Merge branch 'main' into fix-predict

Nixtla · Jan 2, 2024 · 3a3c07d · 3a3c07d
2 parents 8561672 + 6d4c59d
commit 3a3c07d
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Showing 6 changed files with 32 additions and 21 deletions.
diff --git a/environment.yml b/environment.yml
@@ -28,5 +28,5 @@ dependencies:
     - polars
     - ray<2.8
     - triad==0.9.1
-    - utilsforecast>=0.0.22
+    - utilsforecast>=0.0.24
     - xgboost_ray
diff --git a/local_environment.yml b/local_environment.yml
@@ -21,4 +21,4 @@ dependencies:
     - datasetsforecast
     - nbdev
     - polars
-    - utilsforecast>=0.0.22
+    - utilsforecast>=0.0.24
diff --git a/mlforecast/core.py b/mlforecast/core.py
@@ -774,7 +774,11 @@ def update(self, df: DataFrame) -> None:
         df = ufp.sort(df, by=[self.id_col, self.time_col])
         values = df[self.target_col].to_numpy()
         id_counts = ufp.counts_by_id(df, self.id_col)
-        sizes = ufp.join(uids, id_counts, on=self.id_col, how="outer")
+        try:
+            sizes = ufp.join(uids, id_counts, on=self.id_col, how="outer_coalesce")
+        except (KeyError, ValueError):
+            # pandas raises key error, polars before coalesce raises value error
+            sizes = ufp.join(uids, id_counts, on=self.id_col, how="outer")
         sizes = ufp.fill_null(sizes, {"counts": 0})
         sizes = ufp.sort(sizes, by=self.id_col)
         new_groups = ~ufp.is_in(sizes[self.id_col], uids)

diff --git a/nbs/core.ipynb b/nbs/core.ipynb
@@ -1247,7 +1247,11 @@
     "        df = ufp.sort(df, by=[self.id_col, self.time_col])\n",
     "        values = df[self.target_col].to_numpy()        \n",
     "        id_counts = ufp.counts_by_id(df, self.id_col)\n",
-    "        sizes = ufp.join(uids, id_counts, on=self.id_col, how='outer')\n",
+    "        try:\n",
+    "            sizes = ufp.join(uids, id_counts, on=self.id_col, how='outer_coalesce')\n",
+    "        except (KeyError, ValueError):\n",
+    "            # pandas raises key error, polars before coalesce raises value error\n",
+    "            sizes = ufp.join(uids, id_counts, on=self.id_col, how='outer')\n",
     "        sizes = ufp.fill_null(sizes, {'counts': 0})\n",
     "        sizes = ufp.sort(sizes, by=self.id_col)\n",
     "        new_groups = ~ufp.is_in(sizes[self.id_col], uids)\n",
@@ -2181,7 +2185,7 @@
     ")\n",
     "last_val_id0 = last_vals_two_series.filter(pl.col('unique_id') == 'id_0')\n",
     "new_values = last_val_id0.with_columns(\n",
-    "    pl.col('unique_id').cast(pl.Utf8),\n",
+    "    pl.col('unique_id').cast(pl.Categorical),\n",
     "    pl.col('ds').dt.offset_by('1d'),\n",
     "    pl.col('static_0').cast(pl.Int64),\n",
     "    pl.col('static_1').cast(pl.Int64),\n",
@@ -2192,7 +2196,10 @@
     "    'y': [5.0, 6.0],\n",
     "    'static_0': [0, 0],\n",
     "    'static_1': [1, 1],\n",
-    "}).with_columns(pl.col('ds').dt.cast_time_unit('ns'))\n",
+    "}).with_columns(\n",
+    "    pl.col('ds').dt.cast_time_unit('ns'),\n",
+    "    pl.col('unique_id').cast(pl.Categorical),\n",
+    ")\n",
     "new_values = pl.concat([new_values, new_serie])\n",
     "ts.update(new_values)\n",
     "preds = ts.predict({'Naive': NaiveModel()}, 1)\n",

diff --git a/nbs/forecast.ipynb b/nbs/forecast.ipynb
@@ -1476,7 +1476,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L542){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L564){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.make_future_dataframe\n",
        "\n",
@@ -1492,7 +1492,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L542){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L564){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.make_future_dataframe\n",
        "\n",
@@ -1600,7 +1600,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L588){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.get_missing_future\n",
        "\n",
@@ -1619,7 +1619,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L588){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.get_missing_future\n",
        "\n",
@@ -2111,7 +2111,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L554){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L607){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.predict\n",
        "\n",
@@ -2141,7 +2141,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L554){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L607){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.predict\n",
        "\n",
@@ -2700,7 +2700,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L216){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L202){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.preprocess\n",
        "\n",
@@ -2732,7 +2732,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L216){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L202){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.preprocess\n",
        "\n",
@@ -3052,7 +3052,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L272){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L258){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.fit_models\n",
        "\n",
@@ -3071,7 +3071,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L272){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L258){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.fit_models\n",
        "\n",
@@ -3196,7 +3196,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L693){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L746){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.cross_validation\n",
        "\n",
@@ -3249,7 +3249,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L693){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L746){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.cross_validation\n",
        "\n",
@@ -4190,7 +4190,7 @@
       "text/markdown": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L202){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L188){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.from_cv\n",
        "\n",
@@ -4199,7 +4199,7 @@
       "text/plain": [
        "---\n",
        "\n",
-       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L202){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
+       "[source](https://github.com/Nixtla/mlforecast/blob/main/mlforecast/forecast.py#L188){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
        "\n",
        "### MLForecast.from_cv\n",
        "\n",

diff --git a/settings.ini b/settings.ini
@@ -15,7 +15,7 @@ language = English
 custom_sidebar = True
 license = apache2
 status = 3
-requirements = numba packaging pandas scikit-learn utilsforecast>=0.0.22 window-ops
+requirements = numba packaging pandas scikit-learn utilsforecast>=0.0.24 window-ops
 dask_requirements = fugue dask[complete] lightgbm xgboost
 ray_requirements = fugue[ray] lightgbm_ray xgboost_ray
 spark_requirements = fugue pyspark lightgbm xgboost