code to generate sinusoidal embeddings
This package is poetry managed can be installed via
poetry add git+https://github.com/enveda/sinusoidal-embedding.git
The example below is a brief example of how to instantiate and use sinusoidal peak embeddings. The output of the peak_embedding is a tensor with dimensions [batch, peak sequence, embedding] and can be passed to any torch sequence model.
import numpy as np
from torch.utils.data import DataLoader
from matchms import Spectrum
from sinusoidal_embedding.peak_embedding import PeakEmbedding
from sinusoidal_embedding.peak_embedding import SinusoidalMZEmbedding
from sinusoidal_embedding.data.spectrum_dataset import SpectrumDataset
from sinusoidal_embedding.data.spectrum_transform import SpectrumNumericalMZTransform
from sinusoidal_embedding.data.spectrum_collater import SpectrumNumericalMZCollater
# Generate some random spectra for testing
spectra = [
Spectrum(
np.sort(np.random.rand(n)*1000),
np.random.rand(n),
metadata={'precursor_mz': np.random.rand() * 1000}
)
for n in np.random.randint(0,1000, 200)
]
# Instantiate data loader
spectrum_transform = SpectrumNumericalMZTransform()
spectrum_collater = SpectrumNumericalMZCollater()
spectrum_dataset = SpectrumDataset(spectra, transform=spectrum_transform)
data_loader = DataLoader(
spectrum_dataset,
batch_size=8,
collate_fn=spectrum_collater,
)
# Instantiate Sinusoidal Peak Embedding
peak_embedding = PeakEmbedding(SinusoidalMZEmbedding(embd_dim=64))
# Generate peak embeddings
data = next(iter(data_loader))
peak_embedding(data)