def compute_data_from_recipe(
self,
num_steps: int,
constant: Optional[float] = None,
one_to_zero: float = 0.1,
zero_to_one: float = 0.1,
scale_features: float = 200,
) -> TrainDatasets:
recipe = []
recipe_type = Constant(constant)
if self.is_noise:
recipe_type += RandomGaussian() # Use default stddev = 1.0
if self.is_trend:
recipe_type += LinearTrend()
if self.is_promotions:
recipe.append(
("binary_causal", BinaryMarkovChain(one_to_zero, zero_to_one))
)
recipe.append(
(FieldName.FEAT_DYNAMIC_REAL, Stack(["binary_causal"]))
)
recipe_type += scale_features * Lag("binary_causal", lag=0)
if self.holidays:
# Compute dates array
dates = list(
pd.period_range(self.start, periods=num_steps, freq=self.freq)
)
recipe.append(
("binary_holidays", BinaryHolidays(dates, self.holidays))
)
recipe.append(
(FieldName.FEAT_DYNAMIC_REAL, Stack(["binary_holidays"]))
)
recipe_type += scale_features * Lag("binary_holidays", lag=0)
recipe.append((FieldName.TARGET, recipe_type))
max_train_length = num_steps - self.prediction_length
data = RecipeDataset(
recipe=recipe,
metadata=self.metadata,
max_train_length=max_train_length,
prediction_length=self.prediction_length,
# Add 1 time series at a time in the loop for different constant
# valus per time series
num_timeseries=1,
)
generated = data.generate()
return generated
def compute_data_from_recipe(
self,
num_steps: int,
constant: Optional[float] = None,
one_to_zero: float = 0.1,
zero_to_one: float = 0.1,
scale_features: float = 200,
) -> TrainDatasets:
recipe = []
recipe_type = Constant(constant)
if self.is_noise:
recipe_type += RandomGaussian() # Use default stddev = 1.0
if self.is_trend:
recipe_type += LinearTrend()
if self.is_promotions:
recipe.append(
('binary_causal', BinaryMarkovChain(one_to_zero, zero_to_one)))
recipe.append(('feat_dynamic_real', Stack(['binary_causal'])))
recipe_type += scale_features * Lag('binary_causal', lag=0)
if self.holidays:
timestamp = self.init_date()
# Compute dates array
dates = []
for i in range(num_steps):
dates.append(timestamp)
timestamp += 1
recipe.append(('binary_holidays', Binary(dates, self.holidays)))
recipe.append(('feat_dynamic_real', Stack(['binary_holidays'])))
recipe_type += scale_features * Lag('binary_holidays', lag=0)
recipe.append(('target', recipe_type))
max_train_length = num_steps - self.prediction_length
data = RecipeDataset(
recipe=recipe,
metadata=self.metadata,
max_train_length=max_train_length,
prediction_length=self.prediction_length,
num_timeseries=
1, # Add 1 time series at a time in the loop for different constant valus per time series
)
generated = data.generate()
return generated
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)
np.random.seed(0)
ret = func(
x,
field_name="bar",
length=10,
global_state=global_state.copy(),
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)
kwargs = dict(foo=42, bar=23)
np.random.seed(0)
ret = func(
x,
field_name='bar',
length=10,
global_state=global_state.copy(),
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)
kwargs = dict(foo=42, bar=23)
np.random.seed(0)
ret = func(
x,
field_name="bar",
length=10,
global_state=global_state.copy(),
