def test_different_implementations():
random_seed = 1233
X_train, y_train = load_gunpoint(return_X_y=True)
# Compare with chained transformations.
tran1 = RandomIntervalSegmenter(n_intervals='sqrt', random_state=random_seed)
tran2 = RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))
A = tran2.fit_transform(tran1.fit_transform(X_train))
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt', features=[np.mean], random_state=random_seed)
B = tran.fit_transform(X_train)
np.testing.assert_array_equal(A, B)
# Compare with transformer pipeline using TSFeatureUnion.
steps = [
('segment', RandomIntervalSegmenter(n_intervals='sqrt', check_input=False)),
('transform', TSFeatureUnion([
('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))),
('std', RowwiseTransformer(FunctionTransformer(func=np.std, validate=False))),
])),
]
pipe = TSPipeline(steps, random_state=random_seed)
a = pipe.fit_transform(X_train)
n_ints = a.shape[1] // 2 # Rename columns for comparing re-ordered arrays.
a.columns = [*a.columns[:n_ints] + '_mean', *a.columns[n_ints:n_ints * 2] + '_std']
a = a.reindex(np.sort(a.columns), axis=1)
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt', features=[np.mean, np.std],
random_state=random_seed)
b = tran.fit_transform(X_train)
b = b.reindex(np.sort(b.columns), axis=1)
np.testing.assert_array_equal(a, b)
def test_rowwise_transformer_function_transformer_series_to_primitives():
X, y = load_gunpoint(return_X_y=True)
ft = FunctionTransformer(func=np.mean, validate=False)
t = RowwiseTransformer(ft)
Xt = t.fit_transform(X, y)
assert Xt.shape == X.shape
assert isinstance(Xt.iloc[0, 0], float) # check series-to-primitive transforms
def test_rowwise_transformer_transform_inverse_transform():
X, y = load_gunpoint(return_X_y=True)
t = RowwiseTransformer(StandardScaler())
Xt = t.fit_transform(X)
Xit = t.inverse_transform(Xt)
assert Xit.shape == X.shape
assert isinstance(Xit.iloc[0, 0], (pd.Series, np.ndarray)) # check series-to-series transforms
np.testing.assert_array_almost_equal(tabularise(X).values, tabularise(Xit).values, decimal=5)
def test_rowwise_transformer_sklearn_transfomer():
mu = 10
sd = 5
X = generate_df_from_array(np.random.normal(loc=mu, scale=5, size=(100,)), n_rows=10, n_cols=1)
t = StandardScaler(with_mean=True, with_std=True)
r = RowwiseTransformer(t)
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_rowwise_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 = RowwiseTransformer(ft)
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
def test_different_implementations():
random_seed = 1233
X_train, y_train = load_gunpoint(return_X_y=True)
# Compare with chained transformations.
tran1 = RandomIntervalSegmenter(n_intervals='sqrt',
random_state=random_seed)
tran2 = RowwiseTransformer(
FunctionTransformer(func=np.mean, validate=False))
A = tran2.fit_transform(tran1.fit_transform(X_train))
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt',
features=[np.mean],
random_state=random_seed)
B = tran.fit_transform(X_train)
np.testing.assert_array_equal(A, B)
