def _kernel_approximation(self): attrs = [ 'AdditiveChi2Sampler', 'Nystroem', 'RBFSampler', 'SkewedChi2Sampler' ] return _AccessorMethods(self, module_name='sklearn.kernel_approximation', attrs=attrs)
def correlation_models(self): """Property to access ``sklearn.gaussian_process.correlation_models``""" module_name = 'sklearn.gaussian_process.correlation_models' attrs = ['absolute_exponential', 'squared_exponential', 'generalized_exponential', 'pure_nugget', 'cubic', 'linear'] return _AccessorMethods(self._df, module_name=module_name, attrs=attrs)
def _text(self): attrs = [ 'CountVectorizer', 'HashingVectorizer', 'TfidfTransformer', 'TfidfVectorizer' ] return _AccessorMethods(self._df, module_name='sklearn.feature_extraction.text', attrs=attrs)
def correlation_models(self): """Property to access ``sklearn.gaussian_process.correlation_models``""" module_name = 'sklearn.gaussian_process.correlation_models' attrs = [ 'absolute_exponential', 'squared_exponential', 'generalized_exponential', 'pure_nugget', 'cubic', 'linear' ] return _AccessorMethods(self._df, module_name=module_name, attrs=attrs)
def _mixture(self): return _AccessorMethods(self, module_name='sklearn.mixture')
def _cross_decomposition(self): attrs = ['PLSRegression', 'PLSCanonical', 'CCA', 'PLSSVD'] return _AccessorMethods(self, module_name='sklearn.cross_decomposition', attrs=attrs)
def _dummy(self): attrs = ['DummyClassifier', 'DummyRegressor'] return _AccessorMethods(self, module_name='sklearn.dummy', attrs=attrs)
def _over_sampling(self): return _AccessorMethods(self._df, module_name='imblearn.over_sampling')
def _ensemble(self): return _AccessorMethods(self._df, module_name='imblearn.ensemble')
def _image(self): return _AccessorMethods(self._df, module_name='sklearn.feature_extraction.image')
def _combine(self): return _AccessorMethods(self._df, module_name='imblearn.combine')
def _semi_supervised(self): return _AccessorMethods(self, module_name='sklearn.semi_supervised')
def _multiclass(self): from distutils.version import LooseVersion import sklearn if str(sklearn.__version__) < LooseVersion('0.16.0'): warnings.warn('sklern.multiclass may not be loaded properly') return _AccessorMethods(self, module_name='sklearn.multiclass')
def _qda(self): return _AccessorMethods(self, module_name='sklearn.qda')
def _text(self): attrs = ['CountVectorizer', 'HashingVectorizer', 'TfidfTransformer', 'TfidfVectorizer'] return _AccessorMethods(self._df, module_name='sklearn.feature_extraction.text', attrs=attrs)
def _multioutput(self): return _AccessorMethods(self, module_name='sklearn.multioutput')
def _neural_network(self): return _AccessorMethods(self, module_name='sklearn.neural_network')
def _naive_bayes(self): return _AccessorMethods(self, module_name='sklearn.naive_bayes')
def _random_projection(self): return _AccessorMethods(self, module_name='sklearn.random_projection')
def _bicluster(self): return _AccessorMethods(self._df, module_name='sklearn.cluster.bicluster')
def _tree(self): return _AccessorMethods(self, module_name='sklearn.tree')
def _calibration(self): attrs = ['CalibratedClassifierCV'] return _AccessorMethods(self, module_name='sklearn.calibration', attrs=attrs)
def _da(self): return _AccessorMethods(self, module_name='sklearn.discriminant_analysis')
def _kernel_ridge(self): attrs = ['KernelRidge'] return _AccessorMethods(self, module_name='sklearn.kernel_ridge', attrs=attrs)
def _kernel_approximation(self): attrs = ['AdditiveChi2Sampler', 'Nystroem', 'RBFSampler', 'SkewedChi2Sampler'] return _AccessorMethods(self, module_name='sklearn.kernel_approximation', attrs=attrs)
def _multiclass(self): return _AccessorMethods(self, module_name='sklearn.multiclass')