def __init__(self, wrapped, max_plotted_predictions=None):
if max_plotted_predictions is None:
max_plotted_predictions = 10
FeatureUnion.__init__(self, [Identity(), wrapped],
joiner=PlotPredictionsJoiner(
plot_predictions, max_plotted_predictions),
n_jobs=1)
def __init__(self, steps, hyperparams=None):
FeatureUnion.__init__(self, steps)
self._make_all_steps_optional()
if hyperparams is None:
self.set_hyperparams(HyperparameterSamples({
CHOICE_HYPERPARAM: list(self.keys())[0]
}))
def __init__(self, steps, hyperparams=None):
FeatureUnion.__init__(self, steps)
if hyperparams is None:
self.set_hyperparams(HyperparameterSamples({}))
else:
self.set_hyperparams(hyperparams)
self._make_all_steps_optional()
def __init__(self, steps, hyperparams=None):
FeatureUnion.__init__(self, steps, joiner=SelectNonEmptyDataInputs())
self._make_all_steps_optional()
if hyperparams is None:
choices = list(self.keys())[:-1]
self.set_hyperparams({CHOICE_HYPERPARAM: choices[0]})
self.set_hyperparams_space({CHOICE_HYPERPARAM: Choice(choices)})
def __init__(self, column_chooser_steps_as_tuple: ColumnChooserTupleList, n_dimension: int = 3):
# Make unique names from the indices in case we have many steps for transforming the same column(s).
self.string_indices = [
str(name) + "_" + str(step.__class__.__name__)
for name, step in column_chooser_steps_as_tuple
]
FeatureUnion.__init__(self, [
(string_indices, Pipeline([
ColumnsSelectorND(indices, n_dimension=n_dimension),
step
]))
for string_indices, (indices, step) in zip(self.string_indices, column_chooser_steps_as_tuple)
])
def __init__(self, steps, hyperparams=None):
FeatureUnion.__init__(self, steps, joiner=SelectNonEmptyDataInputs())
self._make_all_steps_optional()
choices = list(self.keys())[:-1]
if hyperparams is None:
self.update_hyperparams(
{ChooseOneStepOf.CHOICE_HYPERPARAM: choices[0]})
else:
self.update_hyperparams(
{ChooseOneStepOf.CHOICE_HYPERPARAM: hyperparams})
self.update_hyperparams_space(
{ChooseOneStepOf.CHOICE_HYPERPARAM: Choice(choices)})
def __init__(self, steps, joiner: NonFittableMixin = None):
if joiner is None:
joiner = NumpyConcatenateOnCustomAxisIfNotEmpty(axis=-1)
FeatureUnion.__init__(self, steps_as_tuple=steps, joiner=joiner)
self.set_hyperparams(HyperparameterSamples({}))
self._make_all_steps_optional()
def __init__(self, steps):
FeatureUnion.__init__(self, steps_as_tuple=steps, joiner=NumpyConcatenateOnCustomAxisIfNotEmpty(axis=-1))
self.set_hyperparams(HyperparameterSamples({}))
self._make_all_steps_optional()
def __init__(self, steps):
FeatureUnion.__init__(self, steps)
self.set_hyperparams(HyperparameterSamples({}))
self._make_all_steps_optional()