Howto BF-STREAMS-005: Visualizing Multivariate Point Outlier Stream Provided By MLPro
Executable code
## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - The integrative middleware framework for standardized machine learning
## -- Module : howto_bf_streams_005_native_stream_PointOutliersND.py
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd Ver. Auth. Description
## -- 2024-02-06 1.0.0 DA Creation/First implementation
## -- 2024-04-26 1.1.0 DA Refactoring
## -------------------------------------------------------------------------------------------------
"""
Ver. 1.1.0 (2024-04-26)
This module demonstrates and visualizes the native stream PointOutliersND which generates an infinite
instances number of n-dimensional instances. Each feature is based on a configurable baseline function.
Additionally, random point outliers are induced.
You will learn:
1) The properties and use of native stream PointOutliersND.
2) How to set up a stream workflow without a stream task.
3) How to set up a stream scenario based on a stream and a processing stream workflow.
4) How to run a stream scenario dark or with default visualization.
"""
from mlpro.bf.streams import *
from mlpro.bf.streams.streams import *
from mlpro.bf.various import Log
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## -------------------------------------------------------------------------------------------------
class MyScenario (StreamScenario):
"""
Example of a custom stream scenario including a stream and a stream workflow. See class
mlpro.bf.streams.models.StreamScenario for further details and explanations.
"""
C_NAME = 'My stream scenario'
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def _setup(self, p_mode, p_visualize:bool, p_logging):
# 1 Import a native stream from MLPro
stream = StreamMLProPOutliers( p_functions = ['sin', 'cos', 'const'],
p_outlier_rate = 0.01,
p_visualize = p_visualize,
p_logging = p_logging )
# 2 Set up a stream workflow
workflow = StreamWorkflow( p_name = 'wf1',
p_range_max = StreamWorkflow.C_RANGE_NONE,
p_visualize = p_visualize,
p_logging = logging )
# 3 Return stream and workflow
return stream, workflow
# 1 Preparation of demo/unit test mode
if __name__ == "__main__":
# 1.1 Parameters for demo mode
cycle_limit = 720
logging = Log.C_LOG_ALL
visualize = True
step_rate = 2
else:
# 1.2 Parameters for internal unit test
cycle_limit = 2
logging = Log.C_LOG_NOTHING
visualize = False
step_rate = 1
# 2 Instantiate the stream scenario
myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
p_cycle_limit=cycle_limit,
p_visualize=visualize,
p_logging=logging )
# 3 Reset and run own stream scenario
myscenario.reset()
if __name__ == '__main__':
myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_ND,
p_view_autoselect = False,
p_step_rate = step_rate ) )
input('Press ENTER to start stream processing...')
myscenario.run()
if __name__ == '__main__':
input('Press ENTER to exit...')
Results
