examples/sample_notebooks/regression_performance_bike_sharing_demand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Performance Dashboard for Bike Sharing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import zipfile\n",
    "import io\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from evidently import ColumnMapping\n",
    "from evidently.dashboard import Dashboard\n",
    "from evidently.dashboard.tabs import RegressionPerformanceTab\n",
    "\n",
    "from evidently.model_profile import Profile\n",
    "from evidently.model_profile.sections import RegressionPerformanceProfileSection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bike Sharing Demand Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More information about the dataset can be found in UCI machine learning repository: https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset\n",
    "\n",
    "Acknowledgement: Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "content = requests.get(\"https://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip\").content\n",
    "with zipfile.ZipFile(io.BytesIO(content)) as arc:\n",
    "    raw_data = pd.read_csv(arc.open(\"day.csv\"), header=0, sep=',', parse_dates=['dteday'], index_col='dteday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data = raw_data[:120]\n",
    "prod_data = raw_data[120:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dteday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-26</th>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.631667</td>\n",
       "      <td>0.594083</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.326500</td>\n",
       "      <td>678</td>\n",
       "      <td>3722</td>\n",
       "      <td>4400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-27</th>\n",
       "      <td>117</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.575142</td>\n",
       "      <td>0.835417</td>\n",
       "      <td>0.312200</td>\n",
       "      <td>547</td>\n",
       "      <td>3325</td>\n",
       "      <td>3872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-28</th>\n",
       "      <td>118</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.617500</td>\n",
       "      <td>0.578929</td>\n",
       "      <td>0.700833</td>\n",
       "      <td>0.320908</td>\n",
       "      <td>569</td>\n",
       "      <td>3489</td>\n",
       "      <td>4058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-29</th>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.497463</td>\n",
       "      <td>0.457083</td>\n",
       "      <td>0.240063</td>\n",
       "      <td>878</td>\n",
       "      <td>3717</td>\n",
       "      <td>4595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-30</th>\n",
       "      <td>120</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.472500</td>\n",
       "      <td>0.464021</td>\n",
       "      <td>0.503333</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>1965</td>\n",
       "      <td>3347</td>\n",
       "      <td>5312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            instant  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "dteday                                                                \n",
       "2011-01-01        1       1   0     1        0        6           0   \n",
       "2011-01-02        2       1   0     1        0        0           0   \n",
       "2011-01-03        3       1   0     1        0        1           1   \n",
       "2011-01-04        4       1   0     1        0        2           1   \n",
       "2011-01-05        5       1   0     1        0        3           1   \n",
       "...             ...     ...  ..   ...      ...      ...         ...   \n",
       "2011-04-26      116       2   0     4        0        2           1   \n",
       "2011-04-27      117       2   0     4        0        3           1   \n",
       "2011-04-28      118       2   0     4        0        4           1   \n",
       "2011-04-29      119       2   0     4        0        5           1   \n",
       "2011-04-30      120       2   0     4        0        6           0   \n",
       "\n",
       "            weathersit      temp     atemp       hum  windspeed  casual  \\\n",
       "dteday                                                                    \n",
       "2011-01-01           2  0.344167  0.363625  0.805833   0.160446     331   \n",
       "2011-01-02           2  0.363478  0.353739  0.696087   0.248539     131   \n",
       "2011-01-03           1  0.196364  0.189405  0.437273   0.248309     120   \n",
       "2011-01-04           1  0.200000  0.212122  0.590435   0.160296     108   \n",
       "2011-01-05           1  0.226957  0.229270  0.436957   0.186900      82   \n",
       "...                ...       ...       ...       ...        ...     ...   \n",
       "2011-04-26           1  0.631667  0.594083  0.729167   0.326500     678   \n",
       "2011-04-27           2  0.620000  0.575142  0.835417   0.312200     547   \n",
       "2011-04-28           2  0.617500  0.578929  0.700833   0.320908     569   \n",
       "2011-04-29           1  0.510000  0.497463  0.457083   0.240063     878   \n",
       "2011-04-30           1  0.472500  0.464021  0.503333   0.235075    1965   \n",
       "\n",
       "            registered   cnt  \n",
       "dteday                        \n",
       "2011-01-01         654   985  \n",
       "2011-01-02         670   801  \n",
       "2011-01-03        1229  1349  \n",
       "2011-01-04        1454  1562  \n",
       "2011-01-05        1518  1600  \n",
       "...                ...   ...  \n",
       "2011-04-26        3722  4400  \n",
       "2011-04-27        3325  3872  \n",
       "2011-04-28        3489  4058  \n",
       "2011-04-29        3717  4595  \n",
       "2011-04-30        3347  5312  \n",
       "\n",
       "[120 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'cnt'\n",
    "prediction = 'prediction'\n",
    "datetime = 'dteday'\n",
    "\n",
    "numerical_features = ['temp', 'atemp', 'hum', 'windspeed', 'weekday']\n",
    "categorical_features = ['season', 'holiday', 'workingday', 'weathersit']\n",
    "\n",
    "features = numerical_features + categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(ref_data[features], ref_data[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_data['prediction']  = model.predict(ref_data[features])\n",
    "prod_data['prediction'] = model.predict(prod_data[features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Perfomance Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = ColumnMapping(target,\n",
    "                               'prediction',\n",
    "                               numerical_features=numerical_features,\n",
    "                               categorical_features=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Perfomance Report full (verbose_level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = Dashboard(tabs=[RegressionPerformanceTab(verbose_level=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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\"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45], \"showgrid\": true, \"title\": {\"text\": \"Actual Value\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.55, 1.0], \"showgrid\": true, \"title\": {\"text\": \"Actual Value\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"showgrid\": true, \"title\": {\"text\": \"Predicted Value\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 1.0], \"showgrid\": true, \"title\": {\"text\": \"Predicted Value\"}}}}};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_7ca80bc8861f4d538c1762725abe5785,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_7ca80bc8861f4d538c1762725abe5785)),\n",
       "        \"root_evidently_dashboard_7ca80bc8861f4d538c1762725abe5785\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_7ca80bc8861f4d538c1762725abe5785\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_7ca80bc8861f4d538c1762725abe5785\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dashboard.save('bike_sharing_demand_model_perfomance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Perfomance Report short (verbose_level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = Dashboard(tabs=[RegressionPerformanceTab(verbose_level=0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/icon?family=Material+Icons\"/>\n",
       "<style>\n",
       ".reset-this-parent {\n",
       "  all: initial;\n",
       "}\n",
       ".reset-this-parent h5 {\n",
       "  all: initial;\n",
       "  font: initial;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "    var evidently_dashboard_a9aff064d5104394b79e151c6de100a0 = {\"name\": \"evidently_dashboard_a9aff064d5104394b79e151c6de100a0\", \"widgets\": [{\"type\": \"counter\", \"title\": \"\", \"size\": 2, \"details\": \"\", \"alertsPosition\": null, \"alertStats\": null, \"params\": {\"counters\": [{\"value\": \"\", \"label\": \"Regression Model Performance Report. 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\"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"title\": {\"text\": \"Error (Predicted - Actual)\"}}, \"yaxis\": {\"title\": {\"text\": \"Percentage\"}}}}, \"insights\": [], \"additionalGraphs\": [], \"alerts\": [], \"tabs\": []}]};\n",
       "    var additional_graphs_evidently_dashboard_a9aff064d5104394b79e151c6de100a0 = {};\n",
       "</script>\n",
       "<script>\n",
       "function domReady(fn) {\n",
       "  // If we're early to the party\n",
       "  document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "  // If late; I mean on time.\n",
       "  if (document.readyState === \"interactive\" || document.readyState === \"complete\" ) {\n",
       "    fn();\n",
       "  }\n",
       "}\n",
       "\n",
       "domReady(function () {\n",
       "    requirejs([\"evidently\"], function(ev) {\n",
       "        drawDashboard(evidently_dashboard_a9aff064d5104394b79e151c6de100a0,\n",
       "        new Map(Object.entries(additional_graphs_evidently_dashboard_a9aff064d5104394b79e151c6de100a0)),\n",
       "        \"root_evidently_dashboard_a9aff064d5104394b79e151c6de100a0\");\n",
       "    },\n",
       "    function(err) {\n",
       "        $(\"#root_evidently_dashboard_a9aff064d5104394b79e151c6de100a0\").innerHTML = \"Failed to load\";\n",
       "    })\n",
       "});\n",
       "</script>\n",
       "<div class=\"reset-this-parent\" id=\"root_evidently_dashboard_a9aff064d5104394b79e151c6de100a0\">Loading...</div>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dashboard.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dashboard.save('bike_sharing_demand_model_perfomance.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Model Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_regression_performance_profile = Profile(sections=[RegressionPerformanceProfileSection()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_regression_performance_profile.calculate(ref_data, prod_data, column_mapping=column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_profile = bike_regression_performance_profile.json() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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