DropNA — sktime documentation (2024)

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()[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__.

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 to True.

• if self.get_tag("remember_data") is True, memorizes X asself._X, coerced to self.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, or np.ndarray (1D or 2D)

• Panel scitype = collection of time series.pd.DataFrame with 2-level row MultiIndex (instance, time),3D np.ndarray (instance, variable, time),list of Series typed pd.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") is True,must be passed in fit, not optional.For required format, see class docstring for details.

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, or np.ndarray (1D or 2D)

• Panel scitype = collection of time series.pd.DataFrame with 2-level row MultiIndex (instance, time),3D np.ndarray (instance, variable, time),list of Series typed pd.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

See Also

pandas.DataFrame.groupby — pandas 2.2.2 documentation

yoptional, data in sktime compatible data format, default=None

Additional data, e.g., labels for transformationIf self.get_tag("requires_y") is True,must be passed in fit, not optional.For required format, see class docstring for details.

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(sort=True)[source]#

Get object’s parameter names.

Parameters:

sortbool, default=True

Whether to return the parameter names sorted in alphabetical order (True),or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]

List of parameter names of cls.If sort=False, in same order as they appear in the class __init__.If sort=True, alphabetically ordered.

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, or np.ndarray (1D or 2D)

• Panel scitype = collection of time series.pd.DataFrame with 2-level row MultiIndex (instance, time),3D np.ndarray (instance, variable, time),list of Series typed pd.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.

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, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

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 selfif path is a file location, stores self at that location as a zip file

saved 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 file estimator.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

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, requires dask 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 for joblib.Parallel can be passed here, e.g.,n_jobs, with the exception of backend which is directlycontrolled by backend.If n_jobs is not passed, it will default to -1, other parameterswill default to joblib defaults.

• “joblib”: custom and 3rd party joblib backends,e.g., spark. Any valid keys for joblib.Parallelcan be passed here, e.g., n_jobs,backend must be passed as a key of backend_params in this case.If n_jobs is not passed, it will default to -1, other parameterswill default to joblib 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 methods

• valid 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_transformis directly returned

• valid 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 via estimator.get_params,and sets them to integers derived from random_state via set_params.These integers are sampled from chain hashing via sample_dependent_seed,and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending onself_policy, and remaining component estimatorsif and only if deep=True.

Note: calls set_params even if self does not have a random_state,or none of the components have a random_state parameter.Therefore, set_random_state will reset any scikit-base estimator,even those without a random_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’s random_state parameter, if exists.If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

• “copy” : estimator.random_state is set to input random_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, or np.ndarray (1D or 2D)

• Panel scitype = collection of time series.pd.DataFrame with 2-level row MultiIndex (instance, time),3D np.ndarray (instance, variable, time),list of Series typed pd.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.

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 to self._X,updated by values in X, via update_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, or np.ndarray (1D or 2D)

• Panel scitype = collection of time series.pd.DataFrame with 2-level row MultiIndex (instance, time),3D np.ndarray (instance, variable, time),list of Series typed pd.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.

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) or MyClass(**params[i]) creates a valid testinstance.create_test_instance uses the first (or only) dictionary in params