LostTech.TensorFlow : API Documentation

Type MultiForestMultiHeadEstimator

Namespace tensorflow.contrib.tensor_forest.random_forest

Parent Estimator

Interfaces IMultiForestMultiHeadEstimator

Methods

Properties

Public instance methods

object predict(IEnumerable<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
IEnumerable<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(IEnumerable<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
IEnumerable<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(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
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(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
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.

Public static methods

MultiForestMultiHeadEstimator NewDyn(object params_list, 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> num_trainers, ImplicitContainer<T> trainer_id, ImplicitContainer<T> report_feature_importances, ImplicitContainer<T> local_eval)

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;