def test_bad_input_args(bad_num_intervals):
X = _make_nested_from_array(np.ones(10), n_instances=10, n_columns=1)
if not isinstance(bad_num_intervals, int):
with pytest.raises(TypeError):
PAA(num_intervals=bad_num_intervals).fit(X).transform(X)
else:
with pytest.raises(ValueError):
PAA(num_intervals=bad_num_intervals).fit(X).transform(X)
def test_paa_performs_correcly_along_each_dim():
X = _make_nested_from_array(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
n_instances=1,
n_columns=2)
p = PAA(num_intervals=3).fit(X)
res = p.transform(X)
orig = convert_list_to_dataframe([[2.2, 5.5, 8.8], [2.2, 5.5, 8.8]])
orig.columns = X.columns
assert check_if_dataframes_are_equal(res, orig)
def test_output_of_transformer():
X = _make_nested_from_array(np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),
n_instances=1,
n_columns=1)
p = PAA(num_intervals=3).fit(X)
res = p.transform(X)
orig = convert_list_to_dataframe([[2.2, 5.5, 8.8]])
orig.columns = X.columns
assert check_if_dataframes_are_equal(res, orig)
def test_output_dimensions():
# test with univariate
X = _make_nested_from_array(np.ones(12), n_instances=10, n_columns=1)
p = PAA(num_intervals=5).fit(X)
res = p.transform(X)
# get the dimension of the generated dataframe.
corr_time_series_length = res.iloc[0, 0].shape[0]
num_rows = res.shape[0]
num_cols = res.shape[1]
assert corr_time_series_length == 5
assert num_rows == 10
assert num_cols == 1
# test with multivariate
X = _make_nested_from_array(np.ones(12), n_instances=10, n_columns=5)
p = PAA(num_intervals=5).fit(X)
res = p.transform(X)
# get the dimension of the generated dataframe.
corr_time_series_length = res.iloc[0, 0].shape[0]
num_rows = res.shape[0]
num_cols = res.shape[1]
assert corr_time_series_length == 5
assert num_rows == 10
assert num_cols == 5
def _get_transformer(self, tName):
"""
Function to extract the appropriate transformer
Parameters
-------
self : the ShapeDTW object.
tName : the name of the required transformer.
Returns
-------
output : Base Transformer object corresponding to the class
(or classes if its a compound transformer) of the
required transformer. The transformer is
configured with the parameters given in self.metric_params.
throws : ValueError if a shape descriptor doesn't exist.
"""
parameters = self.metric_params
tName = tName.lower()
if parameters is None:
parameters = {}
parameters = {k.lower(): v for k, v in parameters.items()}
self._check_metric_params(parameters)
if tName == "raw":
return None
elif tName == "paa":
num_intervals = parameters.get("num_intervals_paa")
if num_intervals is None:
return PAA()
return PAA(num_intervals)
elif tName == "dwt":
num_levels = parameters.get("num_levels_dwt")
if num_levels is None:
return DWTTransformer()
return DWTTransformer(num_levels)
elif tName == "slope":
num_intervals = parameters.get("num_intervals_slope")
if num_intervals is None:
return SlopeTransformer()
return SlopeTransformer(num_intervals)
elif tName == "derivative":
return DerivativeSlopeTransformer()
elif tName == "hog1d":
num_intervals = parameters.get("num_intervals_hog1d")
num_bins = parameters.get("num_bins_hog1d")
scaling_factor = parameters.get("scaling_factor_hog1d")
# All 3 paramaters are None
if num_intervals is None and num_bins is None and \
scaling_factor is None:
return HOG1DTransformer()
# 2 parameters are None
if num_intervals is None and num_bins is None:
return HOG1DTransformer(scaling_factor=scaling_factor)
if num_intervals is None and scaling_factor is None:
return HOG1DTransformer(num_bins=num_bins)
if num_bins is None and scaling_factor is None:
return HOG1DTransformer(num_intervals=num_intervals)
# 1 parameter is None
if num_intervals is None:
return HOG1DTransformer(scaling_factor=scaling_factor,
num_bins=num_bins)
if scaling_factor is None:
return HOG1DTransformer(num_intervals=num_intervals,
num_bins=num_bins)
if num_bins is None:
return HOG1DTransformer(scaling_factor=scaling_factor,
num_intervals=num_intervals)
# All parameters are given
return HOG1DTransformer(num_intervals=num_intervals,
num_bins=num_bins,
scaling_factor=scaling_factor)
else:
raise ValueError("Invalid shape desciptor function.")