tfma.extractors.FeaturesExtractor
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Creates an extractor for extracting features.
tfma.extractors.FeaturesExtractor(
eval_config: tfma.EvalConfig,
tensor_representations: Optional[Mapping[Text, schema_pb2.TensorRepresentation]] = None
) -> tfma.extractors.Extractor
The extractor acts as follows depending on the existence of certain keys
within the incoming extracts:
1) Extracts contains tfma.ARROW_RECORD_BATCH_KEY
The features stored in the RecordBatch will be extracted and added to the
output extract under the key tfma.FEATURES_KEY and the raw serialized inputs
will be added under the tfma.INPUT_KEY. Any extracts that already exist will
be merged with the values from the RecordBatch with the RecordBatch values
taking precedence when duplicate keys are detected. The
tfma.ARROW_RECORD_BATCH_KEY key will be removed from the output extracts.
2) Extracts contains tfma.FEATURES_KEY (but not tfma.ARROW_RECORD_BATCH_KEY)
The operation will be a no-op and the incoming extracts will be passed as is
to the output.
3) Extracts contains neither tfma.FEATURES_KEY | tfma.ARROW_RECORD_BATCH_KEY
An exception will be raised.
Args |
|---|
eval_config
|
Eval config.
|
tensor_representations
|
Optional tensor representations to use when parsing
the data. If tensor_representations are not passed or a representation is
not found for a given feature name a default representation will be used
where possible, otherwise an exception will be raised.
|
Returns |
|---|
|
Extractor for extracting features.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tfma.extractors.FeaturesExtractor\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/model-analysis/blob/v0.46.0/tensorflow_model_analysis/extractors/features_extractor.py#L36-L77) |\n\nCreates an extractor for extracting features. \n\n tfma.extractors.FeaturesExtractor(\n eval_config: ../../tfma/EvalConfig,\n tensor_representations: Optional[Mapping[Text, schema_pb2.TensorRepresentation]] = None\n ) -\u003e ../../tfma/extractors/Extractor\n\nThe extractor acts as follows depending on the existence of certain keys\nwithin the incoming extracts:\n\n1) Extracts contains tfma.ARROW_RECORD_BATCH_KEY\n\nThe features stored in the RecordBatch will be extracted and added to the\noutput extract under the key tfma.FEATURES_KEY and the raw serialized inputs\nwill be added under the tfma.INPUT_KEY. Any extracts that already exist will\nbe merged with the values from the RecordBatch with the RecordBatch values\ntaking precedence when duplicate keys are detected. The\ntfma.ARROW_RECORD_BATCH_KEY key will be removed from the output extracts.\n\n2) Extracts contains tfma.FEATURES_KEY (but not tfma.ARROW_RECORD_BATCH_KEY)\n\nThe operation will be a no-op and the incoming extracts will be passed as is\nto the output.\n\n3) Extracts contains neither tfma.FEATURES_KEY \\| tfma.ARROW_RECORD_BATCH_KEY\n\nAn exception will be raised.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `eval_config` | Eval config. |\n| `tensor_representations` | Optional tensor representations to use when parsing the data. If tensor_representations are not passed or a representation is not found for a given feature name a default representation will be used where possible, otherwise an exception will be raised. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Extractor for extracting features. ||\n\n\u003cbr /\u003e"]]
View source on GitHub