def test_fit_predict(self): metric = Metric({"name": "logloss"}) params = {"ml_task": "binary_classification"} la = LinearAlgorithm(params) la.fit(self.X, self.y) y_predicted = la.predict(self.X) self.assertTrue(metric(self.y, y_predicted) < 0.6)
def test_reproduce_fit(self): metric = Metric({"name": "logloss"}) params = {"seed": 1, "ml_task": "binary_classification"} prev_loss = None for _ in range(3): model = LinearAlgorithm(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): metric = Metric({"name": "logloss"}) model = LinearAlgorithm({"ml_task": "binary_classification"}) model.fit(self.X, self.y) y_predicted = model.predict(self.X) loss = metric(self.y, y_predicted) model2 = LinearAlgorithm({}) model2 = model.copy() self.assertEqual(type(model), type(model2)) y_predicted = model2.predict(self.X) loss2 = metric(self.y, y_predicted) assert_almost_equal(loss, loss2)
def test_save_and_load(self): metric = Metric({"name": "logloss"}) model = LinearAlgorithm({"ml_task": "binary_classification"}) model.fit(self.X, self.y) y_predicted = model.predict(self.X) loss = metric(self.y, y_predicted) filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex()) model.save(filename) model2 = LinearAlgorithm({"ml_task": "binary_classification"}) model2.load(filename) # Finished with the file, delete it os.remove(filename) y_predicted = model2.predict(self.X) loss2 = metric(self.y, y_predicted) assert_almost_equal(loss, loss2)
def test_save_and_load(self): metric = Metric({"name": "logloss"}) model = LinearAlgorithm({"ml_task": "binary_classification"}) model.fit(self.X, self.y) y_predicted = model.predict(self.X) loss = metric(self.y, y_predicted) with tempfile.NamedTemporaryFile() as tmp: model.save(tmp.name) model2 = LinearAlgorithm({"ml_task": "binary_classification"}) model2.load(tmp.name) y_predicted = model2.predict(self.X) loss2 = metric(self.y, y_predicted) assert_almost_equal(loss, loss2)