matchms.similarity.CosineGreedy module

class matchms.similarity.CosineGreedy.CosineGreedy(tolerance: float = 0.1, mz_power: float = 0.0, intensity_power: float = 1.0)

Bases: 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 CosineHungarian). In practice this will rarely affect similarity scores notably, in particular for smaller tolerances.

For example

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

Cosine score is 0.83 with 1 matched peaks

__init__(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.

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

Optional: Provide optimized method to calculate an np.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

• 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) → Tuple[float, int]

Calculate cosine score between two spectra.

Parameters:

• reference – Single reference spectrum.

• query – Single query spectrum.

Returns:

Tuple with cosine score and number of matched peaks.

Return type:

Score

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.

to_dict() → dict

Return a dictionary representation of a similarity function.