| """Class mapping: one vocabulary, one index space, shared by data, models and API.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
| import config |
| from backend.preprocessing import include50 |
|
|
|
|
| def test_class_mapping_is_vocabulary_order(vocabulary): |
| mapping = include50.class_mapping() |
| assert list(mapping.keys()) == vocabulary |
| assert list(mapping.values()) == list(range(len(vocabulary))) |
|
|
|
|
| def test_dataset_metadata_class_names_are_uppercase_glosses(vocabulary): |
| for name in vocabulary: |
| assert name == name.upper().strip() |
| assert len(name) <= 32 |
|
|
|
|
| def test_vocabulary_file_records_provenance(): |
| payload = json.loads((Path(config.METADATA_DIR) / "vocabulary.json").read_text()) |
| assert payload["num_classes"] == 50 |
| assert "INCLUDE" in payload["dataset"].upper() |
| assert payload["citation"] and payload["doi"], "the dataset must be cited, not just used" |
| assert len(payload["classes"]) == 50 |
| assert all(c["videos"] > 0 for c in payload["classes"]) |
|
|
|
|
| def test_processed_classes_are_vocabulary(processed, vocabulary): |
| assert [str(c) for c in processed["classes"]] == vocabulary |
|
|
|
|
| def test_label_mapping_file_matches_vocabulary(): |
| p = Path(config.PROCESSED_DIR) / "label_mapping.json" |
| if not p.exists(): |
| import pytest |
|
|
| pytest.skip("label_mapping.json not written yet") |
| payload = json.loads(p.read_text()) |
| assert payload["classes"] == include50.load_vocabulary() |
| assert payload["num_classes"] == 50 |
|
|
|
|
| def test_model_class_mapping_matches_vocabulary(vocabulary): |
| for name in config.AVAILABLE_MODELS: |
| p = Path(config.MODEL_PATHS[name]) / "class_mapping.json" |
| if not p.exists(): |
| continue |
| payload = json.loads(p.read_text()) |
| assert payload["classes"] == vocabulary, f"{name}: class order differs from the vocabulary" |
| assert payload["num_classes"] == len(vocabulary) |
|
|
|
|
| def test_model_output_width_matches_num_classes(all_trained_models): |
| for name, model in all_trained_models.items(): |
| assert model.output_shape[-1] == config.NUM_CLASSES, name |
| assert model.input_shape[-1] == config.MODEL_INPUT_DIM, name |
|
|
|
|
| def test_preprocessing_config_is_recorded_per_model(): |
| for name in config.AVAILABLE_MODELS: |
| p = Path(config.MODEL_PATHS[name]) / "preprocessing_config.json" |
| if not p.exists(): |
| continue |
| cfg = json.loads(p.read_text()) |
| assert cfg["sequence_length"] == config.SEQUENCE_LENGTH |
| assert cfg["model_input_dim"] == config.MODEL_INPUT_DIM |
| assert cfg["split_protocol"] == config.SPLIT_PROTOCOL |
|
|
|
|
| def test_label_indices_are_dense(processed): |
| assert set(np.unique(processed["y_train"]).tolist()) == set(range(config.NUM_CLASSES)) |
| for key in ("y_val", "y_test"): |
| present = set(np.unique(processed[key]).tolist()) |
| assert present <= set(range(config.NUM_CLASSES)) |
| assert len(present) == config.NUM_CLASSES, f"{key} should be class-stratified" |
|
|
