similarity_service

Similarity analysis service that orchestrates the similarity workflow.

Module Contents

exception similarity_service.SimilarityCacheOnlyMiss

A strict synchronous run could not use its preflight snapshot.

similarity_service.analyze_sequence_similarity(csv_file, session_id: str = 'default') → Dict[str, Any]

Analyze sequence similarity against target databases.

Parameters:

  • csv_file – Uploaded CSV file containing protein sequences

  • session_id – Session ID for logging

Returns:

Dictionary containing similarity analysis results

Raises:

  • ValueError – If CSV is invalid or contains no sequences

  • Exception – If analysis fails

similarity_service.analyze_similarity_for_method(query_db: str, target_db: str, query_file_path: str, method_name: str, original_sequences: List[str], seq_to_unique_id: Dict[str, str], session_id: str) → Dict[str, Any]

Analyze similarity for a specific method/database.

Parameters:

  • query_db – Path to query database

  • target_db – Path to target database

  • query_file_path – Path to original FASTA file

  • method_name – Name of the method

  • original_sequences – Original sequence list

  • seq_to_unique_id – Sequence to unique ID mapping

  • session_id – Session ID for logging

Returns:

Dictionary containing method-specific results

similarity_service.similarity_cache_label_for_method(method_key: str) → str

Return the persistent-cache dataset label used by output enrichment.

similarity_service.append_kcat_similarity_columns_to_output_csv(output_csv_path: str, kcat_method_key: str, recon_xkg: bool = False, cached_similarity_snapshot: dict[str, tuple[float | None, float | None]] | None = None, cache_only: bool = False, selected_sequences_by_row: list[str] | None = None) → None

Best-effort enrichment for completed kcat jobs.

Adds two per-row columns to output.csv:

  • mean similarity to {method} training data

  • max similarity to {method} training data

When recon_xkg is set, per-sequence similarity is served from the persistent SimilarityStore and MMseqs2 runs only for sequences not yet cached. A cached NULL/NULL pair is a negative cache hit and renders as blank cells, so repeated deterministic failures do not retry MMseqs2. On any error, both columns are still created with blank values.

similarity_service.kcat_similarity_sequences_for_output_rows(df: pandas.DataFrame, selected_sequences_by_row: list[str] | None = None) → list[str]

Return the unique sequence keys kcat output enrichment will require.