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Base classes

taxotagger.abc

EmbedModelBase

Bases: ABC

Base class for embedding models.

name instance-attribute 

name: str

The name of the pretrained model used for embedding.

embed abstractmethod 

embed(fasta_file: str) -> dict[str, list[dict[str, Any]]]

Calculate the embeddings for the given FASTA file.

Parameters:

  • fasta_file (str) –

    The path to the FASTA file to embed.

Returns:

  • dict[str, list[dict[str, Any]]] –

    A dictionary of embeddings for each taxonomy level. The dictionary keys are the taxonomy levels, and the values are lists of dictionaries containing the id, embeddings and metadata for each sequence.

    The shape of the list is (n_samples), where n_samples is the number of sequences.

    The keys id and vector must be present in the inside dictionaries to present the accession and the embedding vector of the sequence, respectively.

    For example: 

    {
    "phylum": [
        {"id": "seq1", "vector": [0.1, 0.2, ...], "phylum": "Ascomycota", ...},
        {"id": "seq2", "vector": [0.3, 0.4, ...], "phylum": "Basidiomycota", ...},
        ...
    ],
    "class": [
        {"id": "seq1", "vector": [0.5, 0.6, ...], "class": "Dothideomycetes", ...},
        {"id": "seq2", "vector": [0.7, 0.8, ...], "class": "Agaricomycetes", ...},
        ...
    ],
    ...
}

Source code in src/taxotagger/abc.py 
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@abstractmethod
def embed(self, fasta_file: str) -> dict[str, list[dict[str, Any]]]:
    """Calculate the embeddings for the given FASTA file.

    Args:
        fasta_file: The path to the FASTA file to embed.

    Returns:
        A dictionary of embeddings for each taxonomy level.
            The dictionary keys are the [taxonomy levels][taxotagger.defaults.TAXONOMY_LEVELS],
            and the values are lists of dictionaries containing the id, embeddings and metadata
            for each sequence.

            The shape of the list is `(n_samples)`, where `n_samples` is the number of sequences.

            The keys `id` and `vector` must be present in the inside dictionaries to present the
            accession and the embedding vector of the sequence, respectively.

            For example:
            ```python
            {
                "phylum": [
                    {"id": "seq1", "vector": [0.1, 0.2, ...], "phylum": "Ascomycota", ...},
                    {"id": "seq2", "vector": [0.3, 0.4, ...], "phylum": "Basidiomycota", ...},
                    ...
                ],
                "class": [
                    {"id": "seq1", "vector": [0.5, 0.6, ...], "class": "Dothideomycetes", ...},
                    {"id": "seq2", "vector": [0.7, 0.8, ...], "class": "Agaricomycetes", ...},
                    ...
                ],
                ...
            }
            ```
    """
    ...