TimeBinAggregate

TimeBinAggregate

class TimeBinAggregate(bins, aggfunc=None, return_index='bin_start')[source]

Bins time series and aggregates by bin.

In transform, applies groupby with aggfunc on the temporal coordinate.

More precisely: bins encodes bins \(B_1, \dots, B_k\) where \(B_i\) are intervals, in the reals or in a temporal (time stamp) range.

In transform, the estimator TimeBinAggregate collects values at time stamps of X falling into \(B_i\) as a sample \(S_i\), and then applies aggfunc to \(S_i\) to obtain an aggregate value \(v_i\). The transformed series are values \(v_i\) at time stamps \(t_i\), determined from \(B_i\) per the rule in return_index.

Parameters:

bins1D array-like or pd.IntervalIndex

if 1D array-like, is interpreted as breaks of bins breaks of bins defining intervals considered by aggfunc

aggfunccallable *1D array-like -> float), optional, default=np.mean

Function used to aggregate the values in intervals. Should have signature 1D -> float and defaults to mean if None

return_indexstr, one of the below; optional, default=”range”

“range” = RangeIndex with bins indexed in same order as in bins “bin_start” = transformed pd.DataFrame will be indexed by bin starts “bin_end” = transformed pd.DataFrame will be indexed by bin starts “bin_mid” = transformed pd.DataFrame will be indexed by bin midpoints “bin” = transformed pd.DataFrame will have bins as IntervalIndex

Attributes:

is_fitted

Whether fit has been called.

Examples

from sktime.datatypes import get_examples from sktime.transformations.binning import TimeBinAggregate

bins = [0, 2, 4] X = get_examples(“pd.DataFrame”)[0]

t = TimeBinAggregate([-1, 2, 10])

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.