989ddd036f
* fixed some endian issues * Use dmlc::ByteSwap() to simplify code * Fix lint check * [CI] Add test for s390x * Download latest CMake on s390x * Fix a bug in my code * Save magic number in dmatrix with byteswap on big-endian machine * Save version in binary with byteswap on big-endian machine * Load scalar with byteswap in MetaInfo * Add a debugging message * Handle arrays correctly when byteswapping * EOF can also be 255 * Handle magic number in MetaInfo carefully * Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model * Handle missing packages in Python tests * Don't use boto3 in model compatibility tests * Add s390 Docker file for local testing * Add model compatibility tests * Add R compatibility test * Revert "Add R compatibility test" This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8. Co-authored-by: Qi Zhang <q.zhang@ibm.com> Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
import xgboost
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import os
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import generate_models as gm
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import testing as tm
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import json
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import zipfile
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import pytest
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import copy
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import urllib.request
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def run_model_param_check(config):
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assert config['learner']['learner_model_param']['num_feature'] == str(4)
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assert config['learner']['learner_train_param']['booster'] == 'gbtree'
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def run_booster_check(booster, name):
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config = json.loads(booster.save_config())
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run_model_param_check(config)
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if name.find('cls') != -1:
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assert (len(booster.get_dump()) == gm.kForests * gm.kRounds *
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gm.kClasses)
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assert float(
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config['learner']['learner_model_param']['base_score']) == 0.5
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assert config['learner']['learner_train_param'][
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'objective'] == 'multi:softmax'
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elif name.find('logit') != -1:
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assert len(booster.get_dump()) == gm.kForests * gm.kRounds
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assert config['learner']['learner_model_param']['num_class'] == str(0)
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assert config['learner']['learner_train_param'][
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'objective'] == 'binary:logistic'
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elif name.find('ltr') != -1:
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assert config['learner']['learner_train_param'][
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'objective'] == 'rank:ndcg'
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else:
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assert name.find('reg') != -1
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assert len(booster.get_dump()) == gm.kForests * gm.kRounds
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assert float(
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config['learner']['learner_model_param']['base_score']) == 0.5
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assert config['learner']['learner_train_param'][
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'objective'] == 'reg:squarederror'
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def run_scikit_model_check(name, path):
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if name.find('reg') != -1:
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reg = xgboost.XGBRegressor()
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reg.load_model(path)
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config = json.loads(reg.get_booster().save_config())
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if name.find('0.90') != -1:
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assert config['learner']['learner_train_param'][
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'objective'] == 'reg:linear'
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else:
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assert config['learner']['learner_train_param'][
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'objective'] == 'reg:squarederror'
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assert (len(reg.get_booster().get_dump()) ==
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gm.kRounds * gm.kForests)
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run_model_param_check(config)
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elif name.find('cls') != -1:
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cls = xgboost.XGBClassifier()
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cls.load_model(path)
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if name.find('0.90') == -1:
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assert len(cls.classes_) == gm.kClasses
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assert len(cls._le.classes_) == gm.kClasses
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assert cls.n_classes_ == gm.kClasses
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assert (len(cls.get_booster().get_dump()) ==
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gm.kRounds * gm.kForests * gm.kClasses), path
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config = json.loads(cls.get_booster().save_config())
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assert config['learner']['learner_train_param'][
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'objective'] == 'multi:softprob', path
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run_model_param_check(config)
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elif name.find('ltr') != -1:
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ltr = xgboost.XGBRanker()
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ltr.load_model(path)
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assert (len(ltr.get_booster().get_dump()) ==
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gm.kRounds * gm.kForests)
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config = json.loads(ltr.get_booster().save_config())
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assert config['learner']['learner_train_param'][
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'objective'] == 'rank:ndcg'
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run_model_param_check(config)
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elif name.find('logit') != -1:
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logit = xgboost.XGBClassifier()
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logit.load_model(path)
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assert (len(logit.get_booster().get_dump()) ==
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gm.kRounds * gm.kForests)
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config = json.loads(logit.get_booster().save_config())
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assert config['learner']['learner_train_param'][
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'objective'] == 'binary:logistic'
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else:
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assert False
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_model_compatibility():
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'''Test model compatibility, can only be run on CI as others don't
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have the credentials.
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'''
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path = os.path.dirname(os.path.abspath(__file__))
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path = os.path.join(path, 'models')
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zip_path, _ = urllib.request.urlretrieve('https://xgboost-ci-jenkins-artifacts.s3-us-west-2' +
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'.amazonaws.com/xgboost_model_compatibility_test.zip')
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with zipfile.ZipFile(zip_path, 'r') as z:
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z.extractall(path)
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models = [
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os.path.join(root, f) for root, subdir, files in os.walk(path)
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for f in files
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if f != 'version'
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]
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assert models
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for path in models:
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name = os.path.basename(path)
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if name.startswith('xgboost-'):
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booster = xgboost.Booster(model_file=path)
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run_booster_check(booster, name)
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# Do full serialization.
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booster = copy.copy(booster)
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run_booster_check(booster, name)
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elif name.startswith('xgboost_scikit'):
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run_scikit_model_check(name, path)
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else:
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assert False
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