LayeredFingerprint

class skfp.fingerprints.LayeredFingerprint(fp_size: int = 2048, min_path: int = 1, max_path: int = 7, linear_paths_only: bool = False, sparse: bool = False, n_jobs: int | None = None, batch_size: int | None = None, verbose: int = 0)

Layered fingerprint.

This fingerprint is an RDKit original [1]. This is a hashed fingerprint, where fragments are created from small subgraphs on the molecular graph.

For a given molecule, all paths between min_path and max_path (inclusive) are extracted. Those are any subgraphs, unless linear_paths_only is set to True. Note that all explicit atoms, including hydrogens if present, are used.

Then each subgraph is hashed in “layers” (hence the fingerprint name), using different atom and bond invariants (features). Additionally, information about path length and the number of distinct atoms is included. Those hashes are combined into a single value and hashed into the resulting fingerprint, which is folded at the end.

Layers are: - pure topology (using only subgraph “shape”) - bond order (type), ignoring aromaticiyt (aromatic bonds are treated as single) - atom types (atomic numbers) - presence of rings (whether bond is in a ring) - ring sizes (size of smallest ring that bond is a part of) - aromaticity (whether bond is aromatic or not)

Parameters:

• fp_size (int, default=2048) – Size of output vectors, i.e. number of bits for each fingerprint. Must be positive.

• min_path (int, default=1) – Minimal length of paths used, in bonds. Default value means that at least 2-atom subgraphs are used.

• max_path (int, default=7) – Maximal length of paths used, in bonds.

• linear_paths_only (bool, default=False) – Whether to use only linear paths, instead of any subgraphs.

• sparse (bool, default=False) – Whether to return dense NumPy array, or sparse SciPy CSR array.

• n_jobs (int, default=None) – The number of jobs to run in parallel. transform() is parallelized over the input molecules. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Scikit-learn documentation on n_jobs for more details.

• batch_size (int, default=None) – Number of inputs processed in each batch. None divides input data into equal-sized parts, as many as n_jobs.

• verbose (int, default=0) – Controls the verbosity when computing fingerprints.

n_features_out

Number of output features, size of fingerprints. Equal to fp_size.

Type:

int

requires_conformers

This fingerprint uses only 2D molecular graphs and does not require conformers.

Type:

bool = False

See also

RDKitFingerprint

Related fingerprint, but uses constant bond types and pseudorandom numbers to set multiple bits.

References

[1]

Gregory Landrum “Fingerprints in the RDKit” UGM 2012

Examples

>>> from skfp.fingerprints import LayeredFingerprint
>>> smiles = ["O", "CC", "[C-]#N", "CC=O"]
>>> fp = LayeredFingerprint()
>>> fp
LayeredFingerprint()

>>> fp.transform(smiles)
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)

Methods

fit(X[, y])	Unused, kept for Scikit-learn compatibility.
fit_transform(X[, y])	The same as .transform() method, kept for Scikit-learn compatibility.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
set_transform_request(*[, copy])	Request metadata passed to the transform method.
transform(X[, copy])

fit(X: Sequence[str | Mol], y: Any | None = None, **fit_params)

Unused, kept for Scikit-learn compatibility.

Parameters:

• X (any) – Unused, kept for Scikit-learn compatibility.

• y (any) – Unused, kept for Scikit-learn compatibility.

• **fit_params (dict) – Unused, kept for Scikit-learn compatibility.

Return type:

self

fit_transform(X: Sequence[str | Mol], y: Any | None = None, **fit_params)

The same as .transform() method, kept for Scikit-learn compatibility.

Parameters:

• X (any) – See .transform() method.

• y (any) – See .transform() method.

• **fit_params (dict) – Unused, kept for Scikit-learn compatibility.

Returns:

X_new – See .transform() method.

Return type:

any

get_feature_names_out(input_features=None)

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: [“class_name0”, “class_name1”, “class_name2”].

Parameters:

input_features (array-like of str or None, default=None) – Only used to validate feature names with the names seen in fit.

Returns:

feature_names_out – Transformed feature names.

Return type:

ndarray of str objects

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

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:

self – Estimator instance.

Return type:

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → LayeredFingerprint

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.

Added 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.

Parameters:

copy (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for copy parameter in transform.

Returns:

self – The updated object.

Return type:

object