class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator)):
"""Base class for decision trees.
Warning: This class should not be used directly.
Use derived classes instead.
"""
@abstractmethod
def __init__(self,
criterion,
splitter,
max_depth,
min_samples_split,
min_samples_leaf,
min_weight_fraction_leaf,
max_features,
max_leaf_nodes,
random_state,
min_impurity_decrease,
min_impurity_split,
class_weight=None,
presort=False):
self.criterion = criterion
self.splitter = splitter
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.random_state = random_state
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.class_weight = class_weight
self.presort = presort
def fit(self,
X,
y,
sample_weight=None,
check_input=True,
X_idx_sorted=None):
n_samples, self.n_features_ = X.shape
y = np.atleast_1d(y)
expanded_class_weight = None
# is_classification = is_classifier(self)
# if is_classification:
# y = np.copy(y)
# y_encoded = np.zeros(y.shape, dtype=np.int)
# classes_k , y_encoded = np.unique( y , return_inverse=True)
# y = y_encoded
# else:
self.classes_ = [None]
self.n_classes_ = [1]
# Build tree
criterion = self.criterion
splitter = self.splitter
self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)
builder = DepthFirstTreeBuilder(splitter, self.min_samples_split,
self.min_samples_leaf,
self.min_weight_leaf, self.max_depth,
self.min_impurity_decrease,
self.min_impurity_split)
builder.build(self.tree_, X, y, sample_weight, X_idx_sorted)
return self
def predict(self, X, check_input=True):
proba = self.tree_.predict(X)
return proba[:, 0]
def apply(self, X, check_input=True):
return self.tree_.apply(X)
@property
def feature_importances_(self):
"""Return the feature importances.
The importance of a feature is computed as the (normalized) total
reduction of the criterion brought by that feature.
It is also known as the Gini importance.
Returns
-------
feature_importances_ : array, shape = [n_features]
"""
return self.tree_.compute_feature_importances()
SPLITTERS = DENSE_SPLITTERS
splitter = SPLITTERS[splitter](criterion, max_features, min_samples_leaf,
min_weight_leaf, random_state)
tree_ = Tree(n_features_, n_classes_, n_outputs)
builder = DepthFirstTreeBuilder(splitter, min_samples_split, min_samples_leaf,
min_weight_leaf, max_depth,
min_impurity_decrease, min_impurity_split)
builder.build(tree_, X_train, y_train)
classes_ = classes[0]
n_classes_ = n_classes_[0]
# Prune tree
n_classes_ = np.atleast_1d(n_classes_)
pruned_tree = Tree(n_features_, n_classes_, n_outputs)
_build_pruned_tree_ccp(pruned_tree, tree_, ccp_alpha)
tree_ = pruned_tree
# =============================================================================
# Decision Tree Algorithm - predicting
# =============================================================================
X_test = check_array(X_test, dtype=DTYPE, accept_sparse="csr")
proba = tree_.predict(X_test)
n_samples = X_test.shape[0]
predictions = classes_.take(np.argmax(proba, axis=1), axis=0)
metrics.accuracy_score(y_test, predictions)
