Package mdp :: Package nodes :: Class LabelBinarizerScikitsLearnNode

Class LabelBinarizerScikitsLearnNode

Binarize labels in a one-vs-all fashion. This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Several regression and binary classification algorithms are available in the scikit. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.

At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.

At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.

Attributes

classes_ : array of shape [n_class]: Holds the label for each class.

Examples

>>> from scikits.learn import preprocessing
>>> clf = preprocessing.LabelBinarizer()
>>> clf.fit([1,2,6,4,2])
LabelBinarizer()
>>> clf.classes_
array([1, 2, 4, 6])
>>> clf.transform([1, 6])
array([[ 1.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.]])

>>> clf.fit_transform([(1,2),(3,)])
array([[ 1.,  1.,  0.],
       [ 0.,  0.,  1.]])

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Initializes an object of type 'ScikitsNode'.

_execute(self, x)

list

_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

_stop_training(self, **kwargs)
Concatenate the collected data in a single array.

execute(self, x)
Transform multi-class labels to binary labels This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

stop_training(self, **kwargs)
Fit label binarizer This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Cumulator

_train(self, *args)
Collect all input data in a list.

train(self, *args)
Collect all input data in a list.

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of numpy.dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert y.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

is_invertible()
Return True if the node can be inverted, False otherwise.

bool

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

Initializes an object of type 'ScikitsNode'.

Parameters:

input_dim (int) - Dimensionality of the input. Default is None.
output_dim (int) - Dimensionality of the output. Default is None.
dtype (numpy.dtype or str) - Datatype of the input. Default is None.

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

Returns: list: The list of dtypes supported by this node.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

Transform multi-class labels to binary labels This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

Parameters

y : numpy array of shape [n_samples]: Target values

Returns

Y : numpy array of shape [n_samples, n_classes]

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Returns: bool: A boolean indication whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Fit label binarizer This node has been automatically generated by wrapping the scikits.learn.preprocessing.LabelBinarizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

y : numpy array of shape [n_samples]: Target values

Returns

self : returns an instance of self.

Overrides: Node.stop_training

Class LabelBinarizerScikitsLearnNode

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs) (Constructor)

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

is_invertible()
Static Method

is_trainable()
Static Method