prepare/generate_data+training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "You can change the code below to read your own data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(filepath_or_buffer=\"./input/heart.csv\", header=0, index_col=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a classification model\n",
    "In this step, you can either train a model in the cell below, or skip the training part and read the model prediction of the training data from your own model ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "'''prepare data'''\n",
    "X = df.drop(columns=['target']).values\n",
    "y = df['target'].values\n",
    "y = y.reshape(len(y))\n",
    "\n",
    "train, test, train_labels, test_labels = train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "train_df = pd.DataFrame(train, columns=df.columns[:-1].values)\n",
    "test_df = pd.DataFrame(test, columns=df.columns[:-1].values)\n",
    "\n",
    "'''train the model'''\n",
    "clf = MLPClassifier(random_state=1, max_iter=300)\n",
    "clf.fit(train, train_labels)\n",
    "\n",
    "'''report accuracy'''\n",
    "print(clf.score(test,test_labels ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' obtain the original model's prediction '''\n",
    "y_pred = clf.predict(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_name = 'heart'\n",
    "\n",
    "'''\n",
    "cols: the column names of the input data.\n",
    "data: the training input.\n",
    "target_names: the names for the target classes (grount truth).\n",
    "real_min: minimal values for columns of the input data.\n",
    "real_max: maximal values for columns of the input data.\n",
    "y_pred: the prediction of the input data from the model you want to explain.\n",
    "y_gt: the ground truth of the input data.\n",
    "'''\n",
    "\n",
    "def output_data(cols, data, target_names, real_min, real_max, y_pred, y_gt):\n",
    "    filename = \"./output/\"+data_name+\"/test.json\"\n",
    "    directory = os.path.dirname(filename)\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "        \n",
    "    to_output = {}\n",
    "    to_output['columns'] = cols\n",
    "    to_output['data'] = data\n",
    "    to_output['target_names'] = target_names\n",
    "    to_output['real_min'] = real_min\n",
    "    to_output['real_max'] = real_max\n",
    "    to_output['y_pred'] = y_pred\n",
    "    to_output['y_gt'] = y_gt\n",
    "    with open(filename, 'w') as output:\n",
    "        output.write(json.dumps(to_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' name the target classes '''\n",
    "target_names = [\"False\", \"True\"]\n",
    "\n",
    "''' obtain the min and max values '''\n",
    "min_val = np.min(X, axis=0)\n",
    "max_val = np.max(X, axis=0)\n",
    "\n",
    "''' output data '''\n",
    "output_data(cols=df.drop(columns=['target']).columns.values.tolist(), \n",
    "            data=train.tolist(), \n",
    "            target_names=target_names, \n",
    "            real_min=min_val.tolist(), \n",
    "            real_max=max_val.tolist(),\n",
    "            y_pred=y_pred.tolist(), \n",
    "            y_gt=train_labels.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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