generate feature list feature_list = range(X.shape[1]) #Generate target names target_names = range(len(set(y))) #Name name = 'test_report_without_feature_importances' #Generate report rg = ReportGenerator() report = rg.generate_report(model=svc, y_true=y_true, y_pred=y_pred, y_score=y_score, feature_list=feature_list, target_names=target_names, name=name) #Save report to file report_file = open(os.path.join(models_path, 'classiffier_without_feature_importances_report.html'), 'w') report_file.write(report) report_file.close()
forest.fit(X_train, y_train) y_true = y_test y_pred = forest.predict(X_test) y_score = forest.predict_proba(X_test) #Pickle model joblib.dump(forest, os.path.join(models_path, 'classifier_with_feature_importances_model.pkl')) #Pickle y_true joblib.dump(y_true, os.path.join(models_path, 'classifier_with_feature_importances_y_true.pkl')) #Pickle y_pred joblib.dump(y_pred, os.path.join(models_path, 'classifier_with_feature_importances_y_pred.pkl')) #Pickle y_score joblib.dump(y_score, os.path.join(models_path, 'classifier_with_feature_importances_y_score.pkl')) #Pickle X joblib.dump(X, os.path.join(models_path, 'classifier_with_feature_importances_x.pkl')) #Generate feature list feature_list = range(X.shape[1]) #Generate target names target_names = range(len(set(y))) #Name name = 'test_report' #Generate report rg = ReportGenerator() report = rg.generate_report(model=forest, y_true=y_true, y_pred=y_pred, y_score=y_score, feature_list=feature_list, target_names=target_names, name=name) #Save report to file report_file = open(os.path.join(models_path, 'classiffier_with_feature_importances_report.html'), 'w') report_file.write(report) report_file.close()
