def compute(self, values, accumulator=None):
"""Compute a step in this computation, returning a new accumulator."""
values = base_preprocessing_layer.convert_to_list(
values, sparse_default_value=self._mask_value)
if accumulator is None:
accumulator = self._create_accumulator()
# TODO(momernick): Benchmark improvements to this algorithm.
if not isinstance(values, list):
values = [values]
for document in values:
if not isinstance(document, list):
document = [document]
if self._compute_idf:
current_doc_id = accumulator.data["next_doc_id"]
accumulator.data["next_doc_id"] += 1
for token in document:
accumulator.count_dict[token] += 1
if self._compute_idf:
doc_count = accumulator.per_doc_count_dict[token]
if doc_count["last_doc_id"] != current_doc_id:
doc_count["count"] += 1
doc_count["last_doc_id"] = current_doc_id
return accumulator
def compute(self, values, accumulator=None):
"""Computes a step in this computation, returning a new accumulator."""
values = base_preprocessing_layer.convert_to_list(values)
if accumulator is None:
accumulator = self._create_accumulator()
# TODO(momernick): Benchmark improvements to this algorithm.
for element in values:
if not isinstance(element, list):
element = [element]
current_doc_id = accumulator.data[self.DOC_ID_IDX]
for value in element:
if self._max_tokens is None:
current_max_value = accumulator.data[self.MAX_VALUE_IDX]
if value > current_max_value:
accumulator.data[self.MAX_VALUE_IDX] = value
if self._compute_idf:
doc_count = accumulator.per_doc_count_dict[value]
if doc_count["last_doc_id"] != current_doc_id:
doc_count["count"] += 1
doc_count["last_doc_id"] = current_doc_id
accumulator.data[self.DOC_ID_IDX] += 1
return accumulator
def compute(self, values, accumulator=None):
"""Compute a step in this computation, returning a new accumulator."""
values = base_preprocessing_layer.convert_to_list(values)
if accumulator is None:
accumulator = self._create_accumulator()
# TODO(momernick): Benchmark improvements to this algorithm.
for document in values:
for token in document:
accumulator.count_dict[token] += 1
return accumulator
def compute(self, values, accumulator=None):
"""Compute a step in this computation, returning a new accumulator."""
values = base_preprocessing_layer.convert_to_list(
values, sparse_default_value=self._mask_value)
if accumulator is None:
accumulator = self._create_accumulator()
# TODO(momernick): Benchmark improvements to this algorithm.
if isinstance(values, (str, bytes, np.int64)):
accumulator.count_dict[values] += 1
else:
for document in values:
if not isinstance(document, list):
accumulator.count_dict[document] += 1
else:
for token in document:
accumulator.count_dict[token] += 1
return accumulator
def test_conversion(self, inputs, expected): values = base_preprocessing_layer.convert_to_list(inputs()) self.assertAllEqual(expected, values)
