![]() |
An Estimator for Weighted Matrix Factorization, using the WALS method.
Inherits From: Estimator
tf.contrib.factorization.WALSMatrixFactorization(
num_rows, num_cols, embedding_dimension, unobserved_weight=0.1,
regularization_coeff=None, row_init='random', col_init='random',
num_row_shards=1, num_col_shards=1, row_weights=1, col_weights=1,
use_factors_weights_cache_for_training=True,
use_gramian_cache_for_training=True, max_sweeps=None, model_dir=None,
config=None
)
WALS (Weighted Alternating Least Squares) is an algorithm for weighted matrix
factorization. It computes a low-rank approximation of a given sparse (n x m)
matrix A
, by a product of two matrices, U * V^T
, where U
is a (n x k)
matrix and V
is a (m x k) matrix. Here k is the rank of the approximation,
also called the embedding dimension. We refer to U
as the row factors, and
V
as the column factors.
See tensorflow/contrib/factorization/g3doc/wals.md for the precise problem
formulation.
The training proceeds in sweeps: during a row_sweep, we fix V
and solve for
U
. During a column sweep, we fix U
and solve for V
. Each one of these
problems is an unconstrained quadratic minimization problem and can be solved
exactly (it can also be solved in mini-batches, since the solution decouples
across rows of each matrix).
The alternating between sweeps is achieved by using a hook during training,
which is responsible for keeping track of the sweeps and running preparation
ops at the beginning of each sweep. It also updates the global_step variable,
which keeps track of the number of batches processed since the beginning of
training.
The current implementation assumes that the training is run on a single
machine, and will fail if config.num_worker_replicas
is not equal to one.
Training is done by calling self.fit(input_fn=input_fn)
, where input_fn
provides two tensors: one for rows of the input matrix, and one for rows of
the transposed input matrix (i.e. columns of the original matrix). Note that
during a row sweep, only row batches are processed (ignoring column batches)
and vice-versa.
Also note that every row (respectively every column) of the input matrix
must be processed at least once for the sweep to be considered complete. In
particular, training will not make progress if some rows are not generated by
the input_fn
.
For prediction, given a new set of input rows A'
, we compute a corresponding
set of row factors U'
, such that U' * V^T
is a good approximation of A'
.
We call this operation a row projection. A similar operation is defined for
columns. Projection is done by calling
self.get_projections(input_fn=input_fn)
, where input_fn
satisfies the
constraints given below.
The input functions must satisfy the following constraints: Calling input_fn
must return a tuple (features, labels)
where labels
is None, and
features
is a dict containing the following keys:
TRAIN:
WALSMatrixFactorization.INPUT_ROWS
: float32 SparseTensor (matrix). Rows of the input matrix to process (or to project).WALSMatrixFactorization.INPUT_COLS
: float32 SparseTensor (matrix). Columns of the input matrix to process (or to project), transposed.
INFER:
WALSMatrixFactorization.INPUT_ROWS
: float32 SparseTensor (matrix). Rows to project.WALSMatrixFactorization.INPUT_COLS
: float32 SparseTensor (matrix). Columns to project.WALSMatrixFactorization.PROJECT_ROW
: Boolean Tensor. Whether to project the rows or columns.WALSMatrixFactorization.PROJECTION_WEIGHTS
(Optional): float32 Tensor (vector). The weights to use in the projection.
EVAL:
WALSMatrixFactorization.INPUT_ROWS
: float32 SparseTensor (matrix). Rows to project.WALSMatrixFactorization.INPUT_COLS
: float32 SparseTensor (matrix). Columns to project.WALSMatrixFactorization.PROJECT_ROW
: Boolean Tensor. Whether to project the rows or columns.
Args | |
---|---|
num_rows
|
Total number of rows for input matrix. |
num_cols
|
Total number of cols for input matrix. |
embedding_dimension
|
Dimension to use for the factors. |
unobserved_weight
|
Weight of the unobserved entries of matrix. |
regularization_coeff
|
Weight of the L2 regularization term. Defaults to None, in which case the problem is not regularized. |
row_init
|
Initializer for row factor. Must be either:
|
col_init
|
Initializer for column factor. See row_init. |
num_row_shards
|
Number of shards to use for the row factors. |
num_col_shards
|
Number of shards to use for the column factors. |
row_weights
|
Must be in one of the following three formats:
|
col_weights
|
See row_weights. |
use_factors_weights_cache_for_training
|
Boolean, whether the factors and weights will be cached on the workers before the updates start, during training. Defaults to True. Note that caching is disabled during prediction. |
use_gramian_cache_for_training
|
Boolean, whether the Gramians will be cached on the workers before the updates start, during training. Defaults to True. Note that caching is disabled during prediction. |
max_sweeps
|
integer, optional. Specifies the number of sweeps for which
to train the model, where a sweep is defined as a full update of all the
row factors (resp. column factors).
If steps or max_steps is also specified in model.fit(), training
stops when either of the steps condition or sweeps condition is met.
|
model_dir
|
The directory to save the model results and log files. |
config
|
A Configuration object. See Estimator. |
Raises | |
---|---|
ValueError
|
If config.num_worker_replicas is strictly greater than one. The current implementation only supports running on a single worker. |
Attributes | |
---|---|
config
|
|
model_dir
|
Returns a path in which the eval process will look for checkpoints. |
model_fn
|
Returns the model_fn which is bound to self.params. |
Methods
evaluate
evaluate(
x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None,
metrics=None, name=None, checkpoint_path=None, hooks=None, log_progress=True
)
See Evaluable
. (deprecated arguments)
Raises | |
---|---|
ValueError
|
If at least one of x or y is provided, and at least one of
input_fn or feed_fn is provided.
Or if metrics is not None or dict .
|
export
export(
export_dir, input_fn=export._default_input_fn, input_feature_key=None,
use_deprecated_input_fn=True, signature_fn=None, prediction_key=None,
default_batch_size=1, exports_to_keep=None, checkpoint_path=None
)
Exports inference graph into given dir. (deprecated)
Args | |
---|---|
export_dir
|
A string containing a directory to write the exported graph and checkpoints. |
input_fn
|
If use_deprecated_input_fn is true, then a function that given
Tensor of Example strings, parses it into features that are then
passed to the model. Otherwise, a function that takes no argument and
returns a tuple of (features, labels), where features is a dict of
string key to Tensor and labels is a Tensor that's currently not
used (and so can be None ).
|
input_feature_key
|
Only used if use_deprecated_input_fn is false. String
key into the features dict returned by input_fn that corresponds to a
the raw Example strings Tensor that the exported model will take as
input. Can only be None if you're using a custom signature_fn that
does not use the first arg (examples).
|
use_deprecated_input_fn
|
Determines the signature format of input_fn .
|
signature_fn
|
Function that returns a default signature and a named
signature map, given Tensor of Example strings, dict of Tensor s
for features and Tensor or dict of Tensor s for predictions.
|
prediction_key
|
The key for a tensor in the predictions dict (output
from the model_fn ) to use as the predictions input to the
signature_fn . Optional. If None , predictions will pass to
signature_fn without filtering.
|
default_batch_size
|
Default batch size of the Example placeholder.
|
exports_to_keep
|
Number of exports to keep. |
checkpoint_path
|
the checkpoint path of the model to be exported. If it is
None (which is default), will use the latest checkpoint in
export_dir.
|
Returns | |
---|---|
The string path to the exported directory. NB: this functionality was added ca. 2016/09/25; clients that depend on the return value may need to handle the case where this function returns None because subclasses are not returning a value. |
export_savedmodel
export_savedmodel(
export_dir_base, serving_input_fn, default_output_alternative_key=None,
assets_extra=None, as_text=False, checkpoint_path=None,
graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,), ()),),
strip_default_attrs=False
)
Exports inference graph as a SavedModel into given dir.
Args | |
---|---|
export_dir_base
|
A string containing a directory to write the exported graph and checkpoints. |
serving_input_fn
|
A function that takes no argument and
returns an InputFnOps .
|
default_output_alternative_key
|
the name of the head to serve when none is specified. Not needed for single-headed models. |
assets_extra
|
A dict specifying how to populate the assets.extra directory
within the exported SavedModel. Each key should give the 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'} .
|
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. |
graph_rewrite_specs
|
an iterable of GraphRewriteSpec . Each element will
produce a separate MetaGraphDef within the exported SavedModel, tagged
and rewritten as specified. Defaults to a single entry using the
default serving tag ("serve") and no rewriting.
|
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 an unrecognized export_type is requested. |
fit
fit(
x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None,
max_steps=None
)
See Trainable
. (deprecated arguments)
Raises | |
---|---|
ValueError
|
If x or y are not None while input_fn is not None .
|
ValueError
|
If both steps and max_steps are not None .
|
get_col_factors
get_col_factors()
Returns the column factors of the model, loading them from checkpoint.
Should only be run after training.
Returns | |
---|---|
A list of the column factors of the model. |
get_params
get_params(
deep=True
)
Get parameters for this estimator.
Args | |
---|---|
deep
|
boolean, optional
If |
Returns | |
---|---|
params
|
mapping of string to any Parameter names mapped to their values. |
get_projections
get_projections(
input_fn
)
Computes the projections of the rows or columns given in input_fn.
Runs predict() with the given input_fn, and returns the results. Should only be run after training.
Args | |
---|---|
input_fn
|
Input function which specifies the rows or columns to project. |
Returns | |
---|---|
A generator of the projected factors. |
get_row_factors
get_row_factors()
Returns the row factors of the model, loading them from checkpoint.
Should only be run after training.
Returns | |
---|---|
A list of the row factors of the model. |
get_variable_names
get_variable_names()
Returns list of all variable names in this model.
Returns | |
---|---|
List of names. |
get_variable_value
get_variable_value(
name
)
Returns value of the variable given by name.
Args | |
---|---|
name
|
string, name of the tensor. |
Returns | |
---|---|
Numpy array - value of the tensor. |
partial_fit
partial_fit(
x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None
)
Incremental fit on a batch of samples. (deprecated arguments)
This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training.
This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts.
Args | |
---|---|
x
|
Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, input_fn must be None .
|
y
|
Vector or matrix [n_samples] or [n_samples, n_outpu |