def setUpClass(cls):
cls.sbrl_inst = BRLC(min_rule_len=1,
max_rule_len=2,
iterations=10000,
n_chains=3)
cls.input_data = pd.read_csv('skater/tests/data/sample_data.csv')
# data transformation and cleaning ...
cls.input_data["Sex"] = cls.input_data["Sex"].astype('category')
cls.input_data["Sex_Encoded"] = cls.input_data["Sex"].cat.codes
cls.input_data["Embarked"] = cls.input_data["Embarked"].astype(
'category')
cls.input_data["Embarked_Encoded"] = cls.input_data[
"Embarked"].cat.codes
cls.input_data = cls.input_data.drop(
['Ticket', 'Cabin', 'Name', 'Sex', 'Embarked'], axis=1)
# Remove NaN values
cls.input_data = cls.input_data.dropna()
cls.y = cls.input_data['Survived']
cls.input_data = cls.input_data.drop(['Survived'], axis=1)
# Train a model
cls.sbrl_inst.fit(cls.input_data[1:50],
cls.y[1:50],
undiscretize_feature_list=[
"PassengerId", "Pclass", "SibSp", "Parch",
"Sex_Encoded", "Embarked_Encoded"
])
def setUp(self):
self.sbrl_inst = BRLC(min_rule_len=1,
max_rule_len=2,
iterations=10000,
n_chains=3)
self.input_data = pd.read_csv('skater/tests/data/sample_data.csv')
# data transformation and cleaning ...
self.input_data["Sex"] = self.input_data["Sex"].astype('category')
self.input_data["Sex_Encoded"] = self.input_data["Sex"].cat.codes
self.input_data["Embarked"] = self.input_data["Embarked"].astype(
'category')
self.input_data["Embarked_Encoded"] = self.input_data[
"Embarked"].cat.codes
self.input_data = self.input_data.drop(
['Ticket', 'Cabin', 'Name', 'Sex', 'Embarked'], axis=1)
# Remove NaN values
self.input_data = self.input_data.dropna()
self.y = self.input_data['Survived']
self.input_data = self.input_data.drop(['Survived'], axis=1)
class TestRuleList(unittest.TestCase):
def setUp(self):
self.sbrl_inst = BRLC(min_rule_len=1,
max_rule_len=2,
iterations=10000,
n_chains=3)
self.input_data = pd.read_csv('skater/tests/data/sample_data.csv')
# data transformation and cleaning ...
self.input_data["Sex"] = self.input_data["Sex"].astype('category')
self.input_data["Sex_Encoded"] = self.input_data["Sex"].cat.codes
self.input_data["Embarked"] = self.input_data["Embarked"].astype(
'category')
self.input_data["Embarked_Encoded"] = self.input_data[
"Embarked"].cat.codes
self.input_data = self.input_data.drop(
['Ticket', 'Cabin', 'Name', 'Sex', 'Embarked'], axis=1)
# Remove NaN values
self.input_data = self.input_data.dropna()
self.y = self.input_data['Survived']
self.input_data = self.input_data.drop(['Survived'], axis=1)
def test_discretizer(self):
new_df = self.sbrl_inst.discretizer(self.input_data,
column_list=["Age"])
self.assertEquals(new_df["Age_q_label"].shape[0] > 0, True)
def test_model_build(self):
self.sbrl_inst.fit(self.input_data[1:50],
self.y[1:50],
undiscretize_feature_list=[
"PassengerId", "Pclass", "SibSp", "Parch",
"Sex_Encoded", "Embarked_Encoded"
])
new_data = self.sbrl_inst.discretizer(self.input_data,
column_list=["Age", "Fare"])
result_score = self.sbrl_inst.predict_proba(new_data)
result_labels = self.sbrl_inst.predict(new_data)
# make sure shape of the dataframe is as expected
self.assertEquals(result_score.shape, (77, 2))
self.assertEquals(result_labels[1].shape, (77, ))
generated_labels = np.unique(result_labels[1])
expected_labels = np.array([0, 1])
self.assertEquals(np.array_equal(generated_labels, expected_labels),
True)
def test_model_save_load(self):
self.sbrl_inst.fit(self.input_data[1:50],
self.y[1:50],
undiscretize_feature_list=[
"PassengerId", "Pclass", "SibSp", "Parch",
"Sex_Encoded", "Embarked_Encoded"
])
self.sbrl_inst.save_model("test.pkl", compress=True)
# Explicitly assigning the model instance to 'None' to validate loading of persisted model
# Care is advised when handing the model instance, it might make the model unstable
self.sbrl_inst.model = None
self.assertEquals(self.sbrl_inst.model is None, True)
self.sbrl_inst.load_model("test.pkl")
self.assertEquals(self.sbrl_inst.model is not None, True)
def test_model_output(self):
self.sbrl_inst.fit(self.input_data[1:50],
self.y[1:50],
undiscretize_feature_list=[
"PassengerId", "Pclass", "SibSp", "Parch",
"Sex_Encoded", "Embarked_Encoded"
])
result = self.sbrl_inst.access_learned_rules('23:25')
self.assertEquals(len(result), 2)
@unittest.skip(
"Support for computing validation curve for SBRL is still under development"
)
def test_validation(self):
param_range = [3, 4]
train_scores, test_scores = compute_validation_curve(
self.sbrl_inst,
n_folds=2,
x=self.input_data,
y=self.y,
param_name="rule_minlen",
param_range=param_range)
self.assertEquals(train_scores.shape[0], 2)
self.assertEquals(test_scores.shape[0], 2)