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
)
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.
|
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.
|
Raises |
|---|
ValueError
|
when wrong arguments are given or unsupported functionalities
are requested.
|
Attributes |
|---|
config
|
|
model_dir
|
|
model_fn
|
Returns the model_fn which is bound to self.params.
|
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:
| 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 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
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
View source
experimental_predict_with_explanations(
input_fn, predict_keys=None, hooks=None, checkpoint_path=N
TensorFlow 1 version
View source on GitHub