tf.contrib.kernel_methods.KernelLinearClassifier

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Linear classifier using kernel methods as feature preprocessing.

tf.contrib.kernel_methods.KernelLinearClassifier(
    feature_columns=None, model_dir=None, n_classes=2, weight_column_name=None,
    optimizer=None, kernel_mappers=None, config=None
)

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.

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.

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.

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

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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

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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 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.

export_savedmodel

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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

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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_params

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get_params(
    deep=True
)

Get parameters for this estimator.

Args
deep boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
params mapping of string to any Parameter names mapped to their values.

get_variable_names

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get_variable_names()

Returns list of all variable names in this model.

Returns
List of names.

get_variable_value

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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

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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_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.

predict

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predict(
    x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=True,
    iterate_batches=False
)

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.

Raises
ValueError If x and input_fn are both provided or both None.

predict_classes

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predict_classes(
    input_fn=None
)

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)

predict_proba

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predict_proba(
    input_fn=None
)

Runs inference to determine the class probability predictions.

Args
input_fn The input function providing features.

Returns
A generator of predicted class probabilities for the features provided by input_fn.

set_params

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set_params(
    **params
)

Set the parameters of this estimator.

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.

Args
**params Parameters.

Returns
self

Raises
ValueError If params contain invalid names.