ModelInterpretation_MBocalbos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0465601305038742"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "X_train = pd.read_feather('./X_train.feather')\n",
    "y_train = pd.read_feather('./y_train.feather')['shares']\n",
    "\n",
    "X_val = pd.read_feather('./X_val.feather')\n",
    "y_val = pd.read_feather('./y_val.feather')['shares']\n",
    "\n",
    "rfr = RandomForestRegressor(n_estimators=42, max_features='log2', min_samples_leaf=46)\n",
    "rfr.fit(X_train, y_train)\n",
    "rfr.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/jeff/Documents/jigsaw/curriculum/yeshiva-notes/student-work/mahriya'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import eli5\n",
    "from eli5.sklearn import PermutationImportance\n",
    "pmi_limited = PermutationImportance(rfr).fit(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>weight</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kw_max_avg</td>\n",
       "      <td>0.024703</td>\n",
       "      <td>0.002037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>self_reference_avg_sharess</td>\n",
       "      <td>0.020182</td>\n",
       "      <td>0.004262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kw_min_avg</td>\n",
       "      <td>0.005673</td>\n",
       "      <td>0.001020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data_channel_missing</td>\n",
       "      <td>0.005605</td>\n",
       "      <td>0.000984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dayofyear</td>\n",
       "      <td>0.004981</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num_hrefs</td>\n",
       "      <td>0.004295</td>\n",
       "      <td>0.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>average_token_length</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>0.001330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>num_videos</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.000746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      feature    weight       std\n",
       "0                  kw_max_avg  0.024703  0.002037\n",
       "1  self_reference_avg_sharess  0.020182  0.004262\n",
       "2                  kw_min_avg  0.005673  0.001020\n",
       "3        data_channel_missing  0.005605  0.000984\n",
       "4                   Dayofyear  0.004981  0.000741\n",
       "5                   num_hrefs  0.004295  0.000373\n",
       "6        average_token_length  0.001765  0.001330\n",
       "7                  num_videos  0.001733  0.000746"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eli5.explain_weights_df(pmi_limited, feature_names = X_val.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_hrefs</th>\n",
       "      <th>num_videos</th>\n",
       "      <th>average_token_length</th>\n",
       "      <th>kw_min_avg</th>\n",
       "      <th>kw_max_avg</th>\n",
       "      <th>self_reference_avg_sharess</th>\n",
       "      <th>Dayofyear</th>\n",
       "      <th>data_channel_missing</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.608309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4975.190476</td>\n",
       "      <td>7300.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.346939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4755.043584</td>\n",
       "      <td>34148.666670</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.904040</td>\n",
       "      <td>1124.000000</td>\n",
       "      <td>3287.700428</td>\n",
       "      <td>3266.666667</td>\n",
       "      <td>310.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.767296</td>\n",
       "      <td>1332.454545</td>\n",
       "      <td>6932.138936</td>\n",
       "      <td>14700.000000</td>\n",
       "      <td>336.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.961240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3571.800035</td>\n",
       "      <td>1097.500000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_hrefs  num_videos  average_token_length   kw_min_avg   kw_max_avg  \\\n",
       "0        4.0        11.0              4.608309     0.000000  4975.190476   \n",
       "1        5.0         0.0              5.346939     0.000000  4755.043584   \n",
       "2        4.0         0.0              4.904040  1124.000000  3287.700428   \n",
       "3        7.0         0.0              4.767296  1332.454545  6932.138936   \n",
       "4        3.0         0.0              4.961240     0.000000  3571.800035   \n",
       "\n",
       "   self_reference_avg_sharess  Dayofyear  data_channel_missing  shares  \n",
       "0                 7300.000000       11.0                   1.0     622  \n",
       "1                34148.666670      304.0                   0.0    2600  \n",
       "2                 3266.666667      310.0                   0.0    1400  \n",
       "3                14700.000000      336.0                   0.0    7000  \n",
       "4                 1097.500000       87.0                   0.0     903  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = X_train.merge(y_train, on=y_train.index).drop(columns=['key_0'])\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 25307 entries, 0 to 25306\n",
      "Data columns (total 9 columns):\n",
      "num_hrefs                     25307 non-null float64\n",
      "num_videos                    25307 non-null float64\n",
      "average_token_length          25307 non-null float64\n",
      "kw_min_avg                    25307 non-null float64\n",
      "kw_max_avg                    25307 non-null float64\n",
      "self_reference_avg_sharess    25307 non-null float64\n",
      "Dayofyear                     25307 non-null float64\n",
      "data_channel_missing          25307 non-null float64\n",
      "shares                        25307 non-null int64\n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 1.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotnine import *\n",
    "sampled_train = df_train.sample(n=3000, random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graph import plot, trace_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "                        {},\n",
       "                        {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a08483d1-ee96-446a-a9c4-86455dd1fd8d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = trace_values(sampled_train['num_videos'], sampled_train['shares'])\n",
    "plot([trace])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampled_train['num_videos'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampled_train['shares']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (7552358292)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(sampled_train, aes('num_videos', 'shares')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\plotnine\\stats\\smoothers.py:238: UserWarning: See sklearn.gaussian_process.GaussianProcessRegressor for parameters to pass in as 'method_args'\n",
      "  \"See sklearn.gaussian_process.GaussianProcessRegressor \"\n",
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\plotnine\\layer.py:449: UserWarning: geom_smooth : Removed 54 rows containing missing values.\n",
      "  self.data = self.geom.handle_na(self.data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (137269200574)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(sampled_train, aes('average_token_length', 'shares'))\n",
    " + stat_smooth(se=True, method='gpr') + xlim(0, 25) + ylim(0, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    700\u001b[0m                 \u001b[0mtype_pprinters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype_printers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m                 deferred_pprinters=self.deferred_printers)\n\u001b[1;32m--> 702\u001b[1;33m             \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m             \u001b[0mprinter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mstream\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\lib\\pretty.py\u001b[0m in \u001b[0;36mpretty\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    400\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    401\u001b[0m                                 \u001b[1;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'__repr__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m                             \u001b[1;32mreturn\u001b[0m \u001b[0m_repr_pprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    403\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_default_pprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcycle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\IPython\\lib\\pretty.py\u001b[0m in \u001b[0;36m_repr_pprint\u001b[1;34m(obj, p, cycle)\u001b[0m\n\u001b[0;32m    695\u001b[0m     \u001b[1;34m\"\"\"A pprint that just redirects to the normal repr function.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    696\u001b[0m     \u001b[1;31m# Find newlines and replace them with p.break_()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 697\u001b[1;33m     \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    698\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0moutput_line\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    699\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m__repr__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;31m# in the jupyter notebook.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m'<ggplot: (%d)>'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__hash__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, return_ggplot)\u001b[0m\n\u001b[0;32m    186\u001b[0m         \u001b[1;31m# new frames knowing that they are separate from the original.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moption_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mode.chained_assignment'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreturn_ggplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_draw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_ggplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m_draw\u001b[1;34m(self, return_ggplot)\u001b[0m\n\u001b[0;32m    193\u001b[0m         \u001b[1;31m# assign a default theme\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_build\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m         \u001b[1;31m# If no theme we use the default\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\ggplot.py\u001b[0m in \u001b[0;36m_build\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;31m# Apply and map statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m         \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m         \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\layer.py\u001b[0m in \u001b[0;36mcompute_statistic\u001b[1;34m(self, layout)\u001b[0m\n\u001b[0;32m     82\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m             \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmap_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\layer.py\u001b[0m in \u001b[0;36mcompute_statistic\u001b[1;34m(self, layout)\u001b[0m\n\u001b[0;32m    358\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    359\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 360\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_layer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    361\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\stats\\stat.py\u001b[0m in \u001b[0;36mcompute_layer\u001b[1;34m(cls, data, params, layout)\u001b[0m\n\u001b[0;32m    265\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_panel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpscales\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 267\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mgroupby_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'PANEL'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    268\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    269\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\plotnine\\utils.py\u001b[0m in \u001b[0;36mgroupby_apply\u001b[1;34m(df, cols, func, *args, **kwargs)\u001b[0m\n\u001b[0;32m    627\u001b[0m         \u001b[1;31m# do not mark d as a slice of df i.e no SettingWithCopyWarning\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    628\u001b[0m         \u001b[0mlst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 629\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    630\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    226\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 228\u001b[1;33m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    229\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0m_Concatenator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    230\u001b[0m     \"\"\"\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             \u001b[0mobjs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_not_none\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    263\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m             \u001b[1;31m# #1649\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "(ggplot(sampled_train, aes('num_videos', 'shares'))\n",
    " + stat_smooth(se=True, method='gpr') + xlim(13, 14) + ylim(0, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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