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Use hist as the default tree method. (#9320)

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This commit is contained in:
Jiaming Yuan
2023-06-27 23:04:24 +08:00
committed by GitHub
parent bc267dd729
commit f4798718c7
10 changed files with 138 additions and 178 deletions
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48
tests/python/test_survival.py
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@@ -1,6 +1,6 @@
import json
import os
from typing import Optional, Tuple
from typing import List, Optional, Tuple, cast
import numpy as np
import pytest
@@ -62,8 +62,8 @@ def test_aft_survival_toy_data(
X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
dmat, y_lower, y_upper = toy_data
# "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
# the corresponding predicted label (y_pred)
# "Accuracy" = the number of data points whose ranged label (y_lower, y_upper)
# includes the corresponding predicted label (y_pred)
acc_rec = []
class Callback(xgb.callback.TrainingCallback):
@@ -71,21 +71,33 @@ def test_aft_survival_toy_data(
super().__init__()
def after_iteration(
self, model: xgb.Booster,
self,
model: xgb.Booster,
epoch: int,
evals_log: xgb.callback.TrainingCallback.EvalsLog
evals_log: xgb.callback.TrainingCallback.EvalsLog,
):
y_pred = model.predict(dmat)
acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper)/len(X))
acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper) / len(X))
acc_rec.append(acc)
return False
evals_result = {}
params = {'max_depth': 3, 'objective': 'survival:aft', 'min_child_weight': 0}
bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=evals_result,
callbacks=[Callback()])
evals_result: xgb.callback.TrainingCallback.EvalsLog = {}
params = {
"max_depth": 3,
"objective": "survival:aft",
"min_child_weight": 0,
"tree_method": "exact",
}
bst = xgb.train(
params,
dmat,
15,
[(dmat, "train")],
evals_result=evals_result,
callbacks=[Callback()],
)
nloglik_rec = evals_result['train']['aft-nloglik']
nloglik_rec = cast(List[float], evals_result["train"]["aft-nloglik"])
# AFT metric (negative log likelihood) improve monotonically
assert all(p >= q for p, q in zip(nloglik_rec, nloglik_rec[:1]))
# "Accuracy" improve monotonically.
@@ -94,15 +106,17 @@ def test_aft_survival_toy_data(
assert acc_rec[-1] == 1.0
def gather_split_thresholds(tree):
if 'split_condition' in tree:
return (gather_split_thresholds(tree['children'][0])
| gather_split_thresholds(tree['children'][1])
| {tree['split_condition']})
if "split_condition" in tree:
return (
gather_split_thresholds(tree["children"][0])
| gather_split_thresholds(tree["children"][1])
| {tree["split_condition"]}
)
return set()
# Only 2.5, 3.5, and 4.5 are used as split thresholds.
model_json = [json.loads(e) for e in bst.get_dump(dump_format='json')]
for tree in model_json:
model_json = [json.loads(e) for e in bst.get_dump(dump_format="json")]
for i, tree in enumerate(model_json):
assert gather_split_thresholds(tree).issubset({2.5, 3.5, 4.5})