Waterfall plot for the SHAP of any models class.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Waterfall Plots for SHAP Values of Any Models\n",
    "\n",
    "* Please read [Explain your model with the SHAP values](https://towardsdatascience.com/explain-your-model-with-the-shap-values-bc36aac4de3d) and [The SHAP with More Elegant Charts](https://dataman-ai.medium.com/the-shap-with-more-elegant-charts-bc3e73fa1c0c)\n",
    "\n",
    "### Table of Contents\n",
    "* [Section 1: Matplotlib](#section1)\n",
    "    * [Section 1.1: Random forest](#section11)\n",
    "    * [Section 1.2: XGB](sSection12)\n",
    "* [Section 2: Plotly](#section2)\n",
    "    * [Section 2.1: Random forest](#section21)\n",
    "    * [Section 2.2: XGB](#section22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import shap\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "path = \"/Downloads\"\n",
    "df = pd.read_csv(path + '/winequality-red.csv')\n",
    "\n",
    "features = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', \n",
    "            'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']\n",
    "Y = np.where(df['quality']>6,1,0)\n",
    "X =  df[features]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 1234)\n",
    "df['quality'] = df['quality'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['quality'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1) Matplotlib <a class=\"anchor\" id=\"section1\"></a>\n",
    "\n",
    "### (1.1) Random forest <a class=\"anchor\" id=\"section11\"></a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6940550067343673"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build the model with the random forest regression algorithm:\n",
    "rf_model = RandomForestRegressor(max_depth=6, random_state=0, n_estimators=10)\n",
    "rf_model.fit(X_train, Y_train).score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model_explainer = shap.TreeExplainer(rf_model)\n",
    "rf_shap_values = rf_model_explainer.shap_values(X_test)\n",
    "base_value = Y_train.mean()\n",
    "Y_pred_prob = rf_model.predict(X_test)\n",
    "Y_pred = np.where(Y_pred_prob>base_value,1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1.2) Waterfall plot  <a class=\"anchor\" id=\"section12\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "class waterfall():\n",
    "    def __init__(self, data, shap_values, \n",
    "                       base_value, \n",
    "                       path = \"\",\n",
    "                       green_color ='#29EA38' , \n",
    "                       red_color = '#FB3C62', \n",
    "                       n=8,\n",
    "                       title=\"The Prediction \" ,\n",
    "                       x_lab=\"\",\n",
    "                       y_lab=\"The predicted value\",\n",
    "                       formatting = \"{:,.2f}\",\n",
    "                       rotation_value = 90,\n",
    "                       figsize = (7,5)\n",
    "                \n",
    "                ):\n",
    "        self.data        = data\n",
    "        self.shap_values = shap_values\n",
    "        self.base_value  = base_value\n",
    "        self.green_color = green_color\n",
    "        self.red_color   = red_color\n",
    "        self.n           = n\n",
    "        self.title       = title\n",
    "        self.x_lab       = x_lab\n",
    "        self.y_lab       = y_lab\n",
    "        self.formatting  = formatting\n",
    "        self.rotation_value = rotation_value\n",
    "        self.figsize     = figsize\n",
    "        self._plot       = pd.DataFrame()\n",
    "        self.path        = path\n",
    "\n",
    "    def obs_to_explain(self):\n",
    "        '''\n",
    "          - data: the observation. It is a Pandas series. The index contains the variable names \n",
    "          - shap_values: the shap_values for the above observation \n",
    "          - base_value: the base_value, which is the expected value or the mean of the target value of the training set\n",
    "          - green_color: the color for the up bar\n",
    "          - red_color: the color for the down bar\n",
    "          - for_plot: a sorted data frame by the absolute value of shape in descending order\n",
    "          - n: show the top n (default) variables. The rest variables are summed up into \"others\"\n",
    "        '''\n",
    "        for_plot = pd.DataFrame({'data':np.round(self.data,2),\n",
    "                                 'shap':self.shap_values,\n",
    "                                 'shap_abs': np.abs(self.shap_values),\n",
    "                                 'label': self.data.index\n",
    "                                })\n",
    "        for_plot = for_plot.sort_values(by='shap_abs',ascending=False)\n",
    "\n",
    "        # Split the variables into n and the rest. Only show the top n\n",
    "        for_plot1 = for_plot.iloc[0:self.n,:]\n",
    "        for_plot2 = for_plot.iloc[self.n:,:]\n",
    "\n",
    "        # Sum up the rest as 'others'\n",
    "        rest = pd.DataFrame({'data': '','shap':for_plot2['shap'].sum(), 'label': 'Others'},index=['others'])\n",
    "        for_plot = for_plot1.append(rest)\n",
    "\n",
    "        # Sum up the rest into 'others'\n",
    "        base = pd.DataFrame({'data': np.round(self.base_value,2),'shap':self.base_value, 'label': 'Base value'},index=['base_value'])\n",
    "        for_plot = base.append(for_plot)\n",
    "\n",
    "        for_plot['blank'] = for_plot['shap'].cumsum().shift(1).fillna(0) # +  base_value\n",
    "        for_plot['label'] = + for_plot['label'] + \" =\" + for_plot['data'].map(str) \n",
    "        for_plot['color'] = np.where(for_plot['shap']>0,self.green_color,self.red_color)\n",
    "        for_plot = for_plot.drop(['data','shap_abs'],axis=1)\n",
    "        \n",
    "        self.for_plot = for_plot\n",
    "        \n",
    "        return(for_plot ) \n",
    "    \n",
    "    def plt_plot(self):\n",
    "        '''\n",
    "          - x_lab, y_lab: the x label and y label\n",
    "          - formatting: show the value of each bar \n",
    "        '''\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=self.figsize)\n",
    "\n",
    "        # Plot the waterfall    \n",
    "        plt.bar(range(0,len(self.for_plot.index)), self.for_plot['shap'], width=0.6,\n",
    "              bottom=self.for_plot['blank'],     color=self.for_plot['color'])     \n",
    "\n",
    "        #axis labels\n",
    "        plt.xlabel(\"\\n\" + self.x_lab)\n",
    "        plt.ylabel(self.y_lab + \"\\n\")\n",
    "\n",
    "        #Get the y-axis position for the labels\n",
    "        y_height = self.for_plot.shap.cumsum().shift(1).fillna(0)\n",
    "\n",
    "        plot_max = self.for_plot['shap'].max()\n",
    "        plot_min = self.for_plot['shap'].min()\n",
    "        pos_offset = plot_max / 40\n",
    "        plot_offset = plot_max / 15 \n",
    "        total = self.for_plot.sum().shap \n",
    "\n",
    "        # label the shap values\n",
    "        loop = 0\n",
    "        for index, row in self.for_plot.iterrows():\n",
    "            # For the last item in the list, we don't want to double count\n",
    "            if row['shap'] == total:\n",
    "                    y = y_height[loop]\n",
    "            else:\n",
    "                    y = y_height[loop] + row['shap']\n",
    "\n",
    "            if row['shap'] > 0:\n",
    "                    y += (pos_offset*2)\n",
    "                    plt.annotate(self.formatting.format(row['shap']),(loop,y),ha=\"center\", color = self.green_color, fontsize=10)\n",
    "            else:\n",
    "                    y -= (pos_offset*4)\n",
    "                    plt.annotate(self.formatting.format(row['shap']),(loop,y),ha=\"center\", color = self.red_color, fontsize=10)\n",
    "            loop+=1\n",
    "\n",
    "        # Range of the ylim\n",
    "        plt.ylim(plot_min-round(3.6*plot_offset, 7) ,plot_max+round(3.6*plot_offset, 7))\n",
    "\n",
    "        #Rotate the labels\n",
    "        plt.xticks(range(0,len(self.for_plot)), self.for_plot['label'], rotation=self.rotation_value)\n",
    "\n",
    "        #add the base value line and title\n",
    "        #plt.axhline(base_value, color='black', linewidth = 0.6, linestyle=\"dashed\")\n",
    "        plt.title(self.title)\n",
    "\n",
    "        import matplotlib.patches as mpatches\n",
    "        red_patch = mpatches.Patch(color=self.red_color, label='Down')\n",
    "        green_patch = mpatches.Patch(color=self.green_color, label='Up')\n",
    "        plt.legend(handles=[red_patch,green_patch],bbox_to_anchor=[1, 1], loc='upper left')\n",
    "\n",
    "        #plt.tight_layout()\n",
    "        #return(fig)\n",
    "    \n",
    "    def plotly_plot(self):\n",
    "        import plotly.graph_objects as go\n",
    "        import numpy as np\n",
    "\n",
    "        ys = self.for_plot['shap'].round(2)\n",
    "        ms = list(np.repeat('relative',len(ys)))\n",
    "        xs = list(self.for_plot['label'].values)\n",
    "        texts = self.for_plot['shap'].round(2)\n",
    "\n",
    "        fig = go.Figure(go.Waterfall(\n",
    "            name = \"20\", orientation = \"v\",\n",
    "            measure = ms,\n",
    "            x = xs,\n",
    "            textposition = \"outside\",\n",
    "            text = texts,\n",
    "            y = ys,\n",
    "            connector = {\"line\":{\"color\":\"rgb(63, 63, 63)\"} },\n",
    "           ) )\n",
    "\n",
    "\n",
    "        layout = go.Layout(\n",
    "            paper_bgcolor='rgba(0,0,0,0)',\n",
    "            plot_bgcolor='rgba(0,0,0,0)'\n",
    "        )\n",
    "\n",
    "        fig.update_layout(\n",
    "            template=\"plotly_white\",\n",
    "            title =\"The prediction of this observation is \" ,\n",
    "            margin=dict(l=120, r=120, t=60, b=60),\n",
    "            yaxis=dict(\n",
    "                title_text=\"The predicted value\" \n",
    "            ),\n",
    "            xaxis =dict(\n",
    "                tickangle = -90,\n",
    "                title_text = \"Variables\")\n",
    "        )\n",
    "        fig.write_html(self.path + \"/f.html\", auto_open=True)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>shap</th>\n",
       "      <th>label</th>\n",
       "      <th>blank</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base_value</th>\n",
       "      <td>0.137608</td>\n",
       "      <td>Base value =0.14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>#29EA38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>-0.055662</td>\n",
       "      <td>alcohol =9.5</td>\n",
       "      <td>0.137608</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>-0.036046</td>\n",
       "      <td>total sulfur dioxide =81.0</td>\n",
       "      <td>0.081945</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>-0.022881</td>\n",
       "      <td>sulphates =0.54</td>\n",
       "      <td>0.045899</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>-0.019963</td>\n",
       "      <td>volatile acidity =0.72</td>\n",
       "      <td>0.023018</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>0.014723</td>\n",
       "      <td>free sulfur dioxide =13.0</td>\n",
       "      <td>0.003055</td>\n",
       "      <td>#29EA38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>-0.004883</td>\n",
       "      <td>citric acid =0.22</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>-0.003330</td>\n",
       "      <td>density =1.0</td>\n",
       "      <td>0.012895</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>-0.003327</td>\n",
       "      <td>residual sugar =2.3</td>\n",
       "      <td>0.009564</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>-0.000794</td>\n",
       "      <td>Others =</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>#FB3C62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          shap                       label     blank    color\n",
       "base_value            0.137608            Base value =0.14  0.000000  #29EA38\n",
       "alcohol              -0.055662                alcohol =9.5  0.137608  #FB3C62\n",
       "total sulfur dioxide -0.036046  total sulfur dioxide =81.0  0.081945  #FB3C62\n",
       "sulphates            -0.022881             sulphates =0.54  0.045899  #FB3C62\n",
       "volatile acidity     -0.019963      volatile acidity =0.72  0.023018  #FB3C62\n",
       "free sulfur dioxide   0.014723   free sulfur dioxide =13.0  0.003055  #29EA38\n",
       "citric acid          -0.004883           citric acid =0.22  0.017778  #FB3C62\n",
       "density              -0.003330                density =1.0  0.012895  #FB3C62\n",
       "residual sugar       -0.003327         residual sugar =2.3  0.009564  #FB3C62\n",
       "others               -0.000794                    Others =  0.006237  #FB3C62"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = 10\n",
    "rf_shap_to = waterfall(X_test.iloc[obs,:],\n",
    "                              base_value = base_value,\n",
    "                              shap_values = rf_shap_values[obs],\n",
    "                              path = path,\n",
    "                              title=\"The Prediction \" ,\n",
    "                              x_lab=\"\",\n",
    "                              y_lab=\"The predicted value\",\n",
    "                              formatting = \"{:,.2f}\",\n",
    "                              rotation_value = 90,\n",
    "                              figsize = (7,5)\n",
    "                      \n",
    "                      )\n",
    "rf_shap_to.obs_to_explain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted value is: 0.005\n",
      "The expected value is: 0.138\n",
      "The predicted group is: 0\n",
      "The Actual value is: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs =10\n",
    "print(\"The predicted value is: \" +  str(np.round(Y_pred_prob[obs],3)))\n",
    "print(\"The expected value is: \" +  str(np.round(base_value,3)))\n",
    "print(\"The predicted group is: \" +  str(np.round(Y_pred[obs],0)))\n",
    "print(\"The Actual value is: \" +  str(np.round(Y_test[obs],3)))\n",
    "rf_shap_to.obs_to_explain()\n",
    "rf_shap_to.plt_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1.2) XGB <a class=\"anchor\" id=\"section12\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999638222863754"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "xgb_model = xgb.XGBRegressor(random_state=42)\n",
    "xgb_model.fit(X_train, Y_train).score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model_explainer = shap.TreeExplainer(xgb_model)\n",
    "xgb_shap_values = xgb_model_explainer.shap_values(X_test)\n",
    "base_value = Y_train.mean()\n",
    "Y_pred_prob = xgb_model.predict(X_test)\n",
    "Y_pred = np.where(Y_pred_prob>base_value,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = 10\n",
    "xgb_shap_to = waterfall(X_test.iloc[obs,:],\n",
    "                              base_value = base_value,\n",
    "                              shap_values = xgb_shap_values[obs],\n",
    "                              path = path,\n",
    "                              title=\"The Prediction \" ,\n",
    "                              x_lab=\"\",\n",
    "                              y_lab=\"The predicted value\",\n",
    "                              formatting = \"{:,.2f}\",\n",
    "                              rotation_value = 90,\n",
    "                              figsize = (7,5)\n",
    "                      \n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted value is: 0.001\n",
      "The expected value is: 0.138\n",
      "The predicted group is: 0\n",
      "The Actual value is: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs =10\n",
    "print(\"The predicted value is: \" +  str(np.round(Y_pred_prob[obs],3)))\n",
    "print(\"The expected value is: \" +  str(np.round(base_value,3)))\n",
    "print(\"The predicted group is: \" +  str(np.round(Y_pred[obs],0)))\n",
    "print(\"The Actual value is: \" +  str(np.round(Y_test[obs],3)))\n",
    "\n",
    "xgb_shap_to.obs_to_explain()\n",
    "xgb_shap_to.plt_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2) Plotly <a class=\"anchor\" id=\"section2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2.1) Random forest <a class=\"anchor\" id=\"section21\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted value is: 0.001\n",
      "The expected value is: 0.138\n",
      "The predicted group is: 0\n",
      "The Actual value is: 0\n"
     ]
    }
   ],
   "source": [
    "obs =10\n",
    "print(\"The predicted value is: \" +  str(np.round(Y_pred_prob[obs],3)))\n",
    "print(\"The expected value is: \" +  str(np.round(base_value,3)))\n",
    "print(\"The predicted group is: \" +  str(np.round(Y_pred[obs],0)))\n",
    "print(\"The Actual value is: \" +  str(np.round(Y_test[obs],3)))\n",
    "\n",
    "rf_shap_to.obs_to_explain()\n",
    "rf_shap_to.plotly_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2.2) XGB <a class=\"anchor\" id=\"section22\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model_explainer = shap.TreeExplainer(xgb_model)\n",
    "xgb_shap_values = xgb_model_explainer.shap_values(X_test)\n",
    "base_value = Y_train.mean()\n",
    "Y_pred_prob = xgb_model.predict(X_test)\n",
    "Y_pred = np.where(Y_pred_prob>base_value,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted value is: 0.001\n",
      "The expected value is: 0.138\n",
      "The predicted group is: 0\n",
      "The Actual value is: 0\n"
     ]
    }
   ],
   "source": [
    "obs =10\n",
    "print(\"The predicted value is: \" +  str(np.round(Y_pred_prob[obs],3)))\n",
    "print(\"The expected value is: \" +  str(np.round(base_value,3)))\n",
    "print(\"The predicted group is: \" +  str(np.round(Y_pred[obs],0)))\n",
    "print(\"The Actual value is: \" +  str(np.round(Y_test[obs],3)))\n",
    "\n",
    "xgb_shap_to.obs_to_explain()\n",
    "xgb_shap_to.plotly_plot()"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}