Package mdp :: Package nodes :: Class RFECVScikitsLearnNode
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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)

 

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.

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