def _fit(self, X, y, **fit_params):
# Collect feature name(s)
if (isinstance(X, DataFrame)):
self.active_fields = X.columns.values
elif (isinstance(X, Series)):
self.active_fields = numpy.array([X.name])
# Collect label name
if (isinstance(y, Series)):
self.target_field = y.name
return Pipeline._fit(self, X, y, **fit_params)
class BasePipeline(BaseEstimator):
"""Base class for all pipeline objects.
Notes
-----
This class should not be instantiated, only subclassed."""
__metaclass__ = ABCMeta
def __init__(self, configuration, random_state=None):
self.configuration = configuration
if random_state is None:
self.random_state = check_random_state(1)
else:
self.random_state = check_random_state(random_state)
def fit(self, X, y, fit_params=None, init_params=None):
"""Fit the selected algorithm to the training data.
Parameters
----------
X : array-like or sparse, shape = (n_samples, n_features)
Training data. The preferred type of the matrix (dense or sparse)
depends on the estimator selected.
y : array-like
Targets
fit_params : dict
See the documentation of sklearn.pipeline.Pipeline for formatting
instructions.
init_params : dict
Pass arguments to the constructors of single methods. To pass
arguments to only one of the methods (lets says the
OneHotEncoder), seperate the class name from the argument by a ':'.
Returns
-------
self : returns an instance of self.
Raises
------
NoModelException
NoModelException is raised if fit() is called without specifying
a classification algorithm first.
"""
X, fit_params = self.pre_transform(X,
y,
fit_params=fit_params,
init_params=init_params)
self.fit_estimator(X, y, **fit_params)
return self
def pre_transform(self, X, y, fit_params=None, init_params=None):
# Save all transformation object in a list to create a pipeline object
steps = []
# seperate the init parameters for the single methods
init_params_per_method = defaultdict(dict)
if init_params is not None and len(init_params) != 0:
for init_param, value in init_params.items():
method, param = init_param.split(":")
init_params_per_method[method][param] = value
# Instantiate preprocessor objects
for preproc_name, preproc_class in self._get_pipeline()[:-1]:
preproc_params = {}
for instantiated_hyperparameter in self.configuration:
if not instantiated_hyperparameter.startswith(preproc_name +
":"):
continue
if self.configuration[instantiated_hyperparameter] is None:
continue
name_ = instantiated_hyperparameter.split(":")[-1]
preproc_params[name_] = self.configuration[
instantiated_hyperparameter]
preprocessor_object = preproc_class(random_state=self.random_state,
**preproc_params)
# Ducktyping...
if hasattr(preproc_class, 'get_components'):
preprocessor_object = preprocessor_object.choice
steps.append((preproc_name, preprocessor_object))
# Extract Estimator Hyperparameters from the configuration object
estimator_name = self._get_pipeline()[-1][0]
estimator_object = self._get_pipeline()[-1][1]
estimator_parameters = {}
for instantiated_hyperparameter in self.configuration:
if not instantiated_hyperparameter.startswith(estimator_name):
continue
if self.configuration[instantiated_hyperparameter] is None:
continue
name_ = instantiated_hyperparameter.split(":")[-1]
estimator_parameters[name_] = self.configuration[
instantiated_hyperparameter]
estimator_parameters.update(init_params_per_method[estimator_name])
estimator_object = estimator_object(random_state=self.random_state,
**estimator_parameters)
# Ducktyping...
if hasattr(estimator_object, 'get_components'):
estimator_object = estimator_object.choice
steps.append((estimator_name, estimator_object))
self.pipeline_ = Pipeline(steps)
if fit_params is None or not isinstance(fit_params, dict):
fit_params = dict()
else:
fit_params = {
key.replace(":", "__"): value
for key, value in fit_params.items()
}
X, fit_params = self.pipeline_._fit(X, y, **fit_params)
return X, fit_params
def fit_estimator(self, X, y, **fit_params):
check_is_fitted(self, 'pipeline_')
if fit_params is None:
fit_params = {}
self.pipeline_.steps[-1][-1].fit(X, y, **fit_params)
return self
def iterative_fit(self, X, y, n_iter=1, **fit_params):
check_is_fitted(self, 'pipeline_')
if fit_params is None:
fit_params = {}
self.pipeline_.steps[-1][-1].iterative_fit(X,
y,
n_iter=n_iter,
**fit_params)
def estimator_supports_iterative_fit(self):
return hasattr(self.pipeline_.steps[-1][-1], 'iterative_fit')
def configuration_fully_fitted(self):
check_is_fitted(self, 'pipeline_')
return self.pipeline_.steps[-1][-1].configuration_fully_fitted()
def predict(self, X, batch_size=None):
"""Predict the classes using the selected model.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
batch_size: int or None, defaults to None
batch_size controls whether the pipeline will be
called on small chunks of the data. Useful when calling the
predict method on the whole array X results in a MemoryError.
Returns
-------
array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Returns the predicted values"""
# TODO check if fit() was called before...
if batch_size is None:
return self.pipeline_.predict(X).astype(self._output_dtype)
else:
if type(batch_size) is not int or batch_size <= 0:
raise Exception("batch_size must be a positive integer")
else:
if self.num_targets == 1:
y = np.zeros((X.shape[0], ), dtype=self._output_dtype)
else:
y = np.zeros((X.shape[0], self.num_targets),
dtype=self._output_dtype)
# Copied and adapted from the scikit-learn GP code
for k in range(
max(1, int(np.ceil(float(X.shape[0]) / batch_size)))):
batch_from = k * batch_size
batch_to = min([(k + 1) * batch_size, X.shape[0]])
y[batch_from:batch_to] = \
self.predict(X[batch_from:batch_to], batch_size=None)
return y
@classmethod
def get_hyperparameter_search_space(cls,
include=None,
exclude=None,
dataset_properties=None):
"""Return the configuration space for the CASH problem.
This method should be called by the method
get_hyperparameter_search_space of a subclass. After the subclass
assembles a list of available estimators and preprocessor components,
_get_hyperparameter_search_space can be called to do the work of
creating the actual
ConfigSpace.configuration_space.ConfigurationSpace object.
Parameters
----------
estimator_name : str
Name of the estimator hyperparameter which will be used in the
configuration space. For a classification task, this would be
'classifier'.
estimator_components : dict {name: component}
Dictionary with all estimator components to be included in the
configuration space.
preprocessor_components : dict {name: component}
Dictionary with all preprocessor components to be included in the
configuration space. .
always_active : list of str
A list of components which will always be active in the pipeline.
This is useful for components like imputation which have
hyperparameters to be configured, but which do not have any parent.
default_estimator : str
Default value for the estimator hyperparameter.
Returns
-------
cs : ConfigSpace.configuration_space.Configuration
The configuration space describing the AutoSklearnClassifier.
"""
raise NotImplementedError()
@classmethod
def _get_hyperparameter_search_space(cls, cs, dataset_properties, exclude,
include, pipeline):
if include is None:
include = {}
keys = [pair[0] for pair in pipeline]
for key in include:
if key not in keys:
raise ValueError('Invalid key in include: %s; should be one '
'of %s' % (key, keys))
if exclude is None:
exclude = {}
keys = [pair[0] for pair in pipeline]
for key in exclude:
if key not in keys:
raise ValueError('Invalid key in exclude: %s; should be one '
'of %s' % (key, keys))
if 'sparse' not in dataset_properties:
# This dataset is probaby dense
dataset_properties['sparse'] = False
if 'signed' not in dataset_properties:
# This dataset probably contains unsigned data
dataset_properties['signed'] = False
matches = autosklearn.pipeline.create_searchspace_util.get_match_array(
pipeline, dataset_properties, include=include, exclude=exclude)
# Now we have only legal combinations at this step of the pipeline
# Simple sanity checks
assert np.sum(matches) != 0, "No valid pipeline found."
assert np.sum(matches) <= np.size(matches), \
"'matches' is not binary; %s <= %d, %s" % \
(str(np.sum(matches)), np.size(matches), str(matches.shape))
# Iterate each dimension of the matches array (each step of the
# pipeline) to see if we can add a hyperparameter for that step
for node_idx, n_ in enumerate(pipeline):
node_name, node = n_
is_choice = hasattr(node, "get_available_components")
# if the node isn't a choice we can add it immediately because it
# must be active (if it wouldn't, np.sum(matches) would be zero
if not is_choice:
cs.add_configuration_space(
node_name,
node.get_hyperparameter_search_space(dataset_properties))
# If the node isn't a choice, we have to figure out which of it's
# choices are actually legal choices
else:
choices_list = autosklearn.pipeline.create_searchspace_util.\
find_active_choices(matches, node, node_idx,
dataset_properties,
include.get(node_name),
exclude.get(node_name))
cs.add_configuration_space(
node_name,
node.get_hyperparameter_search_space(dataset_properties,
include=choices_list))
# And now add forbidden parameter configurations
# According to matches
if np.sum(matches) < np.size(matches):
cs = autosklearn.pipeline.create_searchspace_util.add_forbidden(
conf_space=cs,
pipeline=pipeline,
matches=matches,
dataset_properties=dataset_properties,
include=include,
exclude=exclude)
return cs
def __repr__(self):
class_name = self.__class__.__name__
configuration = {}
self.configuration._populate_values()
for hp_name in self.configuration:
if self.configuration[hp_name] is not None:
configuration[hp_name] = self.configuration[hp_name]
configuration_string = ''.join([
'configuration={\n ', ',\n '.join([
"'%s': %s" % (hp_name, repr(configuration[hp_name]))
for hp_name in sorted(configuration)
]), '}'
])
return '%s(%s)' % (class_name, configuration_string)
@classmethod
def _get_pipeline(cls):
if cls == autosklearn.pipelineBaseEstimator:
return []
raise NotImplementedError()
def _get_estimator_hyperparameter_name(self):
raise NotImplementedError()
