Source code for matchms.similarity.CosineGreedy

from typing import Tuple
import numpy
from matchms.typing import SpectrumType
from .BaseSimilarity import BaseSimilarity
from .spectrum_similarity_functions import (collect_peak_pairs,

[docs]class CosineGreedy(BaseSimilarity): """Calculate 'cosine similarity score' between two spectra. 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 in a 'greedy' way. This can perform notably faster, but does occasionally deviate slightly from a fully correct solution (as with the Hungarian algorithm, see :class:`~matchms.similarity.CosineHungarian`). In practice this will rarely affect similarity scores notably, in particular for smaller tolerances. For example .. testcode:: import numpy as np from matchms import Spectrum from matchms.similarity import CosineGreedy reference = Spectrum(mz=np.array([100, 150, 200.]), intensities=np.array([0.7, 0.2, 0.1])) query = Spectrum(mz=np.array([100, 140, 190.]), intensities=np.array([0.4, 0.2, 0.1])) # Use factory to construct a similarity function cosine_greedy = CosineGreedy(tolerance=0.2) score = cosine_greedy.pair(reference, query) print(f"Cosine score is {score['score']:.2f} with {score['matches']} matched peaks") Should output .. testoutput:: Cosine score is 0.83 with 1 matched peaks """ # Set key characteristics as class attributes is_commutative = True # Set output data type, e.g. ("score", "float") or [("score", "float"), ("matches", "int")] score_datatype = [("score", numpy.float64), ("matches", "int")]
[docs] def __init__(self, tolerance: float = 0.1, mz_power: float = 0.0, intensity_power: float = 1.0): """ 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. """ self.tolerance = tolerance self.mz_power = mz_power self.intensity_power = intensity_power
[docs] def pair(self, reference: SpectrumType, query: SpectrumType) -> Tuple[float, int]: """Calculate cosine score between two spectra. Parameters ---------- reference Single reference spectrum. query Single query spectrum. Returns ------- Score Tuple with cosine score and number of matched peaks. """ def get_matching_pairs(): """Get pairs of peaks that match within the given tolerance.""" matching_pairs = collect_peak_pairs(spec1, spec2, self.tolerance, shift=0.0, mz_power=self.mz_power, intensity_power=self.intensity_power) if matching_pairs is None: return None matching_pairs = matching_pairs[numpy.argsort(matching_pairs[:, 2])[::-1], :] return matching_pairs spec1 = reference.peaks.to_numpy spec2 = query.peaks.to_numpy matching_pairs = get_matching_pairs() if matching_pairs is None: return numpy.asarray((float(0), 0), dtype=self.score_datatype) score = score_best_matches(matching_pairs, spec1, spec2, self.mz_power, self.intensity_power) return numpy.asarray(score, dtype=self.score_datatype)