skmultiflow.data.ConceptDriftStream¶

class
skmultiflow.data.
ConceptDriftStream
(stream=AGRAWALGenerator(balance_classes=False, classification_function=0, perturbation=0.0, random_state=112), drift_stream=AGRAWALGenerator(balance_classes=False, classification_function=2, perturbation=0.0, random_state=112), position=5000, width=1000, random_state=None, alpha=0.0)[source]¶ 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 \(f(t) = 1/(1+e^{4(tp)/w})\).
 Parameters
 stream: Stream (default= AGRAWALGenerator(random_state=112))
Original stream concept
 drift_stream: Stream (default= AGRAWALGenerator(random_state=112, classification_function=2))
Drift stream concept
 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.
 alpha: float (optional, default: 0.0)
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].
 position: int (default: 5000)
Central position of concept drift change.
 width: int (Default: 1000)
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 rounddown 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 roundup to 1 to avoid division by zero errors.
Methods
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 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
Retrieve the names of the features.
Retrieve the number of integer features.
Retrieve the number of features.
Retrieve the number of numerical features.
Retrieve the number of targets
Retrieve the names of the targets
Retrieve all target_values in the stream for each target.

property
feature_names
¶ Retrieve the names of the features.
 Returns
 list
names of the features

get_data_info
(self)[source]¶ 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)[source]¶ Collects and returns the information about the configuration of the estimator
 Returns
 string
Configuration of the estimator.

get_params
(self, deep=True)[source]¶ 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)[source]¶ Checks if stream has more samples.
 Returns
 Boolean
True if stream has more samples.

is_restartable
(self)[source]¶ Determine if the stream is restartable.
 Returns
 Boolean
True if stream is restartable.

last_sample
(self)[source]¶ Retrieves last batch_size samples in the stream.
 Returns
 tuple or tuple list
A numpy.ndarray of shape (batch_size, n_features) and an arraylike 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)[source]¶ 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)[source]¶ Returns next sample from the stream.
 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 for the batch_size samples that were requested.

static
prepare_for_use
()[source]¶ Prepare the stream for use.
Deprecated in v0.5.0 and will be removed in v0.7.0

set_params
(self, **params)[source]¶ 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