Package mdp :: Package nodes :: Class VarianceThresholdScikitsLearnNode

Class VarianceThresholdScikitsLearnNode


Feature selector that removes all low-variance features.

This node has been automatically generated by wrapping the ``sklearn.feature_selection.variance_threshold.VarianceThreshold`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.

Read more in the :ref:`User Guide <variance_threshold>`.

**Parameters**

threshold : float, optional
    Features with a training-set variance lower than this threshold will
    be removed. The default is to keep all features with non-zero variance,
    i.e. remove the features that have the same value in all samples.

**Attributes**

``variances_`` : array, shape (n_features,)
    Variances of individual features.

**Examples**

The following dataset has integer features, two of which are the same
in every sample. These are removed with the default setting for threshold::


    >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
    >>> selector = VarianceThreshold()
    >>> selector.fit_transform(X)
    array([[2, 0],
           [1, 4],
           [1, 1]])

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Feature selector that removes all low-variance features.

_execute(self, x)

_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 selected features.

stop_training(self, **kwargs)
Learn empirical variances from X.

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 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.

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 selector that removes all low-variance features.

This node has been automatically generated by wrapping the ``sklearn.feature_selection.variance_threshold.VarianceThreshold`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.

Read more in the :ref:`User Guide <variance_threshold>`.

**Parameters**

threshold : float, optional
    Features with a training-set variance lower than this threshold will
    be removed. The default is to keep all features with non-zero variance,
    i.e. remove the features that have the same value in all samples.

**Attributes**

``variances_`` : array, shape (n_features,)
    Variances of individual features.

**Examples**

The following dataset has integer features, two of which are the same
in every sample. These are removed with the default setting for threshold::


    >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
    >>> selector = VarianceThreshold()
    >>> selector.fit_transform(X)
    array([[2, 0],
           [1, 4],
           [1, 1]])

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.

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 selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.variance_threshold.VarianceThreshold class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array of shape [n_samples, n_features]: The input samples.

Returns

X_r : array of shape [n_samples, n_selected_features]: The input samples with only the selected 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.

Overrides: Node.is_trainable

stop_training(self, **kwargs)

Learn empirical variances from X.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features): Sample vectors from which to compute variances.
y : any: Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns

self

Overrides: Node.stop_training

Class VarianceThresholdScikitsLearnNode

__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