Package mdp :: Package nodes :: Class RFECVScikitsLearnNode

Class RFECVScikitsLearnNode

Feature ranking with Recursive feature elimination and cross validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

estimator : object

A supervised learning estimator with a fit method that updates a coef_ attributes that holds the fitted parameters. The first dimension of the coef_ array must be equal n_features an important features must yield high absolute values in the coef_ array.

For instance this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model package.

n_features : int

Number of features to select

percentage : float

The percentage of features to remove at each iteration Should be between (0, 1]. By default 0.1 will be taken.

Attributes

support_ : array-like, shape = [n_features]: Mask of estimated support
ranking_ : array-like, shape = [n_features]: Mask of the ranking of features

Methods

fit(X, y) : self: Fit the model
transform(X) : array: Reduce X to support

Examples

>>> # TODO!

References

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3), 389--422.

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Feature ranking with Recursive feature elimination and cross validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

_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)
Reduce X to the features selected during the fit This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

stop_training(self, **kwargs)
Fit the RFE model with cross-validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The final size of the support is tuned by cross validation.

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)

estimator : object

For instance this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model package.

n_features : int

Number of features to select

percentage : float

The percentage of features to remove at each iteration Should be between (0, 1]. By default 0.1 will be taken.

Attributes

support_ : array-like, shape = [n_features]: Mask of estimated support
ranking_ : array-like, shape = [n_features]: Mask of the ranking of features

Methods

fit(X, y) : self: Fit the model
transform(X) : array: Reduce X to support

Examples

>>> # TODO!

References

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3), 389--422.

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)

Reduce X to the features selected during the fit This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : array-like, shape = [n_samples, n_features]: Vector, where n_samples in the number of samples and n_features is the number of features.

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 the RFE model with cross-validation This node has been automatically generated by wrapping the scikits.learn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. The final size of the support is tuned by cross validation.

Parameters

X : array-like, shape = [n_samples, n_features]: Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array, shape = [n_samples]: Target values (integers in classification, real numbers in regression)

cv : cross-validation instance

Overrides: Node.stop_training

Class RFECVScikitsLearnNode

__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