schrodinger.application.matsci.mlearn.base module¶

Classes and functions to deal with ML features.

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

BaseFeaturizer

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 (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to 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 to 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. Otherwise it has no effect.

datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for data parameter in fit.
data_ystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for data_y parameter in fit.

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 (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to transform.
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 in transform.

selfobject: The updated object.