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 _image(self):
return _AccessorMethods(self._df,
module_name='sklearn.feature_extraction.image')
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 _qda(self):
return _AccessorMethods(self, module_name='sklearn.qda')
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 _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 _multioutput(self):
return _AccessorMethods(self, module_name='sklearn.multioutput')
def _neural_network(self):
return _AccessorMethods(self, module_name='sklearn.neural_network')
def _ensemble(self):
return _AccessorMethods(self._df, module_name='imblearn.ensemble')
def _neural_network(self):
return _AccessorMethods(self, module_name='sklearn.neural_network')
def _semi_supervised(self):
return _AccessorMethods(self, module_name='sklearn.semi_supervised')
def _naive_bayes(self):
return _AccessorMethods(self, module_name='sklearn.naive_bayes')
def _over_sampling(self):
return _AccessorMethods(self._df, module_name='imblearn.over_sampling')
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 _combine(self):
return _AccessorMethods(self._df, module_name='imblearn.combine')
def _cross_decomposition(self):
attrs = ['PLSRegression', 'PLSCanonical', 'CCA', 'PLSSVD']
return _AccessorMethods(self, module_name='sklearn.cross_decomposition',
attrs=attrs)
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 _da(self):
return _AccessorMethods(self,
module_name='sklearn.discriminant_analysis')
def _dummy(self):
attrs = ['DummyClassifier', 'DummyRegressor']
return _AccessorMethods(self, module_name='sklearn.dummy', attrs=attrs)
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')
def _kernel_ridge(self):
attrs = ['KernelRidge']
return _AccessorMethods(self, module_name='sklearn.kernel_ridge',
attrs=attrs)
def _naive_bayes(self):
return _AccessorMethods(self, module_name='sklearn.naive_bayes')
def _mixture(self):
return _AccessorMethods(self, module_name='sklearn.mixture')
def _random_projection(self):
return _AccessorMethods(self, module_name='sklearn.random_projection')
def _multiclass(self):
return _AccessorMethods(self, module_name='sklearn.multiclass')
def _tree(self):
return _AccessorMethods(self, module_name='sklearn.tree')
def _bicluster(self):
return _AccessorMethods(self._df, module_name='sklearn.cluster.bicluster')
