Post-process classifier output in scikit learn Pipeline

We developed PipeGraph, an extension to Scikit-Learn Pipeline that allows you to get intermediate data, build graph like workflows, and in particular, solve this problem (see the examples in the gallery at http://mcasl.github.io/PipeGraph )

You can use the class sklearn.preprocessing.TransformedTargetRegressor with your SVM classifier as the regressor and use the inverse_func argument to transform your labels after classification.

However, since TransformedTargetRegressor is supposed to transform your labels to a new space before fitting and remap the predicted ones to the original space, it expects an array of labels to transform before fitting and does not accept an empty or None target as input. Therefore, you need to provide a dummy target to your pipeline, which can make your code a bit confusing.

Example:

import numpy as np
from sklearn.compose import TransformedTargetRegressor
from sklearn.svm import OneClassSVM
from sklearn.pipeline import Pipeline

X = np.random.random((10, 2))

regressor = OneClassSVM(gamma='auto')
svm = TransformedTargetRegressor(regressor=regressor,
    inverse_func=lambda x: (x+1)//2, # Function that remaps your labels
    check_inverse=False) # If not set to False, this code will generate an error since the provided inverse_func is not the inverse of the default func argument, which is the identity function

pipeline = Pipeline([
    ('svm', svm)
])

pipeline.fit(X, np.zeros((1,1))) # An array of fake label is provided to the pipeline
pipeline.predict(X)

Output:

array([[0],
       [1],
       [1],
       [1],
       [1],
       [0],
       [1],
       [0],
       [0],
       [0]])

Note that if you need to pass parameters to your OneClassSVM classifier via the Pipeline with a dictionary, for instance in a grid search with GridSearchCV, you need to add regressor__ to your parameter key name between svm__ and your parameter name. For instance, svm__kernel becomes svm__regressor__kernel.