def test_fit_predict(self):
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
self.assertTrue(loss < 0.7)
def test_fit_predict(self):
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
loss_prev = None
for _ in range(3):
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
if loss_prev is not None:
self.assertTrue(loss + 0.001 < loss_prev)
loss_prev = loss
def test_reproduce_fit(self):
metric = Metric({"name": "logloss"})
prev_loss = None
for i in range(3):
model = LightgbmAlgorithm(self.params)
model.fit(self.X, self.y)
y_predicted = model.predict(self.X)
loss = metric(self.y, y_predicted)
if prev_loss is not None:
assert_almost_equal(prev_loss, loss)
prev_loss = loss
def test_copy(self):
# train model #1
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
# create model #2
lgb2 = LightgbmAlgorithm(self.params)
# model #2 is set to None, while initialized
self.assertTrue(lgb2.model is None)
# do a copy and use it for predictions
lgb2 = lgb.copy()
self.assertEqual(type(lgb), type(lgb2))
y_predicted = lgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
self.assertEqual(loss, loss2)
def test_reproduce_fit(self):
metric = Metric({"name": "logloss"})
prev_loss = None
for i in range(3):
model = LightgbmAlgorithm(self.params)
model.fit(self.X, self.y)
y_predicted = model.predict(self.X)
loss = metric(self.y, y_predicted)
if prev_loss is not None:
assert_almost_equal(prev_loss, loss)
prev_loss = loss
losses = []
for i in range(3):
params = copy.deepcopy(self.params)
params["seed"] = 1 + i
model = LightgbmAlgorithm(params)
model.fit(self.X, self.y)
y_predicted = model.predict(self.X)
losses += [metric(self.y, y_predicted)]
for i in range(1, 3):
self.assertNotEqual(losses[i - 1], losses[i])
def test_restricted_characters_in_feature_name(self):
df = pd.DataFrame({
"y": np.random.randint(0, 2, size=100),
"[test1]": np.random.uniform(0, 1, size=100),
"test2 < 1": np.random.uniform(0, 1, size=100),
})
y = df.iloc[:, 0]
X = df.iloc[:, 1:]
metric = Metric({"name": "logloss"})
params = {"objective": "binary:logistic", "eval_metric": "logloss"}
lgb = LightgbmAlgorithm(self.params)
lgb.fit(X, y)
lgb.predict(X)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex())
lgb.save(filename)
lgb2 = LightgbmAlgorithm({})
self.assertTrue(lgb.uid != lgb2.uid)
self.assertTrue(lgb2.model is None)
lgb2.load(filename)
#Finished with the file, delete it
os.remove(filename)
y_predicted = lgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
json_desc = lgb.save()
lgb2 = LightgbmAlgorithm({})
self.assertTrue(lgb.uid != lgb2.uid)
self.assertTrue(lgb2.model is None)
lgb2.load(json_desc)
self.assertTrue(lgb.uid == lgb2.uid)
y_predicted = lgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
lgb = LightgbmAlgorithm(self.params)
lgb.fit(self.X, self.y)
y_predicted = lgb.predict(self.X)
loss = metric(self.y, y_predicted)
with tempfile.NamedTemporaryFile() as tmp:
lgb.save(tmp.name)
lgb2 = LightgbmAlgorithm({})
self.assertTrue(lgb.uid != lgb2.uid)
self.assertTrue(lgb2.model is None)
lgb2.load(tmp.name)
y_predicted = lgb2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_get_metric_name(self):
model = LightgbmAlgorithm(self.params)
self.assertEqual(model.get_metric_name(), "logloss")
def test_is_fitted(self):
model = LightgbmAlgorithm(self.params)
self.assertFalse(model.is_fitted())
model.fit(self.X, self.y)
self.assertTrue(model.is_fitted())