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, )