def test_bin_class_sigmoid_output_transform():
estimator = lightgbm.LGBMClassifier(n_estimators=1,
random_state=1,
max_depth=1,
sigmoid=0.5)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(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.NumVal(0.5),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(20),
ast.NumVal(16.795),
ast.CompOpType.GT),
ast.NumVal(0.5500419366076967),
ast.NumVal(1.2782342253678096)),
ast.BinNumOpType.MUL), 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 = lightgbm.LGBMClassifier(n_estimators=2,
random_state=1,
max_depth=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(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),
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.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_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.SubroutineExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(-0.0),
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.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 = lightgbm.LGBMClassifier(n_estimators=2,
random_state=1,
max_depth=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(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(23),
ast.NumVal(868.2000000000002),
ast.CompOpType.GT),
ast.NumVal(0.26400127816506497),
ast.NumVal(0.633133056485969)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(22),
ast.NumVal(105.95000000000002),
ast.CompOpType.GT),
ast.NumVal(-0.18744882409486507),
ast.NumVal(0.13458899352064668)),
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 = lightgbm.LGBMClassifier(n_estimators=2,
random_state=1,
max_depth=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(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.GT),
ast.NumVal(0.27502096830384837),
ast.NumVal(0.6391171126839048)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(27),
ast.NumVal(0.14205),
ast.CompOpType.GT),
ast.NumVal(-0.21340153096570616),
ast.NumVal(0.11583109256834748)),
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_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(0.16218889967390476),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(0.10012761963766906),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.6289276652681673),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(0.17618420156072845),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(4),
ast.NumVal(0.0010492096607182045),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(5),
ast.NumVal(-0.0029135563693806913),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(6),
ast.NumVal(-0.005923882409142498),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(7),
ast.NumVal(-0.0023293599172479755),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(8),
ast.NumVal(0.0020808828960210517),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(9),
ast.NumVal(0.0009846430705550103),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(10),
ast.NumVal(0.0010399810925427265),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(11),
ast.NumVal(0.011203056917272093),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(12),
ast.NumVal(-0.007271351370867731),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(13),
ast.NumVal(-0.26333437096804224),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(14),
ast.NumVal(1.8533543368532444e-05),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(15),
ast.NumVal(-0.0008266341686278445),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(16),
ast.NumVal(-0.0011090316301215724),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(17),
ast.NumVal(-0.0001910857095336291),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(18),
ast.NumVal(0.00010735116208006556),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(19),
ast.NumVal(-4.076097659514017e-05),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(20),
ast.NumVal(0.15300712110146406),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(21),
ast.NumVal(0.06316277258339074),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(22),
ast.NumVal(0.495291178977687),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(23),
ast.NumVal(-0.29589136204657845),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(24),
ast.NumVal(0.000771932729567487),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(25),
ast.NumVal(-0.011877978242492428),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(26),
ast.NumVal(-0.01678004536869617),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(27),
ast.NumVal(-0.004070431062579625),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(28),
ast.NumVal(0.001158641497209262),
ast.BinNumOpType.MUL),
ast.BinNumExpr(
ast.FeatureRef(29),
ast.NumVal(0.00010737287732588742),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD,
ast.NumVal(0.0),
*feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def classification_binary(model, test_fraction=0.02):
return (
model,
utils.get_binary_classification_model_trainer(test_fraction),
CLASSIFICATION,
)
regression(linear_model.Ridge(random_state=RANDOM_SEED)),
regression(linear_model.RidgeCV()),
regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)),
regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)),
regression(linear_model.TweedieRegressor(power=0.0)),
regression(linear_model.TweedieRegressor(power=1.0)),
regression(linear_model.TweedieRegressor(power=1.5)),
regression(linear_model.TweedieRegressor(power=2.0)),
regression(linear_model.TweedieRegressor(power=3.0)),
# Statsmodels Linear Regression
classification_binary(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(fit_constrained=dict(constraints=(
np.eye(utils.get_binary_classification_model_trainer().
X_train.shape[-1])[0], [1]))))),
classification_binary(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
classification_binary(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(
family=sm.families.Binomial(sm.families.links.cauchy())),
fit=dict(maxiter=2)))),
classification_binary(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(
family=sm.families.Binomial(sm.families.links.cloglog())),
def test_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.1605265174),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.1045225083),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.6237391536),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.1680225811),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(0.0011013688),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(-0.0027528486),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0058878714),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.0023719811),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.0019944105),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(0.0009924456),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0003994860),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0124697033),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.0123674096),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(13), ast.NumVal(-0.2844204905),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(14), ast.NumVal(0.0000273704),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(15), ast.NumVal(-0.0007498013),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(16), ast.NumVal(-0.0010784399),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(17), ast.NumVal(-0.0001848694),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(18), ast.NumVal(0.0000632254),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(19), ast.NumVal(-0.0000369618),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(20), ast.NumVal(0.1520223021),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(21), ast.NumVal(0.0925348635),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(22), ast.NumVal(0.4861047372),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(23), ast.NumVal(-0.2798670185),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(24), ast.NumVal(0.0009925506),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(25), ast.NumVal(-0.0103414976),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(26), ast.NumVal(-0.0155024577),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(27), ast.NumVal(-0.0038881538),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(28), ast.NumVal(0.0010126166),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(29), ast.NumVal(0.0002312558),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(0.0), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_lightning_binary_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_binary_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.1617602138),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0931034793),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.6279180888),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.1856722189),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(0.0009999878),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(-0.0028974470),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0059948515),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.0024173728),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.0020429247),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(0.0009604400),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(0.0010933747),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0078588761),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.0069150246),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(13), ast.NumVal(-0.2583249885),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(14), ast.NumVal(0.0000097479),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(15), ast.NumVal(-0.0007210600),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(16), ast.NumVal(-0.0011295195),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(17), ast.NumVal(-0.0001966115),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(18), ast.NumVal(0.0001358314),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(19), ast.NumVal(-0.0000378118),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(20), ast.NumVal(0.1555921773),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(21), ast.NumVal(0.0621307817),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(22), ast.NumVal(0.5138354949),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(23), ast.NumVal(-0.2418579612),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(24), ast.NumVal(0.0007953821),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(25), ast.NumVal(-0.0110760214),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(26), ast.NumVal(-0.0162178044),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(27), ast.NumVal(-0.0040277699),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(28), ast.NumVal(0.0015067033),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(29), ast.NumVal(0.0001536614),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(0.0), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def classification_binary(model):
return (
model,
utils.get_binary_classification_model_trainer(),
CLASSIFICATION,
)
