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Clarify documentation regarding unequal length series limitation #2589

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Clarify documentation regarding unequal length series limitation #2589

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44 changes: 28 additions & 16 deletions aeon/transformations/collection/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,10 +64,13 @@ def fit(self, X, y=None):
X : np.ndarray or list
Data to fit transform to, of valid collection type. Input data,
any number of channels, equal length series of shape ``(
n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number
of channels, unequal length series) of shape ``[n_cases]``, 2D np.array
``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of
series ``i``. Other types are allowed and converted into one of the above.
n_cases, n_channels, n_timepoints)``.
A list of numpy arrays of shape ``[n_cases]``, where each element is a
2D array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i``
represents the length of series ``i``. This format is primarily used
for handling unequal-length series, but support for it depends on the
estimator's capabilities. Other input types are allowed and converted
into one of the above.

Different estimators have different capabilities to handle different
types of input. If ``self.get_tag("capability:multivariate")`` is False,
Expand Down Expand Up @@ -118,10 +121,13 @@ def transform(self, X, y=None):
X : np.ndarray or list
Data to fit transform to, of valid collection type. Input data,
any number of channels, equal length series of shape ``(
n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number
of channels, unequal length series) of shape ``[n_cases]``, 2D np.array
``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of
series ``i``. Other types are allowed and converted into one of the above.
n_cases, n_channels, n_timepoints)``.
A list of numpy arrays of shape ``[n_cases]``, where each element is a
2D array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i``
represents the length of series ``i``. This format is primarily used
for handling unequal-length series, but support for it depends on the
estimator's capabilities. Other input types are allowed and converted
into one of the above.

Different estimators have different capabilities to handle different
types of input. If ``self.get_tag("capability:multivariate")`` is False,
Expand Down Expand Up @@ -172,10 +178,13 @@ def fit_transform(self, X, y=None):
X : np.ndarray or list
Data to fit transform to, of valid collection type. Input data,
any number of channels, equal length series of shape ``(n_cases,
n_channels, n_timepoints)`` or list of numpy arrays (any number of
channels, unequal length series) of shape ``[n_cases]``, 2D np.array ``(
n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of
series ``i``. Other types are allowed and converted into one of the above.
n_channels, n_timepoints)``.
A list of numpy arrays of shape ``[n_cases]``, where each element is a
2D array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i``
represents the length of series ``i``. This format is primarily used
for handling unequal-length series, but support for it depends on the
estimator's capabilities. Other input types are allowed and converted
into one of the above.

Different estimators have different capabilities to handle different
types of input. If ``self.get_tag("capability:multivariate")`` is False,
Expand Down Expand Up @@ -222,10 +231,13 @@ def inverse_transform(self, X, y=None):
X : np.ndarray or list
Data to fit transform to, of valid collection type. Input data,
any number of channels, equal length series of shape ``(
n_cases, n_channels, n_timepoints)`` or list of numpy arrays (any number
of channels, unequal length series) of shape ``[n_cases]``, 2D np.array
``(n_channels, n_timepoints_i)``, where ``n_timepoints_i`` is length of
series ``i``. Other types are allowed and converted into one of the above.
n_cases, n_channels, n_timepoints)``.
A list of numpy arrays of shape ``[n_cases]``, where each element is a
2D array ``(n_channels, n_timepoints_i)``, where ``n_timepoints_i``
represents the length of series ``i``. This format is primarily used
for handling unequal-length series, but support for it depends on the
estimator's capabilities. Other input types are allowed and converted
into one of the above.

Different estimators have different capabilities to handle different
types of input. If ``self.get_tag("capability:multivariate")`` is False,
Expand Down