data_science_tools/Yellowbrick.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:39.830177Z",
     "start_time": "2020-09-11T14:50:38.783489Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/khuyentran/anaconda3/envs/scraping/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from yellowbrick.datasets.loaders import load_occupancy\n",
    "from yellowbrick.features import Rank1D\n",
    "from yellowbrick.features import Rank2D\n",
    "import datapane as dp \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use a list to save all the plots and generate a report with Datapane. Make sure to log in [Datapane](https://datapane.com/) if you would like to create a [report of this notebook](https://datapane.com/khuyentran1401/reports/yellow_brick/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:39.834867Z",
     "start_time": "2020-09-11T14:50:39.832421Z"
    }
   },
   "outputs": [],
   "source": [
    "report = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:39.883996Z",
     "start_time": "2020-09-11T14:50:39.836237Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = load_occupancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:39.894982Z",
     "start_time": "2020-09-11T14:50:39.885029Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>temperature</th>\n",
       "      <th>relative humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.180</td>\n",
       "      <td>27.2720</td>\n",
       "      <td>426.0</td>\n",
       "      <td>721.250000</td>\n",
       "      <td>0.004793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.150</td>\n",
       "      <td>27.2675</td>\n",
       "      <td>429.5</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>0.004783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.150</td>\n",
       "      <td>27.2450</td>\n",
       "      <td>426.0</td>\n",
       "      <td>713.500000</td>\n",
       "      <td>0.004779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.150</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>708.250000</td>\n",
       "      <td>0.004772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>426.0</td>\n",
       "      <td>704.500000</td>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>701.000000</td>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>701.666667</td>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>699.000000</td>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.100</td>\n",
       "      <td>27.2000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>689.333333</td>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.075</td>\n",
       "      <td>27.1750</td>\n",
       "      <td>419.0</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>0.004745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temperature  relative humidity  light         CO2  humidity\n",
       "0       23.180            27.2720  426.0  721.250000  0.004793\n",
       "1       23.150            27.2675  429.5  714.000000  0.004783\n",
       "2       23.150            27.2450  426.0  713.500000  0.004779\n",
       "3       23.150            27.2000  426.0  708.250000  0.004772\n",
       "4       23.100            27.2000  426.0  704.500000  0.004757\n",
       "5       23.100            27.2000  419.0  701.000000  0.004757\n",
       "6       23.100            27.2000  419.0  701.666667  0.004757\n",
       "7       23.100            27.2000  419.0  699.000000  0.004757\n",
       "8       23.100            27.2000  419.0  689.333333  0.004757\n",
       "9       23.075            27.1750  419.0  688.000000  0.004745"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:39.967111Z",
     "start_time": "2020-09-11T14:50:39.895992Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Table(pd.concat([X, y],axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.148687Z",
     "start_time": "2020-09-11T14:50:39.968073Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7997dd6220>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "visualizer = Rank2D(algorithm='pearson')\n",
    "visualizer.fit(X, y)           # Fit the data to the visualizer\n",
    "visualizer.transform(X)        # Transform the data\n",
    "visualizer.show()              # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.464010Z",
     "start_time": "2020-09-11T14:50:40.150255Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.611171Z",
     "start_time": "2020-09-11T14:50:40.465609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968bd32e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.target import ClassBalance\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "visualizer = ClassBalance(labels=[\"unoccupied\", \"occupied\"])\n",
    "\n",
    "visualizer.fit(y)        # Fit the data to the visualizer\n",
    "visualizer.show()        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.636030Z",
     "start_time": "2020-09-11T14:50:40.612458Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results of the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find f1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.718421Z",
     "start_time": "2020-09-11T14:50:40.637071Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9813041495668035"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Specify the target classes\n",
    "classes = [\"unoccupied\", \"occupied\"]\n",
    "\n",
    "# Create the training and test data\n",
    "tscv = TimeSeriesSplit()\n",
    "for train_index, test_index in tscv.split(X):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "f1_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.721858Z",
     "start_time": "2020-09-11T14:50:40.719644Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.870752Z",
     "start_time": "2020-09-11T14:50:40.723074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "visualizer = classification_report(\n",
    "    model, X_train, y_train, X_test, y_test, classes=classes, support=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.903495Z",
     "start_time": "2020-09-11T14:50:40.871926Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:40.932952Z",
     "start_time": "2020-09-11T14:50:40.904455Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import ConfusionMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.020310Z",
     "start_time": "2020-09-11T14:50:40.934129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968ab6490>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "cm = ConfusionMatrix(model, classes=classes, percent=True)\n",
    "\n",
    "cm.fit(X_train, y_train)\n",
    "\n",
    "cm.score(X_test, y_test)\n",
    "\n",
    "cm.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.037435Z",
     "start_time": "2020-09-11T14:50:41.021357Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROCAUC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ROCAUC plot allows the user to visualize the tradeoff between the classifier’s sensitivity and specificity.\n",
    "\n",
    "a ROC curve displays the true positive rate on the Y axis and the false positive rate on the X axis on both a global average and per-class basis. \n",
    "\n",
    "The ideal point is therefore the top-left corner of the plot: false positives are zero and true positives are one.\n",
    "\n",
    "The higher the area under the curve (AUC), the better the model generally is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.040883Z",
     "start_time": "2020-09-11T14:50:41.038459Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import ROCAUC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.189593Z",
     "start_time": "2020-09-11T14:50:41.041988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968a7fc70>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "visualizer = ROCAUC(model, classes=classes)\n",
    "visualizer.fit(X_train, y_train)        \n",
    "visualizer.score(X_test, y_test)        \n",
    "visualizer.show()                       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.231439Z",
     "start_time": "2020-09-11T14:50:41.190648Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:41.351518Z",
     "start_time": "2020-09-11T14:50:41.232391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968a52af0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.classifier import ClassPredictionError\n",
    "\n",
    "\n",
    "# Instantiate the classification model and visualizer\n",
    "visualizer = ClassPredictionError(model, classes=classes)\n",
    "\n",
    "# Fit the training data to the visualizer\n",
    "visualizer.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "visualizer.score(X_test, y_test)\n",
    "\n",
    "# Draw visualization\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:43.611844Z",
     "start_time": "2020-09-11T14:50:41.352499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968a0df70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.classifier import DiscriminationThreshold\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "visualizer = DiscriminationThreshold(model)\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:43.666496Z",
     "start_time": "2020-09-11T14:50:43.614178Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:43.669413Z",
     "start_time": "2020-09-11T14:50:43.667635Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.model_selection import validation_curve\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:47.005667Z",
     "start_time": "2020-09-11T14:50:43.670272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "viz = validation_curve(\n",
    "    model, X, y, param_name=\"max_depth\",\n",
    "    param_range=np.arange(1, 11), cv=10, scoring=\"f1\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:47.045576Z",
     "start_time": "2020-09-11T14:50:47.006724Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:47.048531Z",
     "start_time": "2020-09-11T14:50:47.046602Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.model_selection import LearningCurve\n",
    "from sklearn.model_selection import StratifiedKFold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:47.058514Z",
     "start_time": "2020-09-11T14:50:47.049501Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the learning curve visualizer\n",
    "cv = StratifiedKFold(n_splits=12)\n",
    "sizes = np.linspace(0.3, 1.0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:50.617040Z",
     "start_time": "2020-09-11T14:50:47.059768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f796884c310>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "visualizer = LearningCurve(\n",
    "    model, cv=cv, scoring='f1', train_sizes=sizes, \n",
    ")\n",
    "\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:50.657570Z",
     "start_time": "2020-09-11T14:50:50.618163Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:50.660496Z",
     "start_time": "2020-09-11T14:50:50.658627Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.model_selection import CVScores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:51.280211Z",
     "start_time": "2020-09-11T14:50:50.661379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79687c6760>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a cross-validation strategy\n",
    "cv = StratifiedKFold(n_splits=12, random_state=42)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Instantiate the classification model and visualizer\n",
    "visualizer = CVScores(model, cv=cv, scoring='f1_weighted')\n",
    "\n",
    "visualizer.fit(X, y)        # Fit the data to the visualizer\n",
    "visualizer.show()           # Finalize and render the figure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:51.327277Z",
     "start_time": "2020-09-11T14:50:51.281308Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the features ranked according to the explained variance each feature contributes to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:51.331858Z",
     "start_time": "2020-09-11T14:50:51.328864Z"
    }
   },
   "outputs": [],
   "source": [
    "from yellowbrick.model_selection import FeatureImportances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:51.457691Z",
     "start_time": "2020-09-11T14:50:51.333556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VoEEDubi4yMkp669Rb29vxcTEKC0tTdeuXdP27dslKc/756ZRo0bau3evrl+/rqSkpBxvSM6JzWbTunXrtGfPHseLdm7ngq+vry5duqQffvhBkvT6669n+5PpadOmOS41VqtWTRUqVMhyvri4uKhdu3aOc+V///ufvvvuuzy9sOdVp06d9PHHH+vatWuSbvyb2rRp0y3PlbJly+rpp59WaGiozpw541g/bdo0/fTTT7rnnnsc7cfFxcnNzU01a9aU1Wp1HEtKSoo2btzoGBMPDw/VrFlTknJdntfj+fDDD2Wz2WQYhl5//XV98cUXf3OU7j7M0KBIzZgxQ9OnT9eHH34oSerZs6fuvfdede/eXZ9++qkCAwN13333KSQkRCNHjtS8efMUHBysRYsWyc/PT/v27dPo0aP19NNPy9vb+5Z/9dGgQQONGDFCwcHBstvt8vLy0syZM/P1eDw9PdWrVy/HO7xy5cqpcePGGj58uAYNGiS73a569eppxowZOe7ftm1b/fvf/9ajjz6qt956K9cxyGmWJdO4ceMcU97u7u5atGiRSpUqpZCQEM2cOdPxTtPPz08PPPCAbDabPv30UwUFBcnV1VWenp6aM2dOtnZDQkI0Y8YMdenSRU5OTho+fHiWafGc9O/fX6dPn1bv3r3l5OSkf/zjH5o7d27eB/T/3AnnSYkSJdSvXz/H/RlOTk6aMmVKlhdCSWrZsqWaNGmioKAgVa1aVZ06ddLXX3+d5/1zExgYqL1796pLly6qXr26HnnkEe3fvz/HbW02m+NSVnJysho3bqy1a9c6Zg9zOxdcXFwUHh7uuM+pevXqmjdvXpaZoX/+85+aNm2aZs+eLcMw5O/vr9atWys6OtqxzcyZMzVlyhRFRESoRIkSCgsL07333qvY2Ng8HevtBAQE6OTJk+rTp4+kG8HqlVdekZT7uSJJo0ePVrly5TRy5EjZbDY5OTmpU6dO2f491q1bV+3bt3fc+zZhwgQdOnRIwcHBevfddzVp0iR17txZzs7OjjGSlOPyzBB9K5l/idetWzcZhqGGDRtq6NCh+TJWdxOLYfzhb9UA/CVF9WF8uHtkXjKTpHXr1umbb77Jtw+dA8yOS04AYAI///yzOnXqpMTERFmtVu3YsUNNmzYt6rKAOwaXnADABOrVq6fevXurb9++cnZ2VtOmTbPdFAvczbjkBAAATI9LTgAAwPS45FRA7Ha7UlJSVKJEiRz/BBYAAORd5odcuru75/hxBwSaApKSkqITJ04UdRkAABQrderUyfJJ1JkINAWkRIkSkm4M/J/5EDH8OceOHXN8nxEKDuNcOBjngscYF46CGOf09HSdOHHC8fr6RwSaApJ5mcnV1TXHLx5E/mF8CwfjXDgY54LHGBeOghrn3G7j4KZgAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgegQaAABgenyXUwE7dfgfctKFoi6j2HKXdPxAUVdR/DHOhYNxLniMcSEpEVXoXTJDAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATI9AAwAATO+uDTQRERGaPHmypk2blus2UVFRGjNmTLblx48fV0xMTEGWBwAA/oS7NtBIUtmyZTVr1qw/vV9kZKROnz6d/wUBAIC/xKWoCyhK586dU9++fRUREaHNmzdr5cqVqly5ssqXL69WrVqpSpUqSklJ0csvv6xffvlFQUFBCgwM1Pr16+Xp6SkvLy81bty4qA8DAIC73l0daDLZ7Xa99tprioiIkJubm7p3765WrVpJkk6dOqVt27bJbrerU6dOGjVqlPz8/BQUFESYAQDgDkGgkRQfH6/SpUurQoUKkqTWrVs71tWvX1/33HOPJMkwjCKpDwAA3NpdfQ9NJsMw5OT0/4fCYrE4Hru4kPkAALjTEWgkeXh4KCEhQYmJibp+/bq+/fbbW25vsVhks9kKqToAAHA7TD/oxizMyJEjNWjQIFWvXl0NGzbMMmPzRw899JDCwsLk7u6e5fIUAAAoGhaDG0MkSdu3b1erVq3k4eGhp556Ss8//7wefPDBv9xeWlqajh07pnsyestJF/KxUgAA7mwpJaLk6+ubr21mvq42bNhQJUuWzLaeGZr/c/36dQ0dOlT33HOP6tWr97fCDAAAKFwEmv/Tu3dv9e7du6jLAAAAfwE3BQMAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANMj0AAAANNzKeoCirtazX5VyZIli7qMYis6Olq+vr5FXUaxxzgXDsa54DHGhSM6OrrQ+2SGBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB7f5VTAPq41RtYLSbfcZqDt/UKqBgCA4okZGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHouRV1AYTt9+rTmzJmjuLg42e12NWvWTKGhobLZbJo7d65++OEHubi4qEKFCpo+fbruvfdeSdKCBQsUHR0tq9WqZ599Vp07dy7iIwEAAJnuqkBjs9k0evRoTZ06VS1atJBhGAoLC9Py5csVHx8vb29vbd68WZIUHR2tp59+Wps3b1Z0dLROnjypDRs2KD4+Xn369CHQAABwB7mrAs3XX3+tmjVrqkWLFpIki8WicePGKSMjQz179lRkZKRjW19fXzVu3Fi7du1SYGCgGjduLEkqW7asrl27JpvNJmdn5yI5DgAAkNVddQ/Nf//7X9WrVy/LslKlSik2NlY1a9aUi0vWfFevXj3FxMTI2dlZbm5ukqSPPvpI7du3J8wAAHAHuatmaCwWi2w2W56XG4aRJbjs3LlTH330kd59990CrRMAAPw5d9UMTc2aNXX06NEsy9LT02WxWBQTE6P09PQs644fP65atWpJkr788ku9+eabevvtt1WmTJlCqxkAANzeXRVo2rZtq3Pnzmn37t2SJLvdroULF2rbtm3q2LGjli1b5tj20KFD+umnn9ShQwclJydrwYIFeuutt+Th4VFU5QMAgFzcVZecnJyctHLlSk2bNk3Lli2Tq6ur2rRpo1GjRslqterVV19Vz5495erqKk9PTy1ZskTOzs767LPPFB8fr5CQEEdb8+fPl4+PTxEeDQAAyHRXBRpJ8vb21ptvvpltuaurqyZOnJjjPo8//rgef/zxgi4NAAD8RXfVJScAAFA8EWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpEWgAAIDpuRR1AcVdr1NLVbJkyaIuAwCAYo0ZGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHoEGgAAYHp8l1MB+6HBP6TfLjieN79qLcJqAAAonpihAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApkegAQAApleogebzzz8vzO5u6fjx44qJiSnqMgAAQD4otEATGxurTz/9tLC6u63IyEidPn26qMsAAAD5wKWwOpo1a5Z++OEHLVu2TCdOnFBiYqJsNpumTJmiunXrKiAgQI899pi2b9+u6tWrq0GDBo7Hr776qiZMmCA3Nzf997//VXx8vObOnav69etr3bp12rJli5ycnBQQEKBhw4YpPDxcZ8+eVWxsrFatWqWJEyfq4sWLSk1N1ejRo+Xj46P169fL09NTXl5eCgkJ0ZYtW+Tu7q758+erdu3akqQvvvhCv//+uxYvXqydO3dm6wcAANwZCm2G5qmnnlKLFi1ksVjk5+en//znP5oxY4bmz58vSbLb7apfv742btyoQ4cOqUqVKvroo48UHR2tpKQkSZLVatWqVav0wgsvaPny5Tp79qy2b9+u999/X+vWrdOOHTt0/vx5SVJGRobee+89JScnq127dlq7dq2WLFmi8PBwPfDAA/Lz89OLL76oxo0b51rzhQsXtG7dOqWnp+faDwAAKHqFNkOT6fDhw4qLi9Mnn3wiSbp27ZpjXePGjWWxWOTl5aX69etLkjw9PZWcnCxJatOmjSSpadOmWrRokY4ePaozZ85oyJAhkqSUlBSdO3fO0ZYklS1bVkePHtWGDRvk5OSkhISEPNfaqFEjWSyWXPvx8fH5O0MBAADySaEHmhIlSmjq1Klq1qxZtnXOzs45PjYMQ9KNWZxMFotFJUqUUIcOHTRr1qws7Rw4cEAlSpSQJG3dulWJiYl67733lJCQoH79+t2yvoyMjCy1Zv4/p34AAMCdodAuOTk5OclqtapJkybauXOnJOnXX3/Vv//97zy3ER0dLenGLE+tWrXUoEEDRUVF6dq1azIMQ2FhYbp+/XqWfeLj41W1alU5OTkpMjJS6enpkm4EIpvNJkkqXbq0Ll26JJvNpiNHjmTrNy/9AACAolNoMzS1atXSTz/9pKpVq+rChQsaOHCg7Ha7Jk+enOc20tLS9Oyzz+rChQtauHChfHx8NGTIEA0aNEjOzs4KCAhQqVKlsuzTuXNnjRw5Ut9//70effRRVa5cWcuWLdNDDz2ksLAwubu7a/DgwRoxYoRq1Kihf/zjH9n6zUs/AACg6FiMzOs5d7gJEyYoKChIHTt2LOpS8iQtLU3Hjh2T/fHe0m8XHMubX7UWYVXFT3R0tHx9fYu6jGKPcS4cjHPBY4wLR0GMc+brasOGDVWyZMls6/mkYAAAYHqFflPwXzVv3ryiLgEAANyhmKEBAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACm51LUBRR3jX/8VSVLlizqMgAAKNaYoQEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKZHoAEAAKbHl1MWsI++m68MI1WS9ES7eUVcDQAAxRMzNAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPQINAAAwPT+dqAJDw/X2rVrc11//PhxxcTESJLGjh2r69ev/6V+YmNj1bdv37+0b05yqmXPnj2aMGGCLl26pGnTpkmSDh48qCtXruRbvwAAIP8V+AxNZGSkTp8+LUlavHixSpUqVdBd5smtaqlYsaJmzZolSdq4cSOBBgCAO5zLrVZGREToiy++0O+//67Fixdr586d2rJli5ycnBQQEKBhw4Y5trVarQoNDdXFixeVmpqq0aNHy8fHR+vXr5enp6e8vLwUEhKiNWvWaNiwYfr8888lSZs2bdLx48c1bNgwTZ48WRkZGXJ2dlZYWJh8fHyy1GMYhqZPn66jR4+qQYMGmj17tiZMmKCgoCB17NhRe/bs0eeff65RoxYB4GUAABSYSURBVEZp/Pjxqlatmg4fPqwBAwbol19+0ZEjRzRo0CANGjRI/v7+2rJli2JjYxUaGqpy5cqpWrVqkm7MBo0ZM0YvvfSSdu7cqZMnT6pjx46yWq0KCQmRJD355JMKDQ1V3bp18/UHAgAA/rxbBhpJunDhgtavX6/Y2Fht375d77//viRpwIAB6tKli2O7xMREtWvXTn369NHZs2f1wgsvKCIiQn5+fgoKClLjxo0lSR4eHqpcubJOnjyp2rVra9euXRo2bJiWLFmiYcOGqU2bNtq3b59ef/11hYWFZanl9OnTWrFihby8vNShQwclJSXlWvfPP/+s5cuXKzExUd27d9euXbuUlpam0aNHa9CgQY7tXn/9dY0aNUoBAQGaPn16ljbatm2revXqaerUqfL29lZwcLBCQkKUnJyshIQEwgwAAHeI2waaRo0ayWKx6OjRozpz5oyGDBkiSUpJSdG5c+cc25UtW1ZHjx7Vhg0b5OTkpISEhFzb7Ny5s/bs2aNq1arp5MmTatasmSZPnqyYmBi98cYbstls8vT0zLZftWrVVLFiRUlShQoVlJycnGsf1apVU/ny5eXq6ipPT09VqlRJKSkp2fY5deqUHnzwQUlSy5Yt9cUXX+TYnoeHh6pXr64ff/xRMTExWcIcAAAoWrcNNCVKlHD8v0OHDo57SzIdOHBAkrR161YlJibqvffeU0JCgvr165drmwEBAQoJCVHt2rXl5+cni8WiEiVKaMmSJfL29s51P2dn5yzPDcOQxWJxPLdarTlu6+KS+2He3Ibdbs91O0nq3bu3tm/frvPnz2vs2LG33BYAABSePN8U3KBBA0VFRenatWsyDENhYWFZ/kooPj5eVatWlZOTkyIjI5Weni5JslgsstlsWdqqVKmSLBaLtm7dqqCgIElSkyZNtHPnTknS/v37tWXLljzV5e7urkuXLkmSoqOj83o4DjVq1NCxY8ckSVFRUdnW31x/+/btdfDgQSUlJalq1ap/ui8AAFAw8hxofHx8NGTIEA0aNEiPPfaYKlasmOWvhDp37qzdu3dr6NChuueee1S5cmUtW7ZMDz30kMLCwrR///4s7fn7++vgwYPy9fWVJI0aNUq7du3SoEGDtHz5cjVt2jRPdfXq1UsrV67UU089dcuZmNyMHDlSCxcu1DPPPOOYjbpZixYtNGbMGJ08eVKurq6qVauWOnbs+Kf7AQAABcdiGIZR1EWYRVpamgYOHKhVq1apTJkyt9322LFjOn79U2UYqZKkJ9rNK4wy7yrR0dGOUIyCwzgXDsa54DHGhaMgxjnzdbVhw4YqWbJktvV8UnAeff/99+rfv7+GDBly2zADAAAK15+/RnOXatq0qT755JOiLgMAAOSAGRoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6BBoAAGB6LkVdQHHX76FQlSxZsqjLAACgWGOGBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BBgAAmB6BpoDVemWTnF9aU9RlAABQrBFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6RFoAACA6Zkq0ERFRWnMmDH50tbIkSOzLVu7dq3Cw8P1888/a+nSpZKkXbt2KT09PV/6BAAABcOlqAsoKm+88Uau6+rVq6d69epJklatWqVWrVrJ1dW1sEoDAAB/kukCTUpKil5++WX98ssvCgoKUlRUlKZOnao6depo7dq1io+PV4sWLbR69Wo5Ozvrp59+0ogRI/Tll1/q559/1vjx4xUQEKCWLVsqKipK+/fv15w5c1ShQgVVrFhR9913n6KiorRu3Tr5+/vr+++/1zPPPKOGDRuqdu3a6t+/vySpa9euWrduncqXL1/EIwIAAEwXaE6dOqVt27bJbrerU6dOql27do7b/fzzz9q+fbsOHjyol19+Wbt27dKRI0e0Zs0aBQQEOLZ79dVXtXDhQtWtW1fPPPOM7rvvPse63r17a+nSpXr77bd17tw5zZs3T/3799evv/6q++67jzADAMAdwnSBpn79+rrnnnskSYZh5Lpd3bp15erqqooVK+r++++Xm5ubvLy8lJycnGW7c+fOqW7dupKk5s2bKy0tLcf26tSpo6SkJMXFxWnXrl3q0aNHPh0RAAD4u0x1U7AkubjknsGsVmuO291qHyen/z8EtwpIktS9e3ft2LFD+/fvV6dOnfJSLgAAKASmCzR/VLp0aV26dEmSdOjQoT+9f6VKlfTf//5XhmHo22+/zbbeYrHIZrNJuhFoIiIiVLFiRccsEQAAKHqmu+T0R48//rhmzZql6tWrq1q1an96/5CQEL3wwgvy8fFR5cqVs61v0aKFBg4cqNWrV6tChQpyc3NT9+7d86N0AACQT0wVaFq2bKmWLVs6nkdFRUmSOnTokOO20o17X9asWZPtcea+7du3V/v27XPdf+7cuY5lcXFxSkpKkp+fXz4cDQAAyC+mv+RUWHbu3KknnnhC48aNy3LfDQAAKHqmmqEpSgEBAVn+3BsAANw5mGoAAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACmR6ABAACm51LUBRR3pyb3UcmSJYu6DAAAijVmaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOnxbdsFxDAMSVJ6enoRV1L8paWlFXUJdwXGuXAwzgWPMS4c+T3Oma+nma+vf2QxcluDvyU5OVknTpwo6jIAAChW6tSpozJlymRbTqApIHa7XSkpKSpRooQsFktRlwMAgKkZhqGMjAy5u7vLySn7HTMEGgAAYHrcFAwAAEyPQAMAAEyPQAMAAEyPQAMAAEyPz6EpAHPmzNGRI0dksVg0adIkNW7cuKhLKjYWLFig6OhoWa1WPfvss2rUqJHGjx8vm82mihUrauHChXJ1dS3qMouF69evq3v37nruuefUunVrxjmfffLJJ3rnnXfk4uKiMWPG6IEHHmCM81lKSopCQ0OVmJiojIwMPf/886pYsaJmzJghSXrggQc0c+bMoi3SxE6cOKHnnntOTzzxhAYPHqwLFy7keA5/8skn+s9//iMnJyc99thj6t+/f4HUwwxNPvv222915swZbdiwQa+88opeeeWVoi6p2Dhw4IBOnjypDRs26J133tGcOXO0dOlSDRw4UO+9956qV6+ujz76qKjLLDbeeOMNlStXTpIY53wWHx+v5cuX67333tObb76pXbt2McYFYNOmTapRo4bWrFmjJUuWOH4nT5o0SevXr9fVq1e1b9++oi7TlFJTUzV79my1bt3asSynczg1NVXLly/XqlWrtGbNGv3nP/9RQkJCgdREoMln+/fvV0BAgCSpVq1aSkxM1NWrV4u4quKhefPmWrJkiSSpbNmyunbtmqKiotSpUydJUseOHbV///6iLLHYOHXqlH799Vd16NBBkhjnfLZ//361bt1apUuXlre3t2bPns0YF4Dy5cs7XjyTkpLk4eGhc+fOOWbNGee/ztXVVW+//ba8vb0dy3I6h48cOaJGjRqpTJkyKlWqlB588EEdOnSoQGoi0OSzy5cvq3z58o7nnp6eunTpUhFWVHw4OzvLzc1NkvTRRx+pffv2unbtmmNa3svLi7HOJ/Pnz9eECRMczxnn/BUbG6vr169rxIgRGjhwoPbv388YF4Bu3brp/PnzCgwM1ODBgzV+/HiVLVvWsZ5x/utcXFxUqlSpLMtyOocvX74sT09PxzYF+ZrIPTQFjM8tzH87d+7URx99pHfffVedO3d2LGes88fmzZvVtGlT3XfffTmuZ5zzR0JCgpYtW6bz589ryJAhWcaVMc4fH3/8sXx8fLRy5UodP35czz//fJaPzGecC05uY1uQY06gyWfe3t66fPmy4/nvv/+uihUrFmFFxcuXX36pN998U++8847KlCkjNzc3Xb9+XaVKldLFixezTH/ir9m7d6/Onj2rvXv36rfffpOrqyvjnM+8vLzUrFkzubi4qFq1anJ3d5ezszNjnM8OHTqkdu3aSZLq1q2rtLQ0Wa1Wx3rGOX/l9Hsip9fEpk2bFkj/XHLKZ23bttXnn38uSfrxxx/l7e2t0qVLF3FVxUNycrIWLFigt956Sx4eHpKkNm3aOMZ7x44d8vPzK8oSi4V//etf2rhxoz744AP1799fzz33HOOcz9q1a6cDBw7IbrcrPj5eqampjHEBqF69uo4cOSJJOnfunNzd3VWrVi199913khjn/JbTOdykSRMdPXpUSUlJSklJ0aFDh/TQQw8VSP98l1MBWLRokb777jtZLBZNnz5ddevWLeqSioUNGzYoPDxcNWrUcCybN2+epkyZorS0NPn4+Gju3LkqUaJEEVZZvISHh6tKlSpq166dQkNDGed8tH79esdfMo0cOVKNGjVijPNZSkqKJk2apCtXrshqteqFF15QxYoVNW3aNNntdjVp0kQTJ04s6jJN6dixY5o/f77OnTsnFxcXVapUSYsWLdKECROyncPbt2/XypUrZbFYNHjwYPXs2bNAaiLQAAAA0+OSEwAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDQAAMD0CDYBCFRERoZdffvmW2/z666/68ccfJUkrVqzQ3r17/3afH3744d9q48+4uX4AhYNPCgZwx4mMjFSFChXUoEEDDR8+/G+317dv33yoKu9urh9A4SDQAMgXUVFRev3111WyZEkFBgaqV69emjVrls6cOaOUlBR1795dw4YNy7JPZGSk3nnnHbm6uspms2nBggW6dOmS1q5dq9KlS6tUqVL6+uuv5evrqy+//FKBgYHq0aOHJGny5Mlq0KCBunXrpunTpysuLk5Xr17Vk08+6dgmU3h4uKxWq8aOHatmzZpp5MiR2r17tzIyMjRixAh98MEHiomJ0YwZM9SuXTsFBwerfv36OnnypC5duqRnn31W3bt31+XLlzV58mSlpqYqPT1dTz/9tAIDAxUeHq7Y2FidP39eISEhWeqvX7++pk+fLmdnZ129elUhISHy8/NTeHi4EhIS9Ntvv+nMmTNq2bKlpk6dKrvdrrCwMB07dkyS9OSTT+qRRx7R8ePHNX/+fFmtVmVkZGjatGmqX79+4fxwARMg0ADIN8eOHdOuXbvk4eGhd955R97e3goLC5PNZtNjjz2mNm3aZNk+KSlJixcvlo+Pj9566y2tW7dOoaGh8vPzk6+vr3r06KGvv/5aktSzZ09t3rxZPXr0UEZGhvbt26dx48bpX//6l/z8/PToo48qNTVVvXr1Utu2bbN8w+/NUlNT1bBhQw0fPlzBwcHavXu33n77bUVEROi9995zfPeP1WrVu+++qzNnzmjAgAHq2rWrli5dqubNm+vpp5/WlStX1LNnT7Vu3VrSjW/QXrt2rSwWS5b6o6Ki9MILL6h58+Y6fPiwZs+e7fi4/Z9++klr165VRkaGWrdurTFjxmjPnj26fPmyPvjgAyUlJenll19W586dNW7cOC1fvlzVqlXT8ePHNWnSJEVERBTUjxIwHQINgHxTo0YNx/dsRUVF6bffftPBgwclSenp6frf//6XZfsKFSooNDRUhmHo0qVLatasWa5tt2/fXjNnzlRqaqoOHjyoxo0by8PDQ1FRUTp69Kg2b94sSXJxcVFsbGyugUaSfH19JUmVKlXSgw8+KEmqXLmykpOTHdtkBpvq1avLYrHoypUrOnLkiAYMGCDpxhdMVqpUSTExMZKkJk2ayGKxZOurYsWKWrBggRYvXqyMjAwlJCRkqcPZ2VnOzs4qX768EhMT9cMPP6hly5aSpLJly2rFihW6cuWKYmJiNHnyZMe+V69eld1ul5MTt0ICEoEGQD66+buHXF1d9fzzz6tLly5ZtsmcVcjIyFBISIg2bdqk+++/X2vXrnVcZsmJq6urHn74Ye3du1f79u1Tr169HMunT5+uRo0a5blOZ2fnHB/fzG63Ox4bhiGLxZJjYMlcltv3Ls2ePVvdunVTv379dOLECY0YMSLXvjP7ublv6cYxlihRQmvWrLnNkQF3L6I9gALh6+urbdu2SboRDubOnZtldiIlJUVOTk6qUqWK0tLStGvXLqWnp0u6ERIyMjKytdmjRw9FRkYqOjpaHTt2zNbP9evXNWPGDFmt1r9d/4EDByRJMTExcnJykqenp5o0aaIvv/xSknTx4kX9/vvvWb4sNdPN9V++fFm1a9eWJH322WeOY8xNs2bNHH1cvXpV/fv3V8mSJVW1alXt27fPUdOyZcv+9jECxQkzNAAKxKBBg3Ty5Ek9/vjjstls6tChg+NylCR5eHioe/fu6tevn3x8fPTUU09p/Pjx2rZtm1q1aqUFCxboj9+d27x5c02cOFFt27aVq6urJGnUqFGaMmWKBgwYoPT0dD3++ONycfn7v9qsVqtGjhyp2NhYTZ06VU5OThozZowmT56s4OBgpaWlafbs2XJ3d8+27831Dxs2TOPHj1fVqlX1xBNPKDIyUvPmzctxP0l65JFHdOjQIf3zn/+UzWbTk08+KVdXV82fP19hYWFasWKFrFarJkyY8LePEShO+LZtAPiD4OBgjRw5MttNzADuXFxyAgAApscMDQAAMD1maAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOkRaAAAgOn9P7Gm0LO1u6MjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7968753730>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "viz = FeatureImportances(model)\n",
    "viz.fit(X, y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:51.482710Z",
     "start_time": "2020-09-11T14:50:51.458708Z"
    }
   },
   "outputs": [],
   "source": [
    "report.append(dp.Plot(fig))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-11T14:50:55.594132Z",
     "start_time": "2020-09-11T14:50:51.483706Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Publishing report and associated data - please wait..\n",
      "Report successfully published at https://datapane.com/khuyentran1401/reports/yellow_brick/\n"
     ]
    }
   ],
   "source": [
    "dp.Report(*report).publish(name='yellow_brick', visibily='PUBLIC')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}