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_multi_class_best_ntree_limit():
base_score = 0.5
estimator = xgb.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 = 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)
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(0.09686334892116512),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(0.32572202133211947),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(-0.48444233646554424),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(-0.219719145605816),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.1089136473832088),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(-0.16956003333433572),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.0365531256007199),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(-0.01016100116780896),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.16690339219780817),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(1),
ast.NumVal(-0.19466284646233858),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(2),
ast.NumVal(0.2953585236360389),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.FeatureRef(3),
ast.NumVal(0.21288203082531384),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD)])
assert utils.cmp_exprs(actual, expected)
("ruby", m2c.export_to_ruby, "rb"),
("f_sharp", m2c.export_to_f_sharp, "fs"),
]
EXAMPLE_MODELS = [
(
"regression",
"linear",
linear_model.LinearRegression(),
utils.get_regression_model_trainer(),
),
(
"classification",
"linear",
linear_model.LogisticRegression(random_state=RANDOM_SEED),
utils.get_classification_model_trainer(),
),
(
"regression",
"decision_tree",
tree.DecisionTreeRegressor(**TREE_PARAMS),
utils.get_regression_model_trainer(),
),
(
"classification",
"decision_tree",
tree.DecisionTreeClassifier(**TREE_PARAMS),
utils.get_classification_model_trainer(),
),
(
"regression",
def classification(model, test_fraction=0.02):
return (
model,
utils.get_classification_model_trainer(test_fraction),
CLASSIFICATION,
)
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(0.0895848274),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.3258329434),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.4900856238),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.2214482506),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1074247041),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.1693225196),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0357417324),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.0161614171),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1825063678),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.2185655665),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.3053017646),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.2175198459),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
])
assert utils.cmp_exprs(actual, expected)
def test_lightning_multi_class():
estimator = AdaGradClassifier(random_state=1)
utils.get_classification_model_trainer()(estimator)
assembler = assemblers.SklearnLinearModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.VectorVal([
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(0.0935146297),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.3213921354),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.4855914264),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.2214295302),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1103262586),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.1662457692),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0379823341),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(-0.0128634938),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.1685751402),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(1),
ast.NumVal(-0.2045901693),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.2932121798),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(0.2138148665),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
])
assert utils.cmp_exprs(actual, expected)
("ruby", m2c.export_to_ruby, "rb"),
("f_sharp", m2c.export_to_f_sharp, "fs"),
]
EXAMPLE_MODELS = [
(
"regression",
"linear",
linear_model.LinearRegression(),
utils.get_regression_model_trainer(),
),
(
"classification",
"linear",
linear_model.LogisticRegression(random_state=RANDOM_SEED),
utils.get_classification_model_trainer(),
),
(
"regression",
"decision_tree",
tree.DecisionTreeRegressor(**TREE_PARAMS),
utils.get_regression_model_trainer(),
),
(
"classification",
"decision_tree",
tree.DecisionTreeClassifier(**TREE_PARAMS),
utils.get_classification_model_trainer(),
),
(
"regression",
def classification(model):
return (
model,
utils.get_classification_model_trainer(),
CLASSIFICATION,
)
