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Fix Crossvalidation when dataframe is not provided #801

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Fix Crossvalidation when dataframe is not provided #801

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b194aa4
Fix cv without df
americast Oct 30, 2023
ce176b8
Merge remote-tracking branch 'origin/main' into fix_cv_nodf
americast Oct 30, 2023
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4 changes: 3 additions & 1 deletion neuralforecast/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,6 +190,7 @@ def __init__(
self.models = [deepcopy(model) for model in self.models_init]
self.freq = pd.tseries.frequencies.to_offset(freq)
self.local_scaler_type = local_scaler_type
self.dataset_df = None

# Flags and attributes
self._fitted = False
Expand Down Expand Up @@ -239,6 +240,7 @@ def fit(
if (df is None) and not (hasattr(self, "dataset")):
raise Exception("You must pass a DataFrame or have one stored.")

self.dataset_df = df
# Model and datasets interactions protections
if (any(model.early_stop_patience_steps > 0 for model in self.models)) and (
val_size == 0
Expand Down Expand Up @@ -539,7 +541,7 @@ def cross_validation(
fcsts_df = pd.concat([fcsts_df, fcsts], axis=1)

# Add original input df's y to forecasts DataFrame
fcsts_df = fcsts_df.merge(df, how="left", on=["unique_id", "ds"])
fcsts_df = fcsts_df.merge(df, how="left", on=["unique_id", "ds"]) if df is not None else fcsts_df.merge(self.dataset_df, how="left", on=["unique_id", "ds"])
return fcsts_df

def predict_insample(self, step_size: int = 1):
Expand Down