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fix: X_df handling in direct approach #468

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Jan 6, 2025
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fix: X_df handling in direct approach #468

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13 changes: 10 additions & 3 deletions mlforecast/core.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -703,6 +703,7 @@ def _predict_multi(
result = df_constructor({self.id_col: uids, self.time_col: dates})
for name, model in models.items():
with self._backup():
self._predict_setup()
new_x = self._get_features_for_next_step(X_df)
if before_predict_callback is not None:
new_x = before_predict_callback(new_x)
Expand Down Expand Up @@ -789,10 +790,16 @@ def predict(
raise ValueError(
f"The following features were provided through `X_df` but were considered as static during fit: {common}.\n"
"Please re-run the fit step using the `static_features` argument to indicate which features are static. "
"If all your features are dynamic please pass an empty list (static_features=[])."
"If all your features are dynamic please provide an empty list (static_features=[])."
)
starts = ufp.offset_times(self.last_dates, self.freq, 1)
ends = ufp.offset_times(self.last_dates, self.freq, horizon)
if getattr(self, "max_horizon", None) is None:
ends = ufp.offset_times(self.last_dates, self.freq, horizon)
expected_rows_X = len(self.uids) * horizon
else:
# direct approach uses only the immediate next timestamp
ends = starts
expected_rows_X = len(self.uids)
dates_validation = type(X_df)(
{
self.id_col: self.uids,
Expand All @@ -803,7 +810,7 @@ def predict(
X_df = ufp.join(X_df, dates_validation, on=self.id_col)
mask = ufp.between(X_df[self.time_col], X_df["_start"], X_df["_end"])
X_df = ufp.filter_with_mask(X_df, mask)
if X_df.shape[0] != len(self.uids) * horizon:
if X_df.shape[0] != expected_rows_X:
msg = (
"Found missing inputs in X_df. "
"It should have one row per id and time for the complete forecasting horizon.\n"
Expand Down
21 changes: 14 additions & 7 deletions nbs/core.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1184,6 +1184,7 @@
" result = df_constructor({self.id_col: uids, self.time_col: dates})\n",
" for name, model in models.items():\n",
" with self._backup():\n",
" self._predict_setup()\n",
" new_x = self._get_features_for_next_step(X_df)\n",
" if before_predict_callback is not None:\n",
" new_x = before_predict_callback(new_x)\n",
Expand Down Expand Up @@ -1261,10 +1262,16 @@
" raise ValueError(\n",
" f\"The following features were provided through `X_df` but were considered as static during fit: {common}.\\n\"\n",
" \"Please re-run the fit step using the `static_features` argument to indicate which features are static. \"\n",
" \"If all your features are dynamic please pass an empty list (static_features=[]).\"\n",
" \"If all your features are dynamic please provide an empty list (static_features=[]).\"\n",
" )\n",
" starts = ufp.offset_times(self.last_dates, self.freq, 1)\n",
" ends = ufp.offset_times(self.last_dates, self.freq, horizon)\n",
" if getattr(self, 'max_horizon', None) is None:\n",
" ends = ufp.offset_times(self.last_dates, self.freq, horizon)\n",
" expected_rows_X = len(self.uids) * horizon\n",
" else:\n",
" # direct approach uses only the immediate next timestamp\n",
" ends = starts\n",
" expected_rows_X = len(self.uids)\n",
" dates_validation = type(X_df)(\n",
" {\n",
" self.id_col: self.uids,\n",
Expand All @@ -1275,7 +1282,7 @@
" X_df = ufp.join(X_df, dates_validation, on=self.id_col)\n",
" mask = ufp.between(X_df[self.time_col], X_df['_start'], X_df['_end'])\n",
" X_df = ufp.filter_with_mask(X_df, mask)\n",
" if X_df.shape[0] != len(self.uids) * horizon:\n",
" if X_df.shape[0] != expected_rows_X:\n",
" msg = (\n",
" \"Found missing inputs in X_df. \"\n",
" \"It should have one row per id and time for the complete forecasting horizon.\\n\"\n",
Expand Down Expand Up @@ -2015,7 +2022,7 @@
"text/markdown": [
"---\n",
"\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L757){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L758){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"## TimeSeries.predict\n",
"\n",
Expand All @@ -2029,7 +2036,7 @@
"text/plain": [
"---\n",
"\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L757){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L758){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"## TimeSeries.predict\n",
"\n",
Expand Down Expand Up @@ -2167,7 +2174,7 @@
"text/markdown": [
"---\n",
"\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L862){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L869){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"## TimeSeries.update\n",
"\n",
Expand All @@ -2180,7 +2187,7 @@
"text/plain": [
"---\n",
"\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L862){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L869){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
"\n",
"## TimeSeries.update\n",
"\n",
Expand Down
28 changes: 28 additions & 0 deletions nbs/forecast.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1149,6 +1149,7 @@
"import numpy as np\n",
"import xgboost as xgb\n",
"from sklearn.linear_model import LinearRegression\n",
"from utilsforecast.feature_engineering import time_features\n",
"from utilsforecast.plotting import plot_series\n",
"\n",
"from mlforecast.lag_transforms import ExpandingMean, ExponentiallyWeightedMean, RollingMean\n",
Expand Down Expand Up @@ -5439,6 +5440,33 @@
"preds2 = fcst2.predict(10)\n",
"pd.testing.assert_frame_equal(preds, preds2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a39447b-3d6b-4960-83c9-4f927c472ebb",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"# direct approach requires only one timestamp and produces same results for two models\n",
"series = generate_daily_series(5)\n",
"h = 5\n",
"freq = 'D'\n",
"train, future = time_features(series, freq=freq, features=['day'], h=h)\n",
"models = [LinearRegression(), lgb.LGBMRegressor(n_estimators=5)]\n",
"\n",
"fcst1 = MLForecast(models=models, freq=freq, date_features=['dayofweek'])\n",
"fcst1.fit(train, max_horizon=h, static_features=[])\n",
"preds1 = fcst1.predict(h=h, X_df=future) # extra timestamps\n",
"\n",
"fcst2 = MLForecast(models=models[::-1], freq=freq, date_features=['dayofweek'])\n",
"fcst2.fit(train, max_horizon=h, static_features=[])\n",
"X_df_one = future.groupby('unique_id', observed=True).head(1)\n",
"preds2 = fcst2.predict(h=h, X_df=X_df_one) # only needed timestamp\n",
"\n",
"pd.testing.assert_frame_equal(preds1, preds2[preds1.columns])"
]
}
],
"metadata": {
Expand Down
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