def test_takens_embedding_3D_no_flatten():
"""Test the return values of TakensEmbedding on 3D input or list of 2D
input, with `flatten` set to ``False``."""
signals = np.arange(20).reshape(2, 2, 5)
TE = TakensEmbedding(flatten=False)
signals_emb = TE.fit_transform(signals)
signals_emb_list = TE.fit_transform(list(signals))
signals_emb_exp = np.array([[[[0, 1],
[1, 2],
[2, 3],
[3, 4]],
[[5, 6],
[6, 7],
[7, 8],
[8, 9]]],
[[[10, 11],
[11, 12],
[12, 13],
[13, 14]],
[[15, 16],
[16, 17],
[17, 18],
[18, 19]]]])
assert np.array_equal(signals_emb, signals_emb_exp)
assert np.array_equal(np.asarray(signals_emb_list), signals_emb_exp)
def test_takens_embedding_plot():
trace_params = {"mode": "lines+markers"}
layout_params = {"title": "New title"}
TE = TakensEmbedding()
plotly_params = {"trace": trace_params, "layout": layout_params}
TE.fit_transform_plot([np.arange(20)], sample=0,
plotly_params=plotly_params)
def test_embedder_resample():
embedder = TakensEmbedding(parameters_type='fixed',
time_delay=3,
dimension=2,
stride=3)
embedder.fit(signal)
y_resampled = embedder.resample(y)
assert_almost_equal(y_resampled, y[np.arange(4, 20, 3)])
def test_takens_embedding_validation(params):
if "flatten" not in params and "ensure_last_value" not in params:
exception_type = ValueError
else:
exception_type = TypeError
with pytest.raises(exception_type):
TE = TakensEmbedding(**params)
TE.fit(signal[None, :])
def tokens_embeddings_(self, X):
X_transformed = list()
for series in X:
te = TakensEmbedding(parameters_type='search',
dimension=self.tokens_embedding_dim_,
time_delay=self.tokens_embedding_delay_,
n_jobs=self.n_job)
X_transformed.append(te.fit_transform(series))
return X_transformed
def test_takens_embedding_3D_default():
"""Test the return values of TakensEmbedding on 3D input or list of 2D
input, with default parameters."""
signals = np.arange(20).reshape(2, 2, 5)
TE = TakensEmbedding()
signals_emb = TE.fit_transform(signals)
signals_emb_list = TE.fit_transform(list(signals))
signals_emb_exp = np.array([[[0, 1, 5, 6],
[1, 2, 6, 7],
[2, 3, 7, 8],
[3, 4, 8, 9]],
[[10, 11, 15, 16],
[11, 12, 16, 17],
[12, 13, 17, 18],
[13, 14, 18, 19]]])
assert np.array_equal(signals_emb, signals_emb_exp)
assert np.array_equal(np.asarray(signals_emb_list), signals_emb_exp)
def test_takens_embedding_2D():
"""Test the return values of TakensEmbedding on 2D input or list of 1D
input, with default parameters."""
signals = np.arange(10).reshape(2, 5)
TE = TakensEmbedding()
signals_emb = TE.fit_transform(signals)
signals_emb_list = TE.fit_transform(list(signals))
signals_emb_exp = np.array([[[0, 1],
[1, 2],
[2, 3],
[3, 4]],
[[5, 6],
[6, 7],
[7, 8],
[8, 9]]])
assert np.array_equal(signals_emb, signals_emb_exp)
assert np.array_equal(np.asarray(signals_emb_list), signals_emb_exp)
def test_takens_embedding_consistent_with_single_takens_embedding(
time_delay, dimension, stride
):
"""Test that TakensEmbedding and SingleTakensEmbedding give identical
outputs (up to shape) on arrays representing single univariate time
series."""
n_points = (len(signal) - time_delay * (dimension - 1) - 1) // stride + 1
single_embedder = SingleTakensEmbedding(parameters_type='fixed',
time_delay=time_delay,
dimension=dimension, stride=stride)
embedder = TakensEmbedding(time_delay=time_delay, dimension=dimension,
stride=stride)
if n_points <= 0:
with pytest.raises(ValueError):
single_embedder.fit_transform(signal)
with pytest.raises(ValueError):
embedder.fit_transform(signal[None, :])
else:
single_embedder_res = single_embedder.fit_transform(signal)
embedder_res = embedder.fit_transform(signal[None, :])[0]
assert np.array_equal(single_embedder_res, embedder_res)
def test_embedder_transform(parameters_type, expected):
embedder = TakensEmbedding(parameters_type=parameters_type)
assert_almost_equal(embedder.fit_transform(signal), expected)
def test_embedder_not_fitted():
embedder = TakensEmbedding()
with pytest.raises(NotFittedError):
embedder.transform(signal)
def test_embedder_params():
parameters_type = 'not_defined'
embedder = TakensEmbedding(parameters_type=parameters_type)
with pytest.raises(ValueError):
embedder.fit(signal)
stride = 2 SW = SlidingWindow(size=window_size, stride=stride) X_sw, yr = SW.fit_transform_resample(X, y) X_sw, yr # In[11]: X, y # In[12]: time_delay = 1 dimension = 2 TE = TakensEmbedding(time_delay=time_delay, dimension=dimension) X_te = TE.fit_transform(X_sw) X_te # In[13]: VR = VietorisRipsPersistence() # No "precomputed" for point clouds Ampl = Amplitude() RFR = RandomForestRegressor() pipe = make_pipeline(SW, TE, VR, Ampl, RFR) pipe # In[14]: pipe.fit(X, y)
def test_takens_embedding_plot():
TE = TakensEmbedding()
_ = TE.fit_transform_plot([np.arange(20)], sample=0)
