def test_leaves_cutoff_threshold():
estimator = lightgbm.LGBMClassifier(n_estimators=2, random_state=1,
max_depth=1)
utils.train_model_classification_binary(estimator)
assembler = assemblers.LightGBMModelAssembler(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),
ast.SubroutineExpr(
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(23),
ast.NumVal(868.2000000000002),
ast.CompOpType.GT),
ast.NumVal(0.25986931215073095),
ast.NumVal(0.6237178414050242)))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(7),
ast.NumVal(0.05142),
ast.CompOpType.GT),
ast.NumVal(-0.1909605544006228),
ast.NumVal(0.1293965108676673)))),
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_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_leaves_cutoff_threshold():
estimator = lightgbm.LGBMClassifier(n_estimators=2,
random_state=1,
max_depth=1)
utils.train_model_classification_binary(estimator)
assembler = assemblers.LightGBMModelAssembler(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),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(23),
ast.NumVal(868.2000000000002),
ast.CompOpType.GT),
ast.NumVal(0.2762557140263451),
ast.NumVal(0.6399134166614473))),
ast.BinNumOpType.ADD),
ast.SubroutineExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(27),
ast.NumVal(0.14205000000000004),
ast.CompOpType.GT),
ast.NumVal(-0.2139321843285849),
ast.NumVal(0.1151466338793227))),
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)