ColumnTransformer#

class ColumnTransformer(transformers, remainder='drop', sparse_threshold=0.3, n_jobs=1, transformer_weights=None, preserve_dataframe=True)[source]#

Column-wise application of transformers.

Applies transformations to columns of an array or pandas DataFrame. Simply takes the column transformer from sklearn and adds capability to handle pandas dataframe.

This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data, to combine several feature extraction mechanisms or transformations into a single transformer.

Parameters:
transformerslist of tuples

List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data. name : string

Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using set_params and searched in grid search.

transformerestimator or {“passthrough”, “drop”}

Estimator must support fit and transform. Special-cased strings “drop” and “passthrough” are accepted as well, to indicate to drop the columns or to pass them through untransformed, respectively.

column(s) : str or int, array-like of string or int, slice, boolean mask array or callable

Indexes the data on its second axis. Integers are interpreted as positional columns, while strings can reference DataFrame columns by name. A scalar string or int should be used where transformer expects X to be a 1d array-like (vector), otherwise a 2d array will be passed to the transformer. A callable is passed the input data X and can return any of the above.

remainder{“drop”, “passthrough”} or estimator, default “drop”

By default, only the specified columns in transformations are transformed and combined in the output, and the non-specified columns are dropped. (default of "drop"). By specifying remainder="passthrough", all remaining columns that were not specified in transformations will be automatically passed through. This subset of columns is concatenated with the output of the transformations. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform.

sparse_thresholdfloat, default = 0.3

If the output of the different transformations contains sparse matrices, these will be stacked as a sparse matrix if the overall density is lower than this value. Use sparse_threshold=0 to always return dense. When the transformed output consists of all dense data, the stacked result will be dense, and this keyword will be ignored.

n_jobsint or None, optional (default=None)

Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

transformer_weightsdict, optional

Multiplicative weights for features per transformer. The output of the transformer is multiplied by these weights. Keys are transformer names, values the weights.

preserve_dataframeboolean

If True, pandas dataframe is returned. If False, numpy array is returned.

Attributes:
transformers_list

The collection of fitted transformations as tuples of (name, fitted_transformer, column). fitted_transformer can be an estimator, “drop”, or “passthrough”. In case there were no columns selected, this will be the unfitted transformer. If there are remaining columns, the final element is a tuple of the form: (“remainder”, transformer, remaining_columns) corresponding to the remainder parameter. If there are remaining columns, then len(transformers_)==len(transformations)+1, otherwise len(transformers_)==len(transformations).

named_transformers_Bunch object, a dictionary with attribute access

Access the fitted transformer by name.

sparse_output_bool

Boolean flag indicating whether the output of transform is a sparse matrix or a dense numpy array, which depends on the output of the individual transformations and the sparse_threshold keyword.

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 the transformer.

fit_transform(X[, y])

Fit and transform, shorthand.

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_feature_names_out([input_features])

Get output feature names for transformation.

get_fitted_params([deep])

Get fitted parameters.

get_metadata_routing()

Get metadata routing of this object.

get_param_defaults()

Get object's parameter defaults.

get_param_names()

Get object's parameter names.

get_params([deep])

Get parameters for this estimator.

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()

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_output(*[, transform])

Set the output container when "transform" and "fit_transform" are called.

set_params(**kwargs)

Set the parameters of this estimator.

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 the data.

update(X[, y, update_params])

Update transformer with X, optionally y.

classmethod get_test_params()[source]#

Return testing parameter settings for the estimator.

Returns:
paramsdict or list of dict, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

fit(X, y=None)[source]#

Fit the transformer.

transform(X, y=None)[source]#

Transform the data.

fit_transform(X, y=None)[source]#

Fit and transform, shorthand.

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 used as tag_names.

Returns:
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic 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 no special 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, and constructs 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 no special 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 tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

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, returns tag_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 return information 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 _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

Returns:
config_dictdict

Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: [“x0”, “x1”, …, “x(n_features_in_ - 1)”].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Transformed feature names.

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 : paramvalue keys-value pairs include:

  • always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

  • if deep=True, also contains keys/value pairs of component parameters parameters 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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

New in version 1.4.

Returns:
routingMetadataRouter

A MetadataRouter encapsulating routing information.

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 parameters for this estimator.

Returns the parameters given in the constructor as well as the estimators contained within the transformers of the ColumnTransformer.

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, 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 if raise_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 _tags class attribute via nested inheritance and then any overrides and 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: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:
Xtime series in sktime compatible data container format

Data to inverse transform, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series 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 values are 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)
property named_transformers_[source]#

Access the fitted transformer by name.

Read-only attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects.

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 retaining the 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 self if 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 object if 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 be stored 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 require installation 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 differ from 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 backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

  • “joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can 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 parameters will 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 out before 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_transform is 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_output(*, transform=None)[source]#

Set the output container when “transform” and “fit_transform” are called.

Calling set_output will set the output of all estimators in transformers and transformers_.

Parameters:
transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

New in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

set_params(**kwargs)[source]#

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in transformers of ColumnTransformer.

Parameters:
**kwargsdict

Estimator parameters.

Returns:
selfColumnTransformer

This estimator.

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 on self_policy, and remaining component estimators if 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 random integers. 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.

update(X, y=None, update_params=True)[source]#

Update transformer with X, optionally y.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : accessed by _update and by update_data, if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _update

Writes to self: _X : updated by values in X, via update_data, if remember_data tag is True fitted model attributes (ending in “_”) : only if update_params=True

type and nature of update are dependent on estimator

Parameters:
Xtime series in sktime compatible data container format

Data to update transform with, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series 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