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Group aware GPU sketching. (#5551)

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* Group aware GPU weighted sketching.

* Distribute group weights to each data point.
* Relax the test.
* Validate input meta info.
* Fix metainfo copy ctor.
This commit is contained in:
Jiaming Yuan
2020-04-20 17:18:52 +08:00
committed by GitHub
parent 397d8f0ee7
commit 29a4cfe400
9 changed files with 296 additions and 124 deletions
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68
tests/python-gpu/test_gpu_ranking.py
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@@ -1,14 +1,13 @@
import numpy as np
from scipy.sparse import csr_matrix
import xgboost
import os
import math
import unittest
import itertools
import shutil
import urllib.request
import zipfile
class TestRanking(unittest.TestCase):
@classmethod
def setUpClass(cls):
@@ -22,7 +21,7 @@ class TestRanking(unittest.TestCase):
target = cls.dpath + '/MQ2008.zip'
if os.path.exists(cls.dpath) and os.path.exists(target):
print ("Skipping dataset download...")
print("Skipping dataset download...")
else:
urllib.request.urlretrieve(url=src, filename=target)
with zipfile.ZipFile(target, 'r') as f:
@@ -50,17 +49,30 @@ class TestRanking(unittest.TestCase):
cls.qid_test = qid_test
cls.qid_valid = qid_valid
def setup_weighted(x, y, groups):
# Setup weighted data
data = xgboost.DMatrix(x, y)
groups_segment = [len(list(items))
for _key, items in itertools.groupby(groups)]
data.set_group(groups_segment)
n_groups = len(groups_segment)
weights = np.ones((n_groups,))
data.set_weight(weights)
return data
cls.dtrain_w = setup_weighted(x_train, y_train, qid_train)
cls.dtest_w = setup_weighted(x_test, y_test, qid_test)
cls.dvalid_w = setup_weighted(x_valid, y_valid, qid_valid)
# model training parameters
cls.params = {'booster': 'gbtree',
'tree_method': 'gpu_hist',
'gpu_id': 0,
'predictor': 'gpu_predictor'
}
'predictor': 'gpu_predictor'}
cls.cpu_params = {'booster': 'gbtree',
'tree_method': 'hist',
'gpu_id': -1,
'predictor': 'cpu_predictor'
}
'predictor': 'cpu_predictor'}
@classmethod
def tearDownClass(cls):
@@ -81,30 +93,46 @@ class TestRanking(unittest.TestCase):
# specify validations set to watch performance
watchlist = [(cls.dtest, 'eval'), (cls.dtrain, 'train')]
num_trees=2500
check_metric_improvement_rounds=10
num_trees = 2500
check_metric_improvement_rounds = 10
evals_result = {}
cls.params['objective'] = rank_objective
cls.params['eval_metric'] = metric_name
bst = xgboost.train(cls.params, cls.dtrain, num_boost_round=num_trees,
early_stopping_rounds=check_metric_improvement_rounds,
evals=watchlist, evals_result=evals_result)
bst = xgboost.train(
cls.params, cls.dtrain, num_boost_round=num_trees,
early_stopping_rounds=check_metric_improvement_rounds,
evals=watchlist, evals_result=evals_result)
gpu_map_metric = evals_result['train'][metric_name][-1]
evals_result = {}
cls.cpu_params['objective'] = rank_objective
cls.cpu_params['eval_metric'] = metric_name
bstc = xgboost.train(cls.cpu_params, cls.dtrain, num_boost_round=num_trees,
early_stopping_rounds=check_metric_improvement_rounds,
evals=watchlist, evals_result=evals_result)
bstc = xgboost.train(
cls.cpu_params, cls.dtrain, num_boost_round=num_trees,
early_stopping_rounds=check_metric_improvement_rounds,
evals=watchlist, evals_result=evals_result)
cpu_map_metric = evals_result['train'][metric_name][-1]
print("{0} gpu {1} metric {2}".format(rank_objective, metric_name, gpu_map_metric))
print("{0} cpu {1} metric {2}".format(rank_objective, metric_name, cpu_map_metric))
print("gpu best score {0} cpu best score {1}".format(bst.best_score, bstc.best_score))
assert np.allclose(gpu_map_metric, cpu_map_metric, tolerance, tolerance)
assert np.allclose(bst.best_score, bstc.best_score, tolerance, tolerance)
assert np.allclose(gpu_map_metric, cpu_map_metric, tolerance,
tolerance)
assert np.allclose(bst.best_score, bstc.best_score, tolerance,
tolerance)
evals_result_weighted = {}
watchlist = [(cls.dtest_w, 'eval'), (cls.dtrain_w, 'train')]
bst_w = xgboost.train(
cls.params, cls.dtrain_w, num_boost_round=num_trees,
early_stopping_rounds=check_metric_improvement_rounds,
evals=watchlist, evals_result=evals_result_weighted)
weighted_metric = evals_result_weighted['train'][metric_name][-1]
# GPU Ranking is not deterministic due to `AtomicAddGpair`,
# remove tolerance once the issue is resolved.
# https://github.com/dmlc/xgboost/issues/5561
assert np.allclose(bst_w.best_score, bst.best_score,
tolerance, tolerance)
assert np.allclose(weighted_metric, gpu_map_metric,
tolerance, tolerance)
def test_training_rank_pairwise_map_metric(self):
"""