{
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   "id": "b6ea6c3b",
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    {
     "ename": "TypeError",
     "evalue": "'numpy.ndarray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[32m     18\u001b[39m X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=\u001b[32m0.2\u001b[39m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m     19\u001b[39m X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=\u001b[32m0.2\u001b[39m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m X_train = \u001b[43mX_train\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     23\u001b[39m n_features = \u001b[32m10\u001b[39m\n\u001b[32m     24\u001b[39m fig=plt.figure( figsize=(\u001b[32m15\u001b[39m, \u001b[32m15\u001b[39m) )\n",
      "\u001b[31mTypeError\u001b[39m: 'numpy.ndarray' object is not callable"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "\n",
    "data = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
    "\n",
    "# Features\n",
    "X = data.drop(columns=['income'])\n",
    "\n",
    "# Labels\n",
    "y = data['income']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "\n",
    "n_features = 10\n",
    "fig=plt.figure( figsize=(15, 15) )\n",
    "plt_num = 1\n",
    "for i in range(n_features):\n",
    "    for j in range(n_features):\n",
    "        ax = fig.add_subplot(n_features, n_features, plt_num)\n",
    "        if(i == j):\n",
    "            ax.hist(X_train[:, i], bins=25, color='gray')\n",
    "        else:\n",
    "            ax.scatter(X_train[:, j], X_train[:, i], c=np.array(colors)[y_train], s=30, alpha=0.3)\n",
    "                \n",
    "        if(i == n_features-1):\n",
    "            ax.set_xlabel(f'$x_{{{j}}}$', fontsize=22)\n",
    "        \n",
    "        if(j==0):\n",
    "            ax.set_ylabel(f'$x_{{{i}}}$', fontsize=22)\n",
    "\n",
    "        ax.grid(True)\n",
    "        plt_num +=1\n"
   ]
  }
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