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.StatsmodelsLinearModelAssembler(estimator)
assembler.assemble()
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.StatsmodelsLinearModelAssembler(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_wo_const():
estimator = utils.StatsmodelsSklearnLikeWrapper(sm.GLS, {})
_, __, estimator = utils.get_regression_model_trainer()(estimator)
assembler = assemblers.StatsmodelsLinearModelAssembler(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)