ensure static features are constant (#391)

mlforecast/__init__.py

@@ -1,3 +1,3 @@
-__version__ = "0.13.2"
+__version__ = "0.13.3"
 __all__ = ['MLForecast']
 from mlforecast.forecast import MLForecast

mlforecast/core.py

@@ -293,19 +293,34 @@ def _fit(
                     tfm.set_column_names(id_col, time_col, target_col)
                     sorted_df = tfm.fit_transform(sorted_df)
                     ga.data = sorted_df[target_col].to_numpy()
-        self.ga = ga
-        last_idxs_per_serie = self.ga.indptr[1:] - 1
         to_drop = [id_col, time_col, target_col]
         if static_features is None:
             static_features = [c for c in df.columns if c not in [time_col, target_col]]
         elif id_col not in static_features:
             static_features = [id_col] + static_features
         else:  # static_features defined and contain id_col
             to_drop = [time_col, target_col]
+        self.ga = ga
+        series_starts = ga.indptr[:-1]
+        series_ends = ga.indptr[1:] - 1
         if self._sort_idxs is not None:
-            last_idxs_per_serie = self._sort_idxs[last_idxs_per_serie]
-        self.static_features_ = ufp.take_rows(df, last_idxs_per_serie)[static_features]
-        self.static_features_ = ufp.drop_index_if_pandas(self.static_features_)
+            series_starts = self._sort_idxs[series_starts]
+            series_ends = self._sort_idxs[series_ends]
+        statics_on_starts = ufp.drop_index_if_pandas(
+            ufp.take_rows(df, series_starts)[static_features]
+        )
+        statics_on_ends = ufp.drop_index_if_pandas(
+            ufp.take_rows(df, series_ends)[static_features]
+        )
+        for feat in static_features:
+            if (statics_on_starts[feat] != statics_on_ends[feat]).any():
+                raise ValueError(
+                    f"{feat} is declared as a static feature but its values change "
+                    "over time. Please set the `static_features` argument to "
+                    "indicate which features are static.\nIf all of your features "
+                    "are dynamic please set `static_features=[]`."
+                )
+        self.static_features_ = statics_on_ends
         self.features_order_ = [
             c for c in df.columns if c not in to_drop
         ] + self.features

nbs/core.ipynb

@@ -776,19 +776,34 @@
     "                    tfm.set_column_names(id_col, time_col, target_col)\n",
     "                    sorted_df = tfm.fit_transform(sorted_df)\n",
     "                    ga.data = sorted_df[target_col].to_numpy()\n",
-    "        self.ga = ga\n",
-    "        last_idxs_per_serie = self.ga.indptr[1:] - 1\n",
     "        to_drop = [id_col, time_col, target_col]\n",
     "        if static_features is None:\n",
     "            static_features = [c for c in df.columns if c not in [time_col, target_col]]\n",
     "        elif id_col not in static_features:\n",
     "            static_features = [id_col] + static_features\n",
     "        else:  # static_features defined and contain id_col\n",
     "            to_drop = [time_col, target_col]\n",
+    "        self.ga = ga\n",
+    "        series_starts = ga.indptr[:-1]\n",
+    "        series_ends = ga.indptr[1:] - 1\n",
     "        if self._sort_idxs is not None:\n",
-    "            last_idxs_per_serie = self._sort_idxs[last_idxs_per_serie]\n",
-    "        self.static_features_ = ufp.take_rows(df, last_idxs_per_serie)[static_features]\n",
-    "        self.static_features_ = ufp.drop_index_if_pandas(self.static_features_)\n",
+    "            series_starts = self._sort_idxs[series_starts]\n",
+    "            series_ends = self._sort_idxs[series_ends]\n",
+    "        statics_on_starts = ufp.drop_index_if_pandas(\n",
+    "            ufp.take_rows(df, series_starts)[static_features]\n",
+    "        )\n",
+    "        statics_on_ends = ufp.drop_index_if_pandas(\n",
+    "            ufp.take_rows(df, series_ends)[static_features]\n",
+    "        )\n",
+    "        for feat in static_features:\n",
+    "            if (statics_on_starts[feat] != statics_on_ends[feat]).any():\n",
+    "                raise ValueError(\n",
+    "                    f'{feat} is declared as a static feature but its values change '\n",
+    "                    'over time. Please set the `static_features` argument to '\n",
+    "                    'indicate which features are static.\\nIf all of your features '\n",
+    "                    'are dynamic please set `static_features=[]`.'\n",
+    "                )\n",
+    "        self.static_features_ = statics_on_ends\n",
     "        self.features_order_ = [c for c in df.columns if c not in to_drop] + self.features\n",
     "        return self\n",
     "\n",
@@ -1647,7 +1662,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L466){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L481){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.fit_transform\n",
        "\n",
@@ -1660,16 +1675,16 @@
        ">                                max_horizon:Optional[int]=None,\n",
        ">                                return_X_y:bool=False, as_numpy:bool=False)\n",
        "\n",
-       "Add the features to `data` and save the required information for the predictions step.\n",
+       "*Add the features to `data` and save the required information for the predictions step.\n",
        "\n",
        "If not all features are static, specify which ones are in `static_features`.\n",
        "If you don't want to drop rows with null values after the transformations set `dropna=False`\n",
-       "If `keep_last_n` is not None then that number of observations is kept across all series for updates."
+       "If `keep_last_n` is not None then that number of observations is kept across all series for updates.*"
       ],
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L466){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L481){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.fit_transform\n",
        "\n",
@@ -1682,11 +1697,11 @@
        ">                                max_horizon:Optional[int]=None,\n",
        ">                                return_X_y:bool=False, as_numpy:bool=False)\n",
        "\n",
-       "Add the features to `data` and save the required information for the predictions step.\n",
+       "*Add the features to `data` and save the required information for the predictions step.\n",
        "\n",
        "If not all features are static, specify which ones are in `static_features`.\n",
        "If you don't want to drop rows with null values after the transformations set `dropna=False`\n",
-       "If `keep_last_n` is not None then that number of observations is kept across all series for updates."
+       "If `keep_last_n` is not None then that number of observations is kept across all series for updates.*"
       ]
      },
      "execution_count": null,
@@ -1917,7 +1932,9 @@
     "ts = TimeSeries(**flow_config)\n",
     "df = ts.fit_transform(series, id_col='unique_id', time_col='ds', target_col='y')\n",
     "non_std_series = series.reset_index().rename(columns={'unique_id': 'some_id', 'ds': 'timestamp', 'y': 'value'})\n",
-    "non_std_res = ts.fit_transform(non_std_series, id_col='some_id', time_col='timestamp', target_col='value')\n",
+    "non_std_res = ts.fit_transform(\n",
+    "    non_std_series, id_col='some_id', time_col='timestamp', target_col='value', static_features=[]\n",
+    ")\n",
     "non_std_res = non_std_res.reset_index(drop=True)\n",
     "pd.testing.assert_frame_equal(\n",
     "    df.reset_index(),\n",
@@ -1960,7 +1977,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L711){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L726){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.predict\n",
        "\n",
@@ -1975,7 +1992,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L711){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L726){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.predict\n",
        "\n",
@@ -2114,28 +2131,28 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L817){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L831){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.update\n",
        "\n",
        ">      TimeSeries.update\n",
        ">                         (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
        ">                         .frame.DataFrame])\n",
        "\n",
-       "Update the values of the stored series."
+       "*Update the values of the stored series.*"
       ],
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L817){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/core.py#L831){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "## TimeSeries.update\n",
        "\n",
        ">      TimeSeries.update\n",
        ">                         (df:Union[pandas.core.frame.DataFrame,polars.dataframe\n",
        ">                         .frame.DataFrame])\n",
        "\n",
-       "Update the values of the stored series."
+       "*Update the values of the stored series.*"
       ]
      },
      "execution_count": null,

nbs/docs/how-to-guides/transforming_exog.ipynb

@@ -605,7 +605,7 @@
     "    lags=[1],\n",
     "    date_features=['dayofweek'],\n",
     ")\n",
-    "fcst.preprocess(series_with_prices, dropna=True).head()"
+    "fcst.preprocess(series_with_prices, static_features=[], dropna=True).head()"
    ]
   },
   {

nbs/forecast.ipynb

@@ -4525,21 +4525,12 @@
    "execution_count": null,
    "id": "4e9188fd-8264-41d1-a4d7-89fa51d915d4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_79672/2131933040.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  non_std_series['ds'] = non_std_series.groupby('unique_id').cumcount()\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#| hide\n",
     "series = generate_daily_series(100, equal_ends=True, n_static_features=2, static_as_categorical=False)\n",
     "non_std_series = series.copy()\n",
-    "non_std_series['ds'] = non_std_series.groupby('unique_id').cumcount()\n",
+    "non_std_series['ds'] = non_std_series.groupby('unique_id', observed=True).cumcount()\n",
     "non_std_series = non_std_series.rename(columns={'unique_id': 'some_id', 'ds': 'time', 'y': 'value'})\n",
     "models = [\n",
     "    lgb.LGBMRegressor(n_jobs=1, random_state=0, verbosity=-1),\n",
@@ -4705,7 +4696,9 @@
     "    fitted_pl = fcst_pl.forecast_fitted_values()\n",
     "    preds_pl = fcst_pl.predict(X_df=prices_pl, **predict_kwargs)\n",
     "    preds_pl_subset = fcst_pl.predict(X_df=prices_pl, ids=fcst_pl.ts.uids[[0, 6]], **predict_kwargs)\n",
-    "    cv_pl = fcst_pl.cross_validation(series_pl, n_windows=2, h=horizon, fitted=True, as_numpy=as_numpy)\n",
+    "    cv_pl = fcst_pl.cross_validation(\n",
+    "        series_pl, n_windows=2, h=horizon, fitted=True, static_features=['product_id', 'static_1'], as_numpy=as_numpy\n",
+    "    )\n",
     "    cv_fitted_pl = fcst_pl.cross_validation_fitted_values()\n",
     "    \n",
     "    fcst_pd = MLForecast(**cfg)\n",
@@ -4714,7 +4707,9 @@
     "    preds_pd = fcst_pd.predict(X_df=prices_pd, **predict_kwargs)\n",
     "    preds_pd_subset = fcst_pd.predict(X_df=prices_pd, ids=fcst_pd.ts.uids[[0, 6]], **predict_kwargs)\n",
     "    assert preds_pd_subset['unique_id'].unique().tolist() == ['id_0', 'id_6']\n",
-    "    cv_pd = fcst_pd.cross_validation(series_pd, n_windows=2, h=horizon, fitted=True, as_numpy=as_numpy)\n",
+    "    cv_pd = fcst_pd.cross_validation(\n",
+    "        series_pd, n_windows=2, h=horizon, fitted=True, static_features=['product_id', 'static_1'], as_numpy=as_numpy\n",
+    "    )\n",
     "    cv_fitted_pd = fcst_pd.cross_validation_fitted_values()\n",
     "\n",
     "    if max_horizon is not None:\n",

settings.ini

@@ -8,7 +8,7 @@ author = José Morales
 author_email = [email protected]
 copyright = Nixtla
 branch = main
-version = 0.13.2
+version = 0.13.3
 min_python = 3.8
 audience = Developers
 language = English
@@ -35,8 +35,8 @@ title = mlforecast
 tst_flags = polars
 black_formatting = True
 readme_nb = index.ipynb
-allowed_metadata_keys =
-allowed_cell_metadata_keys =
+allowed_metadata_keys = 
+allowed_cell_metadata_keys = 
 jupyter_hooks = True
 clean_ids = True
 clear_all = False