The difference is the functional form versus the context manager. you can't add extra parameters to the function, only to the context manager.
On 27 Jul 2017 12:56 am, "Andreas Mueller" <t3k...@gmail.com> wrote: > Hm, it would be nice to do this in a way that relies less on pytest, but I > guess that would be tricky. > One way would be to use assert_raise_message to make clear what the > expected error is. > But that would make the current test more strict - not necessarily that > bad, I guess? > > It looks like all asserts in unittest have a "msg" argument... apart from > assertRaises: > https://docs.python.org/2/library/unittest.html# > unittest.TestCase.assertRaises > > That has been fixed in Python 3.3, though: > https://docs.python.org/3/library/unittest.html# > unittest.TestCase.assertRaises > > So maybe we should just do a backport for assert_raises and > assert_raises_regex? > > > On 07/25/2017 07:58 PM, Joel Nothman wrote: > > One advantage of moving to pytest is that we can put messages into > pytest.raises, and we should emphasise this in moving the check_estimator > assertions to pytest. But I'm also not sure how we do the deprecation of > nosetests for check_estimator in a way that is friendly to our > contribbers... > > On 26 July 2017 at 06:31, Andreas Mueller <t3k...@gmail.com> wrote: > >> Indeed, it makes sure that the transform is applied to data with the same >> number of samples as the input. >> PR welcome to provide a better error message on this! >> >> On 07/25/2017 08:15 AM, Sam Barnett wrote: >> >> 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 <sambarnet...@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/util >>> s/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( >>>>> >>>>> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > ...
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