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)
utils.get_regression_model_trainer()(estimator)
estimator.get_booster().best_ntree_limit = 2
assembler = assemblers.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():
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)
assembler = assemblers.XGBoostModelAssemblerSelector(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_random_forest():
base_score = 0.6
estimator = xgboost.XGBRFRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.get_regression_model_trainer()(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_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.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_random_forest():
base_score = 0.6
estimator = xgboost.XGBRFRegressor(n_estimators=2,
random_state=1,
max_depth=1,
base_score=base_score)
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(base_score),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5),
ast.NumVal(6.94099998), ast.CompOpType.GTE),
ast.NumVal(18.38124656677246), ast.NumVal(9.772658348083496)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.539999961853027),
ast.CompOpType.GTE),
ast.NumVal(8.342499732971191), ast.NumVal(15.027499198913574)),
ast.BinNumOpType.ADD), 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.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
exponent = ast.ExpExpr(ast.SubroutineExpr(
ast.BinNumExpr(ast.NumVal(0.5), ast.SubroutineExpr(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.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.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():
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)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(base_score),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(9.725),
ast.CompOpType.GTE),
ast.NumVal(4.98425627),
ast.NumVal(8.75091362)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.941),
ast.CompOpType.GTE),
ast.NumVal(8.34557438),
ast.NumVal(3.9141891)),
ast.BinNumOpType.ADD),
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.get_regression_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(base_score),
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.94099998),
ast.CompOpType.GTE),
ast.NumVal(18.1008453),
ast.NumVal(9.60167599)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.79699993),
ast.CompOpType.GTE),
ast.NumVal(17.780262),
ast.NumVal(9.51712894)),
ast.BinNumOpType.ADD),
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.get_classification_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(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.get_classification_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
estimator_exp_class1 = ast.ExpExpr(ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.45000005),
ast.CompOpType.GTE), ast.NumVal(-0.219950154),
ast.NumVal(0.430243909)), ast.BinNumOpType.ADD),
to_reuse=True)
estimator_exp_class2 = ast.ExpExpr(ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.45000005),
ast.CompOpType.GTE), ast.NumVal(0.103241883),
ast.NumVal(-0.215121984)), ast.BinNumOpType.ADD),
to_reuse=True)
estimator_exp_class3 = ast.ExpExpr(ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(3), ast.NumVal(1.6500001),
ast.CompOpType.GTE), ast.NumVal(0.402985066),
ast.NumVal(-0.193333372)), 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.XGBoostModelAssemblerSelector(
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_linear_model():
# Default updater ("shotgun") is nondeterministic
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
updater="coord_descent",
feature_selector="shuffle",
booster="gblinear")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.151436),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.084474),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.10035),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(4.71537),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(1.39071),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(0.330592),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(0.0610453),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(0.476255),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(-0.0677851),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.000543615),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0717916),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.010832),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.139375),
ast.BinNumOpType.MUL),
]
expected = ast.BinNumExpr(
ast.NumVal(0.5),
assemblers.utils.apply_op_to_expressions(ast.BinNumOpType.ADD,
ast.NumVal(11.1287),
*feature_weight_mul),
ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_linear_model():
# Default updater ("shotgun") is nondeterministic
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
updater="coord_descent",
feature_selector="shuffle",
booster="gblinear")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.154567),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0815865),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.0979713),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(4.80472),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(1.35478),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(0.327222),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(0.0610654),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(0.46989),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(-0.0674318),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.000506212),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0732867),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0108842),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.140096),
ast.BinNumOpType.MUL),
]
expected = ast.BinNumExpr(
ast.NumVal(0.5),
assemblers.utils.apply_op_to_expressions(ast.BinNumOpType.ADD,
ast.NumVal(11.138),
*feature_weight_mul),
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.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
num_expr = ast.BinNumExpr(ast.NumVal(0.5),
ast.NumVal(-7.663454759665456e-09),
ast.BinNumOpType.ADD)
expected = ast.SoftmaxExpr([num_expr] * 3)
assert utils.cmp_exprs(actual, expected)
def test_linear_model():
estimator = xgboost.XGBRegressor(n_estimators=2,
random_state=1,
feature_selector="shuffle",
booster="gblinear")
utils.get_regression_model_trainer()(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)
def test_binary_classification():
estimator = xgboost.XGBClassifier(n_estimators=2, random_state=1,
max_depth=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
sigmoid = 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(20),
ast.NumVal(16.7950001),
ast.CompOpType.GTE),
ast.NumVal(-0.519171),
ast.NumVal(0.49032259)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(27),
ast.NumVal(0.142349988),
ast.CompOpType.GTE),
ast.NumVal(-0.443304211),
ast.NumVal(0.391988248)),
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.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
sigmoid = 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(20),
ast.NumVal(16.795),
ast.CompOpType.GTE),
ast.NumVal(-0.5178947448730469),
ast.NumVal(0.4880000054836273)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(27),
ast.NumVal(0.142349988),
ast.CompOpType.GTE),
ast.NumVal(-0.4447747468948364),
ast.NumVal(0.39517202973365784)),
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_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)
utils.get_classification_model_trainer()(estimator)
estimator.get_booster().best_ntree_limit = 1
assembler = assemblers.XGBoostModelAssemblerSelector(estimator)
actual = assembler.assemble()
estimator_class1 = ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.450000047683716),
ast.CompOpType.GTE), ast.NumVal(-0.21995015442371368),
ast.NumVal(0.43024390935897827)), ast.BinNumOpType.ADD)
estimator_class2 = ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2), ast.NumVal(2.450000047683716),
ast.CompOpType.GTE), ast.NumVal(0.10324188321828842),
ast.NumVal(-0.21512198448181152)), ast.BinNumOpType.ADD)
estimator_class3 = ast.BinNumExpr(
ast.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(3), ast.NumVal(1.6500000953674316),
ast.CompOpType.GTE), ast.NumVal(0.4029850661754608),
ast.NumVal(-0.19333337247371674)), ast.BinNumOpType.ADD)
expected = ast.SoftmaxExpr(
[estimator_class1, estimator_class2, estimator_class3])
assert utils.cmp_exprs(actual, expected)
