def testFlattenNested(self):
input_array = pa.array([[[1, 2]], None, [None, [3]]])
flattened, parent_indices = arrow_util.flatten_nested(
input_array, return_parent_indices=False)
expected = pa.array([1, 2, 3])
expected_parent_indices = [0, 0, 2]
self.assertIs(parent_indices, None)
self.assertTrue(flattened.equals(expected))
flattened, parent_indices = arrow_util.flatten_nested(
input_array, return_parent_indices=True)
self.assertTrue(flattened.equals(expected))
np.testing.assert_array_equal(parent_indices, expected_parent_indices)
def update(self,
feature_array: pa.Array,
values_quantiles_combiner: Any,
weights: Optional[np.ndarray] = None) -> None:
"""Update the partial numeric statistics using the input value."""
# np.max / np.min below cannot handle empty arrays. And there's nothing
# we can collect in this case.
if not feature_array:
return
flattened_value_array, value_parent_indices = arrow_util.flatten_nested(
feature_array, weights is not None)
# Note: to_numpy will fail if flattened_value_array is empty.
if not flattened_value_array:
return
values = np.asarray(flattened_value_array)
nan_mask = np.isnan(values)
self.num_nan += np.sum(nan_mask)
non_nan_mask = ~nan_mask
values_no_nan = values[non_nan_mask]
# We do this check to avoid failing in np.min/max with empty array.
if values_no_nan.size == 0:
return
# This is to avoid integer overflow when computing sum or sum of squares.
values_no_nan_as_double = values_no_nan.astype(np.float64)
self.sum += np.sum(values_no_nan_as_double)
self.sum_of_squares += np.sum(values_no_nan_as_double *
values_no_nan_as_double)
# Use np.minimum.reduce(values_no_nan, initial=self.min) once we upgrade
# to numpy 1.16
curr_min = np.min(values_no_nan)
curr_max = np.max(values_no_nan)
self.min = min(self.min, curr_min)
self.max = max(self.max, curr_max)
if curr_min == float('-inf') or curr_max == float('inf'):
finite_values = values_no_nan[np.isfinite(values_no_nan)]
if finite_values.size > 0:
self.finite_min = min(self.finite_min, np.min(finite_values))
self.finite_max = max(self.finite_max, np.max(finite_values))
self.num_zeros += values_no_nan.size - np.count_nonzero(values_no_nan)
self.quantiles_summary = values_quantiles_combiner.add_input(
self.quantiles_summary,
[values_no_nan, np.ones_like(values_no_nan)])
if weights is not None:
flat_weights = weights[value_parent_indices]
flat_weights_no_nan = flat_weights[non_nan_mask]
weighted_values = flat_weights_no_nan * values_no_nan
self.weighted_sum += np.sum(weighted_values)
self.weighted_sum_of_squares += np.sum(weighted_values *
values_no_nan)
self.weighted_quantiles_summary = values_quantiles_combiner.add_input(
self.weighted_quantiles_summary,
[values_no_nan, flat_weights_no_nan])
self.weighted_total_num_values += np.sum(flat_weights_no_nan)
def add_input(self, accumulator: _PartialImageStats,
feature_path: types.FeaturePath,
feature_array: pa.Array) -> _PartialImageStats:
"""Return result of folding a batch of inputs into accumulator.
Args:
accumulator: The current accumulator.
feature_path: The path of the feature.
feature_array: An arrow array representing a batch of feature values
which should be added to the accumulator.
Returns:
The accumulator after updating the statistics for the batch of inputs.
"""
if accumulator.invalidate:
return accumulator
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, feature_array.type)
# Ignore null array.
if feature_type is None:
return accumulator
# If we see a different type, invalidate.
if feature_type != statistics_pb2.FeatureNameStatistics.STRING:
accumulator.invalidate = True
return accumulator
# Consider using memoryview to avoid copying after upgrading to
# arrow 0.12. Note that this would involve modifying the subsequent logic
# to iterate over the values in a loop.
values = np.asarray(arrow_util.flatten_nested(feature_array)[0])
accumulator.total_num_values += values.size
image_formats = self._image_decoder.get_formats(values)
valid_mask = ~pd.isnull(image_formats)
valid_formats = image_formats[valid_mask]
format_counts = np.unique(valid_formats, return_counts=True)
for (image_format, count) in zip(*format_counts):
accumulator.counter_by_format[image_format] += count
unknown_count = image_formats.size - valid_formats.size
if unknown_count > 0:
accumulator.counter_by_format[''] += unknown_count
if self._enable_size_stats:
# Get image height and width.
image_sizes = self._image_decoder.get_sizes(values[valid_mask])
if image_sizes.any():
max_sizes = np.max(image_sizes, axis=0)
# Update the max image height/width with all image values.
accumulator.max_height = max(accumulator.max_height,
max_sizes[0])
accumulator.max_width = max(accumulator.max_width,
max_sizes[1])
return accumulator
def add_input(self, accumulator: _PartialTimeStats,
feature_path: types.FeaturePath,
feature_array: pa.Array) -> _PartialTimeStats:
"""Returns result of folding a batch of inputs into the current accumulator.
Args:
accumulator: The current accumulator.
feature_path: The path of the feature.
feature_array: An arrow Array representing a batch of feature values
which should be added to the accumulator.
Returns:
The accumulator after updating the statistics for the batch of inputs.
"""
if accumulator.invalidated:
return accumulator
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, feature_array.type)
# Ignore null array.
if feature_type is None:
return accumulator
if feature_type == statistics_pb2.FeatureNameStatistics.STRING:
def _maybe_get_utf8(val):
return stats_util.maybe_get_utf8(val) if isinstance(
val, bytes) else val
values = np.asarray(arrow_util.flatten_nested(feature_array)[0])
maybe_utf8 = np.vectorize(_maybe_get_utf8, otypes=[object])(values)
if not maybe_utf8.all():
accumulator.invalidated = True
return accumulator
accumulator.update(maybe_utf8, feature_type)
elif feature_type == statistics_pb2.FeatureNameStatistics.INT:
values = np.asarray(arrow_util.flatten_nested(feature_array)[0])
accumulator.update(values, feature_type)
else:
accumulator.invalidated = True
return accumulator
def _get_flattened_feature_values_without_nulls(
feature_array: pa.Array) -> List[Any]:
"""Flattens the feature array into a List and removes null values.
Args:
feature_array: Arrow Array.
Returns:
A list containing the flattened feature values with nulls removed.
"""
non_missing_values = np.asarray(
arrow_util.flatten_nested(feature_array)[0])
return list(non_missing_values[~pd.isnull(non_missing_values)])
def update(self, feature_array: pa.Array) -> None:
"""Update the partial bytes statistics using the input value."""
if pa.types.is_null(feature_array.type):
return
# Iterate through the value array and update the partial stats.'
flattened_values_array, _ = arrow_util.flatten_nested(feature_array)
if (pa.types.is_floating(flattened_values_array.type) or
pa.types.is_integer(flattened_values_array.type)):
raise ValueError('Bytes stats cannot be computed on INT/FLOAT features.')
if flattened_values_array:
num_bytes = array_util.GetElementLengths(
flattened_values_array).to_numpy()
self.min_num_bytes = min(self.min_num_bytes, np.min(num_bytes))
self.max_num_bytes = max(self.max_num_bytes, np.max(num_bytes))
self.total_num_bytes += np.sum(num_bytes)
def _apply_numerical_encoding_to_feature_array(
feature_array: pa.Array, histogram_bin_boundaries: np.ndarray,
encoding_length: int) -> List[int]:
"""Determines encoding of numeric feature array from histogram bins.
Using the provided histogram_bin_boundaries, a histogram is constructed for
each example to obtain an encoding for a feature value.
Args:
feature_array: Arrow Array.
histogram_bin_boundaries: A monotonically increasing np.ndarray representing
the boundaries of each bin in the histogram.
encoding_length: The length of the list containing the encoded feature
values.
Returns:
A list conatining the encoded feature values for each example.
"""
if pa.types.is_null(feature_array.type):
return []
result = [None for _ in range(len(feature_array))] # type: List
flattened, non_missing_parent_indices = arrow_util.flatten_nested(
feature_array, True)
assert non_missing_parent_indices is not None
non_missing_values = np.asarray(flattened)
non_missing_parent_indices = non_missing_parent_indices.astype(np.int32)
values_indices = np.stack((non_missing_values, non_missing_parent_indices),
axis=-1)
nan_mask = pd.isnull(non_missing_values)
for (value, index) in values_indices[~nan_mask]:
index = int(index)
if result[index] is None:
result[index] = []
result[index].append(value)
for (value, index) in values_indices[nan_mask]:
index = int(index)
if result[index] is None:
result[index] = []
for i in range(len(result)):
if result[i] is None:
result[i] = [None] * encoding_length
else:
result[i] = np.bincount(
np.digitize(result[i], histogram_bin_boundaries) - 1,
minlength=encoding_length).tolist()
return result
def _to_topk_tuples(
sliced_record_batch: Tuple[types.SliceKey, pa.RecordBatch],
bytes_features: FrozenSet[types.FeaturePath],
categorical_features: FrozenSet[types.FeaturePath],
example_weight_map: ExampleWeightMap,
) -> Iterable[Tuple[Tuple[types.SliceKey, types.FeaturePathTuple, Any], Union[
int, Tuple[int, Union[int, float]]]]]:
"""Generates tuples for computing top-k and uniques from the input."""
slice_key, record_batch = sliced_record_batch
has_any_weight = bool(example_weight_map.all_weight_features())
for feature_path, feature_array, weights in arrow_util.enumerate_arrays(
record_batch,
example_weight_map=example_weight_map,
enumerate_leaves_only=True):
feature_array_type = feature_array.type
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, feature_array_type)
if feature_path in bytes_features:
continue
if ((feature_type == statistics_pb2.FeatureNameStatistics.INT
and feature_path in categorical_features) or feature_type
== statistics_pb2.FeatureNameStatistics.STRING):
flattened_values, parent_indices = arrow_util.flatten_nested(
feature_array, weights is not None)
if weights is not None and flattened_values:
# Slow path: weighted uniques.
flattened_values_np = np.asarray(flattened_values)
weights_ndarray = weights[parent_indices]
for value, count, weight in _weighted_unique(
flattened_values_np, weights_ndarray):
yield (slice_key, feature_path.steps(), value), (count,
weight)
else:
value_counts = flattened_values.value_counts()
values = value_counts.field('values').to_pylist()
counts = value_counts.field('counts').to_pylist()
if has_any_weight:
for value, count in zip(values, counts):
yield ((slice_key, feature_path.steps(), value),
(count, 1))
else:
for value, count in zip(values, counts):
yield ((slice_key, feature_path.steps(), value), count)
def add_input(
self, accumulator: Dict[types.FeaturePath, _ValueCounts],
input_record_batch: pa.RecordBatch
) -> Dict[types.FeaturePath, _ValueCounts]:
for feature_path, leaf_array, weights in arrow_util.enumerate_arrays(
input_record_batch,
example_weight_map=self._example_weight_map,
enumerate_leaves_only=True):
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, leaf_array.type)
# if it's not a categorical int feature nor a string feature, we don't
# bother with topk stats.
if ((feature_type == statistics_pb2.FeatureNameStatistics.INT
and feature_path in self._categorical_features)
or feature_type
== statistics_pb2.FeatureNameStatistics.STRING):
flattened_values, parent_indices = arrow_util.flatten_nested(
leaf_array, weights is not None)
unweighted_counts = collections.Counter()
# Compute unweighted counts.
value_counts = flattened_values.value_counts()
values = value_counts.field('values').to_pylist()
counts = value_counts.field('counts').to_pylist()
for value, count in zip(values, counts):
unweighted_counts[value] = count
# Compute weighted counts if a weight feature is specified.
weighted_counts = _WeightedCounter()
if weights is not None:
flattened_values_np = np.asarray(flattened_values)
weighted_counts.weighted_update(flattened_values_np,
weights[parent_indices])
if feature_path not in accumulator:
accumulator[feature_path] = _ValueCounts(
unweighted_counts=unweighted_counts,
weighted_counts=weighted_counts)
else:
accumulator[feature_path].unweighted_counts.update(
unweighted_counts)
accumulator[feature_path].weighted_counts.update(
weighted_counts)
return accumulator
def _apply_categorical_encoding_to_feature_array(
feature_array: pa.Array, categorical_encoding: Dict[Any, int],
encoding_length: int) -> List[Any]:
"""Applies the provided encoding to the feature array.
For each example, the frequency of each category is computed. Using the
categorical_encoding dict, an encoding is created for the example by storing
these counts in the appropriate index of the encoding.
Args:
feature_array: Arrow Array.
categorical_encoding: A dict where the key is the category and the value is
the index in the encoding to which the category corresponds to.
encoding_length: The length of the list containing the encoded feature
values.
Returns:
A list containing the encoded feature values for each example.
"""
if pa.types.is_null(feature_array.type):
return []
result = [None for _ in range(len(feature_array))]
flattened, non_missing_parent_indices = arrow_util.flatten_nested(
feature_array, True)
non_missing_values = flattened.to_pylist()
non_missing_parent_indices = list(non_missing_parent_indices)
for (value, index) in zip(non_missing_values, non_missing_parent_indices):
if result[index] is None:
result[index] = []
result[index].append(value)
for i in range(len(result)):
if result[i] is None:
result[i] = [None] * encoding_length
else:
category_frequencies = collections.Counter(result[i])
encoded_values = [0] * encoding_length
for category in category_frequencies:
if category in categorical_encoding:
encoded_values[categorical_encoding[category]] = (
category_frequencies[category])
elif not pd.isnull(category):
encoded_values[-1] += category_frequencies[category]
result[i] = encoded_values
return result
def _get_univalent_values_with_parent_indices(
self,
examples: pa.RecordBatch) -> Dict[types.FeatureName, DataFrame]:
"""Extracts univalent values for each feature along with parent indices."""
result = {}
for feature_name, feat_arr in zip(examples.schema.names,
examples.columns):
if (self._features_needed is not None
and feature_name not in self._features_needed):
continue
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_name, feat_arr.type)
# Only consider crosses of numeric features.
# TODO(zhuo): Support numeric features nested under structs.
if feature_type in (None,
statistics_pb2.FeatureNameStatistics.STRING,
statistics_pb2.FeatureNameStatistics.STRUCT):
continue
value_lengths = np.asarray(
array_util.ListLengthsFromListArray(feat_arr))
univalent_parent_indices = set((value_lengths == 1).nonzero()[0])
# If there are no univalent values, continue to the next feature.
if not univalent_parent_indices:
continue
flattened, value_parent_indices = arrow_util.flatten_nested(
feat_arr, True)
non_missing_values = np.asarray(flattened)
if feature_type == statistics_pb2.FeatureNameStatistics.FLOAT:
# Remove any NaN values if present.
non_nan_mask = ~np.isnan(non_missing_values)
non_missing_values = non_missing_values[non_nan_mask]
value_parent_indices = value_parent_indices[non_nan_mask]
df = pd.DataFrame({
feature_name: non_missing_values,
'parent_index': value_parent_indices
})
# Only keep the univalent feature values.
df = df[df['parent_index'].isin(univalent_parent_indices)]
result[feature_name] = df
return result
def update(self, feature_array: pa.Array) -> None:
"""Update the partial string statistics using the input value."""
if pa.types.is_null(feature_array.type):
return
# Iterate through the value array and update the partial stats.
flattened_values_array, _ = arrow_util.flatten_nested(feature_array)
if arrow_util.is_binary_like(flattened_values_array.type):
# GetBinaryArrayTotalByteSize returns a Python long (to be compatible
# with Python3). To make sure we do cheaper integer arithemetics in
# Python2, we first convert it to int.
self.total_bytes_length += int(array_util.GetBinaryArrayTotalByteSize(
flattened_values_array))
elif flattened_values_array:
# We can only do flattened_values_array.to_numpy() when it's not empty.
# This could be computed faster by taking log10 of the integer.
def _len_after_conv(s):
return len(str(s))
self.total_bytes_length += np.sum(
np.vectorize(_len_after_conv,
otypes=[np.int32])(np.asarray(flattened_values_array)))
def add_input(self, accumulator: _PartialNLStats,
feature_path: types.FeaturePath,
feature_array: pa.Array) -> _PartialNLStats:
"""Return result of folding a batch of inputs into accumulator.
Args:
accumulator: The current accumulator.
feature_path: The path of the feature.
feature_array: An arrow Array representing a batch of feature values
which should be added to the accumulator.
Returns:
The accumulator after updating the statistics for the batch of inputs.
"""
if accumulator.invalidate:
return accumulator
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, feature_array.type)
# Ignore null array.
if feature_type is None:
return accumulator
# If we see a different type, invalidate.
if feature_type != statistics_pb2.FeatureNameStatistics.STRING:
accumulator.invalidate = True
return accumulator
def _is_non_utf8(value):
return (isinstance(value, bytes)
and stats_util.maybe_get_utf8(value) is None)
is_non_utf_vec = np.vectorize(_is_non_utf8, otypes=[bool])
classify_vec = np.vectorize(self._classifier.classify, otypes=[bool])
values = np.asarray(
arrow_util.flatten_nested(feature_array)[0].slice(
0, _CROP_AT_VALUES))
if np.any(is_non_utf_vec(values)):
accumulator.invalidate = True
return accumulator
accumulator.considered += values.size
accumulator.matched += np.sum(classify_vec(values))
return accumulator
def _update_combined_sketch_for_feature(
self, feature_name: tfdv_types.FeaturePath, values: pa.Array,
weights: Optional[np.ndarray],
accumulator: Dict[tfdv_types.FeaturePath, _CombinedSketch]):
"""Updates combined sketch with values (and weights if provided)."""
flattened_values, parent_indices = arrow_util.flatten_nested(
values, weights is not None)
combined_sketch = accumulator.get(feature_name, None)
if combined_sketch is None:
combined_sketch = _CombinedSketch(
distinct=KmvSketch(self._num_kmv_buckets),
topk_unweighted=MisraGriesSketch(self._num_misragries_buckets),
topk_weighted=MisraGriesSketch(self._num_misragries_buckets),
)
weight_array = None
if weights is not None:
flattened_weights = weights[parent_indices]
weight_array = pa.array(flattened_weights, type=pa.float32())
combined_sketch.add(flattened_values, weight_array)
accumulator[feature_name] = combined_sketch
def _encode_univalent_feature(feature_array: pa.Array) -> List[Any]:
"""Encodes univalent feature values into a fixed length representation.
Univalent features are cast into a Python list. They are not affected by the
encoding with the exception of null values which are replaced by None.
Args:
feature_array: Arrow Array.
Returns:
A list containing the feature values where null values are replaced by None.
"""
result = [[None] for _ in range(len(feature_array))]
flattened, non_missing_parent_indices = arrow_util.flatten_nested(
feature_array, True)
non_missing_values = np.asarray(flattened)
nan_mask = pd.isnull(non_missing_values)
non_nan_pairs = np.stack((non_missing_values, non_missing_parent_indices),
axis=-1)[~nan_mask]
for (value, index) in non_nan_pairs:
result[int(index)] = [value]
return result
def _to_topk_tuples(
sliced_record_batch: Tuple[types.SliceKey, pa.RecordBatch],
bytes_features: FrozenSet[types.FeaturePath],
categorical_features: FrozenSet[types.FeaturePath],
weight_feature: Optional[Text]
) -> Iterable[Tuple[Tuple[types.SliceKey, types.FeaturePathTuple, Any], Union[
int, Tuple[int, Union[int, float]]]]]:
"""Generates tuples for computing top-k and uniques from the input."""
slice_key, record_batch = sliced_record_batch
for feature_path, feature_array, weights in arrow_util.enumerate_arrays(
record_batch,
weight_column=weight_feature,
enumerate_leaves_only=True):
feature_array_type = feature_array.type
feature_type = stats_util.get_feature_type_from_arrow_type(
feature_path, feature_array_type)
# Skip null columns.
if feature_type is None:
continue
if feature_path in bytes_features:
continue
if (feature_path in categorical_features or
feature_type == statistics_pb2.FeatureNameStatistics.STRING):
flattened_values, parent_indices = arrow_util.flatten_nested(
feature_array, weights is not None)
if weights is not None and flattened_values:
# Slow path: weighted uniques.
flattened_values_np = np.asarray(flattened_values)
weights_ndarray = weights[parent_indices]
for value, count, weight in _weighted_unique(
flattened_values_np, weights_ndarray):
yield (slice_key, feature_path.steps(), value), (count, weight)
else:
value_counts = array_util.ValueCounts(flattened_values)
values = value_counts.field('values').to_pylist()
counts = value_counts.field('counts').to_pylist()
for value, count in six.moves.zip(values, counts):
yield ((slice_key, feature_path.steps(), value), count)
def _update_combined_sketch_for_feature(
self, feature_name: tfdv_types.FeaturePath, values: pa.Array,
weights: Optional[np.ndarray],
accumulator: Dict[tfdv_types.FeaturePath, _CombinedSketch]):
"""Updates combined sketch with values (and weights if provided)."""
flattened_values, parent_indices = arrow_util.flatten_nested(
values, weights is not None)
combined_sketch = accumulator.get(feature_name, None)
if combined_sketch is None:
self._num_kmv_buckets_gauge.update(self._num_kmv_buckets)
def make_mg_sketch():
num_buckets = max(self._num_misragries_buckets,
self._num_top_values,
self._num_rank_histogram_buckets)
self._num_mg_buckets_gauge.update(num_buckets)
self._num_top_values_gauge.update(self._num_top_values)
self._num_rank_histogram_buckets_gauge.update(
self._num_rank_histogram_buckets)
return MisraGriesSketch(
num_buckets=num_buckets,
invalid_utf8_placeholder=constants.NON_UTF8_PLACEHOLDER,
# Maximum sketch size:
# _LARGE_STRING_THRESHOLD * num_buckets * constant_factor.
large_string_threshold=_LARGE_STRING_THRESHOLD,
large_string_placeholder=constants.LARGE_BYTES_PLACEHOLDER)
self._num_top_values_gauge.update(self._num_top_values)
combined_sketch = _CombinedSketch(distinct=KmvSketch(
self._num_kmv_buckets),
topk_unweighted=make_mg_sketch(),
topk_weighted=make_mg_sketch())
weight_array = None
if weights is not None:
flattened_weights = weights[parent_indices]
weight_array = pa.array(flattened_weights, type=pa.float32())
combined_sketch.add(flattened_values, weight_array)
accumulator[feature_name] = combined_sketch
def _get_example_value_presence(
record_batch: pa.RecordBatch, path: types.FeaturePath,
boundaries: Optional[Sequence[float]],
weight_column_name: Optional[Text]) -> Optional[pd.DataFrame]:
"""Returns information about which examples contained which values.
This function treats all values for a given path within a single example
as a set and and returns a mapping between each example index and the distinct
values which are present in that example.
The result of calling this function for path 'p' on an arrow record batch with
the two records [{'p': ['a', 'a', 'b']}, {'p': [a]}] will be
pd.Series(['a', 'b', 'a'], index=[0, 0, 1]).
If the array retrieved from get_array is null, this function returns None.
Args:
record_batch: The RecordBatch in which to look up the path.
path: The FeaturePath for which to fetch values.
boundaries: Optionally, a set of bin boundaries to use for binning the array
values.
weight_column_name: Optionally, a weight column to return in addition to the
value and example index.
Returns:
A Pandas DataFrame containing distinct pairs of array values and example
indices, along with the corresponding flattened example weights. The index
will be the example indices and the values will be stored in a column named
'values'. If weight_column_name is provided, a second column will be
returned containing the array values, and 'weights' containing the weights
for the example from which each value came.
"""
arr, example_indices = arrow_util.get_array(record_batch,
path,
return_example_indices=True)
if stats_util.get_feature_type_from_arrow_type(path, arr.type) is None:
return None
arr_flat, parent_indices = arrow_util.flatten_nested(
arr, return_parent_indices=True)
is_binary_like = arrow_util.is_binary_like(arr_flat.type)
assert boundaries is None or not is_binary_like, (
'Boundaries can only be applied to numeric columns')
if is_binary_like:
# use dictionary_encode so we can use np.unique on object arrays
dict_array = arr_flat.dictionary_encode()
arr_flat = dict_array.indices
arr_flat_dict = np.asarray(dict_array.dictionary)
example_indices_flat = example_indices[parent_indices]
if boundaries is not None:
element_indices, bins = bin_util.bin_array(arr_flat, boundaries)
rows = np.vstack([example_indices_flat[element_indices], bins])
else:
rows = np.vstack([example_indices_flat, np.asarray(arr_flat)])
if not rows.size:
return None
# Deduplicate values which show up more than once in the same example. This
# makes P(X=x|Y=y) in the standard lift definition behave as
# P(x \in Xs | y \in Ys) if examples contain more than one value of X and Y.
unique_rows = np.unique(rows, axis=1)
example_indices = unique_rows[0, :]
values = unique_rows[1, :]
if is_binary_like:
# return binary like values a pd.Categorical wrapped in a Series. This makes
# subsqeuent operations like pd.Merge cheaper.
values = pd.Categorical.from_codes(values, categories=arr_flat_dict)
columns = {'example_indices': example_indices, 'values': values}
if weight_column_name:
weights = arrow_util.get_weight_feature(record_batch,
weight_column_name)
columns['weights'] = np.asarray(weights)[example_indices]
df = pd.DataFrame(columns)
return df.set_index('example_indices')
def feature_value_slicer(
record_batch: pa.RecordBatch) -> Iterable[types.SlicedRecordBatch]:
"""A function that generates sliced record batches.
The naive approach of doing this would be to iterate each row, identify
slice keys for the row and keep track of index ranges for each slice key.
And then generate an arrow record batch for each slice key based on the
index ranges. This would be expensive as we are identifying the slice keys
for each row individually and we would have to loop over the feature values
including crossing them when we have to slice on multiple features. The
current approach generates the slice keys for a batch by performing joins
over indices of individual features. And then groups the joined record batch
by slice key to get the row indices corresponding to a slice.
Args:
record_batch: Arrow RecordBatch.
Yields:
Sliced record batch (slice_key, record_batch) where record_batch contains
the rows corresponding to a slice.
"""
per_feature_parent_indices = []
for feature_name, values in six.iteritems(features):
feature_array = record_batch.column(
record_batch.schema.get_field_index(feature_name))
flattened, value_parent_indices = arrow_util.flatten_nested(
feature_array, True)
non_missing_values = np.asarray(flattened)
# Create dataframe with feature value and parent index.
df = DataFrame({
feature_name: non_missing_values,
_PARENT_INDEX_COLUMN: value_parent_indices
})
df.drop_duplicates(inplace=True)
# Filter based on slice values
if values is not None:
df = df.loc[df[feature_name].isin(values)]
per_feature_parent_indices.append(df)
# Join dataframes based on parent indices.
# Note that we want the parent indices per slice key to be sorted in the
# merged dataframe. The individual dataframes have the parent indices in
# sorted order. We use "inner" join type to preserve the order of the left
# keys (also note that same parent index rows would be consecutive). Hence
# we expect the merged dataframe to have sorted parent indices per
# slice key.
merged_df = functools.reduce(
lambda base, update: pd.merge(
base,
update,
how='inner', # pylint: disable=g-long-lambda
on=_PARENT_INDEX_COLUMN),
per_feature_parent_indices)
# Construct a new column in the merged dataframe with the slice keys.
merged_df[_SLICE_KEY_COLUMN] = ''
index = 0
for col_name in sorted(merged_df.columns):
if col_name in [_PARENT_INDEX_COLUMN, _SLICE_KEY_COLUMN]:
continue
slice_key_col = (_to_slice_key(col_name) + '_' +
merged_df[col_name].apply(_to_slice_key))
if index == 0:
merged_df[_SLICE_KEY_COLUMN] = slice_key_col
index += 1
else:
merged_df[_SLICE_KEY_COLUMN] += ('_' + slice_key_col)
# Since the parent indices are sorted per slice key, the groupby would
# preserve the sorted order within each group.
per_slice_parent_indices = merged_df.groupby(
_SLICE_KEY_COLUMN, sort=False)[_PARENT_INDEX_COLUMN]
for slice_key, parent_indices in per_slice_parent_indices:
yield (slice_key,
table_util.RecordBatchTake(
record_batch, pa.array(parent_indices.to_numpy())))
def _flatten_and_impute(
examples: pa.RecordBatch, categorical_features: Set[types.FeaturePath]
) -> Dict[types.FeaturePath, np.ndarray]:
"""Flattens and imputes the values in the input Arrow RecordBatch.
Replaces missing values with CATEGORICAL_FEATURE_IMPUTATION_FILL_VALUE
for categorical features and 10*max(feature_values) for numeric features.
We impute missing values with an extreme value that is far from observed
values so it does not incorrectly impact KNN results. 10*max(feature_values)
is used instead of sys.max_float because max_float is large enough to cause
unexpected float arithmetic errors.
Args:
examples: Arrow RecordBatch containing a batch of examples where all
features are univalent.
categorical_features: Set of categorical feature names.
Returns:
A Dict[FeaturePath, np.ndarray] where the key is the feature path and the
value is a 1D numpy array corresponding to the feature values.
"""
num_rows = examples.num_rows
result = {}
for column_name, feature_array in zip(examples.schema.names,
examples.columns):
feature_path = types.FeaturePath([column_name])
imputation_fill_value = (CATEGORICAL_FEATURE_IMPUTATION_FILL_VALUE
if feature_path in categorical_features else
sys.maxsize)
if pa.types.is_null(feature_array.type):
# If null array, impute all values.
imputed_values_array = np.full(shape=num_rows,
fill_value=imputation_fill_value)
result[feature_path] = imputed_values_array
else:
# to_pandas returns a readonly array. Create a copy as we will be imputing
# the NaN values.
flattened_array, non_missing_parent_indices = arrow_util.flatten_nested(
feature_array, return_parent_indices=True)
assert non_missing_parent_indices is not None
non_missing_values = np.copy(np.asarray(flattened_array))
is_categorical_feature = feature_path in categorical_features
result_dtype = non_missing_values.dtype
if non_missing_parent_indices.size < num_rows and is_categorical_feature:
result_dtype = np.object
flattened_array = np.ndarray(shape=num_rows, dtype=result_dtype)
num_values = np.asarray(
array_util.ListLengthsFromListArray(feature_array))
missing_parent_indices = np.where(num_values == 0)[0]
if feature_path not in categorical_features:
# Also impute any NaN values.
nan_mask = np.isnan(non_missing_values)
if not np.all(nan_mask):
imputation_fill_value = non_missing_values[~nan_mask].max(
) * 10
non_missing_values[nan_mask.nonzero()
[0]] = imputation_fill_value
flattened_array[non_missing_parent_indices] = non_missing_values
if missing_parent_indices.any():
flattened_array[missing_parent_indices] = imputation_fill_value
result[feature_path] = flattened_array
return result
def testFlattenNestedNonList(self):
input_array = pa.array([1, 2])
flattened, parent_indices = arrow_util.flatten_nested(
input_array, return_parent_indices=True)
self.assertTrue(flattened.equals(pa.array([1, 2])))
np.testing.assert_array_equal(parent_indices, [0, 1])