def test_remove_row(self):
data = Table("iris")
with data.unlocked():
data.X[0, 0] = np.nan
pp_data = RemoveNaNRows()(data)
self.assertEqual(len(pp_data), len(data) - 1)
self.assertFalse(np.isnan(pp_data.X).any())
def createinstance(params):
params = dict(params)
method = params.pop("method", ImputeEditor.Average)
if method == ImputeEditor.NoImputation:
return None
elif method == ImputeEditor.Average:
return preprocess.Impute()
elif method == ImputeEditor.Model:
return preprocess.Impute(method=preprocess.impute.Model())
elif method == ImputeEditor.DropRows:
return RemoveNaNRows()
elif method == ImputeEditor.DropColumns:
return preprocess.RemoveNaNColumns()
else:
method, defaults = ImputeEditor.Imputers[method]
defaults = dict(defaults)
defaults.update(params)
return preprocess.Impute(method=method)
class LinearProjector(Projector):
name = "Linear Projection"
supports_sparse = False
preprocessors = [RemoveNaNRows()]
projection = DomainProjection
def __init__(self, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.components_ = None
def fit(self, X, Y=None):
self.components_ = self.get_components(X, Y)
return self.projection(self, self.domain)
def get_components(self, X, Y):
raise NotImplementedError
def transform(self, X):
return np.dot(X, self.components_.T)