def test_fit_predict_ts_with_datetime_in_X_column(ts_data_seasonal):
X, y = ts_data_seasonal
assert isinstance(X.index, pd.DatetimeIndex)
assert isinstance(y.index, pd.DatetimeIndex)
m_clf = ARIMARegressor(d=None)
m_clf.fit(X=X[:250], y=y[:250])
y_pred = m_clf.predict(X=X[250:])
X['Sample'] = pd.date_range(start='1/1/2016', periods=500)
dt_clf = ARIMARegressor(d=None)
dt_clf.fit(X=X[:250], y=y[:250])
y_pred_dt = dt_clf.predict(X=X[250:])
assert isinstance(y_pred_dt, woodwork.DataColumn)
pd.testing.assert_series_equal(y_pred.to_series(), y_pred_dt.to_series())
def test_fit_predict_ts_no_X_out_of_sample(ts_data_seasonal):
X, y = ts_data_seasonal
fh_ = forecasting.ForecastingHorizon(y[250:].index, is_relative=False)
a_clf = sktime_arima.AutoARIMA()
a_clf.fit(y=y[:250])
y_pred_sk = a_clf.predict(fh=fh_)
m_clf = ARIMARegressor(d=None)
m_clf.fit(X=None, y=y[:250])
y_pred = m_clf.predict(X=None, y=y[250:])
assert (y_pred_sk.to_period('D') == y_pred.to_series()).all()
def test_fit_predict_date_index_named_out_of_sample(X_none, ts_data_seasonal):
X, y = ts_data_seasonal
fh_ = forecasting.ForecastingHorizon(y[250:].index, is_relative=False)
a_clf = sktime_arima.AutoARIMA()
if X_none:
clf = a_clf.fit(y=y[:250])
y_pred_sk = clf.predict(fh=fh_)
else:
clf = a_clf.fit(X=X[:250], y=y[:250])
y_pred_sk = clf.predict(fh=fh_, X=X[250:])
X = X.reset_index()
assert not isinstance(X.index, pd.DatetimeIndex)
m_clf = ARIMARegressor(date_index='index', d=None)
if X_none:
m_clf.fit(X=None, y=y[:250])
y_pred = m_clf.predict(X=None, y=y[250:])
else:
m_clf.fit(X=X[:250], y=y[:250])
y_pred = m_clf.predict(X=X[250:], y=y[250:])
assert (y_pred_sk.to_period('D') == y_pred.to_series()).all()
def test_fit_predict_ts_with_X_and_y_index_out_of_sample(ts_data_seasonal):
X, y = ts_data_seasonal
assert isinstance(X.index, pd.DatetimeIndex)
assert isinstance(y.index, pd.DatetimeIndex)
fh_ = forecasting.ForecastingHorizon(y[250:].index, is_relative=False)
a_clf = sktime_arima.AutoARIMA()
clf = a_clf.fit(X=X[:250], y=y[:250])
y_pred_sk = clf.predict(fh=fh_, X=X[250:])
m_clf = ARIMARegressor(d=None)
m_clf.fit(X=X[:250], y=y[:250])
y_pred = m_clf.predict(X=X[250:])
assert (y_pred_sk.to_period('D') == y_pred.to_series()).all()
def test_fit_predict_ts_with_only_datetime_column_in_X(ts_data_seasonal):
X, y = ts_data_seasonal
assert isinstance(X.index, pd.DatetimeIndex)
assert isinstance(y.index, pd.DatetimeIndex)
fh_ = forecasting.ForecastingHorizon(y[250:].index, is_relative=False)
a_clf = sktime_arima.AutoARIMA()
clf = a_clf.fit(y=y[:250])
y_pred_sk = clf.predict(fh=fh_)
X = X.drop(["features"], axis=1)
m_clf = ARIMARegressor(d=None)
m_clf.fit(X=X[:250], y=y[:250])
y_pred = m_clf.predict(X=X[250:])
assert (y_pred_sk.to_period('D') == y_pred.to_series()).all()
def test_fit_predict_ts_with_X_and_y_index(mock_get_dates, mock_format_dates,
ts_data_seasonal):
X, y = ts_data_seasonal
assert isinstance(X.index, pd.DatetimeIndex)
assert isinstance(y.index, pd.DatetimeIndex)
mock_get_dates.return_value = (X.index, X)
mock_format_dates.return_value = (X, y, None)
fh_ = forecasting.ForecastingHorizon(y.index, is_relative=False)
a_clf = sktime_arima.AutoARIMA()
clf = a_clf.fit(X=X, y=y)
y_pred_sk = clf.predict(fh=fh_, X=X)
m_clf = ARIMARegressor(d=None)
m_clf.fit(X=X, y=y)
mock_format_dates.return_value = (X, y, fh_)
y_pred = m_clf.predict(X=X)
assert (y_pred_sk == y_pred.to_series()).all()