# Prediction Interpreter
This package contains a wrapper to interpret any predictor. Use this package:
- to interpret a machine learning model that runs over a service
- to interpret a trained ML- model in python, that has insufficient features for interpretation
- to interpret a trained ML- model in python, using the methods already set up for you
- to get ideas about how to use the interpretation methods
By using established interpretation- techniques, the package only needs a function that takes a dataset and returns a prediction, as well as some information about the data and columns.
The package then automatically sets up a wrapper containing the important information and configurations to run interpretations.
To set up the predictor, state the necessary information as in file StartPredictionInterpreter given:
Here, we use a DummyML-Model that predicts the amount of visitors on a day, depending on whether it is holiday, how much the ticket price is, and what'S the weather like.
(For your additional information: There are many visitors if its holiday, or when the price is low. The weather has a random effect on the amount of visitors. But all of this you will see when running the dummy data.)
Additionally to the DummyMLModel, it is necessary to give
- the testdata as a panda-Dataframe
- as well as the result column's name
- all data columns' names
- the numerical column names (if there are any...)
- the categories in result column (classes_)
- rather the result is a continuous value
#get data and object that has singlepredict in correct format
dm = DummyMLModel()
data = dm.testdata
#define necessary variables for techniques
standardColumns = data.columns.to_list()
resultcolumn = "visitorsOnThisDay"
listOfNumericalColumns = ["ticketPrice"]
_classes_ = data[resultcolumn].unique().tolist()
resultIsContinuous = False
After that, create the the interpreter with the before defined parameters, and run the interpretation techniques that are of interest for you.
#create interpreter
predictionInterpreter = PredictionInterpreterClass(dm.predict, listOfNumericalColumns, standardColumns, resultcolumn, _classes_, data, resultIsContinuous)
#call interpretation technique s you want to use:
predictionInterpreter.plotpdpOfDistanceToTrueResultSklearn() # only works if called without any prior methods
predictionInterpreter.plotpdpOfDistanceToTrueResultSklearn2D()
predictionInterpreter.writeDistribution("visitorsOnThisDay")
predictionInterpreter.plotConfusionTable()
predictionInterpreter.printImportanceEli5(exceptedColumns = resultcolumn)
predictionInterpreter.printImportanceEli5(distanceAnalysis=True)
predictionInterpreter.featureAnnulation(annulationValue = "0")
predictionInterpreter.plotIce()
predictionInterpreter.plotpdpOfDistanceToTrueResultPdpbox(featureToExamine="ticketPrice")
predictionInterpreter.plotpdpOfDistanceToTrueResultPdpbox(featuresToExamine=["holidayYN", "ticketPrice"])
predictionInterpreter.plotpdpOfDistanceToTrueResultPdpbox(featureToExamine="ticketPrice", featuresToExamine=["holidayYN", "ticketPrice"])
predictionInterpreter.globalSurrogateModel()
