def transform(self, X, y=None):
"""Concatenate multivariate time series/panel data into long
univariate time series/panel
data by simply concatenating times series in time.
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, n_features]
Nested dataframe with time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with same number of rows and single
column
"""
self.check_is_fitted()
X = check_X(X)
# We concatenate by tabularizing all columns and then detabularizing
# them into a single column
if isinstance(X, pd.DataFrame):
Xt = from_nested_to_2d_array(X)
else:
Xt = from_3d_numpy_to_2d_array(X)
return from_2d_array_to_nested(Xt)
def fit(self, X, y=None):
"""Fit dilations and biases to input time series.
Parameters
----------
X : pandas DataFrame, input time series (sktime format)
y : array_like, target values (optional, ignored as irrelevant)
Returns
-------
self
"""
_X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
_X = _X[:, 0, :].astype(np.float64)
_X = from_3d_numpy_to_2d_array(_X)
if self.normalise:
_X = (_X - _X.mean(axis=-1, keepdims=True)) / (
_X.std(axis=-1, keepdims=True) + 1e-8)
self.parameter = self._get_parameter(_X)
_X1 = np.diff(_X, 1)
self.parameter1 = self._get_parameter(_X1)
self._is_fitted = True
return self
def transform(self, X, y=None):
"""Transform input time series using random convolutional kernels.
Parameters
----------
X : pandas DataFrame, input time series (sktime format)
y : array_like, target values (optional, ignored as irrelevant)
Returns
-------
pandas DataFrame, transformed features
"""
self.check_is_fitted()
_X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
_X = _X[:, 0, :].astype(np.float64)
_X = from_3d_numpy_to_2d_array(_X)
if self.normalise:
_X = (_X - _X.mean(axis=-1, keepdims=True)) / (
_X.std(axis=-1, keepdims=True) + 1e-8)
X1 = np.diff(_X, 1)
# change n_jobs dependend on value and existing cores
prev_threads = get_num_threads()
if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count():
n_jobs = multiprocessing.cpu_count()
else:
n_jobs = self.n_jobs
set_num_threads(n_jobs)
_X = _transform(
_X,
X1,
self.parameter,
self.parameter1,
self.n_features_per_kernel,
)
_X = np.nan_to_num(_X)
set_num_threads(prev_threads)
# # from_2d_array_to_3d_numpy
# _X = np.reshape(_X, (_X.shape[0], 1, _X.shape[1])).astype(np.float64)
return pd.DataFrame(_X)
def transform(self, X, y=None):
"""Transform nested pandas dataframe into tabular dataframe.
Parameters
----------
X : pandas DataFrame
Nested dataframe with pandas series or numpy arrays in cells.
y : array-like, optional (default=None)
Returns
-------
Xt : pandas DataFrame
Transformed dataframe with only primitives in cells.
"""
self.check_is_fitted()
X = check_X(X)
if isinstance(X, pd.DataFrame):
return from_nested_to_2d_array(X)
else:
return from_3d_numpy_to_2d_array(X)
def test_from_3d_numpy_to_2d_array(n_instances, n_columns, n_timepoints):
"""Test from_3d_numpy_to_2d_array for correctness."""
array = np.random.normal(size=(n_instances, n_columns, n_timepoints))
array_2d = from_3d_numpy_to_2d_array(array)
assert array_2d.shape == (n_instances, n_columns * n_timepoints)