def test_sklearn_glm_unknown_link_func():
estimator = linear_model.TweedieRegressor(
power=1, link="this_link_func_does_not_exist", max_iter=10)
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.SklearnGLMModelAssembler(estimator)
assembler.assemble()
def test_sklearn_glm_identity_link_func():
estimator = linear_model.TweedieRegressor(power=0,
link="identity",
max_iter=10)
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.SklearnGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(0.12),
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.02),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_sklearn_glm_log_link_func():
estimator = linear_model.TweedieRegressor(power=1, link="log", fit_intercept=False, max_iter=10)
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.SklearnGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.4619711397),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)