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_multi_class():
estimator = xgboost.XGBClassifier(n_estimators=1, random_state=1,
max_depth=1)
estimator.fit(np.array([[1], [2], [3]]), np.array([1, 2, 3]))
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
exponent = ast.ExpExpr(
ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.NumVal(0.0),
ast.BinNumOpType.ADD)),
to_reuse=True)
exponent_sum = ast.BinNumExpr(
ast.BinNumExpr(exponent, exponent, ast.BinNumOpType.ADD),
exponent,
ast.BinNumOpType.ADD,
to_reuse=True)
softmax = ast.BinNumExpr(exponent, exponent_sum, ast.BinNumOpType.DIV)
expected = ast.VectorVal([softmax] * 3)
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_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_multi_class_best_ntree_limit():
base_score = 0.5
estimator = xgboost.XGBClassifier(n_estimators=100,
random_state=1,
max_depth=1,
base_score=base_score)
estimator.best_ntree_limit = 1
utils.train_model_classification(estimator)
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
estimator_exp_class1 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2),
ast.NumVal(2.45000005), ast.CompOpType.GTE),
ast.NumVal(-0.0733167157), ast.NumVal(0.143414631))),
ast.BinNumOpType.ADD)),
to_reuse=True)
estimator_exp_class2 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.45000005),
ast.CompOpType.GTE), ast.NumVal(0.0344139598),
ast.NumVal(-0.0717073306))), ast.BinNumOpType.ADD)),
to_reuse=True)
estimator_exp_class3 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(3), ast.NumVal(1.6500001),
ast.CompOpType.GTE), ast.NumVal(0.13432835),
ast.NumVal(-0.0644444525))), ast.BinNumOpType.ADD)),
to_reuse=True)
exp_sum = ast.BinNumExpr(ast.BinNumExpr(estimator_exp_class1,
estimator_exp_class2,
ast.BinNumOpType.ADD),
estimator_exp_class3,
ast.BinNumOpType.ADD,
to_reuse=True)
expected = ast.VectorVal([
ast.BinNumExpr(estimator_exp_class1, exp_sum, ast.BinNumOpType.DIV),
ast.BinNumExpr(estimator_exp_class2, exp_sum, ast.BinNumOpType.DIV),
ast.BinNumExpr(estimator_exp_class3, exp_sum, ast.BinNumOpType.DIV)
])
assert utils.cmp_exprs(actual, expected)
def test_multi_class_best_ntree_limit():
base_score = 0.5
estimator = xgboost.XGBClassifier(n_estimators=100,
random_state=1,
max_depth=1,
base_score=base_score)
estimator.best_ntree_limit = 1
utils.train_model_classification(estimator)
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
estimator_exp_class1 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.5999999),
ast.CompOpType.GTE), ast.NumVal(-0.0731707439),
ast.NumVal(0.142857149)), ast.BinNumOpType.ADD)),
to_reuse=True)
estimator_exp_class2 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.5999999),
ast.CompOpType.GTE), ast.NumVal(0.0341463387),
ast.NumVal(-0.0714285821)), ast.BinNumOpType.ADD)),
to_reuse=True)
estimator_exp_class3 = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(4.85000038),
ast.CompOpType.GTE), ast.NumVal(0.129441619),
ast.NumVal(-0.0681440532)), ast.BinNumOpType.ADD)),
to_reuse=True)
exp_sum = ast.BinNumExpr(ast.BinNumExpr(estimator_exp_class1,
estimator_exp_class2,
ast.BinNumOpType.ADD),
estimator_exp_class3,
ast.BinNumOpType.ADD,
to_reuse=True)
expected = ast.VectorVal([
ast.BinNumExpr(estimator_exp_class1, exp_sum, ast.BinNumOpType.DIV),
ast.BinNumExpr(estimator_exp_class2, exp_sum, ast.BinNumOpType.DIV),
ast.BinNumExpr(estimator_exp_class3, exp_sum, ast.BinNumOpType.DIV)
])
assert utils.cmp_exprs(actual, expected)
def test_leaves_cutoff_threshold():
estimator = xgboost.XGBClassifier(n_estimators=2,
random_state=1,
max_depth=1)
utils.train_model_classification_binary(estimator)
assembler = assemblers.XGBoostModelAssembler(estimator,
leaves_cutoff_threshold=1)
actual = assembler.assemble()
sigmoid = ast.BinNumExpr(
ast.NumVal(1),
ast.BinNumExpr(
ast.NumVal(1),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(-0.0),
ast.SubroutineExpr(
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(20),
ast.NumVal(16.7950001),
ast.CompOpType.GTE),
ast.NumVal(-0.173057005),
ast.NumVal(0.163440868)))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(27),
ast.NumVal(0.142349988),
ast.CompOpType.GTE),
ast.NumVal(-0.161026895),
ast.NumVal(0.149405137)))),
ast.BinNumOpType.ADD)), ast.BinNumOpType.SUB)),
ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV,
to_reuse=True)
expected = ast.VectorVal([
ast.BinNumExpr(ast.NumVal(1), sigmoid, ast.BinNumOpType.SUB), sigmoid
])
assert utils.cmp_exprs(actual, expected)
def test_binary_classification():
estimator = xgboost.XGBClassifier(n_estimators=2, random_state=1,
max_depth=1)
utils.train_model_classification_binary(estimator)
assembler = assemblers.XGBoostModelAssembler(estimator)
actual = assembler.assemble()
sigmoid = ast.BinNumExpr(
ast.NumVal(1),
ast.BinNumExpr(
ast.NumVal(1),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0),
ast.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(-0.0),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(20),
ast.NumVal(16.7950001),
ast.CompOpType.GTE),
ast.NumVal(-0.17062147),
ast.NumVal(0.1638484)),
ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(27),
ast.NumVal(0.142349988),
ast.CompOpType.GTE),
ast.NumVal(-0.16087772),
ast.NumVal(0.149866998)),
ast.BinNumOpType.ADD)),
ast.BinNumOpType.SUB)),
ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV,
to_reuse=True)
expected = ast.VectorVal([
ast.BinNumExpr(ast.NumVal(1), sigmoid, ast.BinNumOpType.SUB),
sigmoid])
assert utils.cmp_exprs(actual, expected)
