def test_regression_saved_without_feature_names():
base_score = 0.6
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.train_model_regression(estimator)
with utils.tmp_dir() as tmp_dirpath:
filename = os.path.join(tmp_dirpath, "tmp.file")
estimator.save_model(filename)
estimator = xgboost.XGBRegressor(base_score=base_score)
estimator.load_model(filename)
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(base_score),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(9.72500038),
ast.CompOpType.GTE),
ast.NumVal(1.6614188), ast.NumVal(2.91697121))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.94099998),
ast.CompOpType.GTE), ast.NumVal(3.33810854),
ast.NumVal(1.71813202))), ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_regression_saved_without_feature_names():
base_score = 0.6
estimator = xgb.XGBRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.get_regression_model_trainer()(estimator)
with utils.tmp_dir() as tmp_dirpath:
filename = tmp_dirpath / "tmp.file"
estimator.save_model(filename)
estimator = xgb.XGBRegressor(base_score=base_score)
estimator.load_model(filename)
assembler = XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(base_score),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(9.72500038), ast.CompOpType.GTE),
ast.NumVal(4.995625019073486), ast.NumVal(8.715502738952637)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5),
ast.NumVal(6.94099998), ast.CompOpType.GTE),
ast.NumVal(8.309040069580078), ast.NumVal(3.930694580078125)),
ast.BinNumOpType.ADD), ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_regression_saved_without_feature_names():
base_score = 0.6
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.get_regression_model_trainer()(estimator)
with utils.tmp_dir() as tmp_dirpath:
filename = os.path.join(tmp_dirpath, "tmp.file")
estimator.save_model(filename)
estimator = xgboost.XGBRegressor(base_score=base_score)
estimator.load_model(filename)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(base_score),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.72500038),
ast.CompOpType.GTE), ast.NumVal(4.98425627),
ast.NumVal(8.75091362)), ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.94099998),
ast.CompOpType.GTE), ast.NumVal(8.34557438),
ast.NumVal(3.9141891)), ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def prepare_then_cleanup(self):
with tmp_dir() as tmp_dirpath:
self._resource_tmp_dir = tmp_dirpath
self.prepare()
try:
yield
finally:
self._resource_tmp_dir = None
def global_tmp_dir():
with tmp_dir() as directory:
yield directory
