schrodinger.application.matsci.mlearn.base module¶
Classes and functions to deal with ML features.
Copyright Schrodinger, LLC. All rights reserved.
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class
schrodinger.application.matsci.mlearn.base.
BaseFeaturizer
¶ Bases:
sklearn.base.BaseEstimator
,sklearn.base.TransformerMixin
Class that MUST be inherited to create sklearn Model.
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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
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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
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__init__
¶ Initialize self. See help(type(self)) for accurate signature.
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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.
- X : numpy array of shape [n_samples, n_features]
- Training set.
- y : numpy array of shape [n_samples]
- Target values.
- X_new : numpy array of shape [n_samples, n_features_new]
- Transformed array.
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get_params
(deep=True)¶ Get parameters for this estimator.
- deep : boolean, optional
- If True, will return the parameters for this estimator and contained subobjects that are estimators.
- params : mapping of string to any
- Parameter names mapped to their values.
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set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.self
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