fix: Re-run example notebooks without API warnings (#190)

Fix notebooks ...

examples/quick-starts/utils/add_noisy_features.ipynb

@@ -2,52 +2,48 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# `utils.add_noisy_features`"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "source": [
-    "import slickml\n",
-    "\n",
-    "print(f\"Loaded SlickML Version = {slickml.__version__}\")"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "Loaded SlickML Version = 0.2.0\n"
+      "Loaded SlickML Version = 0.2.1\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "import slickml\n",
+    "\n",
+    "print(f\"Loaded SlickML Version = {slickml.__version__}\")"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "source": [
-    "from slickml.utils import add_noisy_features\n",
-    "\n",
-    "help(add_noisy_features)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "Help on function add_noisy_features in module slickml.utils._transform:\n",
       "\n",
@@ -88,31 +84,27 @@
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from slickml.utils import add_noisy_features\n",
+    "\n",
+    "help(add_noisy_features)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Example 1: `add_noisy_features` with `pandas.DataFrame` as the input data"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.DataFrame(\n",
-    "    {\n",
-    "        \"foo\": [1, 2, 3, 4, 5],\n",
-    "    }\n",
-    ")\n",
-    "df"
-   ],
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/html": [
        "<div>\n",
@@ -170,24 +162,28 @@
        "4    5"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "execution_count": 4
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {
-    "scrolled": true
-   }
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"foo\": [1, 2, 3, 4, 5],\n",
+    "    }\n",
+    ")\n",
+    "df"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "source": [
-    "df_noisy = add_noisy_features(df)\n",
-    "df_noisy"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/html": [
        "<div>\n",
@@ -251,32 +247,29 @@
        "4    5          3"
       ]
      },
+     "execution_count": 5,
      "metadata": {},
-     "execution_count": 5
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "df_noisy = add_noisy_features(df)\n",
+    "df_noisy"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Example 2: `add_noisy_features` with `numpy.ndarray` as the input data"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "np.random.seed(1367)\n",
-    "X = np.random.rand(4, 3)\n",
-    "X"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "array([[0.88614473, 0.04571154, 0.66280713],\n",
@@ -285,22 +278,25 @@
        "       [0.64470859, 0.91519028, 0.26689842]])"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(1367)\n",
+    "X = np.random.rand(4, 3)\n",
+    "X"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "source": [
-    "X_noisy = add_noisy_features(X)\n",
-    "X_noisy"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/html": [
        "<div>\n",
@@ -378,47 +374,53 @@
        "3  0.644709  0.915190  0.266898   0.713585   0.103395   0.767115"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "execution_count": 7
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "X_noisy = add_noisy_features(X)\n",
+    "X_noisy"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### TODO: Example 3: `add_noisy_features` with `List[List[float]]` as the input data"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "source": [
-    "X = [\n",
-    "    [1, 2, 3],\n",
-    "    [4, 5, 6],\n",
-    "    [7, 8, 9],\n",
-    "]\n",
-    "X"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
       ]
      },
+     "execution_count": 8,
      "metadata": {},
-     "execution_count": 8
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "X = [\n",
+    "    [1, 2, 3],\n",
+    "    [4, 5, 6],\n",
+    "    [7, 8, 9],\n",
+    "]\n",
+    "X"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# This feature is currently not implemented;\n",
     "# https://github.com/slickml/slick-ml/issues/107\n",
@@ -427,23 +429,24 @@
     "# Uncomment below once the feature is implemented\n",
     "# X_noisy = add_noisy_features(X)\n",
     "# X_noisy"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Feel free to add your favorite `Example` via a `pull-request`.\n",
     "### More details can be found in our [Contributing Document](https://github.com/slickml/slick-ml/blob/master/CONTRIBUTING.md)."
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "07bad374b921cdabaf2ef6a1d4ae5a7996d892e7452f8d9d13efced363d002df"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.12 64-bit ('.venv': poetry)"
+   "display_name": "Python 3.9.12 64-bit ('.venv': poetry)",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -455,12 +458,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  },
-  "interpreter": {
-   "hash": "07bad374b921cdabaf2ef6a1d4ae5a7996d892e7452f8d9d13efced363d002df"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}