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  {
   "cell_type": "code",
   "execution_count": null,
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    "tags": []
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   "source": [
    "from sklearn import metrics\n",
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 8, 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_prediction(plot_dict):\n",
    "    for noise in plot_dict:\n",
    "        X, y = datasets.make_regression(n_features=1, random_state = 42, noise = noise)\n",
    "        model = LinearRegression()\n",
    "        model.fit(X, y)\n",
    "        prediction = model.predict(X)\n",
    "        mae = metrics.mean_absolute_error(prediction, y)\n",
    "        mse = metrics.mean_squared_error(prediction, y)\n",
    "        r2 = metrics.r2_score(prediction, y)\n",
    "        \n",
    "        plt.subplot(plot_dict[noise])\n",
    "        plt.xlabel('prediction')\n",
    "        plt.ylabel('actual')\n",
    "        plt.tight_layout()\n",
    "        plt.plot(prediction, y,'.')\n",
    "        plt.title('Plot for noise: %d'%noise + '\\n' + 'mae:%.2f'%mae\n",
    "                 + '\\n' + 'mse:%.2f'%mse\n",
    "                 + '\\n' + 'r2:%.2f'%r2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 觀察不同scatter plot和evaluation metrics的關係\n",
    "plot_dict = {1:141, 9:142, 18:143, 1000:144}\n",
    "linear_prediction(plot_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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