[scikit-learn] Fwd: Custom transformer failing check_estimator test

[Sam Barnett]

Apologies: I've since worked out what the problem was and have resolved
this issue. This was what I was missing in my code:


        # Check that the input is of the same shape as the one passed
        # during fit.
        if X.shape != self.input_shape_:
            raise ValueError('Shape of input is different from what was
seen'
                             'in `fit`')


On Tue, Jul 25, 2017 at 9:41 AM, Sam Barnett <sambarnett95 at gmail.com> wrote:

> 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.nothman at gmail.com>
> wrote:
>
>> what is the failing test? please provide the full traceback.
>>
>> On 24 Jul 2017 10:58 pm, "Sam Barnett" <sambarnett95 at 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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>
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