feat: add forecast bias metric (#122)

nbs/evaluation.ipynb

@@ -228,7 +228,7 @@
     "    models : list of str, optional (default=None)\n",
     "        Names of the models to evaluate.\n",
     "        If `None` will use every column in the dataframe after removing id, time and target.\n",
-    "    train_df :pandas, polars, dask or spark DataFrame, optional (default=None)\n",
+    "    train_df : pandas, polars, dask or spark DataFrame, optional (default=None)\n",
     "        Training set. Used to evaluate metrics such as `mase`.\n",
     "    level : list of int, optional (default=None)\n",
     "        Prediction interval levels. Used to compute losses that rely on quantiles.\n",

nbs/losses.ipynb

@@ -574,6 +574,106 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "@_base_docstring\n",
+    "def bias(\n",
+    "    df: DFType,\n",
+    "    models: List[str],\n",
+    "    id_col: str = 'unique_id',\n",
+    "    target_col: str = 'y',\n",
+    ") -> DFType:\n",
+    "    \"\"\"Forecast estimator bias.\n",
+    "    \n",
+    "    Defined as prediction - actual\"\"\"\n",
+    "    if isinstance(df, pd.DataFrame):\n",
+    "        res = (df[models].sub(df[target_col], axis=0)).groupby(df[id_col], observed=True).mean()\n",
+    "        res.index.name = id_col\n",
+    "        res = res.reset_index()\n",
+    "    else:\n",
+    "        def gen_expr(model):\n",
+    "            return pl.col(model).sub(pl.col(target_col)).alias(model)\n",
+    "\n",
+    "        res = _pl_agg_expr(df, models, id_col, gen_expr)\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "---\n",
+       "\n",
+       "#### bias\n",
+       "\n",
+       ">      bias (df:~DFType, models:List[str], id_col:str='unique_id',\n",
+       ">            target_col:str='y')\n",
+       "\n",
+       "*Forecast estimator bias.\n",
+       "\n",
+       "Defined as prediction - actual*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | DFType |  | Input dataframe with id, actual values and predictions. |\n",
+       "| models | List |  | Columns that identify the models predictions. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| **Returns** | **DFType** |  | **dataframe with one row per id and one column per model.** |"
+      ],
+      "text/plain": [
+       "---\n",
+       "\n",
+       "#### bias\n",
+       "\n",
+       ">      bias (df:~DFType, models:List[str], id_col:str='unique_id',\n",
+       ">            target_col:str='y')\n",
+       "\n",
+       "*Forecast estimator bias.\n",
+       "\n",
+       "Defined as prediction - actual*\n",
+       "\n",
+       "|    | **Type** | **Default** | **Details** |\n",
+       "| -- | -------- | ----------- | ----------- |\n",
+       "| df | DFType |  | Input dataframe with id, actual values and predictions. |\n",
+       "| models | List |  | Columns that identify the models predictions. |\n",
+       "| id_col | str | unique_id | Column that identifies each serie. |\n",
+       "| target_col | str | y | Column that contains the target. |\n",
+       "| **Returns** | **DFType** |  | **dataframe with one row per id and one column per model.** |"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show_doc(bias, title_level=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| polars\n",
+    "pd_vs_pl(\n",
+    "    bias(series, models),\n",
+    "    bias(series_pl, models),\n",
+    "    models,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

settings.ini

@@ -1,7 +1,7 @@
 [DEFAULT]
 repo = utilsforecast
 lib_name = utilsforecast
-version = 0.2.4
+version = 0.2.5
 min_python = 3.8
 license = apache2
 black_formatting = True

utilsforecast/__init__.py

@@ -1 +1 @@
-__version__ = "0.2.4"
+__version__ = "0.2.5"

utilsforecast/_modidx.py

@@ -71,6 +71,7 @@
             'utilsforecast.losses': { 'utilsforecast.losses._base_docstring': ('losses.html#_base_docstring', 'utilsforecast/losses.py'),
                                       'utilsforecast.losses._pl_agg_expr': ('losses.html#_pl_agg_expr', 'utilsforecast/losses.py'),
                                       'utilsforecast.losses._zero_to_nan': ('losses.html#_zero_to_nan', 'utilsforecast/losses.py'),
+                                      'utilsforecast.losses.bias': ('losses.html#bias', 'utilsforecast/losses.py'),
                                       'utilsforecast.losses.calibration': ('losses.html#calibration', 'utilsforecast/losses.py'),
                                       'utilsforecast.losses.coverage': ('losses.html#coverage', 'utilsforecast/losses.py'),
                                       'utilsforecast.losses.mae': ('losses.html#mae', 'utilsforecast/losses.py'),

utilsforecast/evaluation.py

@@ -174,7 +174,7 @@ def evaluate(
     models : list of str, optional (default=None)
         Names of the models to evaluate.
         If `None` will use every column in the dataframe after removing id, time and target.
-    train_df :pandas, polars, dask or spark DataFrame, optional (default=None)
+    train_df : pandas, polars, dask or spark DataFrame, optional (default=None)
         Training set. Used to evaluate metrics such as `mase`.
     level : list of int, optional (default=None)
         Prediction interval levels. Used to compute losses that rely on quantiles.

utilsforecast/losses.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/losses.ipynb.
 
 # %% auto 0
-__all__ = ['mae', 'mse', 'rmse', 'mape', 'smape', 'mase', 'rmae', 'quantile_loss', 'mqloss', 'coverage', 'calibration',
+__all__ = ['mae', 'mse', 'rmse', 'bias', 'mape', 'smape', 'mase', 'rmae', 'quantile_loss', 'mqloss', 'coverage', 'calibration',
            'scaled_crps']
 
 # %% ../nbs/losses.ipynb 3
@@ -141,15 +141,42 @@ def rmse(
         res = res.with_columns(*[pl.col(c).pow(0.5) for c in models])
     return res
 
-# %% ../nbs/losses.ipynb 30
+# %% ../nbs/losses.ipynb 27
+@_base_docstring
+def bias(
+    df: DFType,
+    models: List[str],
+    id_col: str = "unique_id",
+    target_col: str = "y",
+) -> DFType:
+    """Forecast estimator bias.
+
+    Defined as prediction - actual"""
+    if isinstance(df, pd.DataFrame):
+        res = (
+            (df[models].sub(df[target_col], axis=0))
+            .groupby(df[id_col], observed=True)
+            .mean()
+        )
+        res.index.name = id_col
+        res = res.reset_index()
+    else:
+
+        def gen_expr(model):
+            return pl.col(model).sub(pl.col(target_col)).alias(model)
+
+        res = _pl_agg_expr(df, models, id_col, gen_expr)
+    return res
+
+# %% ../nbs/losses.ipynb 33
 def _zero_to_nan(series: Union[pd.Series, "pl.Expr"]) -> Union[pd.Series, "pl.Expr"]:
     if isinstance(series, pd.Series):
         res = series.replace(0, np.nan)
     else:
         res = pl.when(series == 0).then(float("nan")).otherwise(series.abs())
     return res
 
-# %% ../nbs/losses.ipynb 31
+# %% ../nbs/losses.ipynb 34
 @_base_docstring
 def mape(
     df: DFType,
@@ -187,7 +214,7 @@ def gen_expr(model):
         res = _pl_agg_expr(df, models, id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 35
+# %% ../nbs/losses.ipynb 38
 @_base_docstring
 def smape(
     df: DFType,
@@ -225,7 +252,7 @@ def gen_expr(model):
         res = _pl_agg_expr(df, models, id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 41
+# %% ../nbs/losses.ipynb 44
 def mase(
     df: DFType,
     models: List[str],
@@ -293,7 +320,7 @@ def gen_expr(model):
         res = _pl_agg_expr(full_df, models, id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 46
+# %% ../nbs/losses.ipynb 49
 def rmae(
     df: DFType,
     models: List[str],
@@ -347,7 +374,7 @@ def gen_expr(model, baseline) -> pl_Expr:
         res = res.select([id_col, *exprs])
     return res
 
-# %% ../nbs/losses.ipynb 52
+# %% ../nbs/losses.ipynb 55
 def quantile_loss(
     df: DFType,
     models: Dict[str, str],
@@ -409,7 +436,7 @@ def gen_expr(model):
         res = _pl_agg_expr(df, list(models.items()), id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 58
+# %% ../nbs/losses.ipynb 61
 def mqloss(
     df: DFType,
     models: Dict[str, List[str]],
@@ -468,7 +495,7 @@ def mqloss(
             res = ufp.assign_columns(res, model, model_res[model])
     return res
 
-# %% ../nbs/losses.ipynb 64
+# %% ../nbs/losses.ipynb 67
 def coverage(
     df: DFType,
     models: List[str],
@@ -527,7 +554,7 @@ def gen_expr(model):
         res = _pl_agg_expr(df, models, id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 68
+# %% ../nbs/losses.ipynb 71
 def calibration(
     df: DFType,
     models: Dict[str, str],
@@ -577,7 +604,7 @@ def gen_expr(model):
         res = _pl_agg_expr(df, list(models.items()), id_col, gen_expr)
     return res
 
-# %% ../nbs/losses.ipynb 72
+# %% ../nbs/losses.ipynb 75
 def scaled_crps(
     df: DFType,
     models: Dict[str, List[str]],