def test_regression():
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)
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(base_score),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(9.72500038),
ast.CompOpType.GTE),
ast.NumVal(1.67318344),
ast.NumVal(2.92757893)),
ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.94099998),
ast.CompOpType.GTE),
ast.NumVal(3.3400948),
ast.NumVal(1.72118247)),
ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_regression_best_ntree_limit():
base_score = 0.6
estimator = xgboost.XGBRegressor(n_estimators=3,
random_state=1,
max_depth=1,
base_score=base_score)
estimator.best_ntree_limit = 2
utils.train_model_regression(estimator)
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 = 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():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1)
utils.train_model_regression(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.8455),
ast.CompOpType.GT),
ast.NumVal(24.007392728914056),
ast.NumVal(22.35695742616179)),
ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(9.63),
ast.CompOpType.GT),
ast.NumVal(-0.4903836928981587),
ast.NumVal(0.7222498915097475)),
ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_regression_random_forest():
base_score = 0.6
estimator = xgboost.XGBRFRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.train_model_regression(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.6),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.8375001),
ast.CompOpType.GTE),
ast.NumVal(17.3671646), ast.NumVal(9.48354053))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.72500038),
ast.CompOpType.GTE), ast.NumVal(8.31587982),
ast.NumVal(14.7766275))), ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_regression():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1)
utils.train_model_regression(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.918),
ast.CompOpType.GT),
ast.NumVal(24.011454621684155),
ast.NumVal(22.289277544391084))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.63),
ast.CompOpType.GT),
ast.NumVal(-0.49461212269771115),
ast.NumVal(0.7174324413014594))), ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_regression_random_forest():
estimator = lightgbm.LGBMRegressor(boosting_type="rf",
n_estimators=2,
random_state=1,
max_depth=1,
subsample=0.7,
subsample_freq=1)
utils.train_model_regression(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5),
ast.NumVal(6.954000000000001),
ast.CompOpType.GT),
ast.NumVal(37.24347877367631),
ast.NumVal(19.936999995530854))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5),
ast.NumVal(6.971500000000001),
ast.CompOpType.GT),
ast.NumVal(38.48600037864964),
ast.NumVal(20.183783757300255))),
ast.BinNumOpType.ADD), ast.NumVal(0.5), ast.BinNumOpType.MUL))
assert utils.cmp_exprs(actual, expected)
def _get_pickled_trained_model():
estimator = linear_model.LinearRegression()
utils.train_model_regression(estimator)
infile = io.BytesIO()
pickle.dump(estimator, infile)
infile.seek(0)
return infile
def _prepare_pickled_model(tmp_path):
p = tmp_path / "model.pickle"
estimator = linear_model.LinearRegression()
utils.train_model_regression(estimator)
p.write_bytes(pickle.dumps(estimator))
return p
def test_linear_model():
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
feature_selector="shuffle",
booster="gblinear")
utils.train_model_regression(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.00999326),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0520094),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.10447),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.17387),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(0.691745),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(0.296357),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(0.0288206),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(0.417822),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.0551116),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(0.00242449),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.109585),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.00744202),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(0.0731089),
ast.BinNumOpType.MUL),
]
expected = ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
assemblers.utils.apply_op_to_expressions(ast.BinNumOpType.ADD,
ast.NumVal(3.13109),
*feature_weight_mul),
ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
