def test_statsmodels_glm_negativebinomial_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(
family=sm.families.NegativeBinomial(sm.families.links.nbinom())),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(-1.0),
ast.BinNumExpr(
ast.NumVal(1.0),
ast.BinNumExpr(
ast.NumVal(1.0),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-1.1079583217),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD), ast.BinNumOpType.SUB)),
ast.BinNumOpType.SUB), ast.BinNumOpType.MUL),
ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_glm_unknown_link_func():
class ValidPowerLink(sm.families.links.Power):
pass
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(family=sm.families.Tweedie(ValidPowerLink(2))),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
assembler.assemble()
def test_statsmodels_glm_identity_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(family=sm.families.Tweedie(sm.families.links.Power(1))),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2], [3]], [0.1, 0.2, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(0.0791304348),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_glm_log_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(family=sm.families.Poisson(sm.families.links.log())),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-1.0242053933),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD))
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_glm_inverse_squared_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(
init=dict(family=sm.families.Tweedie(sm.families.links.Power(-2))),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(1.0),
ast.SqrtExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(15.1237331741),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD)),
ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_glm_negative_power_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(
init=dict(family=sm.families.Tweedie(sm.families.links.Power(-3))),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsGLMModelAssembler(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(1.0),
ast.PowExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(71.0542398846),
ast.BinNumOpType.MUL), ast.BinNumOpType.ADD),
ast.NumVal(0.3333333333)), ast.BinNumOpType.DIV)
assert utils.cmp_exprs(actual, expected)