Example.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cvplot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from lime.lime_tabular import LimeTabularExplainer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare dataset\n",
    "df = pd.read_csv('https://github.com/nyuvis/partial_dependence/raw/master/example_data/test.csv')\n",
    "X = df.drop('label', axis=1)\n",
    "y = df['label']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=123)\n",
    "\n",
    "# Min-max scaling\n",
    "X_train = (X_train - X.min()) / (X.max() - X.min())\n",
    "X_test = (X_test - X.min()) / (X.max() - X.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446115288220551"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train classifier\n",
    "clf = RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train.values, y_train)\n",
    "\n",
    "clf.score(X_test.values, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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      "text/plain": [
       "CVPlot(value={'contributions': {'alcohol': [[[0.0, 0.01983479451909141], [0.05175438596491228, 0.0159792559987…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw Contribution-Value plot\n",
    "plot = cvplot.CVPlot(X_test, y_test, features=['alcohol', 'pH'], model=clf)\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>alcohol</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>density</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>pH</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>volatile acidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.059347</td>\n",
       "      <td>0.260163</td>\n",
       "      <td>0.078713</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.203883</td>\n",
       "      <td>0.124233</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>0.294915</td>\n",
       "      <td>0.271186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>0.255556</td>\n",
       "      <td>0.109792</td>\n",
       "      <td>0.325203</td>\n",
       "      <td>0.146181</td>\n",
       "      <td>0.349206</td>\n",
       "      <td>0.452962</td>\n",
       "      <td>0.349515</td>\n",
       "      <td>0.115031</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.515254</td>\n",
       "      <td>0.293785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.086053</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.054672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.341463</td>\n",
       "      <td>0.601942</td>\n",
       "      <td>0.069018</td>\n",
       "      <td>0.226667</td>\n",
       "      <td>0.267797</td>\n",
       "      <td>0.519774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.062315</td>\n",
       "      <td>0.284553</td>\n",
       "      <td>0.097712</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.292683</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.096626</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>0.332203</td>\n",
       "      <td>0.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.089021</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.102753</td>\n",
       "      <td>0.269841</td>\n",
       "      <td>0.069686</td>\n",
       "      <td>0.427184</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.216949</td>\n",
       "      <td>0.418079</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.091988</td>\n",
       "      <td>0.276423</td>\n",
       "      <td>0.118263</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.278746</td>\n",
       "      <td>0.398058</td>\n",
       "      <td>0.062883</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.484746</td>\n",
       "      <td>0.180791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142433</td>\n",
       "      <td>0.186992</td>\n",
       "      <td>0.160527</td>\n",
       "      <td>0.396825</td>\n",
       "      <td>0.355401</td>\n",
       "      <td>0.427184</td>\n",
       "      <td>0.136503</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.501695</td>\n",
       "      <td>0.327684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.106825</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.151221</td>\n",
       "      <td>0.317460</td>\n",
       "      <td>0.404181</td>\n",
       "      <td>0.291262</td>\n",
       "      <td>0.095092</td>\n",
       "      <td>0.306667</td>\n",
       "      <td>0.671186</td>\n",
       "      <td>0.225989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.151335</td>\n",
       "      <td>0.227642</td>\n",
       "      <td>0.145405</td>\n",
       "      <td>0.365079</td>\n",
       "      <td>0.125436</td>\n",
       "      <td>0.466019</td>\n",
       "      <td>0.073620</td>\n",
       "      <td>0.186667</td>\n",
       "      <td>0.294915</td>\n",
       "      <td>0.519774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.083086</td>\n",
       "      <td>0.227642</td>\n",
       "      <td>0.079100</td>\n",
       "      <td>0.460317</td>\n",
       "      <td>0.160279</td>\n",
       "      <td>0.359223</td>\n",
       "      <td>0.090491</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.267797</td>\n",
       "      <td>0.361582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      alcohol  chlorides  citric acid   density  fixed acidity  \\\n",
       "667  0.716667   0.059347     0.260163  0.078713       0.380952   \n",
       "991  0.255556   0.109792     0.325203  0.146181       0.349206   \n",
       "982  0.700000   0.086053     0.073171  0.054672       0.000000   \n",
       "934  0.500000   0.062315     0.284553  0.097712       0.396825   \n",
       "229  0.250000   0.089021     0.219512  0.102753       0.269841   \n",
       "..        ...        ...          ...       ...            ...   \n",
       "80   0.433333   0.091988     0.276423  0.118263       0.396825   \n",
       "848  0.333333   0.142433     0.186992  0.160527       0.396825   \n",
       "192  0.233333   0.106825     0.333333  0.151221       0.317460   \n",
       "22   0.166667   0.151335     0.227642  0.145405       0.365079   \n",
       "476  0.766667   0.083086     0.227642  0.079100       0.460317   \n",
       "\n",
       "     free sulfur dioxide        pH  residual sugar  sulphates  \\\n",
       "667             0.195122  0.203883        0.124233   0.146667   \n",
       "991             0.452962  0.349515        0.115031   0.360000   \n",
       "982             0.341463  0.601942        0.069018   0.226667   \n",
       "934             0.292683  0.009709        0.096626   0.413333   \n",
       "229             0.069686  0.427184        0.003067   0.120000   \n",
       "..                   ...       ...             ...        ...   \n",
       "80              0.278746  0.398058        0.062883   0.480000   \n",
       "848             0.355401  0.427184        0.136503   0.253333   \n",
       "192             0.404181  0.291262        0.095092   0.306667   \n",
       "22              0.125436  0.466019        0.073620   0.186667   \n",
       "476             0.160279  0.359223        0.090491   0.466667   \n",
       "\n",
       "     total sulfur dioxide  volatile acidity  \n",
       "667              0.294915          0.271186  \n",
       "991              0.515254          0.293785  \n",
       "982              0.267797          0.519774  \n",
       "934              0.332203          0.169492  \n",
       "229              0.216949          0.418079  \n",
       "..                    ...               ...  \n",
       "80               0.484746          0.180791  \n",
       "848              0.501695          0.327684  \n",
       "192              0.671186          0.225989  \n",
       "22               0.294915          0.519774  \n",
       "476              0.267797          0.361582  \n",
       "\n",
       "[152 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display selected instances\n",
    "X_test.iloc[plot.selection,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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