skmultiflow.data.MIXEDGenerator

class skmultiflow.data.MIXEDGenerator(classification_function=0, random_state=None, balance_classes=False)

Mixed data stream generator.

This generator is an implementation of a data stream with abrupt concept drift and boolean noise-free examples as described in Gama, João, et al [1].

It has four relevant attributes, two boolean attributes \(v, w\) and two numeric attributes \(x, y\) uniformly distributed from 0 to 1. The examples are labeled depending on the classification function chosen from below.

• function 0:

  if \(v\) and \(w\) are true or \(v\) and \(z\) are true or \(w\) are true then 0 else 1, where \(z\) is \(y < 0.5 + 0.3 sin(3 \pi x)\)

• function 1:

  The opposite of function 0.

Concept drift can be introduced by changing the classification function. This can be done manually or using ConceptDriftStream.

Parameters

classification_function: int (default: 0)

Which of the two classification functions to use for the generation. Valid options are 0 or 1.

random_state: int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

balance_classes: bool (Default: False)

Whether to balance classes or not. If balanced, the class distribution will converge to a uniform distribution.

References

1

Gama, Joao, et al. “Learning with drift detection.” Advances in artificial intelligence–SBIA 2004. Springer Berlin Heidelberg, 2004. 286-295”

Examples

>>> # Imports
>>> from skmultiflow.data.mixed_generator import MIXEDGenerator
>>> # Setting up the stream
>>> stream = MIXEDGenerator(classification_function = 1, random_state= 112,
... balance_classes = False)
>>> # Retrieving one sample
>>> stream.next_sample()
(array([[0.        , 1.        , 0.95001658, 0.0756772 ]]), array([1.]))

>>> stream.next_sample(10)
(array([[1.        , 1.        , 0.05480574, 0.81767738],
       [1.        , 1.        , 0.00255603, 0.98119928],
       [0.        , 0.        , 0.39464259, 0.00494492],
       [1.        , 1.        , 0.82060937, 0.344983  ],
       [0.        , 1.        , 0.08623151, 0.54607394],
       [0.        , 0.        , 0.04500817, 0.33218776],
       [1.        , 1.        , 0.70936161, 0.18840112],
       [1.        , 0.        , 0.50315448, 0.76353033],
       [1.        , 1.        , 0.21415209, 0.76309258],
       [0.        , 1.        , 0.42563042, 0.23435109]]),
       array([1., 1., 0., 1., 1., 0., 1., 0., 1., 1.]))

>>> stream.n_remaining_samples()
-1
>>> stream.has_more_samples()
True

Methods

generate_drift(self)	Generate drift by switching the classification function.
get_data_info(self)	Retrieves minimum information from the stream
get_info(self)	Collects and returns the information about the configuration of the estimator
get_params(self[, deep])	Get parameters for this estimator.
has_more_samples(self)	Checks if stream has more samples.
is_restartable(self)	Determine if the stream is restartable.
last_sample(self)	Retrieves last batch_size samples in the stream.
n_remaining_samples(self)	Returns the estimated number of remaining samples.
next_sample(self[, batch_size])	Returns next sample from the stream.
prepare_for_use()	Prepare the stream for use.
reset(self)	Resets the estimator to its initial state.
restart(self)	Restart the stream.
set_params(self, **params)	Set the parameters of this estimator.

Attributes

balance_classes	Retrieve the value of the option: Balance classes
classification_function	Retrieve the index of the current classification function.
feature_names	Retrieve the names of the features.
n_cat_features	Retrieve the number of integer features.
n_features	Retrieve the number of features.
n_num_features	Retrieve the number of numerical features.
n_targets	Retrieve the number of targets
target_names	Retrieve the names of the targets
target_values	Retrieve all target_values in the stream for each target.

property balance_classes

Retrieve the value of the option: Balance classes

Returns

Boolean

True is the classes are balanced

property classification_function

Retrieve the index of the current classification function.

Returns

int

index of the classification function [0,1]

property feature_names

Retrieve the names of the features.

Returns

list

names of the features

generate_drift(self)

Generate drift by switching the classification function.

get_data_info(self)

Retrieves minimum information from the stream

Used by evaluator methods to id the stream.

The default format is: ‘Stream name - n_targets, n_classes, n_features’.

Returns

string

Stream data information

get_info(self)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

has_more_samples(self)

Checks if stream has more samples.

Returns

Boolean

True if stream has more samples.

is_restartable(self)

Determine if the stream is restartable.

Returns

Bool

True if stream is restartable.

last_sample(self)

Retrieves last batch_size samples in the stream.

Returns

tuple or tuple list

A numpy.ndarray of shape (batch_size, n_features) and an array-like of shape (batch_size, n_targets), representing the next batch_size samples.

property n_cat_features

Retrieve the number of integer features.

Returns

int

The number of integer features in the stream.

property n_features

Retrieve the number of features.

Returns

int

The total number of features.

property n_num_features

Retrieve the number of numerical features.

Returns

int

The number of numerical features in the stream.

n_remaining_samples(self)

Returns the estimated number of remaining samples.

Returns

int

Remaining number of samples. -1 if infinite (e.g. generator)

property n_targets

Retrieve the number of targets

Returns

int

the number of targets in the stream.

next_sample(self, batch_size=1)

Returns next sample from the stream.

The sample generation works as follows: The two numeric attributes are generated with the random generator, initialized with the seed passed by the user. The boolean attributes are either 0 or 1 based on the comparison of the random generator and 0.5 , the classification function decides whether to classify the instance as class 0 or class 1. The next step is to verify if the classes should be balanced, and if so, balance the classes.

The generated sample will have 4 relevant features and 1 label (it has one classification task).

Parameters

batch_size: int (optional, default=1)

The number of samples to return.

Returns

tuple or tuple list

Return a tuple with the features matrix and the labels matrix for

static prepare_for_use()

Prepare the stream for use.

Deprecated in v0.5.0 and will be removed in v0.7.0

reset(self)

Resets the estimator to its initial state.

Returns

self

restart(self)

Restart the stream.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self

property target_names

Retrieve the names of the targets

Returns

list

the names of the targets in the stream.

property target_values

Retrieve all target_values in the stream for each target.

Returns

list

list of lists of all target_values for each target