def fit(self, X, y):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed,
column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_pandas=True)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y = self.label_encoder.fit_transform(y)
self.y = y
self.classes_ = self.label_encoder.classes_
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure(self)
self.distance_measure = self.get_distance_measure(self)
self.X_exemplar, self.y_exemplar = self.pick_exemplars(self)
self._is_fitted = True
return self
def fit(self, X, y, input_checks = True):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed, column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
input_checks: boolean
whether to check the X and y parameters
Returns
-------
self : object
"""
if input_checks: validate_X_y(X, y)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
self.label_encoder = LabelEncoder()
self.label_encoder.fit(y)
self.classes_ = self.label_encoder.classes_
self.y = self.label_encoder.transform(y)
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure(self)
self.distance_measure = self.get_distance_measure(self)
self.X_exemplar, self.y_exemplar = self.pick_exemplars(self)
return self
def fit(self, X, y, input_checks = True):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed, column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
input_checks: boolean
whether to check the X and y parameters
Returns
-------
self : object
"""
if input_checks: validate_X_y(X, y)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
self.label_encoder = LabelEncoder()
self.label_encoder.fit(y)
self.classes_ = self.label_encoder.classes_
self.y = self.label_encoder.transform(y)
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure_getter(self)
self.distance_measure = self.get_distance_measure(self)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
self.trees = parallel(delayed(self._fit_tree)(X, y, index, self.random_state.randint(0, self.n_trees))
for index in range(self.n_trees))
else:
self.trees = [self._fit_tree(X, y, index, self.random_state.randint(0, self.n_trees))
for index in range(self.n_trees)]
return self
def fit(self, X, y):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed,
column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_pandas=True)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
if self.find_stump is None:
self.find_stump = best_of_n_stumps(self.n_stump_evaluations)
# setup label encoding
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y = self.label_encoder.fit_transform(y)
self.y = y
self.classes_ = self.label_encoder.classes_
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure(self)
self.distance_measure = self.get_distance_measure(self)
self.stump = self.find_stump(self)
n_branches = len(self.stump.y_exemplar)
self.branches = [None] * n_branches
if self.depth < self.max_depth:
for index in range(n_branches):
sub_y = self.stump.y_branches[index]
if not self.is_leaf(sub_y):
sub_tree = ProximityTree(
random_state=self.random_state,
get_exemplars=self.get_exemplars,
distance_measure=self.distance_measure,
setup_distance_measure=self.setup_distance_measure,
get_distance_measure=self.get_distance_measure,
get_gain=self.get_gain,
is_leaf=self.is_leaf,
verbosity=self.verbosity,
max_depth=self.max_depth,
n_jobs=self.n_jobs,
)
sub_tree.label_encoder = self.label_encoder
sub_tree.depth = self.depth + 1
self.branches[index] = sub_tree
sub_X = self.stump.X_branches[index]
sub_tree.fit(sub_X, sub_y)
self._is_fitted = True
return self
def fit(self, X, y, input_checks = True):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed, column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
input_checks: boolean
whether to check the X and y parameters
Returns
-------
self : object
"""
if input_checks: validate_X_y(X, y)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
self.label_encoder = LabelEncoder()
self.label_encoder.fit(y)
self.classes_ = self.label_encoder.classes_
self.y = self.label_encoder.transform(y)
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure(self)
self.distance_measure = self.get_distance_measure(self)
self.stump = self.find_stump(self)
n_branches = len(self.stump.y_exemplar)
self.branches = [None] * n_branches
if self.depth < self.max_depth:
for index in range(n_branches):
sub_y = self.stump.y_branches[index]
if not self.is_leaf(sub_y):
sub_tree = ProximityTree(
random_state=self.random_state,
get_exemplars=self.get_exemplars,
distance_measure=self.distance_measure,
setup_distance_measure=self.setup_distance_measure,
get_distance_measure=self.get_distance_measure,
get_gain=self.get_gain,
is_leaf=self.is_leaf,
verbosity=self.verbosity,
max_depth=self.max_depth,
n_jobs=self.n_jobs
)
sub_tree.depth = self.depth + 1
self.branches[index] = sub_tree
sub_X = self.stump.X_branches[index]
sub_tree.fit(sub_X, sub_y)
return self
def fit(self, X, y):
"""
Build the classifier on the training set (X, y)
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed,
column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
Returns
-------
self : object
"""
X, y = check_X_y(X, y, enforce_univariate=True, coerce_to_pandas=True)
self.X = dataset_properties.positive_dataframe_indices(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y = self.label_encoder.fit_transform(y)
self.y = y
self.classes_ = self.label_encoder.classes_
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure_getter(self)
self.distance_measure = self.get_distance_measure(self)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
self.trees = parallel(
delayed(self._fit_tree)(
X, y, index, self.random_state.randint(0, self.n_estimators)
)
for index in range(self.n_estimators)
)
else:
self.trees = [
self._fit_tree(
X, y, index, self.random_state.randint(0, self.n_estimators)
)
for index in range(self.n_estimators)
]
self._is_fitted = True
return self
