neurosnap.algos.wolfpsort package#

class neurosnap.algos.wolfpsort.WoLFPSortPredictor(organism_type='fungi')[source]#

Bases: object

Pure Python WoLF PSORT port with structured outputs.

VALID_ORGANISMS = {'animal', 'fungi', 'plant'}#
__init__(organism_type='fungi')[source]#

Initialize a WoLF PSORT predictor for one bundled organism model.

Parameters:

organism_type (str) – Bundled model to use. Supported values are "fungi", "animal", and "plant".

Returns:

None. The predictor is initialized in place.

compute_features(sequences)[source]#

Compute WoLF PSORT features for one or more protein sequences.

Parameters:

sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

Return type:

List[Dict[str, object]]

Returns:

List of dictionaries containing id and every WoLF PSORT feature used by the bundled models.

compute_features_dataframe(sequences)[source]#

Compute WoLF PSORT features and return them as a DataFrame.

Parameters:

sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

Return type:

DataFrame

Returns:

DataFrame with one row per sequence and one column per feature.

predict(sequences, include_features=False, include_neighbors=False)[source]#

Predict localization scores for one or more protein sequences.

Parameters:

  • sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

  • include_features (bool) – When True, attach the computed feature dictionary to each prediction record.

  • include_neighbors (bool) – When True, include the ranked training neighbors used during kNN scoring.

Return type:

List[Dict[str, object]]

Returns:

List of dictionaries containing the predicted class, ranked class scores, human-readable labels, best k value, and optional feature / neighbor details.

predict_dataframe(sequences, include_features=False)[source]#

Predict localization scores and return a tabular summary.

Parameters:

  • sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

  • include_features (bool) – When True, expand the computed features into additional DataFrame columns.

Return type:

DataFrame

Returns:

DataFrame with one row per sequence plus the top prediction metadata and full score dictionary.

neurosnap.algos.wolfpsort.compute_features(sequences)[source]#

Compute WoLF PSORT features using the fungi feature definition.

Parameters:

sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

Return type:

List[Dict[str, object]]

Returns:

List of dictionaries containing structured feature values.

neurosnap.algos.wolfpsort.compute_features_dataframe(sequences)[source]#

Compute WoLF PSORT features and return them in DataFrame form.

Parameters:

sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

Return type:

DataFrame

Returns:

DataFrame with one row per sequence and one column per feature.

neurosnap.algos.wolfpsort.predict_localization(sequences, organism_type='fungi', include_features=False, include_neighbors=False, as_dataframe=True)[source]#

Predict WoLF PSORT localization scores for one or more sequences.

Parameters:

  • sequences (Iterator[Tuple[str, str]]) – Iterator yielding (identifier, sequence) tuples.

  • organism_type (str) – Bundled organism model to use. Supported values are "fungi", "animal", and "plant".

  • include_features (bool) – When True, include the computed feature dictionary in dictionary output or expand feature columns in DataFrame output.

  • include_neighbors (bool) – When True, include the ranked training neighbors in dictionary output. This is not supported in DataFrame mode.

  • as_dataframe (bool) – When True, return a DataFrame summary. When False, return a list of dictionaries.

Returns:

DataFrame or list of dictionaries, depending on as_dataframe.

Submodules#