def test_statsmodels_unknown_constant_position():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLS, dict(init=dict(hasconst=True)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
assembler.assemble()
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.StatsmodelsModelAssemblerSelector(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_cloglog_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(
family=sm.families.Binomial(sm.families.links.cloglog())),
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.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.ExpExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(ast.FeatureRef(0),
ast.NumVal(-0.8914468745),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD)), ast.BinNumOpType.SUB)),
ast.BinNumOpType.SUB)
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.StatsmodelsModelAssemblerSelector(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)
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.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = 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.DIV)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_glm_cauchy_link_func():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLM,
dict(init=dict(
family=sm.families.Binomial(
sm.families.links.cauchy())),
fit=dict(maxiter=1)))
estimator = estimator.fit([[1], [2]], [0.1, 0.2])
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
expected = ast.BinNumExpr(
ast.NumVal(0.5),
ast.BinNumExpr(
ast.AtanExpr(
ast.BinNumExpr(
ast.NumVal(0.0),
ast.BinNumExpr(
ast.FeatureRef(0),
ast.NumVal(-0.7279996905393095),
ast.BinNumOpType.MUL),
ast.BinNumOpType.ADD)),
ast.NumVal(3.141592653589793),
ast.BinNumOpType.DIV),
ast.BinNumOpType.ADD)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_processmle():
estimator = utils.StatsmodelsSklearnLikeWrapper(
ProcessMLE,
dict(init=dict(
exog_scale=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_smooth=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_noise=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
time=np.kron(
np.ones(
len(utils.get_regression_model_trainer().y_train) // 3),
np.arange(3)),
groups=np.kron(
np.arange(
len(utils.get_regression_model_trainer().y_train) // 3),
np.ones(3))),
fit=dict(maxiter=1)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.ProcessMLEModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.0915126856),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0455368812),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.0092227692),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.8566616798),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-2.1208777964),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.9725253309),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0061566965),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.9414114075),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1522429507),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0092123938),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.3928508764),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0134405151),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.4364996490),
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_statsmodels_processmle():
estimator = utils.StatsmodelsSklearnLikeWrapper(
ProcessMLE,
dict(init=dict(
exog_scale=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_smooth=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_noise=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
time=np.kron(
np.ones(
len(utils.get_regression_model_trainer().y_train) // 3),
np.arange(3)),
groups=np.kron(
np.arange(
len(utils.get_regression_model_trainer().y_train) // 3),
np.ones(3))),
fit=dict(maxiter=1)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.ProcessMLEModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.0980302102110356),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.04863869398287732),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.009514054355147874),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.977113829322681),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-2.6048073854474705),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.887987153279099),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.008183580358672775),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.996428929917054),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1618353156581333),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.009213049690188308),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.3634816838591863),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.014700492832969888),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.4384298738156768),
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_statsmodels_processmle():
estimator = utils.StatsmodelsSklearnLikeWrapper(
ProcessMLE,
dict(init=dict(
exog_scale=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_smooth=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
exog_noise=np.ones(
(len(utils.get_regression_model_trainer().y_train), 2)),
time=np.kron(
np.ones(
len(utils.get_regression_model_trainer().y_train) // 3),
np.arange(3)),
groups=np.kron(
np.arange(
len(utils.get_regression_model_trainer().y_train) // 3),
np.ones(3))),
fit=dict(maxiter=1)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.ProcessMLEModelAssembler(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.0932673973),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0480819091),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.0063734439),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.7510656855),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-3.0836268637),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.9605290000),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0077880716),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.9685365627),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1688777882),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0092446419),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.3924930042),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.01506511708295605),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.4177000096),
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_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.StatsmodelsModelAssemblerSelector(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.StatsmodelsModelAssemblerSelector(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.StatsmodelsModelAssemblerSelector(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_w_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLS, dict(init=dict(fit_intercept=True)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.1086131135490779),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.046461486329934965),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.027432259970185422),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.6160671309537693),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-17.51793656329748),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(3.7674418196771957),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-2.1581753172923886e-05),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-1.4711768622633619),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.29567671400629103),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.012233831527258853),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.9220356453705244),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.009038220462695548),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.5425830337142312),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(36.36708074657767),
*feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_w_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLS, dict(init=dict(fit_intercept=True)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.1085910250),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0441988987),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0174669054),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.8323210870),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-18.4837486980),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(3.8354955484),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(0.0001409165),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-1.5040340047),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.3106174852),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0123066500),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.9736183985),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0094039648),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.5203427347),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(37.1353468527), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_w_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(
sm.GLS, dict(init=dict(fit_intercept=True)))
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.1082106941),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0444969007),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.0189847585),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.7998640040),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-16.7498366967),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(3.9040863643),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(0.0014333844),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-1.4436181595),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.2868165881),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0118539736),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.9449930750),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0083181952),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.5415938640),
ast.BinNumOpType.MUL),
]
expected = assemblers.utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, ast.NumVal(35.5746356887), *feature_weight_mul)
assert utils.cmp_exprs(actual, expected)
def test_statsmodels_wo_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(sm.GLS, {})
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.0926871267),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0482139967),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.0075524567),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.9965313383),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-3.0877925575),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.9546630146),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0073548271),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.9828206079),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1727389546),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0094218658),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.3931071261),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0149656744),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.4133835832),
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_statsmodels_wo_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(sm.GLS, {})
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.09519078450227643),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.048952926782237956),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(0.007485539189808044),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.7302631809978273),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-2.5078200782168034),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.891794660307579),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.008663096157185936),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.9742684875268565),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1591703441858682),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.009351831548409096),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.36395034626096245),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.014529018124980565),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.437443877026267),
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_statsmodels_wo_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(sm.GLS, {})
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsModelAssemblerSelector(estimator)
actual = assembler.assemble()
feature_weight_mul = [
ast.BinNumExpr(ast.FeatureRef(0), ast.NumVal(-0.0940752519),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(1), ast.NumVal(0.0461122112),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(2), ast.NumVal(-0.0034800646),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(3), ast.NumVal(2.9669908485),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(4), ast.NumVal(-2.1264724710),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(5), ast.NumVal(5.9738064897),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(6), ast.NumVal(-0.0062638276),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(7), ast.NumVal(-0.9385894841),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(8), ast.NumVal(0.1568975632),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(9), ast.NumVal(-0.0091548228),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(10), ast.NumVal(-0.3949784315),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(11), ast.NumVal(0.0135685532),
ast.BinNumOpType.MUL),
ast.BinNumExpr(ast.FeatureRef(12), ast.NumVal(-0.4392385223),
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)
regression(linear_model.OrthogonalMatchingPursuitCV()),
regression(
linear_model.PassiveAggressiveRegressor(random_state=RANDOM_SEED)),
regression(
linear_model.RANSACRegressor(
base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS),
random_state=RANDOM_SEED)),
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)),
# Statsmodels Linear Regression
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1),
fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR, dict(init=dict(fit_intercept=True, rho=3)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR, dict(iterative_fit=dict(maxiter=2)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
regression(linear_model.OrthogonalMatchingPursuitCV()),
regression(
linear_model.PassiveAggressiveRegressor(random_state=RANDOM_SEED)),
regression(
linear_model.RANSACRegressor(
base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS),
random_state=RANDOM_SEED)),
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)),
# Statsmodels Linear Regression
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLS,
dict(init=dict(sigma=np.eye(
len(utils.get_regression_model_trainer().y_train)) + 1),
fit_regularized=STATSMODELS_LINEAR_REGULARIZED_PARAMS))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR, dict(init=dict(fit_intercept=True, rho=3)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
sm.GLSAR, dict(iterative_fit=dict(maxiter=2)))),
regression(
utils.StatsmodelsSklearnLikeWrapper(
random_state=RANDOM_SEED)),
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,
