def test_row_transformer_sklearn_transfomer():
mu = 10
X = _make_nested_from_array(
np.random.normal(loc=mu, scale=5, size=(100,)), n_instances=10, n_columns=1
)
t = StandardScaler(with_mean=True, with_std=True)
r = SeriesToSeriesRowTransformer(t, check_transformer=False)
Xt = r.fit_transform(X)
assert Xt.shape == X.shape
assert isinstance(
Xt.iloc[0, 0], (pd.Series, np.ndarray)
) # check series-to-series transform
np.testing.assert_almost_equal(Xt.iloc[0, 0].mean(), 0) # check standardisation
np.testing.assert_almost_equal(Xt.iloc[0, 0].std(), 1, decimal=2)
def test_row_transformer_function_transformer_series_to_series():
X, y = load_gunpoint(return_X_y=True)
# series-to-series transform function
def powerspectrum(x):
fft = np.fft.fft(x)
ps = fft.real * fft.real + fft.imag * fft.imag
return ps[: ps.shape[0] // 2]
ft = FunctionTransformer(func=powerspectrum, validate=False)
t = SeriesToSeriesRowTransformer(ft, check_transformer=False)
Xt = t.fit_transform(X, y)
assert Xt.shape == X.shape
assert isinstance(
Xt.iloc[0, 0], (pd.Series, np.ndarray)
) # check series-to-series transforms
EXCLUDED_TESTS = {
"ShapeletTransformClassifier": ["check_fit_idempotent"],
"ContractedShapeletTransform": ["check_fit_idempotent"],
}
# We here configure estimators for basic unit testing, including setting of
# required hyper-parameters and setting of hyper-parameters for faster training.
SERIES_TO_SERIES_TRANSFORMER = StandardScaler()
SERIES_TO_PRIMITIVES_TRANSFORMER = FunctionTransformer(np.mean,
kw_args={"axis": 0},
check_inverse=False)
TRANSFORMERS = [
(
"transformer1",
SeriesToSeriesRowTransformer(SERIES_TO_SERIES_TRANSFORMER,
check_transformer=False),
),
(
"transformer2",
SeriesToSeriesRowTransformer(SERIES_TO_SERIES_TRANSFORMER,
check_transformer=False),
),
]
REGRESSOR = LinearRegression()
TIME_SERIES_CLASSIFIER = TimeSeriesForest(n_estimators=3)
TIME_SERIES_CLASSIFIERS = [
("tsf1", TIME_SERIES_CLASSIFIER),
("tsf2", TIME_SERIES_CLASSIFIER),
]
FORECASTER = ExponentialSmoothing()
FORECASTERS = [("ses1", FORECASTER), ("ses2", FORECASTER)]