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 __init__(self, canvas: ICanvas):
self._tree: Tree = Tree("Manager", canvas)
self._canvas: ICanvas = canvas
if getattr(y_train, "dtype", None) != DOUBLE or not y_train.flags.contigous:
y_train = np.ascontiguousarray(y_train, dtype=DOUBLE)
max_depth = (np.iinfo(np.int32).max if max_depth is None else max_depth)
max_leaf_nodes = (-1 if max_leaf_nodes is None else max_leaf_nodes)
max_features = max(1, int(np.sqrt(n_features_)))
# Training Tree
criterion = CRITERIA_CLF[criterion](n_outputs, n_classes_)
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
