151882dd26
* Implement multi-target for hist. - Add new hist tree builder. - Move data fetchers for tests. - Dispatch function calls in gbm base on the tree type.
131 lines
3.6 KiB
Python
131 lines
3.6 KiB
Python
import os
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from typing import Dict
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import numpy as np
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import pytest
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import xgboost
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from xgboost import testing as tm
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pytestmark = tm.timeout(30)
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def comp_training_with_rank_objective(
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dtrain: xgboost.DMatrix,
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dtest: xgboost.DMatrix,
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rank_objective: str,
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metric_name: str,
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tolerance: float = 1e-02,
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) -> None:
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"""Internal method that trains the dataset using the rank objective on GPU and CPU,
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evaluates the metric and determines if the delta between the metric is within the
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tolerance level.
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"""
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# specify validations set to watch performance
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watchlist = [(dtest, "eval"), (dtrain, "train")]
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params = {
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"booster": "gbtree",
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"tree_method": "gpu_hist",
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"gpu_id": 0,
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"predictor": "gpu_predictor",
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}
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num_trees = 100
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check_metric_improvement_rounds = 10
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evals_result: Dict[str, Dict] = {}
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params["objective"] = rank_objective
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params["eval_metric"] = metric_name
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bst = xgboost.train(
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params,
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dtrain,
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num_boost_round=num_trees,
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early_stopping_rounds=check_metric_improvement_rounds,
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evals=watchlist,
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evals_result=evals_result,
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)
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gpu_scores = evals_result["train"][metric_name][-1]
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evals_result = {}
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cpu_params = {
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"booster": "gbtree",
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"tree_method": "hist",
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"gpu_id": -1,
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"predictor": "cpu_predictor",
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}
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cpu_params["objective"] = rank_objective
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cpu_params["eval_metric"] = metric_name
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bstc = xgboost.train(
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cpu_params,
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dtrain,
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num_boost_round=num_trees,
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early_stopping_rounds=check_metric_improvement_rounds,
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evals=watchlist,
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evals_result=evals_result,
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)
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cpu_scores = evals_result["train"][metric_name][-1]
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info = (rank_objective, metric_name)
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assert np.allclose(gpu_scores, cpu_scores, tolerance, tolerance), info
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assert np.allclose(bst.best_score, bstc.best_score, tolerance, tolerance), info
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evals_result_weighted: Dict[str, Dict] = {}
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dtest.set_weight(np.ones((dtest.get_group().size,)))
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dtrain.set_weight(np.ones((dtrain.get_group().size,)))
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watchlist = [(dtest, "eval"), (dtrain, "train")]
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bst_w = xgboost.train(
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params,
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dtrain,
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num_boost_round=num_trees,
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early_stopping_rounds=check_metric_improvement_rounds,
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evals=watchlist,
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evals_result=evals_result_weighted,
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)
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weighted_metric = evals_result_weighted["train"][metric_name][-1]
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tolerance = 1e-5
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assert np.allclose(bst_w.best_score, bst.best_score, tolerance, tolerance)
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assert np.allclose(weighted_metric, gpu_scores, tolerance, tolerance)
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@pytest.mark.parametrize(
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"objective,metric",
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[
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("rank:pairwise", "auc"),
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("rank:pairwise", "ndcg"),
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("rank:pairwise", "map"),
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("rank:ndcg", "auc"),
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("rank:ndcg", "ndcg"),
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("rank:ndcg", "map"),
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("rank:map", "auc"),
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("rank:map", "ndcg"),
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("rank:map", "map"),
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],
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)
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def test_with_mq2008(objective, metric) -> None:
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(
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x_train,
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y_train,
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qid_train,
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x_test,
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y_test,
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qid_test,
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x_valid,
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y_valid,
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qid_valid,
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) = tm.data.get_mq2008(os.path.join(os.path.join(tm.demo_dir(__file__), "rank")))
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if metric.find("map") != -1 or objective.find("map") != -1:
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y_train[y_train <= 1] = 0.0
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y_train[y_train > 1] = 1.0
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y_test[y_test <= 1] = 0.0
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y_test[y_test > 1] = 1.0
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dtrain = xgboost.DMatrix(x_train, y_train, qid=qid_train)
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dtest = xgboost.DMatrix(x_test, y_test, qid=qid_test)
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comp_training_with_rank_objective(dtrain, dtest, objective, metric)
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