Class BoostedTreesRegressor
A Regressor 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,
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
)
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 fromFeatureColumn
.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 aNumericColumn
created bytf.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 thefeatures
. If it is aNumericColumn
, raw tensor is fetched by keyweight_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.
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
tf.estimator.BoostedTreesRegressor.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.
tf.estimator.BoostedTreesRegressor.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: * Atf.data.Dataset
object: Outputs ofDataset
object must be a tuple(features, labels)
with same constraints as below. * A tuple(features, labels)
: Wherefeatures
is atf.Tensor
or a dictionary of string feature name toTensor
andlabels
is aTensor
or a dictionary of string label name toTensor
. Bothfeatures
andlabels
are consumed bymodel_fn
. They should satisfy the expectation ofmodel_fn
from inputs.steps
: Number of steps for which to evaluate model. IfNone
, evaluates untilinput_fn
raises an end-of-input exception.hooks
: List oftf.train.SessionRunHook
subclass instances. Used for callbacks inside the evaluation call.checkpoint_path
: Path of a specific checkpoint to evaluate. IfNone
, the latest checkpoint inmodel_dir
is used. If there are no checkpoints inmodel_dir
, evaluation is run with newly initializedVariables
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
: Ifsteps <= 0
.ValueError
: If no model has been trained, namelymodel_dir
, or the givencheckpoint_path
is empty.
tf.estimator.BoostedTreesRegressor.experimental_export_all_saved_models
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 Tensor
s. 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.ExportOutput
s, 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 exportedSavedModel
s.input_receiver_fn_map
: dict oftf.estimator.ModeKeys
toinput_receiver_fn
mappings, where theinput_receiver_fn
is a function that takes no arguments and returns the appropriate subclass ofInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory within the exportedSavedModel
, orNone
if no extra assets are needed.as_text
: whether to write theSavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. IfNone
(the default), the most recent checkpoint found within the model directory is chosen.
Returns:
The string path to the exported directory.
Raises:
ValueError
: if anyinput_receiver_fn
isNone
, noexport_outputs
are provided, or no checkpoint can be found.
tf.estimator.BoostedTreesRegressor.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.
tf.estimator.BoostedTreesRegressor.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: * Atf.data.Dataset
object: Outputs ofDataset
object must be a tuple(features, labels)
with same constraints as below. * A tuple(features, labels)
: Wherefeatures
is atf.Tensor
or a dictionary of string feature name toTensor
andlabels
is aTensor
or a dictionary of string label name toTensor
. Bothfeatures
andlabels
are consumed bymodel_fn
. They should satisfy the expectation ofmodel_fn
from inputs.predict_keys
: list ofstr
, name of the keys to predict. It is used if thetf.estimator.EstimatorSpec.predictions
is adict
. Ifpredict_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. IfNone
, returns all keys in prediction dict, as well as two new keys 'dfc' and 'bias'.hooks
: List oftf.train.SessionRunHook
subclass instances. Used for callbacks inside the prediction call.checkpoint_path
: Path of a specific checkpoint to predict. IfNone
, the latest checkpoint inmodel_dir
is used. If there are no checkpoints inmodel_dir
, prediction is run with newly initializedVariables
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.
tf.estimator.BoostedTreesRegressor.export_saved_model
export_saved_model(
export_dir_base,
serving_input_receiver_fn,
assets_extra=None,
as_text=False,
checkpoint_path=None,
experimental_mode=model_fn_lib.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 Tensor
s, 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.ExportOutput
s, 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 exportedSavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns atf.estimator.export.ServingInputReceiver
ortf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory within the exportedSavedModel
, orNone
if no extra assets are needed.as_text
: whether to write theSavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. IfNone
(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 noserving_input_receiver_fn
is provided, noexport_outputs
are provided, or no checkpoint can be found.
tf.estimator.BoostedTreesRegressor.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.
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 Tensor
s, 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.ExportOutput
s, 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 exportedSavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns atf.estimator.export.ServingInputReceiver
ortf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory within the exportedSavedModel
, orNone
if no extra assets are needed.as_text
: whether to write theSavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. IfNone
(the default), the most recent checkpoint found within the model directory is chosen.strip_default_attrs
: Boolean. IfTrue
, default-valued attributes will be removed from theNodeDef
s. For a detailed guide, see Stripping Default-Valued Attributes.
Returns:
The string path to the exported directory.
Raises:
ValueError
: if noserving_input_receiver_fn
is provided, noexport_outputs
are provided, or no checkpoint can be found.
tf.estimator.BoostedTreesRegressor.get_variable_names
get_variable_names()
Returns list of all variable names in this model.
Returns:
List of names.
Raises:
ValueError
: If theEstimator
has not produced a checkpoint yet.
tf.estimator.BoostedTreesRegressor.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 theEstimator
has not produced a checkpoint yet.
tf.estimator.BoostedTreesRegressor.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.
tf.estimator.BoostedTreesRegressor.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 untilinput_fn
raises an end-of-input exception (tf.errors.OutOfRangeError
orStopIteration
). See Premade Estimators for more information. The function should construct and return one of the following:- A
tf.data.Dataset
object: Outputs ofDataset
object must have same constraints as below. - features: A
tf.Tensor
or a dictionary of string feature name toTensor
. features are consumed bymodel_fn
. They should satisfy the expectation ofmodel_fn
from inputs. - A tuple, in which case the first item is extracted as features.
- A
predict_keys
: list ofstr
, name of the keys to predict. It is used if thetf.estimator.EstimatorSpec.predictions
is adict
. Ifpredict_keys
is used then rest of the predictions will be filtered from the dictionary. IfNone
, returns all.hooks
: List oftf.train.SessionRunHook
subclass instances. Used for callbacks inside the prediction call.checkpoint_path
: Path of a specific checkpoint to predict. IfNone
, the latest checkpoint inmodel_dir
is used. If there are no checkpoints inmodel_dir
, prediction is run with newly initializedVariables
instead of ones restored from checkpoint.yield_single_examples
: IfFalse
, yields the whole batch as returned by themodel_fn
instead of decomposing the batch into individual elements. This is useful ifmodel_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 inmodel_dir
.ValueError
: If batch length of predictions is not the same andyield_single_examples
isTrue
.ValueError
: If there is a conflict betweenpredict_keys
andpredictions
. For example ifpredict_keys
is notNone
buttf.estimator.EstimatorSpec.predictions
is not adict
.
tf.estimator.BoostedTreesRegressor.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: * Atf.data.Dataset
object: Outputs ofDataset
object must be a tuple(features, labels)
with same constraints as below. * A tuple(features, labels)
: Wherefeatures
is atf.Tensor
or a dictionary of string feature name toTensor
andlabels
is aTensor
or a dictionary of string label name toTensor
. Bothfeatures
andlabels
are consumed bymodel_fn
. They should satisfy the expectation ofmodel_fn
from inputs.hooks
: List oftf.train.SessionRunHook
subclass instances. Used for callbacks inside the training loop.steps
: Number of steps for which to train the model. IfNone
, train forever or train untilinput_fn
generates thetf.errors.OutOfRange
error orStopIteration
exception.steps
works incrementally. If you call two timestrain(steps=10)
then training occurs in total 20 steps. IfOutOfRange
orStopIteration
occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please setmax_steps
instead. If set,max_steps
must beNone
.max_steps
: Number of total steps for which to train model. IfNone
, train forever or train untilinput_fn
generates thetf.errors.OutOfRange
error orStopIteration
exception. If set,steps
must beNone
. IfOutOfRange
orStopIteration
occurs in the middle, training stops beforemax_steps
steps. Two calls totrain(steps=100)
means 200 training iterations. On the other hand, two calls totrain(max_steps=100)
means that the second call will not do any iteration since first call did all 100 steps.saving_listeners
: list ofCheckpointSaverListener
objects. Used for callbacks that run immediately before or after checkpoint savings.
Returns:
self
, for chaining.
Raises:
ValueError
: If bothsteps
andmax_steps
are notNone
.ValueError
: If eithersteps
ormax_steps <= 0
.