skmultiflow.data.
ConceptDriftStream
Generates a stream with concept drift.
A stream generator that adds concept drift or change by joining several streams. This is done by building a weighted combination of two pure distributions that characterizes the target concepts before and after the change.
The sigmoid function is an elegant and practical solution to define the probability that each new instance of the stream belongs to the new concept after the drift. The sigmoid function introduces a gradual, smooth transition whose duration is controlled with two parameters:
\(p\), the position of the change.
\(w\), the width of the transition.
The sigmoid function at sample t is \(f(t) = 1/(1+e^{-4(t-p)/w})\).
Original stream concept
Drift stream concept
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.
Angle of change to estimate the width of concept drift change. If set will override the width parameter. Valid values are in the range (0.0, 90.0].
Central position of concept drift change.
Width of concept drift change.
Notes
An optional way to estimate the width of the transition \(w\) is based on the angle \(\alpha\): \(w = 1/ tan(\alpha)\). Since width corresponds to the number of samples for the transition, the width is round-down to the nearest smaller integer. Notice that larger values of \(\alpha\) result in smaller widths. For \(\alpha>45.0\), the width is smaller than 1 so values are round-up to 1 to avoid division by zero errors.
Methods
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
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.
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 number of samples to return.
Return a tuple with the features 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