Simulate a stream with anomalies in sine waves
Number of samples
Number of anomalies. Can’t be larger than n_samples.
If True, will add contextual anomalies
Number of contextual anomalies. Can’t be larger than n_samples.
Shift applied when retrieving contextual anomalies
Amount of noise
If True, anomalies are randomly sampled with replacement
The data generated corresponds to sine (attribute 1) and cosine
(attribute 2) functions. Anomalies are induced by replacing values
from attribute 2 with values from a sine function different to the one
used in attribute 1. The contextual flag can be used to introduce
contextual anomalies which are values in the normal global range,
but abnormal compared to the seasonal pattern. Contextual attributes
are introduced by replacing values in attribute 2 with values from
Retrieves minimum information from the stream
Collects and returns the information about the configuration of the estimator
Get parameters for this estimator.
Checks if stream has more samples.
Determine if the stream is restartable.
Retrieves last batch_size samples in the stream.
Get the next sample from the stream.
Prepare the stream for use.
Resets the estimator to its initial state.
Restart the stream to the initial state.
Set the parameters of this estimator.
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.
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.
Number of samples remaining.
the number of targets in the stream.
The number of samples to return.
Return a tuple with the features X and the target y for
the batch_size samples that are 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.
the names of the targets in the stream.
list of lists of all target_values for each target