def test_estimate_rename_ds(self):
p = SkProphet('date', True, [], {})
X = self._get_dataset()
X = X.rename({'ds': 'date'}, axis=1)
y_pred = p.fit(X).predict(X)
self.assertIsInstance(y_pred, np.ndarray)
def test_estimate(self):
p = SkProphet('ds', True, [], {})
X = self._get_dataset()
y_pred = p.fit(X).predict(X)
self.assertIsInstance(y_pred, np.ndarray)
def test_estimate_full_output(self):
p = SkProphet('ds', False, [], {})
X = self._get_dataset()
y_pred = p.fit(X).predict(X)
self.assertIsInstance(y_pred, pd.DataFrame)
def test_fit_X_not_dataframe_error(self):
p = SkProphet('ds', True, [], {})
X = [[1, 2], [3, 4]]
with self.assertRaises(TypeError):
p.fit(X)
def test_fit_y_list(self):
p = SkProphet('date', True, [], {})
X = self._get_dataset()
p2 = p.fit(X[['ds', 'x']], X.y.values.tolist())
self.assertEqual(p, p2)
def test_fit_y_str(self):
p = SkProphet('date', True, [], {})
X = self._get_dataset()
X = X.rename({'y': 'mica'}, axis=1)
p2 = p.fit(X, 'mica')
self.assertEqual(p, p2)
def test_fit_X_only_rename_ds(self):
p = SkProphet('date', True, [], {})
X = self._get_dataset()
X = X.rename({'ds': 'date'}, axis=1)
p2 = p.fit(X)
self.assertEqual(p, p2)
def test_fit_X_only(self):
p = SkProphet('ds', True, [], {})
X = self._get_dataset()
p2 = p.fit(X)
self.assertEqual(p, p2)