spec2vec.Spec2Vec module

class spec2vec.Spec2Vec.Spec2Vec(model: gensim.models.word2vec.Word2Vec, intensity_weighting_power: Union[float, int] = 0, allowed_missing_percentage: Union[float, int] = 10, progress_bar: bool = False)[source]

Bases: matchms.similarity.BaseSimilarity.BaseSimilarity

Calculate spec2vec similarity scores between a reference and a query.

Using a trained model, spectrum documents will be converted into spectrum vectors. The spec2vec similarity is then the cosine similarity score between two spectrum vectors.

The following code example shows how to calculate spec2vec similarities between query and reference spectrums. It uses a dummy model that can be found at ../integration-tests/test_user_workflow_spec2vec.model and a small test dataset that can be found at ../tests/pesticides.mgf.

import os
import gensim
from matchms import calculate_scores
from matchms.filtering import add_losses
from matchms.filtering import default_filters
from matchms.filtering import normalize_intensities
from matchms.filtering import require_minimum_number_of_peaks
from matchms.filtering import select_by_intensity
from matchms.filtering import select_by_mz
from matchms.importing import load_from_mgf
from spec2vec import Spec2Vec

def spectrum_processing(s):
    '''This is how a user would typically design his own pre- and post-
    processing pipeline.'''
    s = default_filters(s)
    s = normalize_intensities(s)
    s = select_by_mz(s, mz_from=0, mz_to=1000)
    s = select_by_intensity(s, intensity_from=0.01)
    s = add_losses(s, loss_mz_from=10.0, loss_mz_to=200.0)
    s = require_minimum_number_of_peaks(s, n_required=5)
    return s

spectrums_file = os.path.join(os.getcwd(), "..", "tests", "pesticides.mgf")

# Load data and apply the above defined filters to the data
spectrums = [spectrum_processing(s) for s in load_from_mgf(spectrums_file)]

# Omit spectrums that didn't qualify for analysis
spectrums = [s for s in spectrums if s is not None]

# Load pretrained model (here dummy model)
model_file = os.path.join(os.getcwd(), "..", "integration-tests", "test_user_workflow_spec2vec.model")
model = gensim.models.Word2Vec.load(model_file)

# Define similarity_function
spec2vec = Spec2Vec(model=model, intensity_weighting_power=0.5)

# Calculate scores on all combinations of references and queries
scores = calculate_scores(spectrums[10:], spectrums[:10], spec2vec)

# Select top-10 candidates for first query spectrum
spectrum0_top10 = scores.scores_by_query(spectrums[0], sort=True)[:10]

# Display spectrum IDs for top-10 matches
print([s[0].metadata['spectrumid'] for s in spectrum0_top10])

Should output

['CCMSLIB00001058300', 'CCMSLIB00001058289', 'CCMSLIB00001058303', ...
__init__(model: gensim.models.word2vec.Word2Vec, intensity_weighting_power: Union[float, int] = 0, allowed_missing_percentage: Union[float, int] = 10, progress_bar: bool = False)[source]
Parameters
  • model – Expected input is a gensim word2vec model that has been trained on the desired set of spectrum documents.

  • intensity_weighting_power – Spectrum vectors are a weighted sum of the word vectors. The given word intensities will be raised to the given power. The default is 0, which means that no weighing will be done.

  • allowed_missing_percentage – Set the maximum allowed percentage of the document that may be missing from the input model. This is measured as percentage of the weighted, missing words compared to all word vectors of the document. Default is 10, which means up to 10% missing words are allowed. If more words are missing from the model, an empty embedding will be returned (leading to similarities of 0) and a warning is raised.

  • progress_bar – Set to True to monitor the embedding creating with a progress bar. Default is False.

matrix(references: Union[List[spec2vec.SpectrumDocument.SpectrumDocument], List[matchms.Spectrum.Spectrum]], queries: Union[List[spec2vec.SpectrumDocument.SpectrumDocument], List[matchms.Spectrum.Spectrum]], is_symmetric: bool = False)numpy.ndarray[source]

Calculate the spec2vec similarities between all references and queries.

Parameters
  • references – Reference spectrums or spectrum documents.

  • queries – Query spectrums or spectrum documents.

  • is_symmetric – Set to True if references == queries to speed up calculation about 2x. Uses the fact that in this case score[i, j] = score[j, i]. Default is False.

Returns

Array of spec2vec similarity scores.

Return type

spec2vec_similarity

pair(reference: Union[spec2vec.SpectrumDocument.SpectrumDocument, matchms.Spectrum.Spectrum], query: Union[spec2vec.SpectrumDocument.SpectrumDocument, matchms.Spectrum.Spectrum])float[source]

Calculate the spec2vec similaritiy between a reference and a query.

Parameters
  • reference – Reference spectrum or spectrum document.

  • query – Query spectrum or spectrum document.

Returns

Spec2vec similarity score.

Return type

spec2vec_similarity