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import copy
import json
import os
import urllib.request
import zipfile
import generate_models as gm
import pytest
import xgboost
from xgboost import testing as tm
def run_model_param_check(config):
assert config['learner']['learner_model_param']['num_feature'] == str(4)
assert config['learner']['learner_train_param']['booster'] == 'gbtree'
def run_booster_check(booster, name):
config = json.loads(booster.save_config())
run_model_param_check(config)
if name.find('cls') != -1:
assert (len(booster.get_dump()) == gm.kForests * gm.kRounds *
gm.kClasses)
assert float(
config['learner']['learner_model_param']['base_score']) == 0.5
assert config['learner']['learner_train_param'][
'objective'] == 'multi:softmax'
elif name.find('logitraw') != -1:
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert config['learner']['learner_model_param']['num_class'] == str(0)
assert config['learner']['learner_train_param']['objective'] == 'binary:logitraw'
elif name.find('logit') != -1:
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert config['learner']['learner_model_param']['num_class'] == str(0)
assert config['learner']['learner_train_param'][
'objective'] == 'binary:logistic'
elif name.find('ltr') != -1:
assert config['learner']['learner_train_param'][
'objective'] == 'rank:ndcg'
else:
assert name.find('reg') != -1
assert len(booster.get_dump()) == gm.kForests * gm.kRounds
assert float(
config['learner']['learner_model_param']['base_score']) == 0.5
assert config['learner']['learner_train_param'][
'objective'] == 'reg:squarederror'
def run_scikit_model_check(name, path):
if name.find('reg') != -1:
reg = xgboost.XGBRegressor()
reg.load_model(path)
config = json.loads(reg.get_booster().save_config())
if name.find('0.90') != -1:
assert config['learner']['learner_train_param'][
'objective'] == 'reg:linear'
else:
assert config['learner']['learner_train_param'][
'objective'] == 'reg:squarederror'
assert (len(reg.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
run_model_param_check(config)
elif name.find('cls') != -1:
cls = xgboost.XGBClassifier()
cls.load_model(path)
if name.find('0.90') == -1:
assert len(cls.classes_) == gm.kClasses
assert cls.n_classes_ == gm.kClasses
assert (len(cls.get_booster().get_dump()) ==
gm.kRounds * gm.kForests * gm.kClasses), path
config = json.loads(cls.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'multi:softprob', path
run_model_param_check(config)
elif name.find('ltr') != -1:
ltr = xgboost.XGBRanker()
ltr.load_model(path)
assert (len(ltr.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(ltr.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'rank:ndcg'
run_model_param_check(config)
elif name.find('logitraw') != -1:
logit = xgboost.XGBClassifier()
logit.load_model(path)
assert (len(logit.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(logit.get_booster().save_config())
assert config['learner']['learner_train_param']['objective'] == 'binary:logitraw'
elif name.find('logit') != -1:
logit = xgboost.XGBClassifier()
logit.load_model(path)
assert (len(logit.get_booster().get_dump()) ==
gm.kRounds * gm.kForests)
config = json.loads(logit.get_booster().save_config())
assert config['learner']['learner_train_param'][
'objective'] == 'binary:logistic'
else:
assert False
@pytest.mark.skipif(**tm.no_sklearn())
def test_model_compatibility():
"""Test model compatibility, can only be run on CI as others don't
have the credentials.
"""
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, "models")
if not os.path.exists(path):
zip_path, _ = urllib.request.urlretrieve(
"https://xgboost-ci-jenkins-artifacts.s3-us-west-2"
+ ".amazonaws.com/xgboost_model_compatibility_test.zip"
)
with zipfile.ZipFile(zip_path, "r") as z:
z.extractall(path)
models = [
os.path.join(root, f)
for root, subdir, files in os.walk(path)
for f in files
if f != "version"
]
assert models
for path in models:
name = os.path.basename(path)
if name.startswith("xgboost-"):
booster = xgboost.Booster(model_file=path)
run_booster_check(booster, name)
# Do full serialization.
booster = copy.copy(booster)
run_booster_check(booster, name)
elif name.startswith("xgboost_scikit"):
run_scikit_model_check(name, path)
else:
assert False
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