def load(cls, model_dir: str) -> 'CategoryClassifier':
model_dir_path = pathlib.Path(model_dir)
transformer = joblib.load(str(model_dir_path / cls.TRANSFORMER_PATH))
classifier = joblib.load(str(model_dir_path / cls.CLASSIFIER_PATH))
with open(str(model_dir_path / cls.CATEGORY_TAXONOMY_PATH), 'r') as f:
category_taxonomy_data = json.load(f)
category_taxonomy = Taxonomy.from_dict(category_taxonomy_data)
instance = cls(category_taxonomy)
instance.transformer = transformer
instance.classifier = classifier
return instance
def test_predict(mocker, deepest_only, mock_response, expected_values):
category_taxonomy = Taxonomy.from_dict({
"en:meat": {
"names": "meat",
},
"en:fish": {
"names": "fish",
},
"en:salmon": {
"names": "salmon",
"parents": ["en:fish"],
},
"en:smoked-salmon": {
"names": "salmon",
"parents": ["en:salmon"],
},
})
classifier = CategoryClassifier(category_taxonomy)
mocker.patch(
"robotoff.prediction.category.neural.category_classifier.http_session.post",
return_value=mock_response,
)
predictions = classifier.predict(
{
"ingredients_tags": ["ingredient1"],
"product_name": "Test Product"
},
deepest_only,
)
assert len(predictions) == len(expected_values)
for prediction, (value_tag, confidence) in zip(predictions,
expected_values):
assert prediction.value_tag == value_tag
assert prediction.data.get("confidence") == confidence
def _category_taxonomy() -> Taxonomy:
return Taxonomy.from_dict({"en:mushrooms": {"lang": "fr"}})