Source code for alibi.utils.wrappers

import numpy as np

from functools import singledispatch, update_wrapper

[docs] class Predictor: def __init__(self, clf, preprocessor=None): if not hasattr(clf, 'predict'): raise AttributeError('Classifier object is expected to have a predict method!') self.clf = clf self.predict_fcn = clf.predict self.preprocessor = preprocessor def __call__(self, x): if self.preprocessor: return self.predict_fcn(self.preprocessor.transform(x)) return self.predict_fcn(x)
[docs] class ArgmaxTransformer: """ A transformer for converting classification output probability tensors to class labels. It assumes the predictor is a callable that can be called with a `N`-tensor of data points `x` and produces an `N`-tensor of outputs. """ def __init__(self, predictor): self.predictor = predictor def __call__(self, x): pred = np.atleast_2d(self.predictor(x)) return np.argmax(pred, axis=1)
[docs] def methdispatch(func): """ A decorator that is used to support singledispatch style functionality for instance methods. By default, singledispatch selects a function to call from registered based on the type of args[0]:: def wrapper(*args, **kw): return dispatch(args[0].__class__)(*args, **kw) This uses singledispatch to do achieve this but instead uses `args[1]` since `args[0]` will always be self. """ dispatcher = singledispatch(func) def wrapper(*args, **kw): return dispatcher.dispatch(args[1].__class__)(*args, **kw) wrapper.register = dispatcher.register update_wrapper(wrapper, dispatcher) return wrapper