- class DropNA(axis=0, how=None, thresh=None, remember=None)[source]#
Drop missing values transformation.
Drops rows or columns with missing values from X. Mostly wrapspandas.DataFrame.dropna, but allows specifying thresh as a fraction ofnon-missing observations.
- Parameters:
- axis{0 or ‘index’, 1 or ‘columns’}, default 0
Determine if rows or columns which contain missing values are removed.Must be 0 or ‘index’ for univariate input.
0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.
- how{‘any’, ‘all’}, default ‘any’
Determine if row or column is removed from DataFrame, when we haveat least one NA or all NA.
‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.
- threshint or float, optional
If int, require at least that many non-NA values (as in pandas.dropna).If float, minimum share of non-NA values for rows/columns to beretained. Fraction must be contained within (0,1]. Setting fractionto 1.0 is equivalent to setting how=’any’. thresh cannot be combinedwith how.
- rememberbool, default False if axis==0, True if axis==1
If True, drops the same rows/columns in transform as in fit. If false,drops rows/columns according to the NAs seen in transform (equivalentto PandasTransformAdaptor(method=”dropna”)).
- Attributes:
- is_fitted
Whether
fit
has been called.
Methods
check_is_fitted()
Check if the estimator has been fitted.
clone()
Obtain a clone of the object with same hyper-parameters.
clone_tags(estimator[,tag_names])
Clone tags from another estimator as dynamic override.
create_test_instance([parameter_set])
Construct Estimator instance if possible.
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 a class tag's value.
get_class_tags()
Get class tags from the class and all its 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()
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 estimator class and dynamic tag overrides.
get_tags()
Get tags from estimator class and dynamic tag 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 dynamic tags to given values.
transform(X[,y])
Transform X and return a transformed version.
update(X[,y,update_params])
Update transformer with X, optionally y.
- check_is_fitted()[source]#
Check if the estimator has been fitted.
- Raises:
- NotFittedError
If the estimator has not been fitted yet.
- clone()[source]#
Obtain a clone of the object with same hyper-parameters.
A clone is a different object without shared references, in post-init state.This function is equivalent to returning sklearn.clone of self.
- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
Notes
If successful, equal in value to
type(self)(**self.get_params(deep=False))
.
- clone_tags(estimator, tag_names=None)[source]#
Clone tags from another estimator as dynamic override.
- Parameters:
- estimatorestimator inheriting from :class:BaseEstimator
- tag_namesstr or list of str, default = None
Names of tags to clone. If None then all tags in estimator are usedas tag_names.
- Returns:
- Self
Reference to self.
Notes
Changes object state by setting tag values in tag_set from estimator asdynamic tags in self.
- classmethod create_test_instance(parameter_set='default')[source]#
Construct Estimator instance if possible.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If nospecial parameters are defined for a value, will return “default” set.
- Returns:
- instanceinstance of the class with default parameters
Notes
get_test_params can return dict or list of dict.This function takes first or single dict that get_test_params returns, andconstructs the object with that.
- classmethod create_test_instances_and_names(parameter_set='default')[source]#
Create list of all test instances and a list of names for them.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If nospecial parameters are defined for a value, will return “default” set.
- Returns:
- objslist of instances of cls
i-th instance is cls(**cls.get_test_params()[i])
- nameslist of str, same length as objs
i-th element is name of i-th instance of obj in testsconvention is {cls.__name__}-{i} if more than one instanceotherwise {cls.__name__}
- fit(X, y=None)[source]#
Fit transformer to X, optionally to y.
- State change:
Changes state to “fitted”.
Writes to self:
Sets fitted model attributes ending in “_”, fitted attributes areinspectable via
get_fitted_params
.Sets
self.is_fitted
flag toTrue
.if
self.get_tag("remember_data")
isTrue
, memorizes X asself._X
, coerced toself.get_tag("X_inner_mtype")
.
- Parameters:
- Xtime series in
sktime
compatible data container format Data to fit transform to.
Individual data formats in
sktime
are so-called mtypespecifications, each mtype implements an abstract scitype.Series
scitype = individual time series.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection of time series.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype.For usage, see transformer tutorial
examples/03_transformers.ipynb
- yoptional, data in sktime compatible data format, default=None
Additional data, e.g., labels for transformationIf
self.get_tag("requires_y")
isTrue
,must be passed infit
, not optional.For required format, see class docstring for details.
- Xtime series in
- 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._X : X, coerced copy of X, if remember_data tag is True
possibly coerced to inner type or update_data compatible typeby reference, when possible
model attributes (ending in “_”) : dependent on estimator
- Parameters:
- Xtime series in
sktime
compatible data container format Data to fit transform to, and data to transform.
Individual data formats in
sktime
are so-called mtypespecifications, each mtype implements an abstract scitype.Series
scitype = individual time series.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection of time series.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype.For usage, see transformer tutorial
examples/03_transformers.ipynb
- yoptional, data in sktime compatible data format, default=None
Additional data, e.g., labels for transformationIf
self.get_tag("requires_y")
isTrue
,must be passed infit
, not optional.For required format, see class docstring for details.
- Xtime series in
- Returns:
- transformed version of X
- type depends on type of X and scitype:transform-output tag:
X | tf-output | type of return |
|----------|————–|------------------------|| Series | Primitives | pd.DataFrame (1-row) || Panel | Primitives | pd.DataFrame || Series | Series | Series || Panel | Series | Panel || Series | Panel | Panel |
- instances in return correspond to instances in X
- combinations not in the table are currently not supported
- Explicitly, with examples:
- if X is Series (e.g., pd.DataFrame) and transform-output is Series
then the return is a single Series of the same mtypeExample: detrending a single series
- if X is Panel (e.g., pd-multiindex) and transform-output is Series
- then the return is Panel with same number of instances as X
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
- if X is Series or Panel and transform-output is Primitives
then the return is pd.DataFrame with as many rows as instances in XExample: i-th row of the return has mean and variance of the i-th series
- if X is Series and transform-output is Panel
then the return is a Panel object of type pd-multiindexExample: i-th instance of the output is the i-th window running over X
- classmethod get_class_tag(tag_name, tag_value_default=None)[source]#
Get a class tag’s value.
Does not return information from dynamic tags (set via set_tags or clone_tags)that are defined on instances.
- Parameters:
- tag_namestr
Name of tag value.
- tag_value_defaultany
Default/fallback value if tag is not found.
- Returns:
- tag_value
Value of the tag_name tag in self. If not found, returnstag_value_default.
- classmethod get_class_tags()[source]#
Get class tags from the class and all its parent classes.
Retrieves tag: value pairs from _tags class attribute. Does not returninformation from dynamic tags (set via set_tags or clone_tags)that are defined on instances.
- Returns:
- collected_tagsdict
Dictionary of class tag name: tag value pairs. Collected from _tagsclass attribute via nested inheritance.
- get_config()[source]#
Get config flags for self.
- Returns:
- config_dictdict
Dictionary of config name : config value pairs. Collected from _configclass attribute via nested inheritance and then any overridesand new tags from _onfig_dynamic object attribute.
- get_fitted_params(deep=True)[source]#
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
Whether to return fitted parameters of components.
If True, will return a dict of parameter name : value for this object,including fitted parameters of fittable components(= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object,but not include fitted parameters of components.
- Returns:
- fitted_paramsdict with str-valued keys
Dictionary of fitted parameters, paramname : paramvaluekeys-value pairs include:
always: all fitted parameters of this object, as via get_param_namesvalues are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parametersparameters of components are indexed as [componentname]__[paramname]all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion,e.g., [componentname]__[componentcomponentname]__[paramname], etc
- classmethod get_param_defaults()[source]#
Get object’s parameter defaults.
- Returns:
- default_dict: dict[str, Any]
Keys are all parameters of cls that have a default defined in __init__values are the defaults, as defined in __init__.
- classmethod get_param_names()[source]#
Get object’s parameter names.
- Returns:
- param_names: list[str]
Alphabetically sorted list of parameter names of cls.
- get_params(deep=True)[source]#
Get a dict of parameters values for this object.
- Parameters:
- deepbool, default=True
Whether to return parameters of components.
If True, will return a dict of parameter name : value for this object,including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object,but not include parameters of components.
- Returns:
- paramsdict with str-valued keys
Dictionary of parameters, paramname : paramvaluekeys-value pairs include:
always: all parameters of this object, as via get_param_namesvalues are parameter value for that key, of this objectvalues are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parametersparameters of components are indexed as [componentname]__[paramname]all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion,e.g., [componentname]__[componentcomponentname]__[paramname], etc
- get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#
Get tag value from estimator class and dynamic tag overrides.
- Parameters:
- tag_namestr
Name of tag to be retrieved
- tag_value_defaultany type, optional; default=None
Default/fallback value if tag is not found
- raise_errorbool
whether a ValueError is raised when the tag is not found
- Returns:
- tag_valueAny
Value of the tag_name tag in self. If not found, returns an error ifraise_error is True, otherwise it returns tag_value_default.
- Raises:
- ValueError if raise_error is True i.e. if tag_name is not in
- self.get_tags().keys()
- get_tags()[source]#
Get tags from estimator class and dynamic tag overrides.
- Returns:
- collected_tagsdict
Dictionary of tag name : tag value pairs. Collected from _tagsclass attribute via nested inheritance and then any overridesand new tags from _tags_dynamic object attribute.
- inverse_transform(X, y=None)[source]#
Inverse transform X and return an inverse transformed version.
- Currently it is assumed that only transformers with tags
“scitype:transform-input”=”Series”, “scitype:transform-output”=”Series”,
have an inverse_transform.
- State required:
Requires state to be “fitted”.
Accesses in self:
Fitted model attributes ending in “_”.
self.is_fitted
, must be True
- Parameters:
- Xtime series in
sktime
compatible data container format Data to fit transform to.
Individual data formats in
sktime
are so-called mtypespecifications, each mtype implements an abstract scitype.Series
scitype = individual time series.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection of time series.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype.For usage, see transformer tutorial
examples/03_transformers.ipynb
- yoptional, data in sktime compatible data format, default=None
Additional data, e.g., labels for transformation.Some transformers require this, see class docstring for details.
- Xtime series in
- Returns:
- inverse transformed version of X
of the same type as X, and conforming to mtype format specifications
- is_composite()[source]#
Check if the object is composed of other BaseObjects.
A composite object is an object which contains objects, as parameters.Called on an instance, since this may differ by instance.
- Returns:
- composite: bool
Whether an object has any parameters whose valuesare BaseObjects.
- property is_fitted[source]#
Whether
fit
has been called.
- classmethod load_from_path(serial)[source]#
Load object from file location.
- Parameters:
- serialresult of ZipFile(path).open(“object)
- Returns:
- deserialized self resulting in output at
path
, ofcls.save(path)
- deserialized self resulting in output at
- classmethod load_from_serial(serial)[source]#
Load object from serialized memory container.
- Parameters:
- serial1st element of output of
cls.save(None)
- serial1st element of output of
- Returns:
- deserialized self resulting in output
serial
, ofcls.save(None)
- deserialized self resulting in output
- reset()[source]#
Reset the object to a clean post-init state.
Using reset, runs __init__ with current values of hyper-parameters(result of get_params). This Removes any object attributes, except:
hyper-parameters = arguments of __init__
object attributes containing double-underscores, i.e., the string “__”
Class and object methods, and class attributes are also unaffected.
- Returns:
- self
Instance of class reset to a clean post-init state but retainingthe current hyper-parameter values.
Notes
Equivalent to sklearn.clone but overwrites self. After self.reset()call, self is equal in value to type(self)(**self.get_params(deep=False))
- save(path=None, serialization_format='pickle')[source]#
Save serialized self to bytes-like object or to (.zip) file.
Behaviour:if
path
is None, returns an in-memory serialized selfifpath
is a file location, stores self at that location as a zip filesaved files are zip files with following contents:_metadata - contains class of self, i.e., type(self)_obj - serialized self. This class uses the default serialization (pickle).
- Parameters:
- pathNone or file location (str or Path)
if None, self is saved to an in-memory objectif file location, self is saved to that file location. If:
path=”estimator” then a zip file
estimator.zip
will be made at cwd.path=”/home/stored/estimator” then a zip fileestimator.zip
will bestored in/home/stored/
.- serialization_format: str, default = “pickle”
Module to use for serialization.The available options are “pickle” and “cloudpickle”.Note that non-default formats might requireinstallation of other soft dependencies.
- Returns:
- if
path
is None - in-memory serialized self - if
path
is file location - ZipFile with reference to the file
- if
- set_config(**config_dict)[source]#
Set config flags to given values.
- Parameters:
- config_dictdict
Dictionary of config name : config value pairs.Valid configs, values, and their meaning is listed below:
- displaystr, “diagram” (default), or “text”
how jupyter kernels display instances of self
“diagram” = html box diagram representation
“text” = string printout
- print_changed_onlybool, default=True
whether printing of self lists only self-parameters that differfrom defaults (False), or all parameter names and values (False).Does not nest, i.e., only affects self and not component estimators.
- warningsstr, “on” (default), or “off”
whether to raise warnings, affects warnings from sktime only
“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime
- backend:parallelstr, optional, default=”None”
backend to use for parallelization when broadcasting/vectorizing, one of
“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses
joblib.Parallel
“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
“dask”: uses
dask
, requiresdask
package in environment
- backend:parallel:paramsdict, optional, default={} (no parameters passed)
additional parameters passed to the parallelization backend as config.Valid keys depend on the value of
backend:parallel
:“None”: no additional parameters,
backend_params
is ignored“loky”, “multiprocessing” and “threading”: default
joblib
backendsany valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
, with the exception ofbackend
which is directlycontrolled bybackend
.Ifn_jobs
is not passed, it will default to-1
, other parameterswill default tojoblib
defaults.“joblib”: custom and 3rd party
joblib
backends,e.g.,spark
. Any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
,backend
must be passed as a key ofbackend_params
in this case.Ifn_jobs
is not passed, it will default to-1
, other parameterswill default tojoblib
defaults.“dask”: any valid keys for
dask.compute
can be passed,e.g.,scheduler
- input_conversionstr, one of “on” (default), “off”, or valid mtype string
controls input checks and conversions,for
_fit
,_transform
,_inverse_transform
,_update
"on"
- input check and conversion is carried out"off"
- input check and conversion are not carried outbefore passing data to inner methodsvalid mtype string - input is assumed to specified mtype,conversion is carried out but no check
- output_conversionstr, one of “on”, “off”, valid mtype string
controls output conversion for
_transform
,_inverse_transform
"on"
- if input_conversion is “on”, output conversion is carried out"off"
- output of_transform
,_inverse_transform
is directly returnedvalid mtype string - output is converted to specified mtype
- Returns:
- selfreference to self.
Notes
Changes object state, copies configs in config_dict to self._config_dynamic.
- set_params(**params)[source]#
Set the parameters of this object.
The method works on simple estimators as well as on composite objects.Parameter key strings
<component>__<parameter>
can be used for composites,i.e., objects that contain other objects, to access<parameter>
inthe component<component>
.The string<parameter>
, without<component>__
, can also be used ifthis makes the reference unambiguous, e.g., there are no two parameters ofcomponents with the name<parameter>
.- Parameters:
- **paramsdict
BaseObject parameters, keys must be
<component>__<parameter>
strings.__ suffixes can alias full strings, if unique among get_params keys.
- Returns:
- selfreference to self (after parameters have been set)
- set_random_state(random_state=None, deep=True, self_policy='copy')[source]#
Set random_state pseudo-random seed parameters for self.
Finds
random_state
named parameters viaestimator.get_params
,and sets them to integers derived fromrandom_state
viaset_params
.These integers are sampled from chain hashing viasample_dependent_seed
,and guarantee pseudo-random independence of seeded random generators.Applies to
random_state
parameters inestimator
depending onself_policy
, and remaining component estimatorsif and only ifdeep=True
.Note: calls
set_params
even ifself
does not have arandom_state
,or none of the components have arandom_state
parameter.Therefore,set_random_state
will reset anyscikit-base
estimator,even those without arandom_state
parameter.- Parameters:
- random_stateint, RandomState instance or None, default=None
Pseudo-random number generator to control the generation of the randomintegers. Pass int for reproducible output across multiple function calls.
- deepbool, default=True
Whether to set the random state in sub-estimators.If False, will set only
self
’srandom_state
parameter, if exists.If True, will setrandom_state
parameters in sub-estimators as well.- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
estimator.random_state
is set to inputrandom_state
“keep” :
estimator.random_state
is kept as is“new” :
estimator.random_state
is set to a new random state,
derived from input
random_state
, and in general different from it
- Returns:
- selfreference to self
- set_tags(**tag_dict)[source]#
Set dynamic tags to given values.
- Parameters:
- **tag_dictdict
Dictionary of tag name: tag value pairs.
- Returns:
- Self
Reference to self.
Notes
Changes object state by setting tag values in tag_dict as dynamic tags in self.
- transform(X, y=None)[source]#
Transform X and return a transformed version.
- State required:
Requires state to be “fitted”.
Accesses in self:
Fitted model attributes ending in “_”.
self.is_fitted
, must be True
- Parameters:
- Xtime series in
sktime
compatible data container format Data to transform.
Individual data formats in
sktime
are so-called mtypespecifications, each mtype implements an abstract scitype.Series
scitype = individual time series.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection of time series.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype.For usage, see transformer tutorial
examples/03_transformers.ipynb
- yoptional, data in sktime compatible data format, default=None
Additional data, e.g., labels for transformation.Some transformers require this, see class docstring for details.
- Xtime series in
- Returns:
- transformed version of X
- type depends on type of X and scitype:transform-output tag:
transform
X
-output
type of return
Series
Primitives
pd.DataFrame (1-row)
Panel
Primitives
pd.DataFrame
Series
Series
Series
Panel
Series
Panel
Series
Panel
Panel
- instances in return correspond to instances in X
- combinations not in the table are currently not supported
- Explicitly, with examples:
- if X is Series (e.g., pd.DataFrame) and transform-output is Series
then the return is a single Series of the same mtypeExample: detrending a single series
- if X is Panel (e.g., pd-multiindex) and transform-output is Series
- then the return is Panel with same number of instances as X
(the transformer is applied to each input Series instance)
Example: all series in the panel are detrended individually
- if X is Series or Panel and transform-output is Primitives
then the return is pd.DataFrame with as many rows as instances in XExample: i-th row of the return has mean and variance of the i-th series
- if X is Series and transform-output is Panel
then the return is a Panel object of type pd-multiindexExample: i-th instance of the output is the i-th window running over X
- update(X, y=None, update_params=True)[source]#
Update transformer with X, optionally y.
- State required:
Requires state to be “fitted”.
Accesses in self:
Fitted model attributes ending in “_”.
self.is_fitted
, must be True
Writes to self:
Fitted model attributes ending in “_”.
if
remember_data
tag is True, writes toself._X
,updated by values inX
, viaupdate_data
.
- Parameters:
- Xtime series in
sktime
compatible data container format Data to update transformation with
Individual data formats in
sktime
are so-called mtypespecifications, each mtype implements an abstract scitype.Series
scitype = individual time series.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection of time series.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype.For usage, see transformer tutorial
examples/03_transformers.ipynb
- yoptional, data in sktime compatible data format, default=None
Additional data, e.g., labels for transformation.Some transformers require this, see class docstring for details.
- Xtime series in
- Returns:
- selfa fitted instance of the estimator
- classmethod get_test_params(parameter_set='default')[source]#
Return testing parameter settings for the estimator.
- Parameters:
- parameter_setstr, default=”default”
Name of the set of test parameters to return, for use in tests. If nospecial parameters are defined for a value, will return
"default"
set.There are currently no reserved values for transformers.
- Returns:
- paramsdict or list of dict, default = {}
Parameters to create testing instances of the classEach dict are parameters to construct an “interesting” test instance, i.e.,
MyClass(**params)
orMyClass(**params[i])
creates a valid testinstance.create_test_instance
uses the first (or only) dictionary inparams
DropNA — sktime documentation (2024)
References
- https://inmoose.readthedocs.io/en/v0.5.0/generated/inmoose.limma.TestResults.html
- https://sktime-backup.readthedocs.io/en/v0.30.0/api_reference/auto_generated/sktime.transformations.series.dropna.DropNA.html
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