class TreeBuilderTest(TestCase):
def setUp(self):
self.tree_builder = TreeBuilder(split_criterion=GiniCriterion(),
split_chooser=BestSplitChooser(),
feature_selection=AllFeatureSelection())
def tearDown(self):
pass
def test_build_tree_only_root(self):
n_classes = 1
x = np.array([1, 1]).reshape((2, 1))
y = np.array([0, 0])
returned = self.tree_builder.build_tree(x, y, n_classes)
expected_length = 1
expected_root_samples = [2]
self.assertEqual(len(returned.nodes), expected_length)
self.assertEqual(returned.nodes[returned.last_node_id].samples, expected_root_samples)
self.assertIsInstance(returned.nodes[returned.last_node_id], DecisionLeaf)
def test_build_tree(self):
n_classes = 2
x = np.array(['A', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B']).reshape((3, 3))
y = np.array([1, 1, 0])
expected_length = 3
expected_root_value = 'B'
expected_root_feature_id = 1
returned = self.tree_builder.build_tree(x, y, n_classes)
self.assertEqual(len(returned.nodes), expected_length)
self.assertEqual(returned.nodes[0].value, expected_root_value)
self.assertEqual(returned.nodes[0].feature_id, expected_root_feature_id)
self.assertEqual([returned.nodes[1].result, returned.nodes[2].result], [1, 0])
self.assertIsInstance(returned.nodes[0], DecisionForkCategorical)
def test_build_tree_recursive_all_same_class_two_classes(self):
x = np.array(['A', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B']).reshape((3, 3))
y = np.array([1, 1, 1])
self.tree_builder._n_classes = 2
tree = DecisionTree(n_features=3)
tree.last_node_id = tree.root()
self.tree_builder._build_tree_recursive(tree, tree.last_node_id, x, y, depth=1)
expected_length = 1
expected_root_samples = [0, 3]
self.assertEqual(len(tree.nodes), expected_length)
self.assertIsInstance(tree.nodes[tree.last_node_id], DecisionLeaf)
self.assertEqual(tree.nodes[tree.last_node_id].samples, expected_root_samples)
def test_build_tree_recursive_min_samples_split(self):
x = np.array(['A', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B']).reshape((3, 3))
y = np.array([1, 1, 0])
self.tree_builder._n_classes = 2
self.tree_builder._min_samples_split = 4
tree = DecisionTree(n_features=3)
tree.last_node_id = tree.root()
self.tree_builder._build_tree_recursive(tree, tree.last_node_id, x, y, depth=1)
expected_length = 1
expected_root_samples = [1, 2]
self.assertEqual(len(tree.nodes), expected_length)
self.assertIsInstance(tree.nodes[tree.last_node_id], DecisionLeaf)
self.assertEqual(tree.nodes[tree.last_node_id].samples, expected_root_samples)
def test_build_tree_recursive_max_depth(self):
x = np.array(['A', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B']).reshape((3, 3))
y = np.array([1, 1, 0])
self.tree_builder._n_classes = 2
self.tree_builder._max_depth = 0
tree = DecisionTree(n_features=3)
tree.last_node_id = tree.root()
self.tree_builder._build_tree_recursive(tree, tree.last_node_id, x, y, depth=1)
expected_length = 1
expected_root_samples = [1, 2]
self.assertEqual(len(tree.nodes), expected_length)
self.assertIsInstance(tree.nodes[tree.last_node_id], DecisionLeaf)
self.assertEqual(tree.nodes[tree.last_node_id].samples, expected_root_samples)
def test_build_tree_recursive(self):
x = np.array([0, 1, 0, 1, 1, 2, 0, 2, 1]).reshape((3, 3))
y = np.array([1, 1, 0])
self.tree_builder._n_classes = 2
tree = DecisionTree(n_features=3)
tree.last_node_id = tree.root()
self.tree_builder._build_tree_recursive(tree, tree.last_node_id, x, y, depth=1)
expected_length = 3
expected_root_feature_id = 1
expected_root_value = 1.5
self.assertEqual(len(tree.nodes), expected_length)
self.assertIsInstance(tree.nodes[0], DecisionForkNumerical)
self.assertEqual(tree.nodes[0].feature_id, expected_root_feature_id)
self.assertEqual(tree.nodes[0].value, expected_root_value)
self.assertEqual([tree.nodes[1].result, tree.nodes[2].result], [1, 0])
def test_find_best_split_categorical(self):
x = np.array(['A', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B']).reshape((3, 3))
y = np.array([1, 1, 0])
expected_split_value = 'B'
expected_split_feature_id = 1
expected_split_gain = 0.44
returned_split = self.tree_builder._find_split(x, y, 3)
self.assertEqual(returned_split.value, expected_split_value)
self.assertEqual(returned_split.feature_id, expected_split_feature_id)
self.assertAlmostEqual(returned_split.gain, expected_split_gain, places=2)
def test_find_best_split_numerical(self):
x = np.array([0, 1, 0, 1, 1, 2, 0, 2, 1]).reshape((3, 3))
y = np.array([1, 1, 0])
expected_split_value = 1.5
expected_split_feature_id = 1
expected_split_gain = 0.44
returned_split = self.tree_builder._find_split(x, y, 3)
self.assertEqual(returned_split.value, expected_split_value)
self.assertEqual(returned_split.feature_id, expected_split_feature_id)
self.assertAlmostEqual(returned_split.gain, expected_split_gain, places=2)
def test_find_best_split_without_examples(self):
x = np.array([]).reshape((0, 0))
y = np.array([])
expected_split = None
returned_split = self.tree_builder._find_split(x, y, 0)
self.assertEqual(returned_split, expected_split)
class DecisionForestClassifier(BaseEstimator, ClassifierMixin):
def __init__(self,
n_estimators=100,
bootstrap=True,
max_depth=None,
split_chooser='best',
split_criterion='gini',
min_samples_leaf=1,
feature_selection='log',
feature_prob=None,
min_gain_split=0,
min_samples_split=2):
"""
Builds a decision forest for a classification problem.
:param n_estimators: <int> Number of trees in the forest
:param bootstrap: <bool> Whether to use bagging or not
:param max_depth: <int> or <None> Defines the maximum depth of the tree
:param split_chooser: <string> The name of the split chooser:
"best" for selecting the best possible split
"rand" for selecting a random split
:param split_criterion: <string> The name of the split criterion:
"gini" for selecting the Gini criterion
"entropy" for selecting the Entropy criterion
:param min_samples_leaf: <int> Minimum number of instances to place in a leaf
:param feature_selection: <string> The name of the feature selection criteria:
"all" for selecting all features as candidate features
"log" for selecting log(n)+1 as candidate features
"prob" for selecting the candidate features according to its probabilities
:param feature_prob: <list> Feature probabilities
:param min_gain_split: <float> Minimum split gain value to consider splitting a node
:param min_samples_split: <int> Minimum number of instances to consider creating a split
"""
self._trees = None
self._n_features = None
self._n_instances = None
self._tree_builder = None
self._n_classes = None
self._encoder = None
# Ensemble parameters
self._n_estimators = None
self._bootstrap = bootstrap
# Tree parameters
self._max_depth = None
self._min_samples_leaf = None
self._min_samples_split = None
self._feature_prob = None
self._min_gain_split = None
self._split_chooser = None
self._split_criterion = None
self._feature_selection = None
if n_estimators is None or n_estimators > 0:
self._n_estimators = n_estimators
else:
raise ValueError('The number of trees must be greater than 0.')
if bootstrap is not None:
self._bootstrap = bootstrap
else:
raise ValueError('The value of bootstrap can not be None.')
if max_depth is None or max_depth > 0:
self._max_depth = max_depth
else:
raise ValueError('The depth of the tree must be greater than 0.')
if min_samples_leaf is not None and min_samples_leaf > 0:
self._min_samples_leaf = min_samples_leaf
else:
raise ValueError('The minimum number of instances to place in a leaf must be greater than 0.')
if min_samples_split is not None and min_samples_split > 1:
self._min_samples_split = min_samples_split
else:
raise ValueError('The minimum number of instances to make a split must be greater than 1')
if feature_prob is None or (utils.check_array_sum_one(feature_prob) and
utils.check_positive_array(feature_prob)):
self._feature_prob = feature_prob
else:
raise ValueError('The features probabilities must be positive values and the sum must be one')
if min_gain_split is not None and min_gain_split >= 0:
self._min_gain_split = min_gain_split
else:
raise ValueError('The minimum value of gain to make a split must be greater or equal to 0')
if split_chooser is not None:
self._split_chooser = resolve_split_selection(split_chooser)
else:
raise ValueError('The split chooser can not be None.')
if split_criterion is not None:
self._split_criterion = resolve_split_criterion(split_criterion)
else:
raise ValueError('The split criterion can not be None.')
if feature_selection is not None:
self._feature_selection = resolve_feature_selection(feature_selection)
else:
raise ValueError('The feature selection criteria can not be None.')
@property
def n_estimators(self):
return self._n_estimators
@n_estimators.setter
def n_estimators(self, n_estimators):
self._n_estimators = n_estimators
@property
def bootstrap(self):
return self._bootstrap
@bootstrap.setter
def bootstrap(self, bootstrap):
self._bootstrap = bootstrap
@property
def max_depth(self):
return self._max_depth
@max_depth.setter
def max_depth(self, max_depth):
self._max_depth = max_depth
@property
def min_samples_leaf(self):
return self._min_samples_leaf
@min_samples_leaf.setter
def min_samples_leaf(self, min_samples_leaf):
self._min_samples_leaf = min_samples_leaf
@property
def min_samples_split(self):
return self._min_samples_split
@min_samples_split.setter
def min_samples_split(self, min_samples_split):
self._min_samples_split = min_samples_split
@property
def feature_prob(self):
return self._feature_prob
@feature_prob.setter
def feature_prob(self, feature_prob):
self._feature_prob = feature_prob
@property
def min_gain_split(self):
return self._min_gain_split
@min_gain_split.setter
def min_gain_split(self, min_gain_split):
self._min_gain_split = min_gain_split
@property
def split_chooser(self):
return self._split_chooser.name
@split_chooser.setter
def split_chooser(self, split_chooser):
self._split_chooser = split_chooser
@property
def split_criterion(self):
return self._split_criterion.name
@split_criterion.setter
def split_criterion(self, split_criterion):
self._split_criterion = split_criterion
@property
def feature_selection(self):
return self._feature_selection.name
@feature_selection.setter
def feature_selection(self, feature_selection):
self._feature_selection = feature_selection
def fit(self, X, y):
"""
Trains the decision forest classifier with (X, y).
:param X: <numpy ndarray> An array containing the feature vectors
:param y: <numpy array> An array containing the target features
:return: self
"""
X, y = check_X_y(X, y, dtype=None)
self._encoder = LabelEncoder()
y = self._encoder.fit_transform(y)
self._n_instances, self._n_features = X.shape
self._n_classes = utils.count_classes(y)
self._trees = []
if self._bootstrap:
set_generator = BaggingSet(self._n_instances)
else:
set_generator = SimpleSet(self._n_instances)
self._tree_builder = TreeBuilder(split_criterion=self._split_criterion,
feature_prob=self._feature_prob,
feature_selection=self._feature_selection,
max_depth=self._max_depth,
min_samples_leaf=self._min_samples_leaf,
min_gain_split=self._min_gain_split,
min_samples_split=self._min_samples_split,
split_chooser=self._split_chooser)
for _ in range(self._n_estimators):
ids = set_generator.training_ids()
X_new = X[ids]
y_new = y[ids]
new_tree = self._tree_builder.build_tree(X_new, y_new, self._n_classes)
if self._bootstrap:
validation_ids = set_generator.oob_ids()
if validation_ids:
new_tree.weight = accuracy_score(y[validation_ids], self._predict_on_tree(X[validation_ids], new_tree))
self._trees.append(new_tree)
set_generator.clear()
return self
def predict(self, X, check_input=True):
"""
Predicts the classes for the new instances in X.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <numpy array>
"""
if check_input:
X = self._validate(X, check_input=check_input)
voter = PerformanceWeightingVoter(self._trees, self._n_classes)
sample_size, features_count = X.shape
result = np.zeros(sample_size, dtype=int)
for i in range(sample_size):
x = X[i]
result[i] = voter.predict(x)
return self._encoder.inverse_transform(result)
def predict_proba(self, X, check_input=True):
"""
Predicts the class distribution probabilities for the new instances in X.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <numpy array>
"""
if check_input:
X = self._validate(X, check_input=check_input)
voter = PerformanceWeightingVoter(self._trees, self._n_classes)
sample_size, features_count = X.shape
result = list(range(sample_size))
for i in range(sample_size):
x = X[i]
result[i] = voter.predict_proba(x)
return result
def feature_importances(self):
"""
Calculates the feature importances according to Breiman 2001.
:return: <numpy array>
"""
importances = np.zeros(self._n_features)
for tree in self._trees:
importances += tree.feature_importances()
importances /= len(self._trees)
return importances
def trees_mean_weight(self):
"""
Calculates the mean weight of the trees in the forest.
:return: <float>
"""
weights = [tree.weight for tree in self._trees]
mean_weight = np.mean(weights)
return mean_weight
def diversity_measure(self, X, y, diversity='pcd'):
"""
Calculates the diversity measure for the forest.
:param X: <numpy ndarray> An array containing the feature vectors
:param y: <numpy array> An array containing the target features
:param diversity: <string> The type of diversity to be calculated
"pcd" for Percentage of Correct Diversity
"qstat" for QStatistic Diversity
:return: <float>
"""
X, y = check_X_y(X, y, dtype=None)
y = self._encoder.transform(y)
if diversity == 'pcd':
metric = PercentageCorrectDiversity()
elif diversity == 'qstat':
metric = QStatisticDiversity()
else:
raise ValueError("It was not possible to recognize the diversity measure.")
forest_diversity = metric.get_measure(self._trees, X, y)
return forest_diversity
def _validate(self, X, check_input):
"""
Validate X whenever one tries to predict or predict_proba.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <bool>
"""
if self._trees is None:
raise NotFittedError("Estimator not fitted, "
"call `fit` before exploiting the model.")
if check_input:
X = check_array(X, dtype=None)
n_features = X.shape[1]
if self._n_features != n_features:
raise ValueError("Number of features of the model must "
" match the input. Model n_features is %s and "
" input n_features is %s "
% (self._n_features, n_features))
return X
def _predict_on_tree(self, X, tree, check_input=True):
"""
Predicts the classes for the new instances in X.
:param X: <numpy ndarray> An array containing the feature vectors
:param tree: <DecisionTree> The tree in which to predict
:param check_input: <bool> If input array must be checked
:return: <numpy array>
"""
if check_input:
X = self._validate(X, check_input=check_input)
sample_size, features_count = X.shape
result = np.zeros(sample_size, dtype=int)
for i in range(sample_size):
x = X[i]
result[i] = tree.predict(x)
return result
class ProactiveForestClassifier(DecisionForestClassifier):
def __init__(self,
n_estimators=100,
bootstrap=True,
max_depth=None,
split_chooser='best',
split_criterion='gini',
min_samples_leaf=1,
feature_selection='log',
feature_prob=None,
min_gain_split=0,
min_samples_split=2,
alpha=0.1):
"""
Builds a proactive forest for a classification problem.
:param n_estimators: <int> Number of trees in the forest
:param bootstrap: <bool> Whether to use bagging or not
:param max_depth: <int> or <None> Defines the maximum depth of the tree
:param split_chooser: <string> The name of the split chooser:
"best" for selecting the best possible split
"rand" for selecting a random split
:param split_criterion: <string> The name of the split criterion:
"gini" for selecting the Gini criterion
"entropy" for selecting the Entropy criterion
:param min_samples_leaf: <int> Minimum number of instances to place in a leaf
:param feature_selection: <string> The name of the feature selection criteria:
"all" for selecting all features as candidate features
"log" for selecting log(n)+1 as candidate features
"prob" for selecting the candidate features according to its probabilities
:param feature_prob: <list> Feature probabilities
:param min_gain_split: <float> Minimum split gain value to consider splitting a node
:param min_samples_split: <int> Minimum number of instances to consider creating a split
:param alpha: <float> Diversity rate. It can take values from (0, 1]
"""
if 0 < alpha <= 1:
self.alpha = alpha
else:
raise ValueError("The diversity rate can only take values from (0, 1].")
super().__init__(n_estimators=n_estimators,
bootstrap=bootstrap,
max_depth=max_depth,
split_chooser=split_chooser,
split_criterion=split_criterion,
min_samples_leaf=min_samples_leaf,
feature_selection=feature_selection,
feature_prob=feature_prob,
min_gain_split=min_gain_split,
min_samples_split=min_samples_split
)
def fit(self, X, y):
"""
Trains the decision forest classifier with (X, y).
:param X: <numpy ndarray> An array containing the feature vectors
:param y: <numpy array> An array containing the target features
:return: self
"""
X, y = check_X_y(X, y, dtype=None)
self._encoder = LabelEncoder()
y = self._encoder.fit_transform(y)
self._n_instances, self._n_features = X.shape
self._n_classes = utils.count_classes(y)
self._trees = []
if self._bootstrap:
set_generator = BaggingSet(self._n_instances)
else:
set_generator = SimpleSet(self._n_instances)
ledger = FIProbabilityLedger(probabilities=self._feature_prob, n_features=self._n_features, alpha=self.alpha)
self._tree_builder = TreeBuilder(split_criterion=self._split_criterion,
feature_prob=ledger.probabilities,
feature_selection=self._feature_selection,
max_depth=self._max_depth,
min_samples_leaf=self._min_samples_leaf,
min_gain_split=self._min_gain_split,
min_samples_split=self._min_samples_split,
split_chooser=self._split_chooser)
for i in range(1, self._n_estimators+1):
ids = set_generator.training_ids()
X_new = X[ids]
y_new = y[ids]
new_tree = self._tree_builder.build_tree(X_new, y_new, self._n_classes)
if self._bootstrap:
validation_ids = set_generator.oob_ids()
if validation_ids:
new_tree.weight = accuracy_score(y[validation_ids], self._predict_on_tree(X[validation_ids], new_tree))
self._trees.append(new_tree)
set_generator.clear()
rate = i/self._n_estimators
ledger.update_probabilities(new_tree, rate=rate)
self._tree_builder.feature_prob = ledger.probabilities
return self
class DecisionTreeClassifier(BaseEstimator, ClassifierMixin):
def __init__(self,
max_depth=None,
split_chooser='best',
split_criterion='gini',
min_samples_leaf=1,
min_samples_split=2,
feature_selection='all',
feature_prob=None,
min_gain_split=0):
"""
Builds a decision tree for a classification problem.
:param max_depth: <int> or <None> Defines the maximum depth of the tree
:param split_chooser: <string> The name of the split chooser:
"best" for selecting the best possible split
"rand" for selecting a random split
:param split_criterion: <string> The name of the split criterion:
"gini" for selecting the Gini criterion
"entropy" for selecting the Entropy criterion
:param min_samples_leaf: <int> Minimum number of instances to place in a leaf
:param min_samples_split: <int> Minimum number of instances to consider creating a split
:param feature_selection: <string> The name of the feature selection criteria:
"all" for selecting all features as candidate features
"log" for selecting log(n)+1 as candidate features
"prob" for selecting the candidate features according to its probabilities
:param feature_prob: <list> Feature probabilities
:param min_gain_split: <float> Minimum split gain value to consider splitting a node
"""
# Classifier parameters
self._tree = None
self._n_features = None
self._n_instances = None
self._tree_builder = None
self._encoder = None
self._n_classes = None
# Tree parameters
self._max_depth = None
self._min_samples_leaf = None
self._min_samples_split = None
self._feature_prob = None
self._min_gain_split = None
self._split_chooser = None
self._split_criterion = None
self._feature_selection = None
if max_depth is None or max_depth > 0:
self._max_depth = max_depth
else:
raise ValueError('The depth of the tree must be greater than 0.')
if min_samples_leaf is not None and min_samples_leaf > 0:
self._min_samples_leaf = min_samples_leaf
else:
raise ValueError('The minimum number of instances to place in a leaf must be greater than 0.')
if min_samples_split is not None and min_samples_split > 1:
self._min_samples_split = min_samples_split
else:
raise ValueError('The minimum number of instances to make a split must be greater than 1')
if feature_prob is None or (utils.check_array_sum_one(feature_prob) and
utils.check_positive_array(feature_prob)):
self._feature_prob = feature_prob
else:
raise ValueError('The features probabilities must be positive values and the sum must be one')
if min_gain_split is not None and min_gain_split >= 0:
self._min_gain_split = min_gain_split
else:
raise ValueError('The minimum value of gain to make a split must be greater or equal to 0')
if split_chooser is not None:
self._split_chooser = resolve_split_selection(split_chooser)
else:
raise ValueError('The split chooser can not be None.')
if split_criterion is not None:
self._split_criterion = resolve_split_criterion(split_criterion)
else:
raise ValueError('The split criterion can not be None.')
if feature_selection is not None:
self._feature_selection = resolve_feature_selection(feature_selection)
else:
raise ValueError('The feature selection criteria can not be None.')
@property
def max_depth(self):
return self._max_depth
@max_depth.setter
def max_depth(self, max_depth):
self._max_depth = max_depth
@property
def min_samples_leaf(self):
return self._min_samples_leaf
@min_samples_leaf.setter
def min_samples_leaf(self, min_samples_leaf):
self._min_samples_leaf = min_samples_leaf
@property
def min_samples_split(self):
return self._min_samples_split
@min_samples_split.setter
def min_samples_split(self, min_samples_split):
self._min_samples_split = min_samples_split
@property
def feature_prob(self):
return self._feature_prob
@feature_prob.setter
def feature_prob(self, feature_prob):
self._feature_prob = feature_prob
@property
def min_gain_split(self):
return self._min_gain_split
@min_gain_split.setter
def min_gain_split(self, min_gain_split):
self._min_gain_split = min_gain_split
@property
def split_chooser(self):
return self._split_chooser.name
@split_chooser.setter
def split_chooser(self, split_chooser):
self._split_chooser = split_chooser
@property
def split_criterion(self):
return self._split_criterion.name
@split_criterion.setter
def split_criterion(self, split_criterion):
self._split_criterion = split_criterion
@property
def feature_selection(self):
return self._feature_selection.name
@feature_selection.setter
def feature_selection(self, feature_selection):
self._feature_selection = feature_selection
def fit(self, X, y):
"""
Trains the decision tree classifier with (X, y).
:param X: <numpy ndarray> An array containing the feature vectors
:param y: <numpy array> An array containing the target features
:return: self
"""
X, y = check_X_y(X, y, dtype=None)
self._encoder = LabelEncoder()
y = self._encoder.fit_transform(y)
self._n_instances, self._n_features = X.shape
self._n_classes = utils.count_classes(y)
self._tree_builder = TreeBuilder(split_criterion=self._split_criterion,
feature_prob=self._feature_prob,
feature_selection=self._feature_selection,
max_depth=self._max_depth,
min_samples_leaf=self._min_samples_leaf,
min_gain_split=self._min_gain_split,
min_samples_split=self._min_samples_split,
split_chooser=self._split_chooser)
self._tree = self._tree_builder.build_tree(X, y, self._n_classes)
return self
def predict(self, X, check_input=True):
"""
Predicts the classes for the new instances in X.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <numpy array>
"""
if check_input:
X = self._validate_predict(X, check_input=check_input)
sample_size, features_count = X.shape
result = np.zeros(sample_size, dtype=int)
for i in range(sample_size):
x = X[i]
result[i] = self._tree.predict(x)
return self._encoder.inverse_transform(result)
def predict_proba(self, X, check_input=True):
"""
Predicts the class distribution probabilities for the new instances in X.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <numpy array>
"""
if check_input:
X = self._validate_predict(X, check_input=check_input)
sample_size, features_count = X.shape
result = list(range(sample_size))
for i in range(sample_size):
x = X[i]
result[i] = self._tree.predict_proba(x)
return result
def _validate_predict(self, X, check_input):
"""
Validate X whenever one tries to predict or predict_proba.
:param X: <numpy ndarray> An array containing the feature vectors
:param check_input: <bool> If input array must be checked
:return: <bool>
"""
if self._tree is None:
raise NotFittedError("Estimator not fitted, "
"call `fit` before exploiting the model.")
if check_input:
X = check_array(X, dtype=None)
n_features = X.shape[1]
if self._n_features != n_features:
raise ValueError("Number of features of the model must "
" match the input. Model n_features is %s and "
" input n_features is %s "
% (self._n_features, n_features))
return X