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- from sklearn.tree._tree import Tree
- from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
- from sklearn.base import ClassifierMixin, RegressorMixin
- from sklearn import __version__
- from collections import namedtuple
- import pandas as pd
- import numpy as np
- from collections import namedtuple
- from sklearn import __version__
- from sklearn.base import ClassifierMixin, RegressorMixin
- from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
- from sklearn.tree._tree import Tree
- import imodels.util.tree
- class Node:
- def __init__(self, impurity, num_samples, num_samples_per_class, predicted_probs):
- self.impurity = impurity
- self.num_samples = num_samples
- self.num_samples_per_class = num_samples_per_class
- self.predicted_probs = predicted_probs
- self.feature_index = 0
- self.threshold = 0
- self.left = None
- self.right = None
- class CustomDecisionTreeClassifier(ClassifierMixin):
- def __init__(self, max_leaf_nodes=None, impurity_func='gini'):
- self.root = None
- self.max_leaf_nodes = max_leaf_nodes
- self.impurity_func = impurity_func
- def fit(self, X, y, feature_costs=None):
- self.n_classes_ = len(set(y))
- self.n_features = X.shape[1]
- self.feature_costs_ = imodels.util.tree._validate_feature_costs(
- feature_costs, self.n_features)
- self.root = self._grow_tree(X, y)
- def _grow_tree(self, X, y):
- stack = []
- num_samples_per_class = np.array([np.sum(y == i)
- for i in range(self.n_classes_)])
- root = Node(
- impurity=self._calc_impurity(y),
- num_samples=y.size,
- num_samples_per_class=num_samples_per_class,
- predicted_probs=num_samples_per_class / y.size,
- )
- root.impurity_reduction = self._best_split(X, y)[-1]
- stack.append((root, X, y))
- self.n_splits = 0
- while stack:
- node, X_node, y_node = stack.pop()
- idx, thr, _ = self._best_split(X_node, y_node)
- if idx is not None:
- self.n_splits += 1
- indices_left = X_node[:, idx] < thr
- X_left, y_left = X_node[indices_left], y_node[indices_left]
- X_right, y_right = X_node[~indices_left], y_node[~indices_left]
- node.feature_index = idx
- node.threshold = thr
- num_samples_per_class_left = np.array([
- np.sum(y_left == i) for i in range(self.n_classes_)])
- node.left = Node(
- impurity=self._calc_impurity(y_left),
- num_samples=y_left.size,
- num_samples_per_class=num_samples_per_class_left,
- predicted_probs=num_samples_per_class_left / y_left.size,
- )
- # some redundant calculation going on here, but it's okay....
- node.left.impurity_reduction = self._best_split(
- X_left, y_left)[-1]
- num_samples_per_class_right = np.array([
- np.sum(y_right == i) for i in range(self.n_classes_)])
- node.right = Node(
- impurity=self._calc_impurity(y_right),
- num_samples=y_right.size,
- num_samples_per_class=num_samples_per_class_right,
- predicted_probs=num_samples_per_class_right / y_right.size,
- )
- node.right.impurity_reduction = self._best_split(
- X_right, y_right)[-1]
- stack.append((node.right, X_right, y_right))
- stack.append((node.left, X_left, y_left))
- # early stop
- if self.max_leaf_nodes and self.n_splits >= self.max_leaf_nodes - 1:
- return root
- # sort stack by impurity_reduction
- stack = sorted(
- stack, key=lambda x: x[0].impurity_reduction, reverse=True)
- return root
- def _best_split(self, X, y):
- n = y.size
- if n <= 1:
- return None, None, 0
- orig_impurity = self._gini(y)
- impurity_reduction = 0
- best_impurity_reduction = 0
- best_idx, best_thr = None, None
- # loop over features
- for idx in range(self.n_features):
- thresholds, y_classes = zip(*sorted(zip(X[:, idx], y)))
- y_classes = np.array(y_classes)
- # consider every point where threshold value changes
- idx_thresholds = (1 + np.where(np.diff(thresholds))[0]).tolist()
- for i in idx_thresholds:
- # calculate impurity for left and right
- y_left = y_classes[:i]
- y_right = y_classes[i:]
- impurity_reduction = orig_impurity - (y_left.size * self._gini(y_left) +
- y_right.size * self._gini(y_right)) / n
- if self.impurity_func == 'information_gain_ratio':
- split_info = - (y_left.size / n * np.log2(y_left.size / n)) - (
- y_right.size / n * np.log2(y_right.size / n))
- if ~np.isnan(split_info):
- impurity_reduction = impurity_reduction / split_info
- if self.impurity_func == 'cost_information_gain_ratio':
- impurity_reduction /= self.feature_costs_[idx]
- if impurity_reduction > best_impurity_reduction:
- best_impurity_reduction = impurity_reduction
- best_idx = idx
- best_thr = (thresholds[i] + thresholds[i - 1]) / 2
- return best_idx, best_thr, impurity_reduction
- def _calc_impurity(self, y):
- if self.impurity_func == 'gini':
- return self._gini(y)
- elif self.impurity_func in ['entropy', 'information_gain_ratio', 'cost_information_gain_ratio']:
- return self._entropy(y)
- def _gini(self, y):
- n = y.size
- return 1.0 - sum((np.sum(y == c) / n) ** 2 for c in range(self.n_classes_))
- def _entropy(self, y):
- n = y.size
- return -sum((np.sum(y == c) / n) * np.log2(np.sum(y == c) / n) for c in range(self.n_classes_))
- def predict(self, X):
- return np.argmax(self.predict_proba(X), axis=1)
- def predict_proba(self, X):
- return np.array([self._predict_single_proba(x) for x in X])
- def _predict_single_proba(self, x):
- node = self.root
- while node.left or node.right:
- if x[node.feature_index] < node.threshold and node.left:
- node = node.left
- elif node.right:
- node = node.right
- return node.predicted_probs
- if __name__ == '__main__':
- from sklearn.datasets import load_breast_cancer, load_iris
- from sklearn.model_selection import train_test_split
- from sklearn.metrics import accuracy_score
- # data = load_breast_cancer()
- data = load_iris()
- X = data.data
- y = data.target
- # print(np.unique(y))
- X_train, X_test, y_train, y_test = train_test_split(
- X, y, random_state=42, test_size=0.5)
- m = CustomDecisionTreeClassifier(
- # max_leaf_nodes=20,
- impurity_func='cost_information_gain_ratio')
- m.fit(X_train, y_train)
- y_pred = m.predict(X_test)
- print("Accuracy:", accuracy_score(y_test, y_pred))
- print('n_nodes', m.n_splits + 1)
- print('shapes', m.predict_proba(X_test).shape, m.predict(X_test).shape)
- cost = imodels.util.tree.calculate_mean_depth_of_points_in_custom_tree(m)
- print('Cost', cost)
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