LostTech.TensorFlow : API Documentation

Type TensorForestEstimator

Namespace tensorflow.contrib.tensor_forest.random_forest

Parent Estimator

Interfaces ITensorForestEstimator

Methods

Properties

Public instance methods

IDictionary<object, object> evaluate(object x, object y, PythonFunctionContainer input_fn, PythonFunctionContainer feed_fn, Nullable<int> batch_size, Nullable<int> steps, IDictionary<string, MetricSpec> metrics, string name, string checkpoint_path, IEnumerable<SessionRunHook> hooks)

See evaluable.Evaluable.

IDictionary<object, object> evaluate(object x, object y, PythonFunctionContainer input_fn, PythonFunctionContainer feed_fn, Nullable<int> batch_size, Nullable<int> steps, IDictionary<string, MetricSpec> metrics, string name, string checkpoint_path, IEnumerable<SessionRunHook> hooks, bool log_progress)

See `Evaluable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object evaluate_dyn(object x, object y, object input_fn, object feed_fn, object batch_size, object steps, object metrics, object name, object checkpoint_path, object hooks)

See evaluable.Evaluable.

object evaluate_dyn(object x, object y, object input_fn, object feed_fn, object batch_size, object steps, object metrics, object name, object checkpoint_path, object hooks, ImplicitContainer<T> log_progress)

See `Evaluable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object export(string export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, string default_batch_size, string exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
string export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
string default_batch_size
Default batch size of the `Example` placeholder.
string exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(string export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, int default_batch_size, string exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
string export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
int default_batch_size
Default batch size of the `Example` placeholder.
string exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(string export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, int default_batch_size, int exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
string export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
int default_batch_size
Default batch size of the `Example` placeholder.
int exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(string export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, string default_batch_size, int exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
string export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
string default_batch_size
Default batch size of the `Example` placeholder.
int exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(Byte[] export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, int default_batch_size, int exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
Byte[] export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
int default_batch_size
Default batch size of the `Example` placeholder.
int exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(Byte[] export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, string default_batch_size, int exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
Byte[] export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
string default_batch_size
Default batch size of the `Example` placeholder.
int exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(Byte[] export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, int default_batch_size, string exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
Byte[] export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
int default_batch_size
Default batch size of the `Example` placeholder.
string exports_to_keep
Number of exports to keep.
object 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
object
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.

object export(Byte[] export_dir, ImplicitContainer<T> input_fn, string input_feature_key, bool use_deprecated_input_fn, PythonFunctionContainer signature_fn, string prediction_key, string default_batch_size, string exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
Byte[] export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
string 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).
bool use_deprecated_input_fn
Determines the signature format of `input_fn`.
PythonFunctionContainer 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.
string 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.
string default_batch_size
Default batch size of the `Example` placeholder.
string exports_to_keep
Number of exports to keep.
object 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
object
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.

object export_dyn(object export_dir, ImplicitContainer<T> input_fn, object input_feature_key, ImplicitContainer<T> use_deprecated_input_fn, object signature_fn, object prediction_key, ImplicitContainer<T> default_batch_size, object exports_to_keep, object checkpoint_path)

Exports inference graph into given dir. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-25. Instructions for updating: Please use Estimator.export_savedmodel() instead.
Parameters
object export_dir
A string containing a directory to write the exported graph and checkpoints.
ImplicitContainer<T> 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`).
object 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).
ImplicitContainer<T> use_deprecated_input_fn
Determines the signature format of `input_fn`.
object 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.
object 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.
ImplicitContainer<T> default_batch_size
Default batch size of the `Example` placeholder.
object exports_to_keep
Number of exports to keep.
object 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
object
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.

object export_savedmodel(object export_dir_base, PythonFunctionContainer serving_input_fn, object default_output_alternative_key, IDictionary<string, object> assets_extra, bool as_text, object checkpoint_path, ImplicitContainer<T> graph_rewrite_specs, bool strip_default_attrs)

Exports inference graph as a SavedModel into given dir.
Parameters
object export_dir_base
A string containing a directory to write the exported graph and checkpoints.
PythonFunctionContainer serving_input_fn
A function that takes no argument and returns an `InputFnOps`.
object default_output_alternative_key
the name of the head to serve when none is specified. Not needed for single-headed models.
IDictionary<string, object> 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'}`.
bool as_text
whether to write the SavedModel proto in text format.
object checkpoint_path
The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
ImplicitContainer<T> 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.
bool strip_default_attrs
Boolean. If `True`, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
Returns
object
The string path to the exported directory.

object export_savedmodel_dyn(object export_dir_base, object serving_input_fn, object default_output_alternative_key, object assets_extra, ImplicitContainer<T> as_text, object checkpoint_path, ImplicitContainer<T> graph_rewrite_specs, ImplicitContainer<T> strip_default_attrs)

Exports inference graph as a SavedModel into given dir.
Parameters
object export_dir_base
A string containing a directory to write the exported graph and checkpoints.
object serving_input_fn
A function that takes no argument and returns an `InputFnOps`.
object default_output_alternative_key
the name of the head to serve when none is specified. Not needed for single-headed models.
object 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'}`.
ImplicitContainer<T> as_text
whether to write the SavedModel proto in text format.
object checkpoint_path
The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
ImplicitContainer<T> 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.
ImplicitContainer<T> strip_default_attrs
Boolean. If `True`, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see [Stripping Default-Valued Attributes](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md#stripping-default-valued-attributes).
Returns
object
The string path to the exported directory.

object fit(ndarray x, ndarray y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(object x, object y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(object x, IEnumerator<ndarray> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(object x, IDictionary<string, object> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(object x, ndarray y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(IDictionary<string, object> x, IDictionary<string, object> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(IDictionary<string, object> x, ndarray y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(ndarray x, object y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(ndarray x, IEnumerator<ndarray> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(ndarray x, IDictionary<string, object> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(IDictionary<string, object> x, object y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit(IDictionary<string, object> x, IEnumerator<ndarray> y, PythonFunctionContainer input_fn, Nullable<int> steps, Nullable<int> batch_size, IEnumerable<BaseMonitor> monitors, Nullable<int> max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

object fit_dyn(object x, object y, object input_fn, object steps, object batch_size, object monitors, object max_steps)

See `Trainable`. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

IDictionary<object, object> get_params(bool deep)

object get_params_dyn(ImplicitContainer<T> deep)

object partial_fit(object x, object y, PythonFunctionContainer input_fn, int steps, object batch_size, object monitors)

Incremental fit on a batch of samples. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

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.
Parameters
object 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`.
object 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`.
PythonFunctionContainer input_fn
Input function. If set, `x`, `y`, and `batch_size` must be `None`.
int steps
Number of steps for which to train model. If `None`, train forever.
object batch_size
minibatch size to use on the input, defaults to first dimension of `x`. Must be `None` if `input_fn` is provided.
object monitors
List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop.
Returns
object
`self`, for chaining.

object partial_fit_dyn(object x, object y, object input_fn, ImplicitContainer<T> steps, object batch_size, object monitors)

Incremental fit on a batch of samples. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(batch_size, x, y)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))

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.
Parameters
object 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`.
object 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`.
object input_fn
Input function. If set, `x`, `y`, and `batch_size` must be `None`.
ImplicitContainer<T> steps
Number of steps for which to train model. If `None`, train forever.
object batch_size
minibatch size to use on the input, defaults to first dimension of `x`. Must be `None` if `input_fn` is provided.
object monitors
List of `BaseMonitor` subclass instances. Used for callbacks inside the training loop.
Returns
object
`self`, for chaining.

object predict(IDictionary<string, object> x, PythonFunctionContainer input_fn, int batch_size, IEnumerable<string> outputs, bool as_iterable, bool iterate_batches)

Returns predictions for given features. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(as_iterable, batch_size, x)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Parameters
IDictionary<string, object> 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`.
PythonFunctionContainer input_fn
Input function. If set, `x` and 'batch_size' must be `None`.
int batch_size
Override default batch size. If set, 'input_fn' must be 'None'.
IEnumerable<string> outputs
list of `str`, name of the output to predict. If `None`, returns all.
bool 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).
bool 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
object
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.

object predict(IDictionary<string, object> x, PythonFunctionContainer input_fn, IEnumerable<object> batch_size, IEnumerable<string> outputs, bool as_iterable, bool iterate_batches)

Returns predictions for given features. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(as_iterable, batch_size, x)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Parameters
IDictionary<string, object> 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`.
PythonFunctionContainer input_fn
Input function. If set, `x` and 'batch_size' must be `None`.
IEnumerable<object> batch_size
Override default batch size. If set, 'input_fn' must be 'None'.
IEnumerable<string> outputs
list of `str`, name of the output to predict. If `None`, returns all.
bool 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).
bool 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
object
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.

object predict_dyn(object x, object input_fn, object batch_size, object outputs, ImplicitContainer<T> as_iterable, ImplicitContainer<T> iterate_batches)

Returns predictions for given features. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: `(as_iterable, batch_size, x)`. They will be removed after 2016-12-01. Instructions for updating: Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn. Example conversion: est = Estimator(...) -> est = SKCompat(Estimator(...))
Parameters
object 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`.
object input_fn
Input function. If set, `x` and 'batch_size' must be `None`.
object batch_size
Override default batch size. If set, 'input_fn' must be 'None'.
object outputs
list of `str`, name of the output to predict. If `None`, returns all.
ImplicitContainer<T> 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).
ImplicitContainer<T> 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
object
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.

Public static methods

TensorForestEstimator NewDyn(object params, object device_assigner, object model_dir, object feature_columns, ImplicitContainer<T> graph_builder_class, object config, object weight_column, object keys_column, object feature_engineering_fn, ImplicitContainer<T> early_stopping_rounds, ImplicitContainer<T> early_stopping_loss_threshold, ImplicitContainer<T> num_trainers, ImplicitContainer<T> trainer_id, ImplicitContainer<T> report_feature_importances, ImplicitContainer<T> local_eval, object version, object head, ImplicitContainer<T> include_all_in_serving)

Public properties

object config get;

object config_dyn get;

string model_dir get;

object model_dir_dyn get;

object model_fn get;

object model_fn_dyn get;

object PythonObject get;