skmultiflow.data.
STAGGERGenerator
STAGGER concepts stream generator.
This generator is an implementation of the dara stream with abrupt concept drift, as described in Gama, Joao, et al [1].
The STAGGER Concepts are boolean functions f three features encoding objects: size (small, medium and large), shape (circle, square and triangle) and colour (red, blue and green). A classification function is chosen among three possible ones:
Function that return 1 if the size is small and the color is red.
Function that return 1 if the color is green or the shape is a circle.
Function that return 1 if the size is medium or large
Concept drift can be introduced by changing the classification function. This can be done manually or using ConceptDriftStream.
ConceptDriftStream
One important feature is the possibility to balance classes, which means the class distribution will tend to a uniform one.
Which of the four classification functions to use for the generation. The value can vary from 0 to 2.
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.
Whether to balance classes or not. If balanced, the class distribution will converge to a uniform distribution.
References
Gama, Joao, et al.’s ‘Learning with drift detection. ‘ Advances in artificial intelligence–SBIA 2004. Springer Berlin Heidelberg, 2004. 286-295.”
Examples
>>> # Imports >>> from skmultiflow.data.stagger_generator import STAGGERGenerator >>> # Setting up the stream >>> stream = STAGGERGenerator(classification_function = 2, random_state = 112, ... balance_classes = False) >>> # Retrieving one sample >>> stream.next_sample() (array([[0., 0., 2.]]), array([0.])) >>> stream.next_sample(10) (array([[1., 0., 1.], [0., 0., 0.], [1., 2., 0.], [1., 0., 2.], [0., 2., 1.], [0., 1., 2.], [0., 1., 1.], [0., 1., 2.], [1., 2., 2.], [1., 2., 0.]]), array([1., 0., 1., 1., 0., 0., 0., 0., 1., 1.])) >>> stream.n_remaining_samples() -1 >>> stream.has_more_samples() True
Methods
classification_function_one(size, color, shape)
classification_function_one
Decides the sample class label as positive if the color is green or shape is a circle.
classification_function_two(size, color, shape)
classification_function_two
Decides the sample class label as positive if the size is medium or large.
classification_function_zero(size, color, shape)
classification_function_zero
Decides the sample class label as positive if the color is red and size is small.
generate_drift(self)
generate_drift
Generate drift by switching the classification function randomly.
get_data_info(self)
get_data_info
Retrieves minimum information from the stream
get_info(self)
get_info
Collects and returns the information about the configuration of the estimator
get_params(self[, deep])
get_params
Get parameters for this estimator.
has_more_samples(self)
has_more_samples
Checks if stream has more samples.
is_restartable(self)
is_restartable
Determine if the stream is restartable.
last_sample(self)
last_sample
Retrieves last batch_size samples in the stream.
n_remaining_samples(self)
n_remaining_samples
Returns the estimated number of remaining samples.
next_sample(self[, batch_size])
next_sample
Returns next sample from the stream.
prepare_for_use()
prepare_for_use
Prepare the stream for use.
reset(self)
reset
Resets the estimator to its initial state.
restart(self)
restart
Restart the stream.
set_params(self, **params)
set_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.
True is the classes are balanced
index of the classification function from 0 to 2.
First numeric attribute.
Second boolean attribute.
Third boolean attribute
Returns the sample class label, either 0 or 1.
names of the features
Used by evaluator methods to id the stream.
The default format is: ‘Stream name - n_targets, n_classes, n_features’.
Stream data information
Configuration of the estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
True if stream has more samples.
True if stream is restartable.
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.
The number of integer features in the stream.
The total number of features.
The number of numerical features in the stream.
Remaining number of samples. -1 if infinite (e.g. generator)
the number of targets in the stream.
The sample generation works as follows: The three attributes are generated with the random int generator, initialized with the seed passed by the user. Then, 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 relevant features and 1 label (it has one classification task).
The number of samples to return.
Return a tuple with the features matrix and the labels matrix for the batch_size samples that were requested.
Deprecated in v0.5.0 and will be removed in v0.7.0
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.
<component>__<parameter>
the names of the targets in the stream.
list of lists of all target_values for each target