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(-0.0915126856),
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(-0.0932673973),
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)