CIMtools.preprocessing package
- class CIMtools.preprocessing.CGR(cgr_type='0')
Bases:
CIMtoolsTransformerMixin
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') CGR
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') CGR
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.CGRToMatrix(charge=True, is_radical=False, isotope=False, hybridization=False, neighbors=False, in_ring=False, adjacent=True)
Bases:
GraphToMatrix
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') CGRToMatrix
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') CGRToMatrix
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.Conditions(temperature=298.15, pressure=100000, solvents=None)
Bases:
object
- property pressure
- property solvents
- property temperature
- class CIMtools.preprocessing.ConditionsToDataFrame(max_solvents=1)
Bases:
CIMtoolsTransformerMixin
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_feature_names()
Get feature names.
- Returns
feature_names – Names of the features produced by transform.
- Return type
list of strings
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') ConditionsToDataFrame
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') ConditionsToDataFrame
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.DictToConditions(temperature=None, pressure=None, solvents=None, amounts=None, default_temperature=298.15, default_pressure=100000, default_first_solvent='water', default_first_amount=1)
Bases:
CIMtoolsTransformerMixin
Dictionary to Conditions mapper
- Parameters
temperature – name of temperature key
pressure – name of pressure key
solvents – names of solvents keys
amounts – names of solvents amounts keys
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') DictToConditions
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') DictToConditions
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.EquationTransformer(equation='x')
Bases:
CIMtoolsTransformerMixin
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_feature_names()
Get feature names.
- Returns
feature_names – Names of the features produced by transform.
- Return type
list of strings
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') EquationTransformer
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') EquationTransformer
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.Fragmentor(fragment_type=3, min_length=2, max_length=10, cgr_dynbonds=0, doallways=False, useformalcharge=False, header=None, workpath='.', version='2017', verbose=False, remove_rare_ratio=0, return_domain=False)
Bases:
BaseEstimator
,TransformerMixin
ISIDA Fragmentor wrapper
- Parameters
fragment_type – fragmentation type. see Fragmentor manual (-t)
min_length – minimal length of fragments. see Fragmentor manual (-l)
max_length – maximal length of fragments. see Fragmentor manual (-u)
cgr_dynbonds – see Fragmentor manual (-d)
doallways – see Fragmentor manual (–DoAllWays)
useformalcharge – see Fragmentor manual (–UseFormalCharge)
header –
if None descriptors will be generated on train set if False Fragmentor will work in headless mode. in this mod fit unusable and Fragmentor return
all found descriptors
else path string to existing header file acceptable
workpath – path for temp files
version – fragmentor version
verbose – silent Fragmentor output
remove_rare_ratio – if descriptors found on train less then given ratio it will be removed from header. if partial fit used, be sure to use finalize method. unusable if headless mode set
return_domain – add AD bool column. False in column is: molecule/CGR has new features
- delete_work_path()
- finalize()
finalize partial fitting procedure
- fit(x, y=None)
Compute the header.
- fit_transform(x, y=None)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_feature_names()
Get feature names.
- Returns
feature_names – Names of the features produced by transform.
- Return type
list of strings
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- partial_fit(x, y=None)
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') Fragmentor
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_partial_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') Fragmentor
Request metadata passed to the
partial_fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topartial_fit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topartial_fit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter inpartial_fit
.- Returns
self – The updated object.
- Return type
object
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') Fragmentor
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- set_work_path(workpath)
- transform(x)
- class CIMtools.preprocessing.FragmentorFingerprint(fingerprint_size=12, bits_count=4, bits_active=2, fragment_type=3, min_length=2, max_length=10, cgr_dynbonds=0, doallways=False, useformalcharge=False, workpath='.', version='2017', verbose=False)
Bases:
BaseEstimator
,TransformerMixin
ISIDA Fragmentor fragments to fingerprints
- Parameters
fingerprint_size – exponent of 2 of fingerprint length
bits_count – include number of fragment descriptors into fingerprint. for example by default: if number is one: only one set of bits will be activated. if number is tho: additional set of bits will be activated (totally up to 2*bits_active). if number is four or greater: will be activated up 4*bits_active bits
bits_active – number of activated bits for each fragment (need for prevent collision bit lost)
workpath – path for temp files.
version – fragmentor version. need for selecting Fragmentor executables named as fragmentor-{version}
- delete_work_path()
- fit(x, y=None)
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') FragmentorFingerprint
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') FragmentorFingerprint
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- set_work_path(workpath)
- transform(x)
- transform_bitset(x)
- class CIMtools.preprocessing.MoleculesToMatrix(charge=True, is_radical=False, isotope=False, hybridization=False, neighbors=False, implicit_hydrogens=False, total_hydrogens=False, in_ring=False, adjacent=True)
Bases:
GraphToMatrix
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') MoleculesToMatrix
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') MoleculesToMatrix
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.RDTool(algorithm='max', verbose=False)
Bases:
CIMtoolsTransformerMixin
- Parameters
algorithm – ‘max’,’min’,’mixture’
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') RDTool
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') RDTool
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.SolventVectorizer(polarizability_form1=True, polarizability_form2=True, permettivity_form1=True, permettivity_form2=True, permettivity_form3=True, permettivity_form4=True, permettivity_polarizability=True, alpha_kamlet_taft=True, beta_kamlet_taft=True, pi_kamlet_taft=True, spp_katalan=True, sb_katalan=True, sa_katalan=True)
Bases:
CIMtoolsTransformerMixin
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_feature_names()
Get feature names.
- Returns
feature_names – Names of the features produced by transform.
- Return type
list of strings
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') SolventVectorizer
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') SolventVectorizer
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)
- class CIMtools.preprocessing.StandardizeCGR
Bases:
CIMtoolsTransformerMixin
Reactions and Molecules standardization
For molecules kekule/thiele and groups standardization procedures will be applied.
- fit(x, y=None)
Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
- fit_transform(X, y=None, **fit_params)
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
routing – A
MetadataRequest
encapsulating routing information.- Return type
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- set_fit_request(*, x: Union[bool, None, str] = '$UNCHANGED$') StandardizeCGR
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter infit
.- Returns
self – The updated object.
- Return type
object
- set_output(*, transform=None)
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_params(**params)
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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance
- set_transform_request(*, x: Union[bool, None, str] = '$UNCHANGED$') StandardizeCGR
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters
x (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
x
parameter intransform
.- Returns
self – The updated object.
- Return type
object
- transform(x)