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DFKI-NLP / thermostat

Collection of NLP model explanations and accompanying analysis tools
Apache License 2.0
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[KernelShap] ZeroDivisionError: Weights sum to zero, can't be normalized #4

Open nfelnlp opened 3 years ago

nfelnlp commented 3 years ago

KernelShap is not working yet and gave the following error:

Traceback (most recent call last):
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/captum/attr/_core/lime.py", line 484, in attribute
    self.interpretable_model.fit(DataLoader(dataset, batch_size=n_samples))
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/captum/_utils/models/linear_model/model.py", line 303, in fit
    return super().fit(train_data=train_data, **kwargs)
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/captum/_utils/models/linear_model/model.py", line 260, in fit
    return super().fit(
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/captum/_utils/models/linear_model/model.py", line 117, in fit
    return self.train_fn(
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/captum/_utils/models/linear_model/train.py", line 329, in sklearn_train_linear_model
    sklearn_model.fit(x, y, sample_weight=w, **fit_kwargs)
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/sklearn/linear_model/_base.py", line 525, in fit
    X, y, X_offset, y_offset, X_scale = self._preprocess_data(
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/sklearn/linear_model/_base.py", line 162, in _preprocess_data
    X_offset = np.average(X, axis=0, weights=sample_weight)
  File "<__array_function__ internals>", line 5, in average
  File "/home/nfel/PycharmProjects/thermostat/venv/lib/python3.8/site-packages/numpy/lib/function_base.py", line 409, in average
    raise ZeroDivisionError(
ZeroDivisionError: Weights sum to zero, can't be normalized
nfelnlp commented 3 years ago

Not an easy fix after discussing it briefly with @rbtsbg . Will not implement KernelShap for the first version/publication.