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[Minor] Improve Season glocal reg invalid parameter handling #1601

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merged 3 commits into from
Jun 26, 2024
Merged

[Minor] Improve Season glocal reg invalid parameter handling #1601

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cabfc1f
reduce warning
ourownstory Jun 26, 2024
274c660
assertionError for wrong values
ourownstory Jun 26, 2024
4d7f519
Merge branch 'main' into season-glocal-reg
ourownstory Jun 26, 2024
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6 changes: 2 additions & 4 deletions neuralprophet/configure.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,7 @@
log.error("Invalid growth for global_local mode '{}'. Set to 'global'".format(self.trend_global_local))
self.trend_global_local = "global"

if self.trend_local_reg < 0:

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Operator "<" not supported for "None" (reportOptionalOperand)
log.error("Invalid negative trend_local_reg '{}'. Set to False".format(self.trend_local_reg))
self.trend_local_reg = False

Expand Down Expand Up @@ -354,13 +354,13 @@
log.error("Invalid global_local mode '{}'. Set to 'global'".format(self.global_local))
self.global_local = "global"

self.periods = OrderedDict(

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No overloads for "__init__" match the provided arguments (reportCallIssue)
{

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Argument of type "dict[str, Season]" cannot be assigned to parameter "iterable" of type "Iterable[list[bytes]]" in function "__init__" (reportArgumentType)
"yearly": Season(
resolution=6,
period=365.25,
arg=self.yearly_arg,
global_local=(

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Argument of type "SeasonGlobalLocalMode | Literal['auto']" cannot be assigned to parameter "global_local" of type "SeasonGlobalLocalMode" in function "__init__" (reportArgumentType)
self.yearly_global_local
if self.yearly_global_local in ["global", "local"]
else self.global_local
Expand All @@ -371,7 +371,7 @@
resolution=3,
period=7,
arg=self.weekly_arg,
global_local=(

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Argument of type "SeasonGlobalLocalMode | Literal['auto']" cannot be assigned to parameter "global_local" of type "SeasonGlobalLocalMode" in function "__init__" (reportArgumentType)
self.weekly_global_local
if self.weekly_global_local in ["global", "local"]
else self.global_local
Expand All @@ -382,7 +382,7 @@
resolution=6,
period=1,
arg=self.daily_arg,
global_local=(

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Argument of type "SeasonGlobalLocalMode | Literal['auto']" cannot be assigned to parameter "global_local" of type "SeasonGlobalLocalMode" in function "__init__" (reportArgumentType)
self.daily_global_local if self.daily_global_local in ["global", "local"] else self.global_local
),
condition_name=None,
Expand All @@ -390,12 +390,10 @@
}
)

if self.seasonality_local_reg < 0:
log.error("Invalid negative seasonality_local_reg '{}'. Set to False".format(self.seasonality_local_reg))
self.seasonality_local_reg = False
assert self.seasonality_local_reg >= 0, "Invalid seasonality_local_reg '{}'.".format(self.seasonality_local_reg)

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Operator ">=" not supported for "None" (reportOptionalOperand)

if self.seasonality_local_reg is True:
log.error("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
log.warning("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
self.seasonality_local_reg = 1

# If Season modelling is global but local regularization is set.
Expand All @@ -410,7 +408,7 @@
resolution=resolution,
period=period,
arg=arg,
global_local=global_local if global_local in ["global", "local"] else self.global_local,

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Argument of type "str" cannot be assigned to parameter "global_local" of type "SeasonGlobalLocalMode" in function "__init__"
  Type "str" is incompatible with type "SeasonGlobalLocalMode"
    "str" is incompatible with type "Literal['global']"
    "str" is incompatible with type "Literal['local']"
    "str" is incompatible with type "Literal['glocal']" (reportArgumentType)
condition_name=condition_name,
)

Expand Down Expand Up @@ -486,7 +484,7 @@
regressors: OrderedDict = field(init=False) # contains RegressorConfig objects

def __post_init__(self):
self.regressors = None

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Cannot assign to attribute "regressors" for class "ConfigFutureRegressors*"
  "None" is incompatible with "OrderedDict[Unknown, Unknown]" (reportAttributeAccessIssue)


@dataclass
Expand Down
23 changes: 22 additions & 1 deletion tests/test_glocal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import pathlib

import pandas as pd
import pytest

from neuralprophet import NeuralProphet

Expand Down Expand Up @@ -341,14 +342,16 @@ def test_glocal_seasonality_reg():
df2_0["ID"] = "df2"
df3_0 = df.iloc[256:384, :].copy(deep=True)
df3_0["ID"] = "df3"
for _ in [-30, 0, False, True]:
for coef_i in [0, 1.5, False, True]:

m = NeuralProphet(
n_forecasts=1,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
learning_rate=LR,
season_global_local="local",
yearly_seasonality_glocal_mode="global",
seasonality_local_reg=coef_i,
)

m.add_seasonality(period=30, fourier_order=8, name="monthly", global_local="global")
Expand All @@ -359,6 +362,24 @@ def test_glocal_seasonality_reg():
metrics = m.test(test_df)
log.info(f"forecast = {forecast}, metrics = {metrics}")

with pytest.raises(AssertionError, match="Invalid seasonality_local_reg"):
m = NeuralProphet(
n_forecasts=1,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
learning_rate=LR,
season_global_local="local",
yearly_seasonality_glocal_mode="global",
seasonality_local_reg=-324,
)

m.add_seasonality(period=30, fourier_order=8, name="monthly", global_local="global")
train_df, test_df = m.split_df(pd.concat((df1_0, df2_0, df3_0)), valid_p=0.33, local_split=True)
m.fit(train_df)
future = m.make_future_dataframe(test_df, n_historic_predictions=True)
forecast = m.predict(future)
metrics = m.test(test_df)


def test_trend_local_reg_if_global():
# SEASONALITY GLOBAL LOCAL MODELLING - NO EXOGENOUS VARIABLES
Expand Down
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