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tfx.components.CsvExampleGen

Official TFX CsvExampleGen component.

Inherits From: FileBasedExampleGen, BaseComponent, BaseNode

tfx.components.CsvExampleGen(
    input: Optional[tfx.types.Channel] = None,
    input_base: Optional[Text] = None,
    input_config: Optional[Union[example_gen_pb2.Input, Dict[Text, Any]]] = None,
    output_config: Optional[Union[example_gen_pb2.Output, Dict[Text, Any]]] = None,
    range_config: Optional[Union[range_config_pb2.RangeConfig, Dict[Text, Any]]] = None,
    example_artifacts: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials

The csv examplegen component takes csv data, and generates train and eval examples for downsteam components.

The csv examplegen encodes column values to tf.Example int/float/byte feature. For the case when there's missing cells, the csv examplegen uses: -- tf.train.Feature(type_list=tf.train.typeList(value=[])), when the type can be inferred. -- tf.train.Feature() when it cannot infer the type from the column.

Note that the type inferring will be per input split. If input isn't a single split, users need to ensure the column types align in each pre-splits.

For example, given the following csv rows of a split:

header:A,B,C,D row1: 1,,x,0.1 row2: 2,,y,0.2 row3: 3,,,0.3 row4:

The output example will be example1: 1(int), empty feature(no type), x(string), 0.1(float) example2: 2(int), empty feature(no type), x(string), 0.2(float) example3: 3(int), empty feature(no type), empty list(string), 0.3(float)

Note that the empty feature is tf.train.Feature() while empty list string feature is tf.train.Feature(bytes_list=tf.train.BytesList(value=[])).

input A Channel of type standard_artifacts.ExternalArtifact, which includes one artifact whose uri is an external directory containing the CSV files. (Deprecated by input_base)
input_base an external directory containing the CSV files.
input_config An example_gen_pb2.Input instance, providing input configuration. If unset, the files under input_base will be treated as a single split. If any field is provided as a RuntimeParameter, input_config should be constructed as a dict with the same field names as Input proto message.
output_config An example_gen_pb2.Output instance, providing output configuration. If unset, default splits will be 'train' and 'eval' with size 2:1. If any field is provided as a RuntimeParameter, output_config should be constructed as a dict with the same field names as Output proto message.
range_config An optional range_config_pb2.RangeConfig instance, specifying the range of span values to consider. If unset, driver will default to searching for latest span with no restrictions.
example_artifacts Optional channel of 'ExamplesPath' for output train and eval examples.
instance_name Optional unique instance name. Necessary if multiple CsvExampleGen components are declared in the same pipeline.

component_id

component_type

downstream_nodes

exec_properties

id Node id, unique across all TFX nodes in a pipeline.

If id is set by the user, return it directly. otherwise, if instance name (deprecated) is available, node id will be: . otherwise, node id will be:

inputs

outputs

type

upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node a component that must run before this node.

from_json_dict

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

View source

@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name (Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

View source

with_id(
    id: Text
) -> "BaseNode"

with_platform_config

View source

with_platform_config(
    config: message.Message
) -> "BaseComponent"

Attaches a proto-form platform config to a component.

The config will be a per-node platform-specific config.

Args
config platform config to attach to the component.

Returns
the same component itself.

EXECUTOR_SPEC tfx.dsl.components.base.executor_spec.ExecutorClassSpec