def fit(self, X, Y, sample_weight=None):
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(
max_depth=self.max_depth)
estimator = sklearn.ensemble.AdaBoostClassifier(
base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = MultilabelClassifier(estimator, n_jobs=1)
self.estimator.fit(X, Y, sample_weight=sample_weight)
else:
self.estimator.fit(X, Y, sample_weight=sample_weight)
return self
def fit(self, X, Y, sample_weight=None):
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(max_depth=self.max_depth)
estimator = sklearn.ensemble.AdaBoostClassifier(
base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state
)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = MultilabelClassifier(estimator, n_jobs=1)
self.estimator.fit(X, Y, sample_weight=sample_weight)
else:
self.estimator.fit(X, Y, sample_weight=sample_weight)
return self
class AdaboostClassifier(ParamSklearnClassificationAlgorithm):
def __init__(self, n_estimators, learning_rate, algorithm, max_depth,
random_state=None):
self.n_estimators = int(n_estimators)
self.learning_rate = float(learning_rate)
self.algorithm = algorithm
self.random_state = random_state
self.max_depth = max_depth
self.estimator = None
def fit(self, X, Y, sample_weight=None):
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(max_depth=self.max_depth)
estimator = sklearn.ensemble.AdaBoostClassifier(
base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state
)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = MultilabelClassifier(estimator, n_jobs=1)
self.estimator.fit(X, Y, sample_weight=sample_weight)
else:
self.estimator.fit(X, Y, sample_weight=sample_weight)
return self
def predict(self, X):
if self.estimator is None:
raise NotImplementedError
return self.estimator.predict(X)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict_proba(X)
@staticmethod
def get_properties(dataset_properties=None):
return {'shortname': 'AB',
'name': 'AdaBoost Classifier',
'handles_missing_values': False,
'handles_nominal_values': False,
'handles_numerical_features': True,
'prefers_data_scaled': False,
'prefers_data_normalized': False,
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': True,
'is_deterministic': True,
'handles_sparse': False,
'input': (DENSE, SPARSE, UNSIGNED_DATA),
'output': (PREDICTIONS,),
# TODO find out what is best used here!
# But rather fortran or C-contiguous?
'preferred_dtype': np.float32}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
# base_estimator = Constant(name="base_estimator", value="None")
n_estimators = cs.add_hyperparameter(UniformIntegerHyperparameter(
name="n_estimators", lower=50, upper=500, default=50, log=False))
learning_rate = cs.add_hyperparameter(UniformFloatHyperparameter(
name="learning_rate", lower=0.0001, upper=2, default=0.1, log=True))
algorithm = cs.add_hyperparameter(CategoricalHyperparameter(
name="algorithm", choices=["SAMME.R", "SAMME"], default="SAMME.R"))
max_depth = cs.add_hyperparameter(UniformIntegerHyperparameter(
name="max_depth", lower=1, upper=10, default=1, log=False))
return cs
class AdaboostClassifier(ParamSklearnClassificationAlgorithm):
def __init__(self,
n_estimators,
learning_rate,
algorithm,
max_depth,
random_state=None):
self.n_estimators = int(n_estimators)
self.learning_rate = float(learning_rate)
self.algorithm = algorithm
self.random_state = random_state
self.max_depth = max_depth
self.estimator = None
def fit(self, X, Y, sample_weight=None):
self.n_estimators = int(self.n_estimators)
self.learning_rate = float(self.learning_rate)
self.max_depth = int(self.max_depth)
base_estimator = sklearn.tree.DecisionTreeClassifier(
max_depth=self.max_depth)
estimator = sklearn.ensemble.AdaBoostClassifier(
base_estimator=base_estimator,
n_estimators=self.n_estimators,
learning_rate=self.learning_rate,
algorithm=self.algorithm,
random_state=self.random_state)
if len(Y.shape) == 2 and Y.shape[1] > 1:
self.estimator = MultilabelClassifier(estimator, n_jobs=1)
self.estimator.fit(X, Y, sample_weight=sample_weight)
else:
self.estimator.fit(X, Y, sample_weight=sample_weight)
return self
def predict(self, X):
if self.estimator is None:
raise NotImplementedError
return self.estimator.predict(X)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict_proba(X)
@staticmethod
def get_properties(dataset_properties=None):
return {
'shortname': 'AB',
'name': 'AdaBoost Classifier',
'handles_missing_values': False,
'handles_nominal_values': False,
'handles_numerical_features': True,
'prefers_data_scaled': False,
'prefers_data_normalized': False,
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': True,
'handles_multilabel': True,
'is_deterministic': True,
'handles_sparse': False,
'input': (DENSE, SPARSE, UNSIGNED_DATA),
'output': (PREDICTIONS, ),
# TODO find out what is best used here!
# But rather fortran or C-contiguous?
'preferred_dtype': np.float32
}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
# base_estimator = Constant(name="base_estimator", value="None")
n_estimators = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="n_estimators",
lower=50,
upper=500,
default=50,
log=False))
learning_rate = cs.add_hyperparameter(
UniformFloatHyperparameter(name="learning_rate",
lower=0.0001,
upper=2,
default=0.1,
log=True))
algorithm = cs.add_hyperparameter(
CategoricalHyperparameter(name="algorithm",
choices=["SAMME.R", "SAMME"],
default="SAMME.R"))
max_depth = cs.add_hyperparameter(
UniformIntegerHyperparameter(name="max_depth",
lower=1,
upper=10,
default=1,
log=False))
return cs
