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tf.estimator.BoostedTreesRegressor

TensorFlow 2.0 version View source on GitHub

Class BoostedTreesRegressor

A Regressor for Tensorflow Boosted Trees models.

Aliases:

  • Class tf.compat.v1.estimator.BoostedTreesRegressor
  • Class tf.compat.v2.estimator.BoostedTreesRegressor

Eager Compatibility

Estimators can be used while eager execution is enabled. Note that input_fn and all hooks are executed inside a graph context, so they have to be written to be compatible with graph mode. Note that input_fn code using tf.data generally works in both graph and eager modes.

__init__

View source

__init__(
    feature_columns,
    n_batches_per_layer,
    model_dir=None,
    label_dimension=_HOLD_FOR_MULTI_DIM_SUPPORT,
    weight_column=None,
    n_trees=100,
    max_depth=6,
    learning_rate=0.1,
    l1_regularization=0.0,
    l2_regularization=0.0,
    tree_complexity=0.0,
    min_node_weight=0.0,
    config=None,
    center_bias=False,
    pruning_mode='none',
    quantile_sketch_epsilon=0.01,
    train_in_memory=False
)

Initializes a BoostedTreesRegressor instance.

Example:

bucketized_feature_1 = bucketized_column(
  numeric_column('feature_1'), BUCKET_BOUNDARIES_1)
bucketized_feature_2 = bucketized_column(
  numeric_column('feature_2'), BUCKET_BOUNDARIES_2)

# Need to see a large portion of the data before we can build a layer, for
# example half of data n_batches_per_layer = 0.5 * NUM_EXAMPLES / BATCH_SIZE
regressor = estimator.BoostedTreesRegressor(
    feature_columns=[bucketized_feature_1, bucketized_feature_2],
    n_batches_per_layer=n_batches_per_layer,
    n_trees=100,
    ... <some other params>
)

def input_fn_train():
  ...
  return dataset

regressor.train(input_fn=input_fn_train)

def input_fn_eval():
  ...
  return dataset

metrics = regressor.evaluate(input_fn=input_fn_eval)

Args:

  • feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from FeatureColumn.
  • n_batches_per_layer: the number of batches to collect statistics per layer. The total number of batches is total number of data divided by batch size.
  • model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
  • label_dimension: Number of regression targets per example. Multi-dimensional support is not yet implemented.
  • weight_column: A string or a NumericColumn created by tf.fc_old.numeric_column defining feature column representing weights. It is used to downweight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a NumericColumn, raw tensor is fetched by key weight_column.key, then weight_column.normalizer_fn is applied on it to get weight tensor.
  • n_trees: number trees to be created.
  • max_depth: maximum depth of the tree to grow.
  • learning_rate: shrinkage parameter to be used when a tree added to the model.
  • l1_regularization: regularization multiplier applied to the absolute weights of the tree leafs.
  • l2_regularization: regularization multiplier applied to the square weights of the tree leafs.
  • tree_complexity: regularization factor to penalize trees with more leaves.
  • min_node_weight: min_node_weight: minimum hessian a node must have for a split to be considered. The value will be compared with sum(leaf_hessian)/(batch_size * n_batches_per_layer).
  • config: RunConfig object to configure the runtime settings.
  • center_bias: Whether bias centering needs to occur. Bias centering refers to the first node in the very first tree returning the prediction that is aligned with the original labels distribution. For example, for regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1.
  • pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- pruning (do not split a node if not enough gain is observed) and post pruning (build the tree up to a max depth and then prune branches with negative gain). For pre and post pruning, you MUST provide tree_complexity >0.
  • quantile_sketch_epsilon: float between 0 and 1. Error bound for quantile computation. This is only used for float feature columns, and the number of buckets generated per float feature is 1/quantile_sketch_epsilon.
  • train_in_memory: bool, when true, it assumes the dataset is in memory, i.e., input_fn should return the entire dataset as a single batch, n_batches_per_layer should be set as 1, num_worker_replicas should be 1, and num_ps_replicas should be 0 in tf.Estimator.RunConfig.

Raises:

  • ValueError: when wrong arguments are given or unsupported functionalities are requested.

Properties

config

model_dir

model_fn

Returns the model_fn which is bound to self.params.

Returns:

The model_fn with following signature: def model_fn(features, labels, mode, config)

params

Methods

eval_dir

View source

eval_dir(name=None)

Shows the directory name where evaluation metrics are dumped.

Args:

  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A string which is the path of directory contains evaluation metrics.

evaluate

View source

evaluate(
    input_fn,
    steps=None,
    hooks=None,
    checkpoint_path=None,
    name=None
)

Evaluates the model given evaluation data input_fn.

For each step, calls input_fn, which returns one batch of data. Evaluates until: - steps batches are processed, or - input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration).

Args:

  • input_fn: A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following: * A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. * A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • steps: Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.
  • hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the evaluation call.
  • checkpoint_path: Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
  • name: Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.

Returns:

A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.

Raises:

  • ValueError: If steps <= 0.

experimental_export_all_saved_models

View source

experimental_export_all_saved_models(
    export_dir_base,
    input_receiver_fn_map,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None
)

Exports a SavedModel with tf.MetaGraphDefs for each requested mode.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures.

For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • input_receiver_fn_map: dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no arguments and returns the appropriate subclass of InputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • as_text: whether to write the SavedModel proto in text format.
  • checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found.

experimental_feature_importances

View source

experimental_feature_importances(normalize=False)

Computes gain-based feature importances.

The higher the value, the more important the corresponding feature.

Args:

  • normalize: If True, normalize the feature importances.

Returns:

  • feature_importances: an OrderedDict, where the keys are the feature column names and the values are importances. It is sorted by importance.

Raises:

  • ValueError: When attempting to normalize on an empty ensemble or an ensemble of trees which have no splits. Or when attempting to normalize and feature importances have negative values.

experimental_predict_with_explanations

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experimental_predict_with_explanations(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None
)

Computes model explainability outputs per example along with predictions.

Currently supports directional feature contributions (DFCs). For each instance, DFCs indicate the aggregate contribution of each feature. See https://arxiv.org/abs/1312.1121 and http://blog.datadive.net/interpreting-random-forests/ for more details.

Args:

  • input_fn: A function that provides input data for predicting as minibatches. See Premade Estimators for more information. The function should construct and return one of the following:
    • A tf.data.Dataset object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below.
    • A tuple (features, labels): Where features is a tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
  • predict_keys: list of str, name of the keys to predict. It is used if the tf.estimator.EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary, with the exception of 'bias' and 'dfc', which will always be in the dictionary. If None, returns all keys in prediction dict, as well as two new keys 'dfc' and 'bias'.
  • hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the prediction call.
  • checkpoint_path: Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint.

Yields:

Evaluated values of predictions tensors. The predictions tensors will contain at least two keys 'dfc' and 'bias' for model explanations. The dfc value corresponds to the contribution of each feature to the overall prediction for this instance (positive indicating that the feature makes it more likely to select class 1 and negative less likely). The dfc is an OrderedDict, where the keys are the feature column names and the values are the contributions. It is sorted by the absolute value of the contribution (e.g OrderedDict([('age', -0.54), ('gender', 0.4), ('fare', 0.21)])). The 'bias' value will be the same across all the instances, corresponding to the probability (classification) or prediction (regression) of the training data distribution.

Raises:

  • ValueError: when wrong arguments are given or unsupported functionalities are requested.

export_saved_model

View source

export_saved_model(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    experimental_mode=ModeKeys.PREDICT
)

Exports inference graph as a SavedModel into the given dir.

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • as_text: whether to write the SavedModel proto in text format.
  • checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
  • experimental_mode: tf.estimator.ModeKeys value indicating with mode will be exported. Note that this feature is experimental.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

export_savedmodel

View source

export_savedmodel(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None,
    strip_default_attrs=False
)

Exports inference graph as a SavedModel into the given dir. (deprecated)

For a detailed guide, see Using SavedModel with Estimators.

This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session.

The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Args:

  • export_dir_base: A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
  • serving_input_receiver_fn: A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
  • assets_extra: A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
  • as_text: whether to write the SavedModel proto in text format.
  • checkpoint_path: The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
  • strip_default_attrs: Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

Returns:

The string path to the exported directory.

Raises:

  • ValueError: if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found.

get_variable_names

View source

get_variable_names()

Returns list of all variable names in this model.

Returns:

List of names.

Raises:

  • ValueError: If the Estimator has not produced a checkpoint yet.

get_variable_value