tfdf.keras.AdvancedArguments

Advanced control of the model that most users won't need to use.

tfdf.keras.AdvancedArguments(
    infer_prediction_signature: Optional[bool] = True,
    yggdrasil_training_config: Optional[tfdf.keras.core.YggdrasilTrainingConfig] = None,
    yggdrasil_deployment_config: Optional[tfdf.keras.core.YggdrasilDeploymentConfig] = None,
    fail_on_non_keras_compatible_feature_name: Optional[bool] = True,
    predict_single_probability_for_binary_classification: Optional[bool] = True,
    metadata_framework: Optional[str] = 'TF Keras',
    metadata_owner: Optional[str] = None,
    populate_history_with_yggdrasil_logs: bool = False,
    disable_categorical_integer_offset_correction: bool = False,
    node_format: Optional[tfdf.keras.core.NodeFormat] = None,
    allow_slow_inference: bool = True,
    force_ydf_port: Optional[int] = None,
    output_secondary_class_predictions: bool = False
)

infer_prediction_signature Instantiate the model graph after training. This allows the model to be saved without specifying an input signature and without calling "predict", "evaluate". Disabling this logic can be useful in two situations: (1) When the exported signature is different from the one used during training, (2) When using a fixed-shape pre-processing that consume 1 dimensional tensors (as keras will automatically expend its shape to rank 2). For example, when using tf.Transform.
yggdrasil_training_config Yggdrasil Decision Forests training configuration. Expose a few extra hyper-parameters. yggdrasil_deployment_config: Configuration of the computing resources used to train the model e.g. number of threads. Does not impact the model quality.
fail_on_non_keras_compatible_feature_name If true (default), training will fail if one of the feature name is not compatible with part of the Keras API. If false, a warning will be generated instead.
predict_single_probability_for_binary_classification Only used for binary classification. If true (default), the prediction of a binary class model is a tensor of shape [None, 1] containing the probability of the positive class (value=1). If false, the prediction of a binary class model is a tensor of shape [None, num_classes=2] containing the probability of the complementary classes.
metadata_framework Metadata describing the framework used to train the model.
metadata_owner Metadata describing who trained the model.
populate_history_with_yggdrasil_logs If false (default) and if a validation dataset is provided, populate the model's history with the final validation evaluation computed by the Keras metric (i.e. one evaluation). If true or if no validation dataset is provided, populate the model's history with the yggdrasil training logs. The yggdrasil training logs contains more metrics, but those might not be comparable with other non TF-DF models.
disable_categorical_integer_offset_correction Yggdrasil Decision Forests reserves the value 0 of categorical integer features to the OOV item, so the value 0 cannot be used directly. If the disable_categorical_integer_offset_correction is true, a +1 offset might be applied before calling the inference code. This attribute should be disabled when creating manually a model with categorical integer features. Ultimately, Yggdrasil Decision Forests will support the value 0 as a normal value and this parameter will be removed. If disable_categorical_integer_offset_correction is false, this +1 offset is never applied.
node_format Yggdrasil Decision Forests node format for the saved model. If not specified, uses the recommended format. The node format is visible in the node summary. For models to be compatible with the open-source version of TensorFlow Decision Forests and TensorFlow Serving, the node format should be BLOB_SEQUENCE.
allow_slow_inference If false, slow inference engines are not allowed. If the model is only available with the slow engine, an error is raised. If true, the fastest compatible inference engine (possibly the slow one) will be used.
force_ydf_port Socket port for YDF GRPC to use during distributed training in addition to the TF GRPC. The chief and the workers should be able to communicate thought this port. If not set, an available port is automatically selected.
output_secondary_class_predictions If true, in the case of a multi-task model, the predictions of secondary tasks are exported in the model predictions. If false, the model only outputs the primary tasks predictions.