Python apache_beam.CoGroupByKey() Examples
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Example #1
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 5 votes |
def _join_x_counts( join_info: Tuple[_SlicedXKey, Dict[Text, Sequence[Any]]] # TODO(b/147153346) update dict value list element type annotation to: # Union[_CountType, Tuple[_YType, _CountType]] ) -> Iterator[Tuple[_SlicedYKey, _ConditionalYRate]]: """Joins x_count with all xy_counts for that x. This function expects the result of a CoGroupByKey, in which the key is a tuple of the form (slice_key, x_path, x), and one of the grouped streams has just one element, the number of examples in a given slice for which x is present in x_path, and the other grouped stream is the set of all (x, y) pairs for that x along with the number of examples in which both x and y are present in their respective paths. Schematically, join_info looks like: (slice, x_path, x), {'x_count': [x_count], 'xy_counts': [(y_1, xy_1_count), ..., (y_k, xy_k_count)]} If the value of x_count is less than min_x_count, no rates will be yielded. Args: join_info: A CoGroupByKey result Yields: Per-(slice, x_path, y, x) tuples of the form (_SlicedYKey(slice, y), _ConditionalYRate(x_path, x, xy_count, x_count)). """ # (slice_key, x_path, x), join_inputs = join_info key, join_inputs = join_info if not join_inputs['x_count']: return x_count = join_inputs['x_count'][0] for y, xy_count in join_inputs['xy_counts']: yield _SlicedYKey(key.slice_key, y), _ConditionalYRate( x_path=key.x_path, x=key.x, xy_count=xy_count, x_count=x_count)
Example #2
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 5 votes |
def _join_example_counts( join_info: Tuple[types.SliceKey, Dict[Text, Sequence[Any]]] # TODO(b/147153346) update dict value list element type annotation to: # Union[_CountType, Tuple[_YType, _CountType]] ) -> Iterator[Tuple[_SlicedYKey, _YRate]]: """Joins slice example count with all values of y within that slice. This function expects the result of a CoGroupByKey, in which the key is the slice_key, one of the grouped streams has just one element, the total number of examples within the slice, and the other grouped stream is the set of all y values and number of times that y value appears in this slice. Schematically, join_info looks like: slice_key, {'example_count': [example_count], 'y_counts': [(y_1, y_1_count), ..., (y_k, y_k_count)]} Args: join_info: A CoGroupByKey result. Yields: Per-(slice, y) tuples (_SlicedYKey(slice, y), _YRate(y_count, example_count)). """ slice_key, join_inputs = join_info example_count = join_inputs['example_count'][0] for y, y_count in join_inputs['y_counts']: yield _SlicedYKey(slice_key, y), _YRate(y_count, example_count)
Example #3
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 5 votes |
def _compute_lifts( join_info: Tuple[_SlicedYKey, Dict[Text, Sequence[Any]]] # TODO(b/147153346) update dict value list element type annotation to: # Sequence[Union[_YRate, _ConditionalYRate]] ) -> Iterator[Tuple[_SlicedFeatureKey, _LiftInfo]]: """Joins y_counts with all x-y pairs for that y and computes lift. This function expects the result of a CoGroupByKey, in which the key is a tuple of the form (slice_key, y), one of the grouped streams has just one element, the y_rate for that value of y, and the other grouped stream is the set of all conditional_y_rate values for that same value of y. Schematically, join_info looks like: (slice_key, y), {'y_rate': [y_count, example_count], 'conditional_y_rate': [ (x_path_1, x_1, x_1_y_count, x_1_count), ..., (x_path_1, x_k, x_k_y_count, x_k_count) ... (x_path_m, x_1, x_1_y_count, x_1_count), ..., (x_path_m, x_k, x_k_y_count, x_k_count)]} Args: join_info: A CoGroupByKey result. Yields: Per-(slice, x_path) tuples of the form ((slice_key, x_path), _LiftInfo(x, y, lift, xy_count, x_count, y_count)). """ (slice_key, y), join_inputs = join_info y_rate = join_inputs['y_rate'][0] for conditional_y_rate in join_inputs['conditional_y_rate']: lift = ((float(conditional_y_rate.xy_count) / conditional_y_rate.x_count) / (float(y_rate.y_count) / y_rate.example_count)) yield (_SlicedFeatureKey(slice_key, conditional_y_rate.x_path), _LiftInfo( x=conditional_y_rate.x, y=y, lift=lift, xy_count=conditional_y_rate.xy_count, x_count=conditional_y_rate.x_count, y_count=y_rate.y_count))
Example #4
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 5 votes |
def expand(self, x_counts_and_ys: Tuple[Tuple[_SlicedXKey, _CountType], _SlicedYKey]): x_counts, y_keys = x_counts_and_ys # slice, y y_keys_by_slice = ( y_keys | 'MoveYToValue_YKey' >> beam.Map(lambda k: (k.slice_key, k.y))) # slice, (x_path, x, x_count) x_counts_by_slice = ( x_counts | 'MoveXToValue_XCountsKey' >> beam.MapTuple( lambda k, v: (k.slice_key, (k.x_path, k.x, v)))) # _SlicedXYKey(slice, x_path, x, y), 0 return ( { 'y_keys': y_keys_by_slice, 'x_counts': x_counts_by_slice } | 'CoGroupByForPlaceholderYRates' >> beam.CoGroupByKey() | 'CrossXYValues' >> beam.FlatMap(_cross_join_y_keys)) # No typehint for input, since it's a multi-input PTransform for which Beam # doesn't yet support typehints (BEAM-3280).
Example #5
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 5 votes |
def expand( self, sliced_record_batchs: beam.pvalue.PCollection) -> beam.pvalue.PCollection: # Compute P(Y=y) # _SlicedYKey(slice, y), _YRate(y_count, example_count) y_rates = sliced_record_batchs | 'GetYRates' >> _GetYRates( self._y_path, self._y_boundaries, self._weight_column_name) y_keys = y_rates | 'ExtractYKeys' >> beam.Keys() # Compute P(Y=y | X=x) # _SlicedYKey(slice, y), _ConditionalYRate(x_path, x, xy_count, x_count) conditional_y_rates = ((sliced_record_batchs, y_keys) | 'GetConditionalYRates' >> _GetConditionalYRates( self._y_path, self._y_boundaries, self._x_paths, self._min_x_count, self._weight_column_name)) return ( { 'conditional_y_rate': conditional_y_rates, 'y_rate': y_rates } | 'CoGroupByForLift' >> beam.CoGroupByKey() | 'ComputeLifts' >> beam.FlatMap(_compute_lifts) | 'FilterLifts' >> _FilterLifts(self._top_k_per_y, self._bottom_k_per_y) | 'GroupLiftsForOutput' >> beam.GroupByKey() | 'MakeProtos' >> beam.Map(_make_dataset_feature_stats_proto, self._y_path, self._y_boundaries, self._weight_column_name is not None, self._output_custom_stats))
Example #6
Source File: linters.py From data-linter with Apache License 2.0 | 5 votes |
def _lint(self, examples): """Returns the result of the TailedDistributionDetector linter. Args: examples: A `PTransform` that yields a `PCollection` of `tf.Example`s. Returns: A `PTransform` that yields a `LintResult` of the format warnings: [feature names] lint_samples: [ [stats: {min: feature_min if outlying, max: feature_max if outlying}] for each warning ] """ feature_values = ( examples | 'FlattenFeatureValue' >> beam.FlatMap( self._flatten_feature_vals(self.numeric_features))) feature_min_trimmed_mean = ( feature_values | self._make_trimmed_averager(self._MIN)) feature_max_trimmed_mean = ( feature_values | self._make_trimmed_averager(self._MAX)) return ( (feature_min_trimmed_mean, feature_max_trimmed_mean) | 'MergeTrimmedMeans' >> beam.CoGroupByKey() | 'AsList' >> beam.combiners.ToList() | 'ToResult' >> beam.Map(self._to_result))
Example #7
Source File: lift_stats_generator.py From data-validation with Apache License 2.0 | 4 votes |
def expand(self, sliced_record_batchs_and_ys: Tuple[types.SlicedRecordBatch, _SlicedYKey]): sliced_record_batchs, y_keys = sliced_record_batchs_and_ys # _SlicedXYKey(slice, x_path, x, y), xy_count partial_copresence_counts = ( sliced_record_batchs | 'ToPartialCopresenceCounts' >> beam.FlatMap( _to_partial_copresence_counts, self._y_path, self._x_paths, self._y_boundaries, self._weight_column_name)) # Compute placerholder copresence counts. # partial_copresence_counts will only include x-y pairs that are present, # but we would also like to keep track of x-y pairs that never appear, as # long as x and y independently occur in the slice. # _SlicedXKey(slice, x_path, x), x_count x_counts = ( sliced_record_batchs | 'ToPartialXCounts' >> beam.FlatMap( _to_partial_x_counts, self._x_paths, self._weight_column_name) | 'SumXCounts' >> beam.CombinePerKey(sum)) if self._min_x_count: x_counts = x_counts | 'FilterXCounts' >> beam.Filter( lambda kv: kv[1] > self._min_x_count) # _SlicedXYKey(slice, x_path, x, y), 0 placeholder_copresence_counts = ( (x_counts, y_keys) | 'GetPlaceholderCopresenceCounts' >> _GetPlaceholderCopresenceCounts( self._x_paths, self._min_x_count)) def move_y_to_value(key, xy_count): return _SlicedXKey(key.slice_key, key.x_path, key.x), (key.y, xy_count) # _SlicedXKey(slice, x_path, x), (y, xy_count) copresence_counts = ( (placeholder_copresence_counts, partial_copresence_counts) | 'FlattenCopresenceCounts' >> beam.Flatten() | 'SumCopresencePairs' >> beam.CombinePerKey(sum) | 'MoveYToValue' >> beam.MapTuple(move_y_to_value)) # _SlicedYKey(slice, y), _ConditionalYRate(x_path, x, xy_count, x_count) return ({ 'x_count': x_counts, 'xy_counts': copresence_counts } | 'CoGroupByForConditionalYRates' >> beam.CoGroupByKey() | 'JoinXCounts' >> beam.FlatMap(_join_x_counts))