def test_discrete_variables(self):
data = Table("zoo")
booster = CatGBClassifier()
cv = CrossValidation(k=3)
results = cv(data, [booster])
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 0.99)
data = Table("titanic")
booster = CatGBClassifier()
cv = CrossValidation(k=3)
results = cv(data, [booster])
ca = CA(results)
self.assertGreater(ca, 0.75)
self.assertLess(ca, 0.99)
def test_GBTrees(self):
booster = CatGBClassifier()
cv = CrossValidation(k=3)
results = cv(self.iris, [booster])
ca = CA(results)
self.assertGreater(ca, 0.9)
self.assertLess(ca, 0.99)
def test_missing_values(self):
data = Table("heart_disease")
booster = CatGBClassifier()
cv = CrossValidation(k=3)
results = cv(data, [booster])
ca = CA(results)
self.assertGreater(ca, 0.8)
self.assertLess(ca, 0.99)
def test_set_params(self):
booster = CatGBClassifier(n_estimators=42, max_depth=4)
self.assertEqual(booster.params["n_estimators"], 42)
self.assertEqual(booster.params["max_depth"], 4)
model = booster(self.iris)
params = model.cat_model.get_params()
self.assertEqual(params["n_estimators"], 42)
self.assertEqual(params["max_depth"], 4)
def test_retain_x(self):
data = Table("heart_disease")
X = data.X.copy()
booster = CatGBClassifier()
model = booster(data)
model(data)
np.testing.assert_array_equal(data.X, X)
self.assertEqual(data.X.dtype, X.dtype)
def test_predict_numpy(self):
booster = CatGBClassifier()
model = booster(self.iris)
pred = model(self.iris.X)
self.assertEqual(pred.shape, (len(self.iris), ))
prob = model(self.iris.X, model.Probs)
self.assertGreaterEqual(prob.all().all(), 0)
self.assertLessEqual(prob.all().all(), 1)
self.assertAlmostEqual(prob.sum(), len(self.iris))
def test_predict_single_instance(self):
booster = CatGBClassifier()
model = booster(self.iris)
for ins in self.iris:
model(ins)
prob = model(ins, model.Probs)
self.assertGreaterEqual(prob.all(), 0)
self.assertLessEqual(prob.all(), 1)
self.assertAlmostEqual(prob.sum(), 1, 3)
def test_predict_sparse(self):
sparse_data = self.iris.to_sparse()
booster = CatGBClassifier()
model = booster(sparse_data)
pred = model(sparse_data)
self.assertEqual(pred.shape, (len(sparse_data), ))
prob = model(sparse_data, model.Probs)
self.assertGreaterEqual(prob.all().all(), 0)
self.assertLessEqual(prob.all().all(), 1)
self.assertAlmostEqual(prob.sum(), len(sparse_data))
def test_default_parameters_cls(self):
data = Table("heart_disease")
booster = CatGBClassifier()
model = booster(data)
params = model.cat_model.get_all_params()
self.assertEqual(self.editor.n_estimators, 100)
self.assertEqual(params["iterations"], 1000)
self.assertEqual(params["depth"], self.editor.max_depth)
self.assertEqual(params["l2_leaf_reg"], self.editor.lambda_)
self.assertEqual(params["rsm"], self.editor.colsample_bylevel)
self.assertEqual(self.editor.learning_rate, 0.3)
def test_doesnt_modify_data(self):
# catgb is called with force-unlocked table, so let us (attempt to)
# test it doesn't actually change it
data = Table("iris")
with data.unlocked():
data[0, 0] = 0
data[1, 0] = np.nan
data[:, 1] = 0
data[:, 2] = np.nan
data.Y[0] = np.nan
x, y = data.X.copy(), data.Y.copy()
booster = CatGBClassifier()
model = booster(data)
model(data)
np.testing.assert_equal(data.X, x)
np.testing.assert_equal(data.Y, y)
with data.unlocked():
data = data.to_sparse()
x = data.X.copy()
booster = CatGBClassifier()
model = booster(data)
model(data)
np.testing.assert_equal(data.X.data, x.data)
def test_scorer(self):
booster = CatGBClassifier()
self.assertIsInstance(booster, Scorer)
booster.score(self.iris)
