def test_split_by_fh(index_type, fh_type, is_relative, values):
"""Test temporal_train_test_split."""
y = _make_series(20, index_type=index_type)
cutoff = y.index[10]
fh = _make_fh(cutoff, values, fh_type, is_relative)
split = temporal_train_test_split(y, fh=fh)
_check_train_test_split_y(fh, split)
def test_predict_time_index_with_X(Forecaster, index_type, fh_type,
is_relative, steps):
"""Check that predicted time index matches forecasting horizon."""
f = _construct_instance(Forecaster)
n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
z, X = make_forecasting_problem(index_type=index_type, make_X=True)
# Some estimators may not support all time index types and fh types, hence we
# need to catch NotImplementedErrors.
for n_columns in n_columns_list:
f = _construct_instance(Forecaster)
y = _make_series(n_columns=n_columns, index_type=index_type)
cutoff = y.index[len(y) // 2]
fh = _make_fh(cutoff, steps, fh_type, is_relative)
y_train, y_test, X_train, X_test = temporal_train_test_split(y,
X,
fh=fh)
try:
f.fit(y_train, X_train, fh=fh)
y_pred = f.predict(X=X_test)
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1],
fh)
except NotImplementedError:
pass
def test_predict_residuals(Forecaster, index_type, fh_type, is_relative,
steps):
"""Check that predict_residuals method works as expected."""
f = Forecaster.create_test_instance()
n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
for n_columns in n_columns_list:
f = Forecaster.create_test_instance()
y_train = _make_series(n_columns=n_columns,
index_type=index_type,
n_timepoints=50)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, steps, fh_type, is_relative)
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
y_test = _make_series(n_columns=n_columns,
index_type=index_type,
n_timepoints=len(y_pred))
y_test.index = y_pred.index
y_res = f.predict_residuals(y_test)
_assert_correct_pred_time_index(y_res.index,
y_train.index[-1],
fh=fh)
except NotImplementedError:
pass
def test_predict_time_index_with_X(self, estimator_instance, n_columns,
index_fh_comb, fh_int_oos):
"""Check that predicted time index matches forecasting horizon."""
index_type, fh_type, is_relative = index_fh_comb
if fh_type == "timedelta":
return None
# todo: ensure check_estimator works with pytest.skip like below
# pytest.skip(
# "ForecastingHorizon with timedelta values "
# "is currently experimental and not supported everywhere"
# )
z, X = make_forecasting_problem(index_type=index_type, make_X=True)
# Some estimators may not support all time index types and fh types, hence we
# need to catch NotImplementedErrors.
y = _make_series(n_columns=n_columns, index_type=index_type)
cutoff = y.index[len(y) // 2]
fh = _make_fh(cutoff, fh_int_oos, fh_type, is_relative)
y_train, _, X_train, X_test = temporal_train_test_split(y, X, fh=fh)
try:
estimator_instance.fit(y_train, X_train, fh=fh)
y_pred = estimator_instance.predict(X=X_test)
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1],
fh)
except NotImplementedError:
pass
def test_predict_residuals(self, estimator_instance, n_columns,
index_fh_comb, fh_int):
"""Check that predict_residuals method works as expected."""
index_type, fh_type, is_relative = index_fh_comb
if fh_type == "timedelta":
# workaround to ensure check_estimator without breaking e.g. debugging
return None
# todo: ensure check_estimator works with pytest.skip like below
# pytest.skip(
# "ForecastingHorizon with timedelta values "
# "is currently experimental and not supported everywhere"
# )
y_train = _make_series(n_columns=n_columns,
index_type=index_type,
n_timepoints=50)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, fh_int, fh_type, is_relative)
try:
estimator_instance.fit(y_train, fh=fh)
y_pred = estimator_instance.predict()
y_test = _make_series(n_columns=n_columns,
index_type=index_type,
n_timepoints=len(y_pred))
y_test.index = y_pred.index
y_res = estimator_instance.predict_residuals(y_test)
_assert_correct_pred_time_index(y_res.index,
y_train.index[-1],
fh=fh)
except NotImplementedError:
pass
def test_predict_time_index(self, estimator_instance, n_columns,
index_fh_comb, fh_int):
"""Check that predicted time index matches forecasting horizon."""
index_type, fh_type, is_relative = index_fh_comb
if fh_type == "timedelta":
return None
# todo: ensure check_estimator works with pytest.skip like below
# pytest.skip(
# "ForecastingHorizon with timedelta values "
# "is currently experimental and not supported everywhere"
# )
y_train = _make_series(n_columns=n_columns,
index_type=index_type,
n_timepoints=50)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, fh_int, fh_type, is_relative)
try:
estimator_instance.fit(y_train, fh=fh)
y_pred = estimator_instance.predict()
_assert_correct_pred_time_index(y_pred.index,
y_train.index[-1],
fh=fh_int)
except NotImplementedError:
pass
def test_predict_time_index(Forecaster, index_type, fh_type, is_relative,
steps):
y_train = make_forecasting_problem(index_type=index_type)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, steps, fh_type, is_relative)
f = _construct_instance(Forecaster)
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh)
except NotImplementedError:
pass
def test_fh(index_type, fh_type, is_relative, steps):
# generate data
y = make_forecasting_problem(index_type=index_type)
assert isinstance(y.index, INDEX_TYPE_LOOKUP.get(index_type))
# split data
y_train, y_test = temporal_train_test_split(y, test_size=10)
# choose cutoff point
cutoff = y_train.index[-1]
# generate fh
fh = _make_fh(cutoff, steps, fh_type, is_relative)
assert isinstance(fh.to_pandas(), INDEX_TYPE_LOOKUP.get(fh_type))
# get expected outputs
if isinstance(steps, int):
steps = np.array([steps])
fh_relative = pd.Int64Index(steps).sort_values()
fh_absolute = y.index[np.where(y.index == cutoff)[0] + steps].sort_values()
fh_indexer = fh_relative - 1
fh_oos = fh.to_pandas()[fh_relative > 0]
is_oos = len(fh_oos) == len(fh)
fh_ins = fh.to_pandas()[fh_relative <= 0]
is_ins = len(fh_ins) == len(fh)
# check outputs
# check relative representation
_assert_index_equal(fh_absolute, fh.to_absolute(cutoff).to_pandas())
assert not fh.to_absolute(cutoff).is_relative
# check relative representation
_assert_index_equal(fh_relative, fh.to_relative(cutoff).to_pandas())
assert fh.to_relative(cutoff).is_relative
# check index-like representation
_assert_index_equal(fh_indexer, fh.to_indexer(cutoff))
# check in-sample representation
# we only compare the numpy array here because the expected solution is
# formatted in a slightly different way than the generated solution
np.testing.assert_array_equal(
fh_ins.to_numpy(), fh.to_in_sample(cutoff).to_pandas()
)
assert fh.to_in_sample(cutoff).is_relative == is_relative
assert fh.is_all_in_sample(cutoff) == is_ins
# check out-of-sample representation
np.testing.assert_array_equal(
fh_oos.to_numpy(), fh.to_out_of_sample(cutoff).to_pandas()
)
assert fh.to_out_of_sample(cutoff).is_relative == is_relative
assert fh.is_all_out_of_sample(cutoff) == is_oos
def test_split_by_fh(index_type, fh_type, is_relative, values):
"""Test temporal_train_test_split."""
if fh_type == "timedelta":
return None
# todo: ensure check_estimator works with pytest.skip like below
# pytest.skip(
# "ForecastingHorizon with timedelta values "
# "is currently experimental and not supported everywhere"
# )
y = _make_series(20, index_type=index_type)
cutoff = y.index[10]
fh = _make_fh(cutoff, values, fh_type, is_relative)
split = temporal_train_test_split(y, fh=fh)
_check_train_test_split_y(fh, split)
def test_predict_time_index_in_sample_full(Forecaster, index_type, fh_type,
is_relative):
# Check that predicted time index matched forecasting horizon for full in-sample
# predictions.
y_train = make_forecasting_problem(index_type=index_type)
cutoff = y_train.index[-1]
steps = -np.arange(len(y_train)) # full in-sample fh
fh = _make_fh(cutoff, steps, fh_type, is_relative)
f = _construct_instance(Forecaster)
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh)
except NotImplementedError:
pass
def test_predict_time_index(Forecaster, index_type, fh_type, is_relative,
steps):
# Check that predicted time index matches forecasting horizon.
y_train = make_forecasting_problem(index_type=index_type)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, steps, fh_type, is_relative)
f = _construct_instance(Forecaster)
# Some estimators may not support all time index types and fh types, hence we
# need to catch NotImplementedErrors.
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh)
except NotImplementedError:
pass
def test_predict_time_index_in_sample_full(Forecaster, index_type, fh_type,
is_relative):
"""Check that predicted time index equals fh for full in-sample predictions."""
y_train = make_forecasting_problem(index_type=index_type)
cutoff = y_train.index[-1]
steps = -np.arange(len(y_train)) # full in-sample fh
fh = _make_fh(cutoff, steps, fh_type, is_relative)
f = _construct_instance(Forecaster)
# Some estimators may not support all time index types and fh types, hence we
# need to catch NotImplementedErrors.
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh)
except NotImplementedError:
pass
def test_predict_time_index(Forecaster, index_type, fh_type, is_relative, steps):
"""Check that predicted time index matches forecasting horizon."""
f = _construct_instance(Forecaster)
n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
for n_columns in n_columns_list:
f = _construct_instance(Forecaster)
y_train = _make_series(
n_columns=n_columns, index_type=index_type, n_timepoints=50
)
cutoff = y_train.index[-1]
fh = _make_fh(cutoff, steps, fh_type, is_relative)
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh=fh)
except NotImplementedError:
pass
def test_predict_time_index_in_sample_full(
Forecaster, index_type, fh_type, is_relative
):
"""Check that predicted time index equals fh for full in-sample predictions."""
f = _construct_instance(Forecaster)
n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
for n_columns in n_columns_list:
f = _construct_instance(Forecaster)
y_train = _make_series(n_columns=n_columns, index_type=index_type)
cutoff = y_train.index[-1]
steps = -np.arange(len(y_train))
fh = _make_fh(cutoff, steps, fh_type, is_relative)
try:
f.fit(y_train, fh=fh)
y_pred = f.predict()
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh)
except NotImplementedError:
pass
def test_predict_time_index_in_sample_full(self, estimator_instance,
n_columns, index_fh_comb):
"""Check that predicted time index equals fh for full in-sample predictions."""
index_type, fh_type, is_relative = index_fh_comb
if fh_type == "timedelta":
return None
# todo: ensure check_estimator works with pytest.skip like below
# pytest.skip(
# "ForecastingHorizon with timedelta values "
# "is currently experimental and not supported everywhere"
# )
y_train = _make_series(n_columns=n_columns, index_type=index_type)
cutoff = y_train.index[-1]
steps = -np.arange(len(y_train))
fh = _make_fh(cutoff, steps, fh_type, is_relative)
try:
estimator_instance.fit(y_train, fh=fh)
y_pred = estimator_instance.predict()
_assert_correct_pred_time_index(y_pred.index, y_train.index[-1],
fh)
except NotImplementedError:
pass
def test_fh(index_type, fh_type, is_relative, steps):
"""Testing ForecastingHorizon conversions."""
int_types = ["int64", "int32"]
steps_is_int = (isinstance(steps, (int, np.integer))
or np.array(steps).dtype in int_types)
steps_is_timedelta = isinstance(steps, pd.Timedelta) or (isinstance(
steps, list) and isinstance(pd.Index(steps), pd.TimedeltaIndex))
steps_and_fh_incompatible = (fh_type == "timedelta"
and steps_is_int) or (fh_type != "timedelta"
and steps_is_timedelta)
if steps_and_fh_incompatible:
pytest.skip("steps and fh_type are incompatible")
# generate data
y = make_forecasting_problem(index_type=index_type)
if index_type == "int":
assert is_integer_index(y.index)
else:
assert isinstance(y.index, INDEX_TYPE_LOOKUP.get(index_type))
# split data
y_train, y_test = temporal_train_test_split(y, test_size=10)
# choose cutoff point
cutoff = y_train.index[-1]
# generate fh
fh = _make_fh(cutoff, steps, fh_type, is_relative)
if fh_type == "int":
assert is_integer_index(fh.to_pandas())
else:
assert isinstance(fh.to_pandas(), INDEX_TYPE_LOOKUP.get(fh_type))
# get expected outputs
if isinstance(steps, int):
steps = np.array([steps])
elif isinstance(steps, pd.Timedelta):
steps = pd.Index([steps])
else:
steps = pd.Index(steps)
if steps.dtype in int_types:
fh_relative = pd.Index(steps, dtype="int64").sort_values()
fh_absolute = y.index[np.where(y.index == cutoff)[0] +
steps].sort_values()
fh_indexer = fh_relative - 1
else:
fh_relative = steps.sort_values()
fh_absolute = (cutoff + steps).sort_values()
fh_indexer = None
if steps.dtype in int_types:
null = 0
else:
null = pd.Timedelta(0)
fh_oos = fh.to_pandas()[fh_relative > null]
is_oos = len(fh_oos) == len(fh)
fh_ins = fh.to_pandas()[fh_relative <= null]
is_ins = len(fh_ins) == len(fh)
# check outputs
# check relative representation
_assert_index_equal(fh_absolute, fh.to_absolute(cutoff).to_pandas())
assert not fh.to_absolute(cutoff).is_relative
# check relative representation
_assert_index_equal(fh_relative, fh.to_relative(cutoff).to_pandas())
assert fh.to_relative(cutoff).is_relative
if steps.dtype in int_types:
# check index-like representation
_assert_index_equal(fh_indexer, fh.to_indexer(cutoff))
else:
with pytest.raises(NotImplementedError):
fh.to_indexer(cutoff)
# check in-sample representation
# we only compare the numpy array here because the expected solution is
# formatted in a slightly different way than the generated solution
np.testing.assert_array_equal(fh_ins.to_numpy(),
fh.to_in_sample(cutoff).to_pandas())
assert fh.to_in_sample(cutoff).is_relative == is_relative
assert fh.is_all_in_sample(cutoff) == is_ins
# check out-of-sample representation
np.testing.assert_array_equal(fh_oos.to_numpy(),
fh.to_out_of_sample(cutoff).to_pandas())
assert fh.to_out_of_sample(cutoff).is_relative == is_relative
assert fh.is_all_out_of_sample(cutoff) == is_oos
