Implement a similar interface to sklearn's make_pipeline about agentmet4fof

[bangxiangyong]

More information here : (https://www.slideshare.net/yongbangxiang/use-cases-agentmet4fof)

[bangxiangyong]

This is how the interface should look like:

This is to specify the pipelines

#option 1 : group of pipelines with 3 levels
ML_pipelines_A = make_agent_pipelines([PCA(), KNN()],
                                [StandardScaler(),RobustScaler()],
                                [LinearRegression(),ANN()], parameters)

#option 2 : multi pipelines of single level
ML_pipelines_B = make_agent_pipelines([CNN(),BCNN(),ANN()], parameters)

And their candidate parameters to be grid-searched

#example of parameters for pipelines
parameters = ([{"n_components":[1,2,3,4,5]}, {"n":[4]}],
              [],
              [0,{"dimensions":[120,233,345,666]}])

Then connection to data, evaluator and monitor looks like this:

#connect data
data_agent.connect(ML_pipelines)

#connect evaluator such as F1 SCORE, PICP, etc
ML_pipelines.connect(evaluator)

#collect experiment statistics with Monitor Agent
evaluator.connect(monitor_agent)

Lastly, to execute and log them as "Experiments" #25

#loop through the parameters and log on MLFLOW
aggregated_results = run_experiment([data_agent_1, data_agent_2], [ML_pipelines_A,ML_pipelines_B,ML_pipeline_C])

[BjoernLudwigPTB]

That appears to be resolved by #33 long ago.