TimeSeriesScaler¶
- class TimeSeriesScaler(copy=True, with_mean=True, with_std=True)[source]¶
StandardScaler for time series.
This class wraps the sklearn StandardScaler so that is standardises time series. We need this because we store collections of time series in arrays of shape (n_cases, n_channels, n_timepoints). Standard scaler would transform a single series (n_channels, n_timepoints) so that each time point is zero mean, unit std dev. We want each channel to be zero mean, unit std dev. This is easily achieved by transposing, but its easy to get wrong and hard to detect if done wrong, hence the wrapper.
The standard score of a time series x is calculated as:
z = (x - u) / s
where u is the mean of the time series or zero if with_mean=False, and s is the standard deviation of the time series or one if with_std=False.
Centering and scaling happen independently on each time series.
- Parameters:
- with_meanbool, default=True
If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.
- with_stdbool, default=True
If True, scale the data to unit variance (or equivalently, unit standard deviation).
See also
sklearn.preprocessing.StandardScaler
class wrapped by this class.
Notes
NaNs are treated as missing values: disregarded in fit, and maintained in transform. We have note used the copy parameter for in place copying, because it doesnt seem to work.
We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.
Examples
>>> from aeon.transformations.collection import TimeSeriesScaler >>> series = np.array([[[0, 0, 0], [0, 0, 0]], [[1, 1, 1], [1, 1, 1]]]) >>> scaler = TimeSeriesScaler() >>> print(scaler.fit(series)) TimeSeriesScaler() >>> scaler.transform(series)[0][0] array([0., 0., 0.])
Methods
clone
([random_state])Obtain a clone of the object with the same hyperparameters.
fit
(X[, y])Fit transformer to X, optionally using y if supervised.
fit_transform
(X[, y])Fit to data, then transform it.
get_class_tag
(tag_name[, raise_error, ...])Get tag value from estimator class (only class tags).
Get class tags from estimator class and all its parent classes.
get_fitted_params
([deep])Get fitted parameters.
Sklearn metadata routing.
get_params
([deep])Get parameters for this estimator.
get_tag
(tag_name[, raise_error, ...])Get tag value from estimator class.
get_tags
()Get tags from estimator.
inverse_transform
(X[, y])Inverse transform X and return an inverse transformed version.
reset
([keep])Reset the object to a clean post-init state.
set_params
(**params)Set the parameters of this estimator.
set_tags
(**tag_dict)Set dynamic tags to given values.
transform
(X[, y])Transform X and return a transformed version.
- clone(random_state=None)[source]¶
Obtain a clone of the object with the same hyperparameters.
A clone is a different object without shared references, in post-init state. This function is equivalent to returning
sklearn.clone
of self. Equal in value totype(self)(**self.get_params(deep=False))
.- Parameters:
- random_stateint, RandomState instance, or None, default=None
Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.
- Returns:
- estimatorobject
Instance of
type(self)
, clone of self (see above)
- fit(X, y=None)[source]¶
Fit transformer to X, optionally using y if supervised.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator
- Parameters:
- Xnp.ndarray or list
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)
, wheren_timepoints_i
is length of seriesi
. Other 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, they cannot handle multivariate series. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- selfa fitted instance of the estimator
- fit_transform(X, y=None)[source]¶
Fit to data, then transform it.
Fits the transformer to X and y and returns a transformed version of X.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator.
- Parameters:
- Xnp.ndarray or list
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)
, wheren_timepoints_i
is length of seriesi
. Other 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, they cannot handle multivariate series. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- transformed version of X
- classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class (only class tags).
- Parameters:
- tag_namestr
Name of tag value.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in cls. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if
raise_error
is True andtag_name
is not inself.get_tags().keys()
Examples
>>> from aeon.classification import DummyClassifier >>> DummyClassifier.get_class_tag("capability:multivariate") True
- classmethod get_class_tags()[source]¶
Get class tags from estimator class and all its parent classes.
- Returns:
- collected_tagsdict
Dictionary of tag name and tag value pairs. Collected from
_tags
class attribute via nested inheritance. These are not overridden by dynamic tags set byset_tags
or class__init__
calls.
- get_fitted_params(deep=True)[source]¶
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.
- Returns:
- fitted_paramsdict
Fitted parameter names mapped to their values.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- get_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class.
Includes dynamic and overridden tags.
- Parameters:
- tag_namestr
Name of tag to be retrieved.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in self. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if raise_error is
True
andtag_name
is not inself.get_tags().keys()
Examples
>>> from aeon.classification import DummyClassifier >>> d = DummyClassifier() >>> d.get_tag("capability:multivariate") True
- get_tags()[source]¶
Get tags from estimator.
Includes dynamic and overridden tags.
- Returns:
- collected_tagsdict
Dictionary of tag name and tag value pairs. Collected from
_tags
class attribute via nested inheritance and then any overridden and new tags from__init__
orset_tags
.
- inverse_transform(X, y=None)[source]¶
Inverse transform X and return an inverse transformed version.
- Currently it is assumed that only transformers with tags
“input_data_type”=”Series”, “output_data_type”=”Series”,
can have an inverse_transform.
- State required:
Requires state to be “fitted”.
- Accesses in self:
_is_fitted : must be True fitted model attributes (ending in “_”) : accessed by _inverse_transform
- Parameters:
- Xnp.ndarray or list
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)
, wheren_timepoints_i
is length of seriesi
. Other 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, they cannot handle multivariate series. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- inverse transformed version of X
of the same type as X
- reset(keep=None)[source]¶
Reset the object to a clean post-init state.
After a
self.reset()
call, self is equal or similar in value totype(self)(**self.get_params(deep=False))
, assuming no other attributes were kept usingkeep
.- Detailed behaviour:
- removes any object attributes, except:
hyper-parameters (arguments of
__init__
) object attributes containing double-underscores, i.e., the string “__”
runs
__init__
with current values of hyperparameters (result ofget_params
)- Not affected by the reset are:
object attributes containing double-underscores class and object methods, class attributes any attributes specified in the
keep
argument
- Parameters:
- keepNone, str, or list of str, default=None
If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.
- Returns:
- selfobject
Reference to self.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_tags(**tag_dict)[source]¶
Set dynamic tags to given values.
- Parameters:
- **tag_dictdict
Dictionary of tag name and tag value pairs.
- Returns:
- selfobject
Reference to self.
- transform(X, y=None)[source]¶
Transform X and return a transformed version.
- State required:
Requires state to be “fitted”.
Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _transform
- Parameters:
- Xnp.ndarray or list
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)
, wheren_timepoints_i
is length of seriesi
. Other 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, they cannot handle multivariate series. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- transformed version of X