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:
matchms.similarity.BaseSimilarity.BaseSimilarity
Calculate ‘cosine similarity score’ between two spectra (using Hungarian algorithm).
The cosine score aims at quantifying the similarity between two mass spectra. The score is calculated by finding best possible matches between peaks of two spectra. Two peaks are considered a potential match if their m/z ratios lie within the given ‘tolerance’. The underlying peak assignment problem is here solved using the Hungarian algorithm. This can perform notably slower than the ‘greedy’ implementation in
CosineGreedy
, but does represent a mathematically proper solution to the problem.- __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.
- matrix(references: List[Spectrum], queries: List[Spectrum], is_symmetric: bool = False) numpy.ndarray ¶
Optional: Provide optimized method to calculate an numpy.array of similarity scores for given reference and query spectrums. 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
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) Tuple[float, int] [source]¶
Calculate cosine score between two spectra.
- Parameters
reference – Single reference spectrum.
query – Single query spectrum.
Returns –
-------- –
peaks. (Tuple with cosine score and number of matched) –
- sort(scores: numpy.ndarray)¶
Return array of indexes for sorted list of scores. This method can be adapted for different styles of scores.
- Parameters
scores – 1D Array of scores.
- Returns
Indexes of sorted scores.
- Return type
idx_sorted