def test_sliding_window_splitter_initial_window_start_with_empty_window_raises_error():
y = _make_series()
cv = SlidingWindowSplitter(
fh=1,
initial_window=15,
start_with_window=False,
)
message = "`start_with_window` must be True if `initial_window` is given"
with pytest.raises(ValueError, match=message):
next(cv.split(y))
def test_sliding_window_splitter_initial_window_smaller_than_window_raise_error():
y = _make_series()
cv = SlidingWindowSplitter(
fh=1,
window_length=10,
initial_window=5,
)
message = "`initial_window` must greater than `window_length`"
with pytest.raises(ValueError, match=message):
next(cv.split(y))
def test_evaluate_initial_window():
initial_window = 20
y = make_forecasting_problem(n_timepoints=30, index_type="int")
forecaster = NaiveForecaster()
fh = 1
cv = SlidingWindowSplitter(fh=fh, initial_window=initial_window)
scoring = sMAPE()
out = evaluate(
forecaster=forecaster, y=y, cv=cv, strategy="update", scoring=scoring
)
_check_evaluate_output(out, cv, y, scoring)
assert out.loc[0, "len_train_window"] == initial_window
# check scoring
actual = out.loc[0, f"test_{scoring.name}"]
train, test = next(cv.split(y))
f = clone(forecaster)
f.fit(y.iloc[train], fh=fh)
expected = scoring(y.iloc[test], f.predict())
np.testing.assert_equal(actual, expected)
strategy="recursive")
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
plot_ys(y_train, y_test, y_pred, labels=["y_train", "y_test", "y_pred"])
st.pyplot()
st.write("smape_loss(y_test, y_pred):", smape_loss(y_test, y_pred))
st.write('''
为了更好地理解先前的数据转换，我们可以看看如何将训练数据划分为多个窗口。
本质上，sktime使用时间时间序列分割器，类似于scikit-learn中的交叉验证分割器。
在这里，我们展示了这对于训练数据的前20个观察结果是如何工作的：
''')
with st.echo():
from sktime.forecasting.model_selection import SlidingWindowSplitter
cv = SlidingWindowSplitter(window_length=10, start_with_window=True)
for input_window, output_window in cv.split(y_train.iloc[:20]):
print(input_window, output_window)
st.write('''
[0 1 2 3 4 5 6 7 8 9] [10]
[ 1 2 3 4 5 6 7 8 9 10] [11]
[ 2 3 4 5 6 7 8 9 10 11] [12]
[ 3 4 5 6 7 8 9 10 11 12] [13]
[ 4 5 6 7 8 9 10 11 12 13] [14]
[ 5 6 7 8 9 10 11 12 13 14] [15]
[ 6 7 8 9 10 11 12 13 14 15] [16]
