FAME3RScoreEstimator

FAME3RScoreEstimator#

class fame3r.FAME3RScoreEstimator(n_neighbors: int = 3)[source]#

Computes the FAME score for a set of features.

The FAME score is defined as the mean Tanimoto similarity of the feature vector to the n closest vectors in the training set.

It is intended for this estimator to only be used with binary feature (“fingerprint”) vectors, as Tanimoto similarity is not well-behaved on arbitrary vectors.

Parameters:
n_neighborsint, default=3

Number of nearest neigbors to consider during FAME score calculation. Defaults to 3, as defined in the original paper.

Examples

>>> from fame3r import FAME3RVectorizer, FAME3RScoreEstimator
>>> from sklearn.pipeline import make_pipeline
>>> pipeline = make_pipeline(
>>>    FAME3RVectorizer(output=["fingerprint"]),
>>>    FAME3RScoreEstimator()
>>> ).fit([["CC[C:1]"], ["CC[N:1]"], ["CC[O:1]"]])
>>> pipeline.predict([["[C:1]CC"]])
array([0.66666667])
fit(X, y=None)[source]#

Fit the estimator to the training set of known samples.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training data.

y(ignored)

Not used, present for API consistency by convention.

Returns:
selfobject

FAME3RVectorizer class instance.

predict(X)[source]#

Compute the FAME score of the given samples.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Query data.

Returns:
yndarray of shape (n_samples,)

The predicted FAME scores.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Not used, present here for API consistency by convention.

Returns:
feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

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

Returns:
paramsdict

Parameter names mapped to their values.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
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.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

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