tf.estimator.BoostedTreesEstimator

TensorFlow 1 version View source on GitHub

An Estimator for Tensorflow Boosted Trees models.

tf.estimator.BoostedTreesEstimator(
    feature_columns, n_batches_per_layer, head, model_dir=None, 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
)

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.
head the Head instance defined for Estimator.
model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into an estimator to continue training a previously saved model.
weight_column A string or a _NumericColumn created by tf.feature_column.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 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.

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

config

model_dir

model_fn Returns the model_fn which is bound to self.params.
params

Methods

eval_dir

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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

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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:

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

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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 path to the exported directory as a bytes object.

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

experimental_feature_importances

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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 constrain