Isolated import cells [Issue #653] (#691)

Nixtla · Nov 10, 2023 · 1a84462 · 1a84462
1 parent 855a621
commit 1a84462
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Showing 9 changed files with 557 additions and 160 deletions.
diff --git a/nbs/src/adapters.prophet.ipynb b/nbs/src/adapters.prophet.ipynb
@@ -29,11 +29,22 @@
    "source": [
     "#| hide\n",
     "import warnings\n",
-    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
     "\n",
     "from nbdev.showdoc import show_doc"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/nbs/src/arima.ipynb b/nbs/src/arima.ipynb
@@ -18,10 +18,21 @@
    "outputs": [],
    "source": [
     "#| hide\n",
-    "import warnings\n",
-    "warnings.simplefilter('ignore')"
+    "import warnings"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "warnings.simplefilter('ignore')\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "markdown",
    "id": "bb0efd1a",

diff --git a/nbs/src/ces.ipynb b/nbs/src/ces.ipynb
@@ -1145,6 +1145,18 @@
     "    return model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import matplotlib.pyplot as plt"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1153,7 +1165,6 @@
    "outputs": [],
    "source": [
     "#| hide\n",
-    "import matplotlib.pyplot as plt\n",
     "res = auto_ces(ap, m=12, model='F')\n",
     "fcst = forecast_ces(res, 12)\n",
     "plt.plot(np.arange(0, len(ap)), ap)\n",

diff --git a/nbs/src/core/core.ipynb b/nbs/src/core/core.ipynb
@@ -56,13 +56,24 @@
    "source": [
     "#| hide\n",
     "import warnings\n",
-    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
-    "warnings.filterwarnings('always', category=UserWarning)\n",
     "\n",
     "from nbdev.showdoc import add_docs, show_doc\n",
     "from statsforecast.models import Naive"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
+    "warnings.filterwarnings('always', category=UserWarning)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2268,12 +2279,21 @@
     "    DynamicOptimizedTheta,\n",
     "    AutoETS,\n",
     "    AutoCES\n",
-    ")\n",
-    "\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
     "# Generate synthetic panel DataFrame for example\n",
     "panel_df = generate_series(n_series=9, equal_ends=False, engine='pandas')\n",
     "panel_df.groupby('unique_id').tail(4)"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -2677,8 +2697,14 @@
     "\n",
     "#from statsforecast.core import StatsForecast\n",
     "from statsforecast.utils import AirPassengersDF as panel_df\n",
-    "from statsforecast.models import AutoARIMA, Naive\n",
-    "\n",
+    "from statsforecast.models import AutoARIMA, Naive\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
     "# Instantiate StatsForecast class\n",
     "fcst = StatsForecast(df=panel_df,\n",
     "                     models=[AutoARIMA(), Naive()],\n",
@@ -2687,7 +2713,10 @@
     "# Efficiently predict without storing memory\n",
     "fcsts_df = fcst.forecast(h=4, fitted=True)\n",
     "fcsts_df.groupby('unique_id').tail(4)"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -2812,8 +2841,14 @@
     "\n",
     "#from statsforecast.core import StatsForecast\n",
     "from statsforecast.utils import AirPassengersDF as panel_df\n",
-    "from statsforecast.models import Naive\n",
-    "\n",
+    "from statsforecast.models import Naive\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
     "# Instantiate StatsForecast class\n",
     "fcst = StatsForecast(df=panel_df,\n",
     "                     models=[AutoARIMA()],\n",
@@ -2823,7 +2858,10 @@
     "fcsts_df = fcst.forecast(h=12, fitted=True, level=(90, 10))\n",
     "insample_fcsts_df = fcst.forecast_fitted_values()\n",
     "insample_fcsts_df.tail(4)"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -2910,8 +2948,14 @@
     "\n",
     "#from statsforecast.core import StatsForecast\n",
     "from statsforecast.utils import AirPassengersDF as panel_df\n",
-    "from statsforecast.models import Naive\n",
-    "\n",
+    "from statsforecast.models import Naive\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
     "# Instantiate StatsForecast class\n",
     "fcst = StatsForecast(df=panel_df,\n",
     "                     models=[Naive()],\n",
@@ -2920,7 +2964,10 @@
     "# Access insample predictions\n",
     "rolled_fcsts_df = fcst.cross_validation(14, n_windows=2)\n",
     "rolled_fcsts_df.head(4)"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
@@ -3097,8 +3144,14 @@
     "\n",
     "#from statsforecast.core import StatsForecast\n",
     "from statsforecast.utils import AirPassengersDF as panel_df\n",
-    "from statsforecast.models import Naive\n",
-    "\n",
+    "from statsforecast.models import Naive\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
     "# Instantiate StatsForecast class\n",
     "fcst = StatsForecast(df=panel_df,\n",
     "                     models=[Naive()],\n",
@@ -3108,7 +3161,10 @@
     "rolled_fcsts_df = fcst.cross_validation(h=12, n_windows=2, fitted=True)\n",
     "insample_rolled_fcsts_df = fcst.cross_validation_fitted_values()\n",
     "insample_rolled_fcsts_df.tail(4)"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",