Package mdp :: Class ClassifierNode

Class ClassifierNode

A ClassifierNode can be used for classification tasks that should not interfere with the normal execution flow. A reason for that is that the labels used for classification do not form a vector space, and so they don't make much sense in a flow.

Instance Methods

[hide private]

__init__(self, execute_method=None, input_dim=None, output_dim=None, dtype=None)
Initialize classifier.

_execute(self, x)

_label(self, x, *args, **kargs)

_prob(self, x, *args, **kargs)

execute(self, x)
Process the data contained in x.

label(self, x, *args, **kwargs)
Returns an array with best class labels.

prob(self, x, *args, **kwargs)
This function does classification or regression on a test vector T given a model with probability information. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

rank(self, x, threshold=None)
Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).

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 PreserveDimNode

_set_input_dim(self, n)

_set_output_dim(self, n)

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_supported_dtypes(self)
Return the list of dtypes supported by this node.

_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)

_stop_training(self, *args, **kwargs)

_train(self, x)

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.

stop_training(self, *args, **kwargs)
Stop the training phase.

train(self, x, *args, **kwargs)
Update the internal structures according to the input data x.

Static Methods

[hide private]

Inherited from Node

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, execute_method=None, input_dim=None, output_dim=None, dtype=None)
(Constructor)

Initialize classifier.

execute_method -- Set to string value 'label', 'rank', or 'prob' to: force the corresponding classification method being used instead of the standard identity execution (which is used when execute_method has the default value None). This can be used when the node is last in a flow, the return value from Flow.execute will then consist of the classification results.

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_label(self, x, *args, **kargs)

_prob(self, x, *args, **kargs)

execute(self, x)

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

Overrides: Node.execute

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

Returns an array with best class labels.

By default, subclasses should overwrite _label to implement their label. The docstring of the '_label' method overwrites this docstring.

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

This function does classification or regression on a test vector T given a model with probability information. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC 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]

Returns

X : array-like, shape = [n_samples, n_classes]: Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.

rank(self, x, threshold=None)

Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).

The optional threshold parameter is used to exclude labels having equal or less probability. E.g. threshold=0 excludes all labels with zero probability.

Class ClassifierNode

__init__(self, execute_method=None, input_dim=None, output_dim=None, dtype=None) (Constructor)

_execute(self, x)

_label(self, x, *args, **kargs)

_prob(self, x, *args, **kargs)

execute(self, x)

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

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

rank(self, x, threshold=None)

init(self, execute_method=None, input_dim=None, output_dim=None, dtype=None)
(Constructor)