def default_synthetic() -> Tuple[DatasetInfo, Dataset, Dataset]: recipe = [ (FieldName.TARGET, LinearTrend() + RandomGaussian()), (FieldName.FEAT_STATIC_CAT, RandomCat([10])), ( FieldName.FEAT_STATIC_REAL, ForEachCat(RandomGaussian(1, (10,)), FieldName.FEAT_STATIC_CAT) + RandomGaussian(0.1, (10,)), ), ] data = RecipeDataset( recipe=recipe, metadata=MetaData( freq="D", feat_static_real=[ BasicFeatureInfo(name=FieldName.FEAT_STATIC_REAL) ], feat_static_cat=[ CategoricalFeatureInfo( name=FieldName.FEAT_STATIC_CAT, cardinality=10 ) ], feat_dynamic_real=[ BasicFeatureInfo(name=FieldName.FEAT_DYNAMIC_REAL) ], ), max_train_length=20, prediction_length=10, num_timeseries=10, trim_length_fun=lambda x, **kwargs: np.minimum( int(np.random.geometric(1 / (kwargs["train_length"] / 2))), kwargs["train_length"], ), ) generated = data.generate() assert generated.test is not None info = data.dataset_info(generated.train, generated.test) return info, generated.train, generated.test
def default_synthetic() -> Tuple[DatasetInfo, Dataset, Dataset]: recipe = [ ('target', LinearTrend() + RandomGaussian()), ('feat_static_cat', RandomCat([10])), ( 'feat_static_real', ForEachCat(RandomGaussian(1, 10), 'feat_static_cat') + RandomGaussian(0.1, 10), ), ] data = RecipeDataset( recipe=recipe, metadata=MetaData( time_granularity='D', feat_static_real=[BasicFeatureInfo(name='feat_static_real')], feat_static_cat=[ CategoricalFeatureInfo(name='feat_static_cat', cardinality=10) ], feat_dynamic_real=[BasicFeatureInfo(name='feat_dynamic_real')], ), max_train_length=20, prediction_length=10, num_timeseries=10, trim_length_fun=lambda x, **kwargs: np.minimum( int(np.random.geometric(1 / (kwargs['train_length'] / 2))), kwargs['train_length'], ), ) generated = data.generate() assert generated.test is not None info = data.dataset_info(generated.train, generated.test) return info, generated.train, generated.test
BASE_RECIPE = [("foo", ConstantVec(1.0)), ("cat", RandomCat([10]))] @pytest.mark.parametrize( "func", [ Debug(), RandomGaussian(), RandomBinary(), RandomSymmetricDirichlet(), BinaryMarkovChain(0.1, 0.1), Constant(1), LinearTrend(), RandomCat([10]), Lag("foo", 1), ForEachCat(RandomGaussian()), Eval("np.random.rand(length)"), SmoothSeasonality(Constant(12), Constant(0)), Add(["foo", "foo"]), Mul(["foo", "foo"]), NanWhere("foo", "foo"), Stack([Ref("foo"), Ref("foo")]), RandomGaussian() + RandomGaussian(), RandomGaussian() * RandomGaussian(), RandomGaussian() / RandomGaussian(), ], ) def test_call_and_repr(func) -> None: global_state = {} x = evaluate(BASE_RECIPE, length=10, global_state=global_state) kwargs = dict(foo=42, bar=23)
BASE_RECIPE = [('foo', ConstantVec(1.0)), ('cat', RandomCat([10]))] @pytest.mark.parametrize( "func", [ Debug(), RandomGaussian(), RandomBinary(), RandomSymmetricDirichlet(), BinaryMarkovChain(0.1, 0.1), Constant(1), LinearTrend(), RandomCat([10]), Lag("foo", 1), ForEachCat(RandomGaussian()), Expr("np.random.rand(length)"), SmoothSeasonality(Constant(12), Constant(0)), Add(['foo', 'foo']), Mul(['foo', 'foo']), NanWhere('foo', 'foo'), NanWhereNot('foo', 'foo'), Stack(['foo', 'foo']), RandomGaussian() + RandomGaussian(), RandomGaussian() * RandomGaussian(), RandomGaussian() / RandomGaussian(), ], ) def test_call_and_repr(func) -> None: global_state = {} x = evaluate_recipe(BASE_RECIPE, length=10, global_state=global_state)