matchms.similarity.CosineHungarian module
- class matchms.similarity.CosineHungarian.CosineHungarian(tolerance: float = 0.1, mz_power: float = 0.0, intensity_power: float = 1.0)[source]
Bases:
BaseSimilarityCalculate ‘cosine similarity score’ between two spectra using the Hungarian algorithm.
The cosine score quantifies the similarity between two mass spectra by finding the optimal one-to-one matching between their peaks. Two peaks are considered a potential match if their m/z ratios lie within the given tolerance.
The peak assignment is solved using the Hungarian algorithm (
scipy.optimize.linear_sum_assignment), which finds the assignment that maximises the sum of intensity products. This is mathematically optimal but can be notably slower than the greedy heuristic inCosineGreedy.- __init__(tolerance: float = 0.1, mz_power: float = 0.0, intensity_power: float = 1.0)[source]
- Parameters:
tolerance – Peaks will be considered a match when <= tolerance apart. Default is 0.1.
mz_power – The power to raise m/z to in the cosine function. The default is 0, in which case the peak intensity products will not depend on the m/z ratios.
intensity_power – The power to raise intensity to in the cosine function. The default is 1.
- 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, progress_bar: bool = True) ndarray
Optional: Provide optimized method to calculate an np.array of similarity scores for given reference and query spectra. If no method is added here, the following naive implementation (i.e. a double for-loop) is used.
- Parameters:
references – List of reference objects
queries – List of query objects
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.
progress_bar – When True a progress bar is shown. Default is True.
- pair(reference: Spectrum, query: Spectrum) Tuple[float, int][source]
Calculate cosine score between two spectra.
- Parameters:
reference – Single reference spectrum.
query – Single query spectrum.
- Return type:
Tuple with cosine score and number of matched peaks.
- sparse_array(references: List[Spectrum], queries: List[Spectrum], idx_row, idx_col, is_symmetric: bool = False, progress_bar: bool = True)
Optional: Provide optimized method to calculate an sparse matrix of similarity scores.
Compute similarity scores for pairs of reference and query spectra 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.
progress_bar – When True a progress bar is shown. Default is True.