def test_fit_logistic(self):
mp = multi_prophet.Prophet(growth="logistic")
self.df["cap"] = 200
self.df["floor"] = 0
mp.fit(self.df)
# will be tested later by checking the performance of the model
self.assertTrue(True)
def test_make_future_dataframe_length(self):
mp = multi_prophet.Prophet()
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
initial_len = len(self.df)
self.assertEqual(initial_len + 7, len(future_df))
def test_plotly_plot(self):
mp = multi_prophet.Prophet()
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
forecast = mp.predict(future_df)
self.assertIsInstance(mp.plot(forecast, plotly=True), plotly.graph_objs.Figure)
def test_plot(self):
mp = multi_prophet.Prophet()
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
forecast = mp.predict(future_df)
self.assertIsInstance(mp.plot(forecast), matplotlib.figure.Figure)
def test_add_country_holiday(self):
mp = multi_prophet.Prophet()
mp.add_country_holidays(country_name="US")
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
self.assertEqual(14, len(mp.prophet.train_holiday_names))
def test_add_seasonality(self):
mp = multi_prophet.Prophet()
mp.add_seasonality(name="monthly", period=30.5, fourier_order=5)
seasonality = mp.prophet.seasonalities["monthly"]
self.assertEqual(30.5, seasonality["period"])
self.assertEqual(5, seasonality["fourier_order"])
self.assertEqual(10, seasonality["prior_scale"])
self.assertEqual("additive", seasonality["mode"])
self.assertIsNone(seasonality["condition_name"])
def test_add_regressor(self):
mp = multi_prophet.Prophet()
mp.add_regressor("Matchday")
extra_regressor = mp.prophet.extra_regressors["Matchday"]
self.assertEqual(10.0, extra_regressor["prior_scale"])
self.assertEqual("auto", extra_regressor["standardize"])
self.assertEqual(0.0, extra_regressor["mu"])
self.assertEqual(1.0, extra_regressor["std"])
self.assertEqual("additive", extra_regressor["mode"])
def test_make_future_dataframe_dates(self):
dates = [
"2016-01-21", "2016-01-22", "2016-01-23", "2016-01-24",
"2016-01-25", "2016-01-26", "2016-01-27"
]
mp = multi_prophet.Prophet()
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
future_dates = [
np.datetime_as_string(d, unit="D")
for d in future_df.tail(7)["ds"].values
]
self.assertEqual(dates, future_dates)
def test_predict(self):
columns = [
"ds", "trend", "yhat_lower", "yhat_upper",
"trend_lower", "trend_upper", "additive_terms", "additive_terms_lower",
"additive_terms_upper", "weekly", "weekly_lower", "weekly_upper",
"yearly", "yearly_lower", "yearly_upper", "multiplicative_terms",
"multiplicative_terms_lower", "multiplicative_terms_upper", "yhat"
]
mp = multi_prophet.Prophet()
mp.fit(self.df)
future_df = mp.make_future_dataframe(7)
forecast = mp.predict(future_df)
self.assertEqual(len(future_df), len(forecast))
self.assertEqual(columns, list(forecast.columns.values))
def test_fit_invalid_kwargs(self):
mp = multi_prophet.Prophet()
with self.assertRaises(ValueError):
mp.fit(self.df, invalid="Newton")
def test_fit_kwargs(self):
mp = multi_prophet.Prophet()
mp.fit(self.df, algorithm="Newton")
# will be tested later by checking the performance of the model
self.assertTrue(True)
def test_fit(self):
mp = multi_prophet.Prophet()
mp.fit(self.df)
# will be tested later by checking the performance of the model
self.assertTrue(True)
def test_constructor_invalid_kwargs(self):
with self.assertRaises(TypeError):
mp = multi_prophet.Prophet(invalid="logistic")
def test_constructor_kwargs(self):
mp = multi_prophet.Prophet(growth="logistic")
self.assertEqual("logistic", mp.prophet.growth)
def test_constructor(self):
mp = multi_prophet.Prophet()
self.assertIsNotNone(mp)
self.assertIsInstance(mp.prophet, fbprophet.Prophet)
