tf.estimator.BoostedTreesClassifier

Class `BoostedTreesClassifier`

Defined in tensorflow/python/estimator/canned/boosted_trees.py.

A Classifier for Tensorflow Boosted Trees models.

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`

__init__(
    feature_columns,
    n_batches_per_layer,
    model_dir=None,
    n_classes=_HOLD_FOR_MULTI_CLASS_SUPPORT,
    weight_column=None,
    label_vocabulary=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'
)

Initializes a BoostedTreesClassifier 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
classifier = estimator.BoostedTreesClassifier(
    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

classifier.train(input_fn=input_fn_train)

def input_fn_eval():
  ...
  return dataset

metrics = classifier.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.
n_classes: number of label classes. Default is binary classification. Multiclass support is not yet implemented.
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.
label_vocabulary: A list of strings represents possible label values. If given, labels must be string type and have any value in label_vocabulary. If it is not given, that means labels are already encoded as integer or float within [0, 1] for n_classes=2 and encoded as integer values in {0, 1,..., n_classes-1} for n_classes>2 . Also there will be errors if vocabulary is not provided and labels are string.
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.

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`

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`

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.
ValueError: If no model has been trained, namely model_dir, or the given checkpoint_path is empty.

`experimental_feature_importances`

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:

sorted_feature_names: 1-D array of feature name which is sorted by its feature importance.
feature_importances: 1-D array of the corresponding feature 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`

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 '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`

export_saved_model(
    export_dir_base,
    serving_input_receiver_fn,
    assets_extra=None,
    as_text=False,
    checkpoint_path=None
)

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'}.

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.

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`

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.

Note that export_to_savedmodel will be renamed to export_saved_model in TensorFlow 2.0. At that time, export_to_savedmodel without the additional underscore will be available only through tf.compat.v1.

Please see tf.estimator.Estimator.export_saved_model for more information.

There is one additional arg versus the new method: strip_default_attrs: This parameter is going away in TF 2.0, and the new behavior will automatically strip all default attributes. Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.

`get_variable_names`

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`

get_variable_value(name)

Returns value of the variable given by name.

Args:

name: string or a list of string, name of the tensor.

Returns:

Numpy array - value of the tensor.

Raises:

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

`latest_checkpoint`

latest_checkpoint()

Finds the filename of the latest saved checkpoint file in model_dir.

Returns:

The full path to the latest checkpoint or None if no checkpoint was found.

`predict`

predict(
    input_fn,
    predict_keys=None,
    hooks=None,
    checkpoint_path=None,
    yield_single_examples=True
)

Yields predictions for given features.

Please note that interleaving two predict outputs does not work. See: issue/20506

Args:

input_fn: A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration). 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 have same constraints as below.
- features: A tf.Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
- A tuple, in which case the first item is extracted as features.
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. If None, returns all.
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.
yield_single_examples: If False, yields the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.

Yields:

Evaluated values of predictions tensors.

Raises:

ValueError: Could not find a trained model in model_dir.
ValueError: If batch length of predictions is not the same and yield_single_examples is True.
ValueError: If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but tf.estimator.EstimatorSpec.predictions is not a dict.

`train`

train(
    input_fn,
    hooks=None,
    steps=None,
    max_steps=None,
    saving_listeners=None
)

Trains a model given training data input_fn.

Args:

input_fn: A function that provides input data for training 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.
hooks: List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop.
steps: Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. steps works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.
max_steps: Number of total steps for which to train model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.
saving_listeners: list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.

Returns:

self, for chaining.

Raises:

ValueError: If both steps and max_steps are not None.
ValueError: If either steps or max_steps <= 0.

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Last updated November 20, 2018.

tf.estimator.BoostedTreesClassifier

Class BoostedTreesClassifier

Eager Compatibility

__init__

Args:

Raises:

Properties

config

model_dir

model_fn

Returns:

params

Methods

eval_dir

Args:

Returns:

evaluate

Args:

Returns:

Raises:

experimental_feature_importances

Args:

Returns:

Raises:

experimental_predict_with_explanations

Args:

Yields:

Raises:

export_saved_model

Args:

Returns:

Raises:

export_savedmodel

get_variable_names

Returns:

Raises:

get_variable_value

Args:

Returns:

Raises:

latest_checkpoint

Returns:

predict

Args:

Yields:

Raises:

train

Args:

Returns:

Raises:

Class `BoostedTreesClassifier`

`init`

`config`

`model_dir`

`model_fn`

`params`

`eval_dir`

`evaluate`

`experimental_feature_importances`

`experimental_predict_with_explanations`

`export_saved_model`

`export_savedmodel`

`get_variable_names`

`get_variable_value`

`latest_checkpoint`

`predict`

`train`