def test_multi_class():
estimator = lightgbm.LGBMClassifier(n_estimators=1,
random_state=1,
max_depth=1)
estimator.fit(np.array([[1], [2], [3]]), np.array([1, 2, 3]))
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
exponent = ast.ExpExpr(ast.BinNumExpr(ast.NumVal(0.0),
ast.NumVal(-1.0986122886681098),
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_random_forest():
estimator = lightgbm.LGBMRegressor(boosting_type="rf",
n_estimators=2,
random_state=1,
max_depth=1,
subsample=0.7,
subsample_freq=1)
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(5.200000000000001), ast.CompOpType.GT),
ast.NumVal(20.206688945020474), ast.NumVal(38.30000037757679)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.837500000000001),
ast.CompOpType.GT), ast.NumVal(36.40634951405711),
ast.NumVal(19.57067132709245)), ast.BinNumOpType.ADD),
ast.NumVal(0.5), ast.BinNumOpType.MUL)
assert utils.cmp_exprs(actual, expected)
def test_regression():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1)
utils.get_regression_model_trainer()(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_categorical():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1,
min_data_per_group=1000)
X = np.column_stack(( np.random.choice([0,1,2,3,4,5], size=[10000, 1]), np.random.choice([0,1,2,3], size=[10000,1])))
y = np.random.normal(size=[10000, 1])
estimator.fit(X, y, categorical_feature=[1])
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.918),
ast.CompOpType.GT),
ast.NumVal(24.011454621684155),
ast.NumVal(22.289277544391084)),
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.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(9.605), ast.CompOpType.GT),
ast.NumVal(17.398543657369768),
ast.NumVal(29.851408659650296)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.888),
ast.CompOpType.GT), ast.NumVal(37.2235298136268),
ast.NumVal(19.948122884684025)), ast.BinNumOpType.ADD),
ast.NumVal(0.5), ast.BinNumOpType.MUL)
assert utils.cmp_exprs(actual, expected)
def test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = 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(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.0026305187),
ast.NumVal(4.0880438137)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(14.895),
ast.CompOpType.GT),
ast.NumVal(-0.0412703078),
ast.NumVal(0.0208393767)), ast.BinNumOpType.ADD),
ast.BinNumOpType.SUB)), ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_maybe_sqr_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
reg_sqrt=True,
objective="regression_l1")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
raw_output = ast.IdExpr(ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.905),
ast.CompOpType.GT), ast.NumVal(4.5658116817),
ast.NumVal(4.6620790482)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.77),
ast.CompOpType.GT), ast.NumVal(-0.0340889740),
ast.NumVal(0.0543687153)), ast.BinNumOpType.ADD),
to_reuse=True)
expected = ast.BinNumExpr(ast.AbsExpr(raw_output), raw_output,
ast.BinNumOpType.MUL)
assert utils.cmp_exprs(actual, expected)
def test_multi_class_sigmoid_output_transform():
estimator = lightgbm.LGBMClassifier(n_estimators=1,
random_state=1,
max_depth=1,
sigmoid=0.5,
objective="ovr")
estimator.fit(np.array([[1], [2], [3]]), np.array([1, 2, 3]))
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.NumVal(-1.3862943611),
ast.BinNumOpType.MUL),
ast.BinNumOpType.SUB)), ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV)
expected = ast.VectorVal([sigmoid] * 3)
assert utils.cmp_exprs(actual, expected)
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_log1p_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6622623010380544),
ast.NumVal(0.6684065452877841)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1404975120475147),
ast.NumVal(0.14535916856709272)), ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def test_maybe_sqr_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
reg_sqrt=True,
objective="regression_l1")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
raw_output = ast.IdExpr(ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.725),
ast.CompOpType.GT), ast.NumVal(4.569350528717041),
ast.NumVal(4.663526439666748)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(11.655),
ast.CompOpType.GT), ast.NumVal(-0.04462450027465819),
ast.NumVal(0.033305134773254384)), ast.BinNumOpType.ADD),
to_reuse=True)
expected = ast.BinNumExpr(ast.AbsExpr(raw_output), raw_output,
ast.BinNumOpType.MUL)
assert utils.cmp_exprs(actual, expected)
def test_regression_with_negative_values():
estimator = lightgbm.LGBMRegressor(n_estimators=3,
random_state=1,
max_depth=1)
utils.get_regression_w_missing_values_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(8),
ast.NumVal(0.0), ast.CompOpType.GT),
ast.NumVal(156.64462853604854),
ast.NumVal(148.40956590509697)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2),
ast.NumVal(0.00780560282464346),
ast.CompOpType.GT), ast.NumVal(4.996373375352607),
ast.NumVal(-3.1063596100284814)), ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(8), ast.NumVal(-0.0010539205031971832),
ast.CompOpType.LTE), ast.NumVal(-3.5131100858883424),
ast.NumVal(3.6285643795846214)), ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = 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(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.002437528537838),
ast.NumVal(4.090096709787509)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(14.895),
ast.CompOpType.GT),
ast.NumVal(-0.0417499606641773),
ast.NumVal(0.02069953712454655)),
ast.BinNumOpType.ADD), ast.BinNumOpType.SUB)),
ast.BinNumOpType.ADD), ast.BinNumOpType.DIV)
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_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1, objective="poisson")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(5),
ast.NumVal(6.918),
ast.CompOpType.GT),
ast.NumVal(3.1480683932),
ast.NumVal(3.1101554907)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(9.63),
ast.CompOpType.GT),
ast.NumVal(-0.0111969636),
ast.NumVal(0.0160298303)),
ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_log1p_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2, random_state=1,
max_depth=1,
objective="cross_entropy_lambda")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.Log1pExpr(
ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(0.6623502468),
ast.NumVal(0.6683497987)),
ast.IfExpr(
ast.CompExpr(
ast.FeatureRef(12),
ast.NumVal(15.145),
ast.CompOpType.GT),
ast.NumVal(0.1405181490),
ast.NumVal(0.1453602134)),
ast.BinNumOpType.ADD)))
assert utils.cmp_exprs(actual, expected)
def test_regression_with_negative_values():
estimator = lightgbm.LGBMRegressor(n_estimators=3,
random_state=1,
max_depth=1)
utils.get_regression_w_missing_values_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(8),
ast.NumVal(0.0), ast.CompOpType.GT),
ast.NumVal(155.96889994777868),
ast.NumVal(147.72971715548434)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(2),
ast.NumVal(0.00780560282464346),
ast.CompOpType.GT), ast.NumVal(4.982244683562974),
ast.NumVal(-2.978315963345233)), ast.BinNumOpType.ADD),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(8), ast.NumVal(-0.0010539205031971832),
ast.CompOpType.LTE), ast.NumVal(-3.488666332734598),
ast.NumVal(3.670539900363904)), 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.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0),
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.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 test_simple_sigmoid_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="cross_entropy")
utils.get_bounded_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = 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(12), ast.NumVal(19.23),
ast.CompOpType.GT),
ast.NumVal(4.0050691250),
ast.NumVal(4.0914737728)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(15.065),
ast.CompOpType.GT),
ast.NumVal(-0.0420531079),
ast.NumVal(0.0202891577)), ast.BinNumOpType.ADD),
ast.BinNumOpType.SUB)), ast.BinNumOpType.ADD),
ast.BinNumOpType.DIV)
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.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(5.200000000000001), ast.CompOpType.GT),
ast.NumVal(20.195681040256623), ast.NumVal(38.30000037757679)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.853000000000001),
ast.CompOpType.GT), ast.NumVal(36.14745794219976),
ast.NumVal(19.778245570310993)), ast.BinNumOpType.ADD),
ast.NumVal(0.5), ast.BinNumOpType.MUL)
assert utils.cmp_exprs(actual, expected)
def test_multi_class():
estimator = lightgbm.LGBMClassifier(n_estimators=1,
random_state=1,
max_depth=1)
estimator.fit(np.array([[1], [2], [3]]), np.array([1, 2, 3]))
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
num_expr = ast.NumVal(-1.0986122886681098)
expected = ast.SoftmaxExpr([num_expr] * 3)
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.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 = 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_regression():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1)
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.837500000000001),
ast.CompOpType.GT), ast.NumVal(23.961356387224317),
ast.NumVal(22.32858336612959)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.725000000000003),
ast.CompOpType.GT), ast.NumVal(-0.5031712645462916),
ast.NumVal(0.6885501354513913)), 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.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.725),
ast.CompOpType.GT), ast.NumVal(22.030283219508686),
ast.NumVal(23.27840740210207)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.8375),
ast.CompOpType.GT), ast.NumVal(1.2777791671888081),
ast.NumVal(-0.2686772850549309)), ast.BinNumOpType.ADD)
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_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="poisson")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.8375),
ast.CompOpType.GT), ast.NumVal(3.1481886430),
ast.NumVal(3.1123367238)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12), ast.NumVal(9.725),
ast.CompOpType.GT), ast.NumVal(-0.0113689739),
ast.NumVal(0.0153551274)), ast.BinNumOpType.ADD))
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_exp_output_transform():
estimator = lightgbm.LGBMRegressor(n_estimators=2,
random_state=1,
max_depth=1,
objective="poisson")
utils.get_regression_model_trainer()(estimator)
assembler = assemblers.LightGBMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.ExpExpr(
ast.BinNumExpr(
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(12),
ast.NumVal(9.725), ast.CompOpType.GT),
ast.NumVal(3.1043985065105892),
ast.NumVal(3.1318783133960197)),
ast.IfExpr(
ast.CompExpr(ast.FeatureRef(5), ast.NumVal(6.8375),
ast.CompOpType.GT),
ast.NumVal(0.028409619436010138),
ast.NumVal(-0.0060740730485278754)), ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
