from sklearn.metrics import make_scorer, silhouette_score
from sklearn.cluster import AgglomerativeClustering
from sklearn import datasets


class Model():
    def __init__(self):
        self.model = AgglomerativeClustering(n_clusters=10)
    def predict(self, inputs):
        self.model.fit(inputs)
        return self.model.labels_

digits = datasets.load_digits(n_class=10)
X = digits.data

scoring = make_scorer(silhouette_score)

model = Model()

print scoring(model, X, y_true=None)

0.178496599406

Traceback (most recent call last):
  File "test_file.py", line 20, in <module>
    print scoring(model, X, y_true=None)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/scorer.py", line 98, in __call__
    **self._kwargs)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/cluster/unsupervised.py", line 100, in silhouette_score
    return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/metrics/cluster/unsupervised.py", line 163, in silhouette_samples
    X, labels = check_X_y(X, labels, accept_sparse=['csc', 'csr'])
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 521, in check_X_y
    ensure_min_features, warn_on_dtype, estimator)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 407, in check_array
    _assert_all_finite(array)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 58, in _assert_all_finite
    " or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

if sample_weight is not None:
    return self._sign * self._score_func(y_true, y_pred, sample_weight=sample_weight, **self._kwargs)
else:
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)

if sample_weight is not None:
    return self._sign * self._score_func(y_true, y_pred, sample_weight=sample_weight, **self._kwargs)
elif y_true == None:
    return self._sign * self._score_func(X, y_pred, **self._kwargs)
else:
    return self._sign * self._score_func(y_true, y_pred, **self._kwargs)