matchms.similarity.MetadataMatch module¶
- class matchms.similarity.MetadataMatch.MetadataMatch(field: str, matching_type: str = 'equal_match', tolerance: float = 0.1)[source]¶
Bases:
BaseSimilarity
Return True if metadata entries of a specified field match between two spectra.
This is supposed to be used to compare a wide range of possible metadata entries and use this to later select related or similar spectra.
Example to calculate scores between 2 pairs of spectrums and iterate over the scores
import numpy as np from matchms import calculate_scores from matchms import Spectrum from matchms.similarity import MetadataMatch spectrum_1 = Spectrum(mz=np.array([]), intensities=np.array([]), metadata={"instrument_type": "orbitrap", "id": 1}) spectrum_2 = Spectrum(mz=np.array([]), intensities=np.array([]), metadata={"instrument_type": "qtof", "id": 2}) spectrum_3 = Spectrum(mz=np.array([]), intensities=np.array([]), metadata={"instrument_type": "qtof", "id": 3}) spectrum_4 = Spectrum(mz=np.array([]), intensities=np.array([]), metadata={"instrument_type": "orbitrap", "id": 4}) references = [spectrum_1, spectrum_2] queries = [spectrum_3, spectrum_4] similarity_score = MetadataMatch(field="instrument_type") scores = calculate_scores(references, queries, similarity_score) for (reference, query, score) in scores: print(f"Metadata match between {reference.get('id')} and {query.get('id')}" + f" is {score}")
Should output
Metadata match between 1 and 4 is [True] Metadata match between 2 and 3 is [True]
- __init__(field: str, matching_type: str = 'equal_match', tolerance: float = 0.1)[source]¶
- Parameters:
field – Specify field name for metadata that should be compared.
matching_type – Specify how field entries should be matched. Can be one of [“equal_match”, “difference”].
tolerance – Specify tolerance below which two values are counted as match. This only applied to numerical values.
- keep_score(score)¶
In the .matrix method scores will be collected in a sparse way. Overwrite this method here if values other than False or 0 should not be stored in the final collection.
- matrix(references: List[Spectrum], queries: List[Spectrum], array_type: str = 'numpy', is_symmetric: bool = False) ndarray [source]¶
Compare parent masses between all references and queries.
- Parameters:
references – List/array of reference spectrums.
queries – List/array of Single query spectrums.
array_type – Specify the output array type. Can be “numpy” or “sparse”. Default is “numpy” and will return a numpy array. “sparse” will return a COO-sparse array.
is_symmetric – Set to True when references and queries are identical (as for instance for an all-vs-all comparison). By using the fact that score[i,j] = score[j,i] the calculation will be about 2x faster.
- pair(reference: Spectrum, query: Spectrum) float [source]¶
Compare precursor m/z between reference and query spectrum.
- Parameters:
reference – Single reference spectrum.
query – Single query spectrum.
- sparse_array(references: List[Spectrum], queries: List[Spectrum], idx_row, idx_col, is_symmetric: bool = False)¶
Optional: Provide optimized method to calculate an sparse matrix of similarity scores.
Compute similarity scores for pairs of reference and query spectrums as given by the indices idx_row (references) and idx_col (queries). If no method is added here, the following naive implementation (i.e. a for-loop) is used.
- Parameters:
references – List of reference objects
queries – List of query objects
idx_row – List/array of row indices
idx_col – List/array of column indices
is_symmetric – Set to True when references and queries are identical (as for instance for an all-vs-all comparison). By using the fact that score[i,j] = score[j,i] the calculation will be about 2x faster.