def test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(1),
ast.BinNumExpr(
ast.NumVal(1),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.0026305187),
ast.NumVal(4.0880438137)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(14.895),
ast.CompOpType.GT),
ast.NumVal(-0.0412703078),
ast.NumVal(0.0208393767)), ast.BinNumOpType.ADD),
ast.BinNumOpType.SUB)), ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_log1p_exp_output_transform():
estimator = lgb.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6622623010380544),
ast.NumVal(0.6684065452877841)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1404975120475147),
ast.NumVal(0.14535916856709272)), ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(1),
ast.BinNumExpr(
ast.NumVal(1),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.0050691250),
ast.NumVal(4.0914737728)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(15.065),
ast.CompOpType.GT),
ast.NumVal(-0.0420531079),
ast.NumVal(0.0202891577)), ast.BinNumOpType.ADD),
ast.BinNumOpType.SUB)), ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(1),
ast.BinNumExpr(
ast.NumVal(1),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.002437528537838),
ast.NumVal(4.090096709787509)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(14.895),
ast.CompOpType.GT),
ast.NumVal(-0.0417499606641773),
ast.NumVal(0.02069953712454655)),
ast.BinNumOpType.ADD), ast.BinNumOpType.SUB)),
ast.BinNumOpType.ADD), ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_log1p_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6623502468),
ast.NumVal(0.6683497987)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1405181490),
ast.NumVal(0.1453602134)),
ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def regression_bounded(model, test_fraction=0.02):
return (
model,
utils.get_bounded_regression_model_trainer(test_fraction),
REGRESSION,
)