Source code for Orange.regression.tree

"""Tree inducers: SKL and Orange's own inducer"""

import numpy as np
import scipy.sparse as sp
import sklearn.tree as skl_tree

from Orange.base import TreeModel as TreeModelInterface
from Orange.tree import Node, DiscreteNode, MappedDiscreteNode, \
    NumericNode, TreeModel
from Orange.regression import SklLearner, SklModel, Learner
from Orange.classification import _tree_scorers

__all__ = ["SklTreeRegressionLearner", "TreeLearner"]

[docs] class TreeLearner(Learner): """ Tree inducer with proper handling of nominal attributes and binarization. The inducer can handle missing values of attributes and target. For discrete attributes with more than two possible values, each value can get a separate branch (`binarize=False`), or values can be grouped into two groups (`binarize=True`, default). The tree growth can be limited by the required number of instances for internal nodes and for leafs, and by the maximal depth of the tree. If the tree is not binary, it can contain zero-branches. Parameters ---------- binarize if `True` the inducer will find optimal split into two subsets for values of discrete attributes. If `False` (default), each value gets its branch. min_samples_leaf the minimal number of data instances in a leaf min_samples_split the minimal number of data instances that is split into subgroups max_depth the maximal depth of the tree Returns ------- instance of OrangeTreeModel """ __returns__ = TreeModel # Binarization is exhaustive, so we set a limit on the number of values MAX_BINARIZATION = 16 def __init__( self, *args, binarize=False, min_samples_leaf=1, min_samples_split=2, max_depth=None, **kwargs): super().__init__(*args, **kwargs) self.params = {} self.binarize = self.params['binarity'] = binarize self.min_samples_leaf = self.params['min_samples_leaf'] = min_samples_leaf self.min_samples_split = self.params['min_samples_split'] = min_samples_split self.max_depth = self.params['max_depth'] = max_depth def _select_attr(self, data): """Select the attribute for the next split. Returns ------- tuple with an instance of Node and a numpy array indicating the branch index for each data instance, or -1 if data instance is dropped """ # Prevent false warnings by pylint attr = attr_no = None REJECT_ATTRIBUTE = 0, None, None, 0 def _score_disc(): n_values = len(attr.values) score = _tree_scorers.compute_grouped_MSE( col_x, col_y, n_values, self.min_samples_leaf) # The score is already adjusted for missing attribute values, so # we don't do it here if score == 0: return REJECT_ATTRIBUTE branches = col_x.flatten() branches[np.isnan(branches)] = -1 return score, DiscreteNode(attr, attr_no, None), branches, n_values def _score_disc_bin(): n_values = len(attr.values) if n_values == 2: return _score_disc() score, mapping = _tree_scorers.find_binarization_MSE( col_x, col_y, n_values, self.min_samples_leaf) # The score is already adjusted for missing attribute values, so # we don't do it here if score == 0: return REJECT_ATTRIBUTE mapping, branches = MappedDiscreteNode.branches_from_mapping( col_x, mapping, len(attr.values)) node = MappedDiscreteNode(attr, attr_no, mapping, None) return score, node, branches, 2 def _score_cont(): """Scoring for numeric attributes""" nans = np.sum(np.isnan(col_x)) non_nans = len(col_x) - nans arginds = np.argsort(col_x)[:non_nans] score, cut = _tree_scorers.find_threshold_MSE( col_x, col_y, arginds, self.min_samples_leaf) if score == 0: return REJECT_ATTRIBUTE score *= non_nans / len(col_x) branches = np.full(len(col_x), -1, dtype=int) mask = ~np.isnan(col_x) branches[mask] = (col_x[mask] > cut).astype(int) node = NumericNode(attr, attr_no, cut, None) return score, node, branches, 2 ####################################### # The real _select_attr starts here is_sparse = sp.issparse(data.X) domain = data.domain col_y = data.Y best_score, *best_res = REJECT_ATTRIBUTE best_res = [Node(None, 0, None), ] + best_res[1:] disc_scorer = _score_disc_bin if self.binarize else _score_disc for attr_no, attr in enumerate(domain.attributes): col_x = data[:, attr_no].X if is_sparse: col_x = col_x.toarray() col_x = col_x.reshape((len(data),)) sc, *res = disc_scorer() if attr.is_discrete else _score_cont() if res[0] is not None and sc > best_score: best_score, best_res = sc, res return best_res def _build_tree(self, data, active_inst, level=1): """Induce a tree from the given data Returns: root node (Node)""" node_insts = data[active_inst] if len(node_insts) < self.min_samples_leaf: return None if len(node_insts) < self.min_samples_split or \ self.max_depth is not None and level > self.max_depth: node, branches, n_children = Node(None, None, None), None, 0 else: node, branches, n_children = self._select_attr(node_insts) mean, var = np.mean(node_insts.Y), np.var(node_insts.Y) node.value = np.array([mean, 1 if np.isnan(var) else var]) node.subset = active_inst if branches is not None: node.children = [ self._build_tree(data, active_inst[branches == br], level + 1) for br in range(n_children)] return node
[docs] def fit_storage(self, data): if self.binarize and any( attr.is_discrete and len(attr.values) > self.MAX_BINARIZATION for attr in data.domain.attributes): # No fallback in the script; widgets can prevent this error # by providing a fallback and issue a warning about doing so raise ValueError("Exhaustive binarization does not handle " "attributes with more than {} values". format(self.MAX_BINARIZATION)) active_inst = np.nonzero(~np.isnan(data.Y))[0].astype(np.int32) root = self._build_tree(data, active_inst) if root is None: root = Node(None, 0, np.array([0., 0.])) root.subset = active_inst model = TreeModel(data, root) return model
class SklTreeRegressor(SklModel, TreeModelInterface): pass
[docs] class SklTreeRegressionLearner(SklLearner): __wraps__ = skl_tree.DecisionTreeRegressor __returns__ = SklTreeRegressor name = 'regression tree' supports_weights = True def __init__(self, criterion="squared_error", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features=None, random_state=None, max_leaf_nodes=None, preprocessors=None): super().__init__(preprocessors=preprocessors) self.params = vars()