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- from sklearn import datasets
- from sklearn.tree import DecisionTreeRegressor
- import numpy as np
- from sklearn.tree._tree import Tree
- from imodels.tree.custom_greedy_tree import CustomDecisionTreeClassifier
- def compute_tree_complexity(tree, complexity_measure='num_rules'):
- """Calculate number of non-leaf nodes
- """
- children_left = tree.children_left
- children_right = tree.children_right
- # num_split_nodes = 0
- complexity = 0
- stack = [(0, 0)] # start with the root node id (0) and its depth (0)
- while len(stack) > 0:
- # `pop` ensures each node is only visited once
- node_id, depth = stack.pop()
- # If the left and right child of a node is not the same we have a split
- # node
- is_split_node = children_left[node_id] != children_right[node_id]
- # If a split node, append left and right children and depth to `stack`
- # so we can loop through them
- if is_split_node:
- if complexity_measure == 'num_rules':
- complexity += 1
- stack.append((children_left[node_id], depth + 1))
- stack.append((children_right[node_id], depth + 1))
- else:
- if complexity_measure != 'num_rules':
- complexity += 1
- return complexity
- def _validate_feature_costs(feature_costs, n_features):
- if feature_costs is None:
- feature_costs = np.ones(n_features, dtype=np.float64)
- else:
- assert len(
- feature_costs) == n_features, f'{len(feature_costs)} != {n_features}'
- np.min(feature_costs) >= 0
- return feature_costs
- def calculate_mean_depth_of_points_in_tree(tree, X, feature_costs=None):
- """Calculate the mean depth of each point in the tree.
- This is the average depth of the path from the root to the point.
- """
- feature_costs = _validate_feature_costs(
- feature_costs, n_features=X.shape[1])
- if isinstance(tree, CustomDecisionTreeClassifier):
- return _mean_depth_custom_tree(tree, X, feature_costs)
- elif hasattr(tree, 'tree_'):
- return _mean_depth_sklearn_tree(tree.tree_, X, feature_costs)
- elif hasattr(tree, 'estimator_'):
- return _mean_depth_sklearn_tree(tree.estimator_.tree_, X, feature_costs)
- else:
- return _mean_depth_coct_tree(tree, X, feature_costs)
- def _mean_depth_custom_tree(custom_tree_, X, feature_costs):
- node = custom_tree_.root
- n_samples = []
- cum_costs = []
- is_leaves = []
- stack = [(node, 0)]
- while len(stack) > 0:
- node, cost = stack.pop()
- n_samples.append(node.num_samples)
- cum_costs.append(cost)
- is_leaves.append(node.left is None and node.right is None)
- if node.left:
- stack.append((node.left, cost + feature_costs[node.feature_index]))
- if node.right:
- stack.append(
- (node.right, cost + feature_costs[node.feature_index]))
- is_leaves = np.array(is_leaves)
- cum_costs = np.array(cum_costs)[is_leaves]
- n_samples = np.array(n_samples)[is_leaves]
- costs = cum_costs * n_samples / np.sum(n_samples)
- return np.sum(costs)
- def _mean_depth_sklearn_tree(tree_, X, feature_costs):
- n_nodes = tree_.node_count
- children_left = tree_.children_left
- children_right = tree_.children_right
- # things to compute
- _node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
- _is_leaves = np.zeros(shape=n_nodes, dtype=bool)
- _cum_costs = np.zeros(shape=n_nodes, dtype=np.float64)
- # start with the root node id (0) and its depth (0) and its cost (0)
- stack = [(0, 0, 0)]
- while len(stack) > 0:
- node_id, depth, cost = stack.pop()
- _node_depth[node_id] = depth
- _cum_costs[node_id] = cost
- is_split_node = children_left[node_id] != children_right[node_id]
- cost += feature_costs[tree_.feature[node_id]]
- if is_split_node:
- stack.append((children_left[node_id], depth + 1, cost))
- stack.append((children_right[node_id], depth + 1, cost))
- else:
- _is_leaves[node_id] = True
- # iterate over leaves and calculate the number of samples in each of them
- n_samples = tree_.n_node_samples
- leaf_samples = n_samples[_is_leaves].astype(np.float64)
- depths = _cum_costs[_is_leaves] * leaf_samples / np.sum(leaf_samples)
- return np.sum(depths)
- def _mean_depth_coct_tree(coct_tree, X, feature_costs):
- indicator = coct_tree.decision_path(X)[:, coct_tree.branch_nodes]
- feature_use_counts = indicator.sum(axis=0)
- n_branch_nodes = indicator.shape[1]
- node_idx_to_feature_cost = {
- i: feature_costs[coct_tree.feature_[i]] for i in range(n_branch_nodes)
- }
- cost = sum(
- [node_idx_to_feature_cost[i] * feature_use_counts[i]
- for i in range(n_branch_nodes)]
- ) / len(X)
- return cost
- def calculate_mean_unique_calls_in_ensemble(ensemble, X, feature_costs=None):
- '''Calculate the mean number of unique calls in the ensemble.
- '''
- if X is None:
- # Should pass X, this is just for testing
- n_features_in = ensemble.n_features_in_
- X = np.random.randint(2, size=(100, n_features_in))
- if feature_costs is None:
- feature_costs = np.ones(n_features_in, dtype=np.float64)
- else:
- assert len(
- feature_costs) == n_features_in, f'{len(feature_costs)} != {n_features_in}'
- np.min(feature_costs) >= 0
- # extract the decision path for each sample
- ests = ensemble.estimators_.flatten()
- feats = [set() for _ in range(len(X))]
- for i in range(len(ests)):
- est = ests[i]
- node_index = est.decision_path(X).toarray()
- feats_est = [
- set([est.tree_.feature[x] for x in np.nonzero(row)[0]])
- for row in node_index
- ]
- for j in range(len(feats)):
- feats[j] = feats[j].union(feats_est[j])
- # -1 for the -2 feature that is always present
- return np.mean([len(f) - 1 for f in feats])
- def compute_mean_llm_calls(model_name, num_prompts, model=None, X=None):
- if model_name == "manual_tree":
- return calculate_mean_depth_of_points_in_tree(model.tree_)
- elif model_name == "manual_hstree":
- return calculate_mean_depth_of_points_in_tree(model.estimator_.tree_)
- elif model_name == "manual_gbdt":
- return calculate_mean_unique_calls_in_ensemble(model, X)
- elif model_name == "manual_tree_cv":
- return calculate_mean_depth_of_points_in_tree(model.best_estimator_.tree_)
- elif model_name in ["manual_single_prompt"]:
- return 1
- elif model_name in ["manual_ensemble", "manual_boosting"]:
- return num_prompts
- else:
- return num_prompts
- if __name__ == '__main__':
- X, y = datasets.fetch_california_housing(return_X_y=True) # regression
- m = DecisionTreeRegressor(random_state=42, max_leaf_nodes=4)
- m.fit(X, y)
- print(compute_tree_complexity(m.tree_, complexity_measure='num_leaves'))
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