This is the Traceback I get:
AssertionErrorTraceback (most recent call last)
<ipython-input-5-166b8f0141db> in <module>()
----> 1 check_estimator(OK.Sqizer)
/Users/Sam/anaconda/lib/python2.7/site-packages/sklearn/utils/estimator_
checks.pyc in check_estimator(Estimator)
253 check_parameters_default_constructible(name, Estimator)
254 for check in _yield_all_checks(name, Estimator):
--> 255 check(name, Estimator)
256
257
/Users/Sam/anaconda/lib/python2.7/site-packages/sklearn/utils/testing.pyc in
wrapper(*args, **kwargs)
353 with warnings.catch_warnings():
354 warnings.simplefilter("ignore", self.category)
--> 355 return fn(*args, **kwargs)
356
357 return wrapper
/Users/Sam/anaconda/lib/python2.7/site-packages/sklearn/utils/estimator_checks.pyc
in check_transformer_general(name, Transformer)
578 X = StandardScaler().fit_transform(X)
579 X -= X.min()
--> 580 _check_transformer(name, Transformer, X, y)
581 _check_transformer(name, Transformer, X.tolist(), y.tolist())
582
/Users/Sam/anaconda/lib/python2.7/site-packages/sklearn/utils/estimator_checks.pyc
in _check_transformer(name, Transformer, X, y)
671 if hasattr(X, 'T'):
672 # If it's not an array, it does not have a 'T' property
--> 673 assert_raises(ValueError, transformer.transform, X.T)
674
675
/Users/Sam/anaconda/lib/python2.7/unittest/case.pyc in assertRaises(self,
excClass, callableObj, *args, **kwargs)
471 return context
472 with context:
--> 473 callableObj(*args, **kwargs)
474
475 def _getAssertEqualityFunc(self, first, second):
/Users/Sam/anaconda/lib/python2.7/unittest/case.pyc in __exit__(self,
exc_type, exc_value, tb)
114 exc_name = str(self.expected)
115 raise self.failureException(
--> 116 "{0} not raised".format(exc_name))
117 if not issubclass(exc_type, self.expected):
118 # let unexpected exceptions pass through
AssertionError: ValueError not raised
On Tue, Jul 25, 2017 at 12:54 AM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> what is the failing test? please provide the full traceback.
>
> On 24 Jul 2017 10:58 pm, "Sam Barnett" <sambarnet...@gmail.com> wrote:
>
>> Dear scikit-learn developers,
>>
>> I am developing a transformer, named Sqizer, that has the ultimate goal
>> of modifying a kernel for use with the sklearn.svm package. When given
>> an input data array X, Sqizer.transform(X) should have as its output the
>> Gram matrix for X using the modified version of the kernel. Here is the
>> code for the class so far:
>>
>> class Sqizer(BaseEstimator, TransformerMixin):
>>
>> def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=1,
>> coef0=0.0, cut_ord_pair=(2,1)):
>> self.C = C
>> self.kernel = kernel
>> self.degree = degree
>> self.gamma = gamma
>> self.coef0 = coef0
>> self.cut_ord_pair = cut_ord_pair
>>
>> def fit(self, X, y=None):
>> # Check that X and y have correct shape
>> X, y = check_X_y(X, y)
>> # Store the classes seen during fit
>> self.classes_ = unique_labels(y)
>>
>> self.X_ = X
>> self.y_ = y
>> return self
>>
>> def transform(self, X):
>>
>> X = check_array(X, warn_on_dtype=True)
>>
>> """Returns Gram matrix corresponding to X, once sqized."""
>> def kPolynom(x,y):
>> return (self.coef0+self.gamma*np.inner(x,y))**self.degree
>> def kGauss(x,y):
>> return np.exp(-self.gamma*np.sum(np.square(x-y)))
>> def kLinear(x,y):
>> return np.inner(x,y)
>> def kSigmoid(x,y):
>> return np.tanh(self.gamma*np.inner(x,y) +self.coef0)
>>
>> def kernselect(kername):
>> switcher = {
>> 'linear': kPolynom,
>> 'rbf': kGauss,
>> 'sigmoid': kLinear,
>> 'poly': kSigmoid,
>> }
>> return switcher.get(kername, "nothing")
>>
>> cut_off = self.cut_ord_pair[0]
>> order = self.cut_ord_pair[1]
>>
>> from SeqKernel import hiSeqKernEval
>>
>> def getGram(Y):
>> gram_matrix = np.zeros((Y.
>>
>> ...
>
> [Message clipped]
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