class TestReportGeneration(TestCase):
def setUp(self):
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data,
iris.target,
test_size=0.30,
random_state=0)
model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_score = model.predict_proba(X_test)
target_names = ['setosa', 'versicolor', 'virginica']
feature_names = range(4)
model_name = 'a model'
self.results = ClassifierEvaluator(estimator=model,
y_true=y_test,
y_pred=y_pred,
y_score=y_score,
feature_names=feature_names,
target_names=target_names,
estimator_name=model_name)
def test_can_create_report(self):
self.results.make_report()
# 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 = RandomForestClassifier()
classifier = classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_score = classifier.predict_proba(X_test)
feature_list = range(4)
target_names = ['setosa', 'versicolor', 'virginica']
# Create a trained model instance
ce = ClassifierEvaluator(classifier,
y_test,
y_pred,
y_score,
feature_list,
target_names,
estimator_name='super awesome SVC')
report = ce.make_report()
# this will automativally render in Jupyter, or you can do report.save('/path')
report