schrodinger.application.matsci.mlearn.base module¶
Classes and functions to deal with ML features.
Copyright Schrodinger, LLC. All rights reserved.
- class schrodinger.application.matsci.mlearn.base.BaseFeaturizer¶
Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Class that MUST be inherited to create sklearn Model.
- fit(data, data_y=None)¶
Fit and return self. Anything that evaluates properties related to the passed data should go here. For example, compute physical properties of a stucture and save them as class property, to be used in the transform method.
- Parameters
data (numpy array of shape [n_samples, n_features]) – Training set
data_y (numpy array of shape [n_samples]) – Target values
- Return type
- Returns
self object with fitted data
- transform(data)¶
Get numerical features. Must be implemented by a child class.
- Parameters
data (numpy array of shape [n_samples, n_features]) – Training set
- Return type
numpy array of shape [n_samples, n_features_new]
- Returns
Transformed array
- set_fit_request(*, data: Union[bool, None, str] = '$UNCHANGED$', data_y: Union[bool, None, str] = '$UNCHANGED$') schrodinger.application.matsci.mlearn.base.BaseFeaturizer ¶
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
. Otherwise it has no effect.- datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
data
parameter infit
.- data_ystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
data_y
parameter infit
.
- selfobject
The updated object.
- set_transform_request(*, data: Union[bool, None, str] = '$UNCHANGED$') schrodinger.application.matsci.mlearn.base.BaseFeaturizer ¶
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
. Otherwise it has no effect.- datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
data
parameter intransform
.
- selfobject
The updated object.