It trains a linear model after possibly mapping initial input features into
a mapped space using explicit kernel mappings. Due to the kernel mappings,
training a linear classifier in the mapped (output) space can detect
non-linearities in the input space.
The user can provide a list of kernel mappers to be applied to all or a subset
of existing feature_columns. This way, the user can effectively provide 2
types of feature columns:
those passed as elements of feature_columns in the classifier's constructor
those appearing as a key of the kernel_mappers dict.
If a column appears in feature_columns only, no mapping is applied to it. If
it appears as a key in kernel_mappers, the corresponding kernel mappers are
applied to it. Note that it is possible that a column appears in both places.
Currently kernel_mappers are supported for _RealValuedColumns only.
Example usage:
real_column_a = real_valued_column(name='real_column_a',...)
sparse_column_b = sparse_column_with_hash_bucket(...)
kernel_mappers = {real_column_a : [RandomFourierFeatureMapper(...)]}
optimizer = ...
# real_column_a is used as a feature in both its initial and its transformed
# (mapped) form. sparse_column_b is not affected by kernel mappers.
kernel_classifier = KernelLinearClassifier(
feature_columns=[real_column_a, sparse_column_b],
model_dir=...,
optimizer=optimizer,
kernel_mappers=kernel_mappers)
# real_column_a is used as a feature in its transformed (mapped) form only.
# sparse_column_b is not affected by kernel mappers.
kernel_classifier = KernelLinearClassifier(
feature_columns=[sparse_column_b],
model_dir=...,
optimizer=optimizer,
kernel_mappers=kernel_mappers)
# Input builders
def train_input_fn: # returns x, y
...
def eval_input_fn: # returns x, y
...
kernel_classifier.fit(input_fn=train_input_fn)
kernel_classifier.evaluate(input_fn=eval_input_fn)
kernel_classifier.predict(...)
Input of fit and evaluate should have following features, otherwise there
will be a KeyError:
if weight_column_name is not None, a feature with
key=weight_column_name whose value is a Tensor.
for each column in feature_columns:
if column is a SparseColumn, a feature with key=column.name
whose value is a SparseTensor.
if column is a WeightedSparseColumn, two features: the first with
key the id column name, the second with key the weight column name.
Both features' value must be a SparseTensor.
if column is a RealValuedColumn, a feature with key=column.name
whose value is a Tensor.
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.
model_dir
Directory to save model parameters, graph etc. This can also be
used to load checkpoints from the directory into an estimator to
continue training a previously saved model.
n_classes
number of label classes. Default is binary classification.
Note that class labels are integers representing the class index (i.e.
values from 0 to n_classes-1). For arbitrary label values (e.g. string
labels), convert to class indices first.
weight_column_name
A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer
The optimizer used to train the model. If specified, it should
be an instance of tf.Optimizer. If None, the Ftrl optimizer is used
by default.
kernel_mappers
Dictionary of kernel mappers to be applied to the input
features before training a (linear) model. Keys are feature columns and
values are lists of mappers to be applied to the corresponding feature
column. Currently only _RealValuedColumns are supported and therefore
all mappers should conform to the DenseKernelMapper interface (see
./mappers/dense_kernel_mapper.py).
config
RunConfig object to configure the runtime settings.
Raises
ValueError
if n_classes < 2.
ValueError
if neither feature_columns nor kernel_mappers are provided.
ValueError
if mappers provided as kernel_mappers values are invalid.
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.
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 Tensors
for features and Tensor or dict of Tensors 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.
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.
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_outputs]. Can be
iterator that returns array of labels. The training label values
(class labels in classification, real numbers in regression). If set,
input_fn must be None.
input_fn
Input function. If set, x, y, and batch_size must be
None.
steps
Number of steps for which to train model. If None, train forever.
batch_size
minibatch size to use on the input, defaults to first
dimension of x. Must be None if input_fn is provided.
monitors
List of BaseMonitor subclass instances. Used for callbacks
inside the training loop.
Returns
self, for chaining.
Raises
ValueError
If at least one of x and y is provided, and input_fn is
provided.
Returns predictions for given features. (deprecated arguments)
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.
input_fn
Input function. If set, x and 'batch_size' must be None.
batch_size
Override default batch size. If set, 'input_fn' must be
'None'.
outputs
list of str, name of the output to predict.
If None, returns all.
as_iterable
If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
iterate_batches
If True, yield the whole batch at once instead of
decomposing the batch into individual samples. Only relevant when
as_iterable is True.
Returns
A numpy array of predicted classes or regression values if the
constructor's model_fn returns a Tensor for predictions or a dict
of numpy arrays if model_fn returns a dict. Returns an iterable of
predictions if as_iterable is True.
Runs inference to determine the predicted class per instance.
Args
input_fn
The input function providing features.
Returns
A generator of predicted classes for the features provided by input_fn.
Each predicted class is represented by its class index (i.e. integer from
0 to n_classes-1)
The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<component>__<parameter> so that it's possible to update each
component of a nested object.
View source on GitHub