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:

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

__class__

alias of builtins.type

__delattr__

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'schrodinger.application.matsci.mlearn.base', '__doc__': '\n Class that MUST be inherited to create sklearn Model.\n ', 'fit': <function BaseFeaturizer.fit>, 'transform': <function BaseFeaturizer.transform>})

__dir__() → list

default dir() implementation

__eq__

Return self==value.

__format__()

default object formatter

__ge__

Return self>=value.

__getattribute__

Return getattr(self, name).

__getstate__()

__gt__

Return self>value.

__hash__

Return hash(self).

__init__

Initialize self. See help(type(self)) for accurate signature.

__init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__

Return self<=value.

__lt__

Return self<value.

__module__ = 'schrodinger.application.matsci.mlearn.base'

__ne__

Return self!=value.

__new__()

Create and return a new object. See help(type) for accurate signature.

__reduce__()

helper for pickle

__reduce_ex__()

helper for pickle

__repr__()

Return repr(self).

__setattr__

Implement setattr(self, name, value).

__setstate__(state)

__sizeof__() → int

size of object in memory, in bytes

__str__

Return str(self).

__subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object (if defined)

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.

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.

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