tft_beam.AnalyzeDataset

Takes a preprocessing_fn and computes the relevant statistics.

tft_beam.AnalyzeDataset(
    preprocessing_fn, pipeline=None
)

AnalyzeDataset accepts a preprocessing_fn in its constructor. When its expand method is called on a dataset, it computes all the relevant statistics required to run the transformation described by the preprocessing_fn, and returns a TransformFn representing the application of the preprocessing_fn.

preprocessing_fn A function that accepts and returns a dictionary from strings to Tensor or 2D SparseTensors.
pipeline (Optional) a beam Pipeline.

label

Methods

default_label

default_label()

default_type_hints

default_type_hints()

display_data

display_data()

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns
Dict[str, Any]: A dictionary containing key:value pairs. The value might be an integer, float or string value; a :class:DisplayDataItem for values that have more data (e.g. short value, label, url); or a :class:HasDisplayData instance that has more display data that should be picked up. For example::

{ 'key1': 'string_value', 'key2': 1234, 'key3': 3.14159265, 'key4': DisplayDataItem('apache.org', url='http://apache.org'), 'key5': subComponent }

expand

View source

expand(
    dataset
)

Analyze the dataset.

Args
dataset A dataset.

Returns
A TransformFn containing the deferred transform function.

Raises
ValueError If preprocessing_fn has no outputs.

from_runner_api

@classmethod
from_runner_api(
    proto, context
)

get_type_hints

get_type_hints()

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order:

  • Using self.default_type_hints().
  • Using self.class type hints.

get_windowing

get_windowing(
    inputs
)

Returns the window function to be associated with transform's output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type

infer_output_type(
    unused_input_type
)

register_urn

@classmethod
register_urn(
    urn, parameter_type, constructor=None
)

runner_api_requires_keyed_input

runner_api_requires_keyed_input()

to_runner_api

to_runner_api(
    context, has_parts=False, **extra_kwargs
)

to_runner_api_parameter

to_runner_api_parameter(
    unused_context
)

to_runner_api_pickled

to_runner_api_pickled(
    unused_context
)

type_check_inputs

type_check_inputs(
    pvalueish
)

type_check_inputs_or_outputs

type_check_inputs_or_outputs(
    pvalueish, input_or_output
)

type_check_outputs

type_check_outputs(
    pvalueish
)

with_input_types

with_input_types(
    input_type_hint
)

Annotates the input type of a :class:PTransform with a type-hint.

Args
input_type_hint (type): An instance of an allowed built-in type, a custom class, or an instance of a :class:~apache_beam.typehints.typehints.TypeConstraint.

Raises
TypeError If input_type_hint is not a valid type-hint. See :obj:apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns
PTransform A reference to the instance of this particular :class:PTransform object. This allows chaining type-hinting related methods.

with_output_types

with_output_types(
    type_hint
)

Annotates the output type of a :class:PTransform with a type-hint.

Args
type_hint (type): An instance of an allowed built-in type, a custom class, or a :class:~apache_beam.typehints.typehints.TypeConstraint.

Raises
TypeError If type_hint is not a valid type-hint. See :obj:~apache_beam.typehints.typehints.validate_composite_type_param() for further details.

Returns
PTransform A reference to the instance of this particular :class:PTransform object. This allows chaining type-hinting related methods.

__or__

__or__(
    right
)

Used to compose PTransforms, e.g., ptransform1 | ptransform2.

__ror__

__ror__(
    left, label=None
)

Used to apply this PTransform to non-PValues, e.g., a tuple.

Class Variables

  • pipeline = None
  • side_inputs = ()