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

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.

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_params(deep=True)¶

Get parameters for this estimator.

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

paramsdict: Parameter names mapped to their values.

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.

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

selfestimator instance: 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.

**paramsdict: Estimator parameters.

selfestimator instance: Estimator instance.