def to_method_object(self):
"""Convert the enum to an instance of `BaselineMethod`."""
if self == self.TF_IDF:
return keyword_based.TfIdfMethod()
elif self == self.BM25:
return keyword_based.BM25Method()
elif self == self.USE_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder/2"))
elif self == self.USE_LARGE_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder-large/3"))
elif self == self.ELMO_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/elmo/1"))
elif self == self.USE_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder/2"))
elif self == self.USE_LARGE_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder-large/3"))
elif self == self.ELMO_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/elmo/1"))
raise ValueError("Unknown method {}".format(self))
def test_rank_responses(self):
mock_encoder = mock.Mock()
mock_encoder.encode.return_value = np.asarray([
[1, 0, 0],
[0, 1, 0],
[0, 1, 1],
],
dtype=np.float32)
method = vector_based.VectorSimilarityMethod(mock_encoder)
assignments = method.rank_responses(["x", "y", "z"], ["a", "b", "c"])
np.testing.assert_allclose([0, 1, 2], assignments)
mock_encoder.encode.assert_has_calls([
mock.call(["x", "y", "z"]),
mock.call(["a", "b", "c"]),
])
def test_train(self):
vector_based.VectorSimilarityMethod(None).train(["x", "y"], ["a", "b"])
def to_method_object(self):
"""Convert the enum to an instance of `BaselineMethod`."""
if self == self.TF_IDF:
return keyword_based.TfIdfMethod()
elif self == self.BM25:
return keyword_based.BM25Method()
elif self == self.USE_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder/2"))
elif self == self.USE_LARGE_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder-large/3"))
elif self == self.ELMO_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/elmo/1"))
elif self == self.USE_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder/2"))
elif self == self.USE_LARGE_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder-large/3"))
elif self == self.ELMO_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/elmo/1"))
elif self == self.BERT_SMALL_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.BERTEncoder(
"https://tfhub.dev/google/"
"bert_uncased_L-12_H-768_A-12/1"))
elif self == self.BERT_SMALL_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.BERTEncoder(
"https://tfhub.dev/google/"
"bert_uncased_L-12_H-768_A-12/1"))
elif self == self.BERT_LARGE_SIM:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.BERTEncoder(
"https://tfhub.dev/google/"
"bert_uncased_L-24_H-1024_A-16/1"))
elif self == self.BERT_LARGE_MAP:
return vector_based.VectorMappingMethod(
encoder=vector_based.BERTEncoder(
"https://tfhub.dev/google/"
"bert_uncased_L-24_H-1024_A-16/1"))
elif self == self.USE_QA:
return vector_based.VectorSimilarityMethod(
encoder=vector_based.TfHubEncoder(
"https://tfhub.dev/google/"
"universal-sentence-encoder-multilingual-qa/1",
is_dual=True))
raise ValueError("Unknown method {}".format(self))