combined_cycle_power_plant_data_set.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Combined Cycle Power Plant Data Set.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kyrcha/deep-learning-pipelines/blob/master/combined_cycle_power_plant_data_set.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vVwx0YLUsXC",
        "colab_type": "text"
      },
      "source": [
        "# Combined Cycle Power Plant Data Set\n",
        "\n",
        "Dataset URL: https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant\n",
        "\n",
        "For this regression task, given the hourly average ambient variables:\n",
        "\n",
        "* Temperature (T), \n",
        "* Ambient Pressure (AP), \n",
        "* Relative Humidity (RH) and \n",
        "* Exhaust Vacuum (V) \n",
        "\n",
        "the goal is to predict the net hourly electrical energy output (EP) of the plant.\n",
        "\n",
        "The authors of the dataset have given us 5 random shuffles of the dataset, one in each xlsx sheet. We will be using one of them and make a 60-20-20 split: 60% for training, 20% for validation (used for early stopping) and 20% for testing.\n",
        "\n",
        "## Data reading\n",
        "\n",
        "Let's go ahead and read the first random shuffle.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yl36kg_ZW0u4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing some necessary libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import zipfile\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMMy1KX1YO_8",
        "colab_type": "code",
        "outputId": "43f2cb73-60d1-4169-c2ee-511fd4e5eb09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# downloading the dataset\n",
        "dataset_path = keras.utils.get_file(\"CCPP.zip\", \"https://archive.ics.uci.edu/ml/machine-learning-databases/00294/CCPP.zip\")\n",
        "dataset_path"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00294/CCPP.zip\n",
            "3678208/3674852 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root/.keras/datasets/CCPP.zip'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjaDzd9xYgI2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# unzipping\n",
        "with zipfile.ZipFile(dataset_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall('.')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2i4SEb7Y8Ca",
        "colab_type": "code",
        "outputId": "fe05f893-2c9a-4dd1-b974-88e380b12184",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# reading the first sheet\n",
        "data = pd.read_excel(open('./CCPP/Folds5x2_pp.xlsx', 'rb'), sheet_name='Sheet1')\n",
        "# check if data is loaded correctly\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AT</th>\n",
              "      <th>V</th>\n",
              "      <th>AP</th>\n",
              "      <th>RH</th>\n",
              "      <th>PE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.96</td>\n",
              "      <td>41.76</td>\n",
              "      <td>1024.07</td>\n",
              "      <td>73.17</td>\n",
              "      <td>463.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.18</td>\n",
              "      <td>62.96</td>\n",
              "      <td>1020.04</td>\n",
              "      <td>59.08</td>\n",
              "      <td>444.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.11</td>\n",
              "      <td>39.40</td>\n",
              "      <td>1012.16</td>\n",
              "      <td>92.14</td>\n",
              "      <td>488.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.86</td>\n",
              "      <td>57.32</td>\n",
              "      <td>1010.24</td>\n",
              "      <td>76.64</td>\n",
              "      <td>446.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10.82</td>\n",
              "      <td>37.50</td>\n",
              "      <td>1009.23</td>\n",
              "      <td>96.62</td>\n",
              "      <td>473.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      AT      V       AP     RH      PE\n",
              "0  14.96  41.76  1024.07  73.17  463.26\n",
              "1  25.18  62.96  1020.04  59.08  444.37\n",
              "2   5.11  39.40  1012.16  92.14  488.56\n",
              "3  20.86  57.32  1010.24  76.64  446.48\n",
              "4  10.82  37.50  1009.23  96.62  473.90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fvrzo0eVaD-j",
        "colab_type": "code",
        "outputId": "82f79556-8630-401a-b65c-61d1adb6165e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# Statistical summary of the dataset\n",
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AT</th>\n",
              "      <th>V</th>\n",
              "      <th>AP</th>\n",
              "      <th>RH</th>\n",
              "      <th>PE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9568.000000</td>\n",
              "      <td>9568.000000</td>\n",
              "      <td>9568.000000</td>\n",
              "      <td>9568.000000</td>\n",
              "      <td>9568.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.651231</td>\n",
              "      <td>54.305804</td>\n",
              "      <td>1013.259078</td>\n",
              "      <td>73.308978</td>\n",
              "      <td>454.365009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.452473</td>\n",
              "      <td>12.707893</td>\n",
              "      <td>5.938784</td>\n",
              "      <td>14.600269</td>\n",
              "      <td>17.066995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.810000</td>\n",
              "      <td>25.360000</td>\n",
              "      <td>992.890000</td>\n",
              "      <td>25.560000</td>\n",
              "      <td>420.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.510000</td>\n",
              "      <td>41.740000</td>\n",
              "      <td>1009.100000</td>\n",
              "      <td>63.327500</td>\n",
              "      <td>439.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.345000</td>\n",
              "      <td>52.080000</td>\n",
              "      <td>1012.940000</td>\n",
              "      <td>74.975000</td>\n",
              "      <td>451.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.720000</td>\n",
              "      <td>66.540000</td>\n",
              "      <td>1017.260000</td>\n",
              "      <td>84.830000</td>\n",
              "      <td>468.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>37.110000</td>\n",
              "      <td>81.560000</td>\n",
              "      <td>1033.300000</td>\n",
              "      <td>100.160000</td>\n",
              "      <td>495.760000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                AT            V           AP           RH           PE\n",
              "count  9568.000000  9568.000000  9568.000000  9568.000000  9568.000000\n",
              "mean     19.651231    54.305804  1013.259078    73.308978   454.365009\n",
              "std       7.452473    12.707893     5.938784    14.600269    17.066995\n",
              "min       1.810000    25.360000   992.890000    25.560000   420.260000\n",
              "25%      13.510000    41.740000  1009.100000    63.327500   439.750000\n",
              "50%      20.345000    52.080000  1012.940000    74.975000   451.550000\n",
              "75%      25.720000    66.540000  1017.260000    84.830000   468.430000\n",
              "max      37.110000    81.560000  1033.300000   100.160000   495.760000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhNkTVFZaJXU",
        "colab_type": "text"
      },
      "source": [
        "We see that the variables are in different scales and ranges so some pre-processing is needed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6L2nk5DjW287",
        "colab_type": "text"
      },
      "source": [
        "## Data preprocessing\n",
        "\n",
        "Neural networks work best with normalized features. So we will extract the min and max from the training set and use those to normalize the values on the training, validation and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LyNhcE1UrNJ",
        "colab_type": "code",
        "outputId": "e12a3e91-303b-4c61-f85a-9ab201d16b62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Check if we have unknown values\n",
        "data.isna().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AT    0\n",
              "V     0\n",
              "AP    0\n",
              "RH    0\n",
              "PE    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z87BCIo6aXBv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# split predictors and target features\n",
        "X = data[['AT', 'V', 'AP', 'RH']]\n",
        "y = data['PE']\n",
        "\n",
        "# split the data (60% training - 40% rest)\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, \n",
        "                                                  random_state=42, \n",
        "                                                    shuffle = False)\n",
        "# split validation and test\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, \n",
        "                                                  random_state=42, \n",
        "                                                shuffle = False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BGkcB7FbxCI",
        "colab_type": "code",
        "outputId": "362d8f15-bee9-4a01-b541-8ffacd86d560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Check the data sizes\n",
        "print(X_train.shape)\n",
        "print(X_val.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5740, 4)\n",
            "(1914, 4)\n",
            "(1914, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iYLAbwaceTm",
        "colab_type": "code",
        "outputId": "663765e3-a9a6-4281-cbd4-d9156d65ad66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "stats = X_train.describe()\n",
        "\n",
        "def standardization(x):\n",
        "  return (x - stats.loc['mean']) / stats.loc['std']\n",
        "\n",
        "def normalization(x):\n",
        "  return (x - stats.loc['min']) / (stats.loc['max'] - stats.loc['min'])\n",
        "\n",
        "normed_train_data = normalization(X_train)\n",
        "normed_val_data = normalization(X_val)\n",
        "normed_test_data = normalization(X_test)\n",
        "\n",
        "# Check if normalization was performed correctly\n",
        "normed_train_data.describe().loc[['min', 'max']]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>AT</th>\n",
              "      <th>V</th>\n",
              "      <th>AP</th>\n",
              "      <th>RH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      AT    V   AP   RH\n",
              "min  0.0  0.0  0.0  0.0\n",
              "max  1.0  1.0  1.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tb9phsMSU4g2",
        "colab_type": "text"
      },
      "source": [
        "## Model Definition\n",
        "\n",
        "With around 6000 training samples a good heuristic is to use a neural network with around 600 weights. Another way is to build a bigger network and add regularization. In our case we will use early stopping.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMuwe3qqeYll",
        "colab_type": "code",
        "outputId": "0eb44ac0-5dbc-463f-8463-b856b63b7262",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "l0 = tf.keras.layers.Dense(units=128, input_shape=(4,), \n",
        "                           activation='sigmoid')\n",
        "l1 = tf.keras.layers.Dense(units=32, activation='relu')\n",
        "l2 = tf.keras.layers.Dense(units=1, activation='linear')  \n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(0.001)\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "model = tf.keras.Sequential([l0, l1, l2])\n",
        "model.compile(optimizer = optimizer, \n",
        "              loss='mean_squared_error', \n",
        "              metrics = ['mean_absolute_error', 'mean_squared_error'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0801 07:35:18.733334 140434851002240 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-78vdBryy3Q",
        "colab_type": "code",
        "outputId": "0c50c067-5383-4da9-9818-93b04adeda9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 128)               640       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                4128      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 4,801\n",
            "Trainable params: 4,801\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JknmEzXcU613",
        "colab_type": "text"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrt5DMlbeulI",
        "colab_type": "code",
        "outputId": "2b868731-e2b9-4cbf-d98e-d8b560e0eb06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "history = model.fit(normed_train_data, y_train, \n",
        "                    epochs=200, \n",
        "                    validation_data=(normed_val_data, y_val),\n",
        "                    verbose=0,\n",
        "                    callbacks=[early_stopping])\n",
        "print('Finished training')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DBEijR-U9Sd",
        "colab_type": "text"
      },
      "source": [
        "## Learning curve visualization\n",
        "\n",
        "Let's plot the errors (training and validation)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4XI-skZg3cu",
        "colab_type": "code",
        "outputId": "f3c9a9a4-3fc8-41df-af51-1be11c9ebbda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "\n",
        "plt.figure()\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Mean Abs Error')\n",
        "plt.plot(hist['epoch'], hist['mean_absolute_error'],\n",
        "         label='Train Error')\n",
        "plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n",
        "         label = 'Val Error')\n",
        "plt.ylim([3,20])\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGBPiLBEYY0yIs0RgjDEh7v8D8I1mW8uGUvIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEBbc8MFVA_Q",
        "colab_type": "text"
      },
      "source": [
        "## Scatter plot of real vs predicted values for the test set\n",
        "\n",
        "And let's also plot the predicted values vs. the true values of the test set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2LE3y50hVIj",
        "colab_type": "code",
        "outputId": "d7150d19-3e38-4504-add7-a0010d8669e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "plt.scatter(y_test.array, test_predictions, s=2)\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predictions')\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([400, plt.xlim()[1]])\n",
        "plt.ylim([400, plt.ylim()[1]])\n",
        "_ = plt.plot([400, 500], [400, 500], color='black', linestyle='dashed')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAEKCAYAAAAbwEBEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztvXl8VPW5+P9+ZjKZZLIOe4CwacIi\naJQgtFrqFSqIWOnVumDVqqBWrai/i1/sr9jvFe+vtNxK8d5rvRbbaq+2Wr2iIKLiWkURUqNgwARQ\nAhhMAtknmczy+f1xlsxkB7JMks/79ZpXZs6cc+bJJOc5z+dZRSmFRqPRWDh6WwCNRhNbaKWg0Wii\n0EpBo9FEoZWCRqOJQisFjUYThVYKGo0mCq0UNBpNFFopaDSaKLRS0Gg0UcT1tgCnwpAhQ9S4ceN6\nWwyNJubJy8srV0oN7cy+fVopjBs3jp07d/a2GBpNzCMiBzu7r14+aDSaKLRS0Gg0UWiloNFootBK\nQaPRRKGVgkajiUIrBY1GE0W3KgUR+UpEdolIvojsNLcNEpE3RKTI/Ok1t4uIPCIi+0TkMxE5pztl\n02g0rdMTlsI/KaVylFK55usVwJtKqSzgTfM1wMVAlvm4BfhdD8im0Wia0RvLh8uAJ83nTwKLIrY/\npQw+AtJFJKMX5NNoBjTdrRQU8LqI5InILea24UqpEvP5UWC4+XwUcCji2MPmNo1G04N0d5rz+Uqp\nIyIyDHhDRPZGvqmUUiJyQu2kTeVyC8CYMWO6TlKNRgN0s6WglDpi/iwFXgTOBb6xlgXmz1Jz9yNA\nZsTho81tzc/5uFIqVymVO3Rop+o7NJoBTSAQOKH9u00piEiSiKRYz4GLgN3Ay8AN5m43AC+Zz18G\nrjejELOAqohlhkajOQnC4TDXXHPNCR3TnZbCcOB9EfkU+Bh4RSm1BVgNfE9EioC55muAzcABYB/w\ne+D2bpRNoxkQOBwOzjnnxKL70pcnROXm5ipdOq3RtCQQCHDgwAEmTpwIgIjkRaQFtIvOaNRo+hmB\nQIBrrrmGb33rW5SVlZ3w8X26yYpGo4nGUggvvPACa9eu5WSc8dpS0GhimLyDFVz/xHbyDlZ0uG9z\nhXD33Xef1GdqpaDRxDDrthbyXlE567YWdrzvunWnrBBALx80mphm2dzsqJ/t8dOf/pTTTz+dRYsW\ndbhve2hLQaOJYaaP9fLUzTOZPtbb6vuBQID777+f8vJy3G73KSsE0EpBo+mzBAIBFi9ezOrVq3n1\n1Ve77LxaKWg0fRBLITz//POsXbuW6667rsvOrZWCRtPHaK4QTsWp2BpaKWg0MUh7ocjjx4+Tn5/f\nLQoBdPRBo4lJrFAkwFM3zwQMC8HhcDB8+HDy8/NJSkrqls/WSkGjiUGahyKtJUNiYiJPPvlktykE\n0MsHTR/hRDL7+gORochIH8I555yDiHTrZ2uloOkTnEhmX1/DUnjPbC9uofi27yvl9G/N7zanYmvo\n5YOmT3AimX2xTt7BCtZtLWTZ3Gymj/XaCm/XkSoqfEaXJMuPsPj6GynOe4uR82/lOz+4ob3Tdhna\nUtD0CTrK7OtLNLd6ls3NZnbWEJbPm8TsrCFRiu++u25j1MW34zrr0natpK5cXmmloNH0MJYSWDY3\nO8pqWDxzDE/dPJNgIMDce9aRd7CCW6++lJd+91ALZdGcrlxe6eWDRtPDWFYPwPVPbI8KPQYCAa68\n+mqK897m2sYEnr7vyqj9m2MplflTjREpXbG80paCRtMDtGXeL5ubTc7oNEqqGvj+uneYf9nlFOe9\nzcj5t9KQMrrDO79lIax5ba/tozhVtFLQaHqAVRs/572iclZt/Dxq+/SxXlITXRSWVPLmo/8vb726\nkbVr1/LyY7+MWmK05S9YNjcbr8dFhS/QZZEZrRQ0mghOxWHX1rF5ByvYX1ZnvGglx2D+1Aw49A98\nhduY+s8/bRFlaMtfYC0dWnNQngrap6DRRNBaevGpHrtuayE1/iBej4uVC6e0OG7L7hJk3LmctvQ/\nqBk0nhXPf0p5XaMdnmwrHHsqsraHVgoaTQSnkg/R1rGR2yPX/Nv3lXLtj2/muhtvhqwMSqqSKSqt\n5Wi131Yi1jGtXfTdlbuh5z5oNB3QPNmoK44LBAKc/q15FOe9zYzF/w9333UXD20qABTXf2scBSXV\n9sVunQMM3wQirFw45YRkOZG5D9pS0Gg64ETNdEsZVDcEyT9Uya4jVSyfN4ktu0tYNjebM0cm21GG\nwXOWMvJbi/jZi7vs49e//yUPXjaV6WO9dshy15Eqxg5OIv9wlS1TVy4ZItFKQaPpgBMx0/MOVrDk\nyR1U+ALkjE6zIwMPvLSbYFgRDgZpeGMtb726Ee+FS0nOvcy+0C2CYcWa1/ayeOYYls3NttOfxw5S\n5IxOA5FuTffWSkGj6YD2koeas25rIRW+gOFUvPQMAFtJeD0ufjonizVvQPZld+CfdDEAKW4nNf5Q\n1Hmuys20P3v9DTNOavlysuiQpEbTSdoLOVpVjtX1AXIy01k+bxLrthbyxdEaxg5OYmSqi4aaSvYf\na+Bvf/sbk7/XNAk6zRMPGBejw4xYFpRU2+9PH+tl2dxsVjz/KdN+8RrPbC/u1t9TWwqaAcuJOhDb\nCzm+V1TOtv3HCIYVOZnprHltLxW+ALuOVHG8pp5jG9fQeOwQP5d1TByRwsqFU7jqvz8kGFZU+Rrt\nZQYKvB4X86dmcP0T25k/NYMtu0uorg9QZOY6PPDSbiaOSOk2q0FbCpoBy4kWETUvZFr0n+/zvYff\npaSqgRS3k2BYEecQ6vxBKnwB4hzCFTkj4O111H3xAalnzSPscLHkyR0APHjZVLweF/cvmMLyeZNI\ncTvJGpbM8nmTWPPaXjt9+b2ichAha2gSTjF8Dt3ZV0JbCpoBS2cciM2tiadunhnlTLTIyUxnf2mN\n4RtQCq/HxfGaeh5/8G6K897mR3c/QH7qt2gMKip8AVa88BkZaQmsv2GGHWWo8Yc4zR3HLzcXUOMP\nkeJ2RkUtpo/1RsnTXWiloBmwdMaBGLlkWDY3m1WbCig8Wo0vEMbjcjAo2U2VL8CVuZn88YMvqSmt\npT4YZuzgJGre/SP7895m6uV3sS3hXEIRzsT9pbUUldYCRprzJ8UVZA1Lps4fbOF0PFGZT5VuVwoi\n4gR2AkeUUgtFZA6wBmPpUgv8WCm1T0TcwFPAdOAYcJVS6qvulk+jaY9Ia2Ld1kLyD1Xa72WPSCU1\nIY73isp5buchjlbVA3C4op7DFfVMmnM1w8eezpFhs6BZjmDY/LnrSJWtdKhqwNrRKVDjD9m+Ceja\nVOb26AmfwjJgT8Tr3wHXKqVygGeAn5vbbwYqlFKnA2uBX/WAbBoN0LlCqGVzs0lxOwEjjLhy4RTb\nz2Dd4VUoSPXOl1HhEAmpg3nkF/eSNSyZFLeTRTkj8bgcuOMc9oUXuQQZkeqmxh/C43IQ53DgcTm5\nKjezS4udOkO3KgURGQ1cAqyP2KyAVPN5GvC1+fwy4Enz+fPAHOnutrUajUlbTsfI7dPHevnTTTOZ\nnTWE+xdMYdXGz1nxwmdU1wdAKVQoSM2r/07Fm4+TVLbbzlMor/VT4w9xvK6R3HGD8AfDJLiceFwO\nvB4XToHbZk9g9RVnMTtrCE6H4A+F8QVCFJRU93gbuu62FH4L3EeTtQSwBNgsIoeB64DV5vZRwCEA\npVQQqAIGd7N8/ZqB1hb9VIiMLLS1PdLJt2V3CfmHqygqrSX/cBWJLiGwdS0Vn7+P98Il5J4/x27K\naiUuLZubzfypGcQ5BF8ghNvlZOwgDyEFz+48BBhLhGtnjsUpkOlNbCFPT/xNu00piMhCoFQpldfs\nrXuABUqp0cAfgYdP8Ly3iMhOEdlZVlbWRdL2T/pzW/Supnlj2Ge2F3P2g6/zxdEa5k/N4Md/+Jjr\n1n/Ee0XlLHlyB/OnZthLieQ4Rd2W31CS/y7eC5cw4tuXU90QZPXmPXxSXEnW0CTW3zADgF9uLiAY\nNvwGFb4AdY0h4hwS1SSloKSakILxQ5JaWAg98TftTkfjecD3RWQBkACkisgrwCSl1HZzn2eBLebz\nI0AmcFhE4jCWFsean1Qp9TjwOBhVkt0of5+nP7VF7y7aSmCyHHxrXttLMBS2IwLWBfzcjmLSPPH4\nGuuZOzLME+veZtwlt6GmLiQUDpN/qNJ2StYfC/HF0RrWvLY3KrLg9bhAKYJhRYrb2eLv1drfrSf+\npj1SOi0iFwD/AiwCjgLfVkoVisjNGFbD5SJyBzBNKXWbiFwN/LNS6sr2zqtLpzUnS/NKxtlZQ6K8\n+89sL2bNa3tZPm8Sj76zj8MV9Qhw6+wJFJRUU90Q5JPiCkSEFLeT1287izmPfoovEMLlFMJhRSji\n0rIyFlPcTtI98VT6Grl/wRSe23mI/EOV5GSms+GO87rt9z2R0ukezWg0fQVLgRdE5FMMn8Jy8+0n\ngMEisg+4F1jRk7JpBhZ2/oFSrfoSFs8cY2cW+oPG3V2B3ecgHApy7OXVVO98iTRPPF83JmCFEwOh\nJoUw2ptIituJy+kgxe3k/gVTGD8kiRp/iC27S1i5cAqzs4a02pGpt+iR5CWl1DvAO+bzF4EXW9mn\nAfhhT8ij0VhKwKotiMSyInZ+VYEvEMIKgXlcDuZPzeDmP3xI0V//DV/hNuJHTgaw6xgsHAIJcU6O\n1zbiC4TsZcPKDbtY+p0Jtgw9kYx0oujOS5oBjdXExOtx2SnF1fUB8g9X4cAIm7mdDvyhMDmZ6STH\nwYsP34evcBvDvreUM+dfy9Gqemr8IZwCGWkJVNUHGZHqtguYHAJKNeUveT0uPnngoh79PWN2+aDR\n9BbNQ3nW6/lTM+z1fmTxUYrbacfRXXFCTmY6P79kMkdf/jW+wm2MuOgWEs+5jPJaP/cvmEKKOw53\nnIPDlQ2cPSad1Vecxej0BADCEQrBKbB83qSe/wJOAK0UNAOC5qE86/WW3SWsv2EGOaPTGJLsJmd0\nGisXTmFEWqJ9bK0/xP7SGh56ZQ9nn/ttfrRsJakzFuHAaIayeOYYRqQlmPUQTuZPzWDd1kKO1zUC\nhqWQNTSJnMx0nrvt2yyeOaY3voJOowuiNP2evIMVdvOTtsJ+B4/7zOhAHCue/5Tiinr7eBUKcuzw\nV9T4x8Ho8zgoPvxmenJBSbU518EobmoIhnhuR3FUi7XThibzxr3f7YlftUvQloKm37NuayH5h6tI\nTYizcxEiHXxWGXScQ6jxBykqq8MfNBYPKhSk/OVfc/R/lhOsPU5dYyiq94FVOWn5GMOKFgNfVl9+\nZo/9rl2BVgqaPk1nC5naKiqy0pBT3E7GD0ki05uIx+VgaEo8EjYUgq9wG+nn/4i45EEcqajn0Xf2\nATBn0jBWPP8pn0ZUTjoFZo0fRKY30a5p6Mm6ha5ALx80fZq2WqR1phlJ3sEKqhuCdofk/EOVeD0u\nfIEwDX4fpS8ZCsF74VJSZ1wGgC8QwmcuLR7/+wEiopA4BUIKnt5+kBp/iJzMdFYsmNwNv3X3opWC\npk8TmW8Q1dPQzFQsqWrgQFktIQXV9QE23Hk+eQcrWPHCZ/Z2p0BGeiJZw5JpCISorg9Q8cmrLRSC\nywGBiNK+SIUgwKpF09iyu4SSqgZqSmuNOKTJyQ6U6Q10noKmX7Dovz4g/1AlKe44avzG3T810cUn\nxRV24lDWsGQy0hI4UF7H4QhHooV1LIBDhfB/lY9r/HTzPScj0hLtbknNEeDffjCNiSNSWp3iZOVD\nNE+n7il0noJm4GHe3Eakuo204UvP4KmbZ3L/gil4XEbDkvpAiPeKyvm6skkhJLudCOCOc5ASD8ff\nWk+49jhhcTLl3NlkDU3C4zIUwo3njcfjctrHup1Nl4/CyFZctfHzFk5NaN+vEWtopaDpF6y89Axm\nZw1h9RVnRZVATxyRgtvlxBcIUeUz8gaUapqvUOsPoYAGfyOf/flBanZsoPGrfwCwv6yOqoYgvkCI\notJatuwuIXt4MgA5o9P4xffPIMUdh6UbQmbkIbL/guUEbV6aHctopaDpc7QWcWjrootscnL/ginE\nOQQFuByC01QMVtix7gvDh5AwdS5gpDiX1vijznesrhEHUF7XyB/fP0CNP2hbDFaLNkuOvtrPQjsa\nNX2Ojga+Wk69+VMz7JkMy+dNsjMJ17y2F58/REgp4lSQoxvXtHAqtobdYBWjOaulVEZ5PWSkJbRw\nIvbVfhZaKWj6DHkHK1i18XPqGkNR2YnWe5YiiJzOZDVG/eP7B5g4IsVOa17xwmcUldbiCPppPHaY\nwXOMYa9gVEP6zDCDEN2I2eNy2Ft9gTBej4vVl5/Z6rIgFisgO4NWCpqYpXkYz8pMBCNSELmflZVo\njW5LcTtJcsfZSmF/WR3Xrd+Ozww5PvT9yTzyZiGX5EzjL5l/4GhtiLJaw+cwyuvhaFU9obCRl2Dh\ndgp/XjKrxVCWvuAnOBG0UtDELJYn38ovWDY3m51fHccXCFPjD7JuayFP3TzT9htYI9Ws7kaHIsKO\nYZou8HA4xM/uWsKeI1V8c93/5VBFA36zK4rH5WB/WS1hZYQwk9xxlNc0UFUf5P4Fk1tNk+5vaEej\nJqbIO1jBov/6gEX/+T51jcZFXNcY4vontvPF0RqcDsupFxeVsDQ7awgThhqRAV9jiNLqBsD4B88a\nlszo9ATcTgdxhHjr0Z/x1qsbaRgykX3lPlshCNAQDDclJSnFhjvOY8LQZGr8Qda8tndAdMbWSkET\nU6za+LnR9PRwFUnxTmZnDSEp3mkPW63xB/F6XPzppnN5bkexMZ1pRzFP3TyT1ZefSZxDjCxFh9H+\nLMHlpKi0lsOVDTQ0NlLy4q/sKEP6uZfZoUkwfAeRWYpWYZM1BKbCFzASk/o5WiloYgvzQkxxO+0E\nJCsH4arcTLtD0vSxXnvfwm9q+N7D77JqUwFLzh+P1+PiojOG42sMRfkEjr32n1FRhsHJ8YTNnIXW\nLoTiYz47x+C0YSlR8vVntFLQxBRWI9M/3TSzxfq9oKSaCl/A7qm4cuEUu4CpqLSW/EOVPL39IGMH\nJ/Hmnm+iuikDpORczKDv3WaHHctqDMdiWFlt16IveH8obOcYxGKD1e5COxo1MUV7Drz5UzPYdaSK\n+VMzAPjiaA3+QAh3nIOhKW6O1zZS4w+Rf6jSXhaoUJD6AzvxZM0iceRE3CMntnrurKFJHKlsgFCT\nZZHpTaS6PhCVkTgQ0JaCJiawshSf2V7cZn+ELbtLbEsh72AFD7y0G18gjD8YZkiym8iMgrAyMxU3\nrqHsfx/Cf3QfYSDd0/I+ONprtF6LXGpkDUtm/JAk8g9X9bmMxFNFWwqamMDKUrQSjqrrA6QmuqLy\nAKZkpLJt/zGmZKSyauPndkv1YSnxlNf67YQjaFIIvi8+wHvhUtwjTifR5aDSF7T3cTkFB0Kiy0nx\nMV+UPDeeN56JIww/Ql/LSDxVtFLQxATN5zBUNwTttOJlc7NZt7WQT4orCIYVz+48xNjBSfax9Y1h\nKswmqdBSIaTNuIzThyW3KHsOhBSgKCqtNTIVmwwFtuwuYfHMMQNmyRCJXj5oYgJrzW7dna/MzWR2\n1hDmT81gyZM7eK+onBq/4T9YPm8SKxdOMVOOIS0xLqr5ScPBT22FkDrjsqb26q38t3s9cTgFcscN\nwutxcdvsCe2WOA+ESd7aUtDEDJHpymAUO13/xHb7NUAorOzCpj8vmcWqTQXs+boq6jyJE6aTcdN/\nEj90HGAoAyttORKXU6gwlxMf7CsnpIzuzO1ZBx0VY/UHOmUpiMgyEUkVgydE5B8i0rMjbjT9nlWb\nCuw261a24pSMVLweF7OzhuAUo4nKM9uLOX/1m1z52Da+Kq/FH1LGkmHTb2g4+BmArRAAQmGipj07\npGVuQpzTQc7otA79B32pWcrJ0llL4Sal1DoRmQd4MQbD/hl4vdsk0ww8zO5Jpw1NsqcxW3dwgAlD\nkigqq+OhTZ/bTsXK+qDdD8FXuI34jGwSxhot1V0OIRBu2W7Q2jRmcBJHq+ppDCmjpbtIh8VNAyE0\n2VmfgpXVsQD4s1Lq84htGk2XYGUurrz0DFtBhJSR3Th/agb7y43ZjIGwwh1n/Ps5wkGOR/ZDmH6p\nfT6HI/pf1CFGSNLjcpA1LJnVl5/Jrn+dz2TTj0Ef7lfalXRWKeSJyOsYSuE1EUkBwh0co9G0oC1H\nXfNS5JWXnmE7EtM98ax5bW9UXYI/qCAU5JuXfk1thFMxksZg9L9oWMGZo9IpWHUxb9z7XdsqiFJG\nmk4vH24GcoADSimfiAwGbuw+sTT9lbYcdc23Tx/rJXt4CvmHqzhm5iB4XE6yhydT+E0tgVAIJYLE\nJ+C9cCnTL7mWG88bz3M7D1F4tBpfIIzCsA5GpieS6HKSFO9s1RcwEJYEJ0KnLAWlVBj4BpgiIrOB\nM4D07hRM0z9py1G3bG42OaPTqG4I2laEdQcflOwGYJQ3kQ13ns+K+VmE6yoQh5PBC+4hdcZlHKmo\n548ffEnh0eqomoekeCfrrj6b1ZefSWqiiy+O1vT7kOKp0qm5DyLyK+AqoICmFA+llPp+J451AjuB\nI0qphSIiwEPAD81z/U4p9Yi5fR3GEsUH/Fgp9Y/2zq3nPvQPrKWDNcAlJzOd1IQ4W3H8+A8fU+MP\n4nE5OH2oh7otv+HvH+5kxI3/gSPeGPfe3KnocTlwOoQaf4jZWUOA6B6LvTV/obc4kbkPnV0+LAIm\nKqX8He7ZkmXAHiDVfP1jIBOYpJQKi8gwc/vFQJb5mAn8zvyp6edYS4cUt5OczHTq/E3Tncpr/dT4\ngzgF6hoaefO/HrKdipZCAFpEGZwOB/cvmMxzO4qpbghyZW6m3aotziH9OqR4qnTW0XgAcJ3oyUVk\nNHAJsD5i80+AB80lCUqpUnP7ZcBTyuAjIF1EMk70MzWxRWcHwHpcTmr8IfZ8Xc0Rs41aUWmtPfw1\nXpQddhx/yU/a7boM2J2SAPIPVbLmtb12r4UHL5va7/oqdiWdVQo+IF9E/ltEHrEenTjut8B9REcq\nTgOuEpGdIvKqiGSZ20cBhyL2O2xu0/Rh2pp98Mz2Ys5+8HWe2V7M9LFeOwXZHwpHVSuCkXz09dtP\n4Svcxr0r/42k6U0KweU0YuNup7AoZ6Sdquz1uKjwBThm1kRU+AIUlFTzyQMX2RmRmtbprFJ4GVgF\nbAPyIh5tIiILgVKlVPP93ECDub75PfCHExFYRG4xFcrOsrKyEzlU0wu05Vi02rBbd/NrZ47FKTA0\nJZ6czHS7nBmMkubUc3/A+Cvu47P080jzuHDHORAgEDIKpv0hxZt7vmHsIA/fO2ME62+YweysIVSa\nU6H0kqHzdHrArIjEA9a3+oVSKtDB/r/EyHwMAgkYPoX/BXKBi5VSX5rOxUqlVJqI/DfwjlLqL+bx\nXwAXKKVK2voM7WjsOzTPQ7j7r5+wIf9r2wlYUtVAUWktKe44/nTTuQD860ufsef1vxCaPJ+wM3r1\nalkCrRHpRHxmezFrXtsbNQxmINLljkYRuQB4EvgKw1rLFJEblFLvtXWMUup+4P6I4/9FKfUjEVkN\n/BPwJfBdwLIrXwbuFJG/YjgYq9pTCJq+RWS/hPU3zODNPYYryUpjTnEbg1tr/EFWbSogOQ7qtvyG\nfa9uZOHEbHY5J5LidhIKw6AkF4nxcfj8QfwhhUPglu9M4KMvj4NSURbB4pljBrQyOBk6u3z4DXCR\nUuq7SqnZwDxg7Ul+5mrgchHZBfwSWGJu34zh0NyHsay4/STPr+lF2nIsGh2RjeEsqzYVMCLVyD1w\niJDidnLtzLHkjE4ja1gy+0oqePHh+3jr1Y2sXbuWr5KNvog1fqMRa12jMfB1zOAkvB4Xt3xnAgUl\n1VyZm6lzEbqAzoYkXUqpL6wXSqlCEel0NEIp9Q7wjvm8EiMi0XwfBdzR2XNqYpPWMhYtEz4tMY4a\nfxCU4sbzJ7Bywy4CYUXAH+Lp7QcZkZZIyfEavvzbL/EVbiP7+3fwnR/cwCsvfEaN2SBFgKtyMyko\nqaa6PkCRL8BTHx7EFwix/cvj+INhdn5VYUyCagiy4Y7zeuur6LN01lLYKSLrReQC8/F7jIQkjcbm\nme3FfFJcQdaw5CgT3nIqVtUHyRmdBiI8t6PYzjx0imEFFJXWUlH6NQ3Fn+G9cCn+yRez5MkdzJk0\nzD6XoqnngZXxaPVmDNi1DuaJdYHTSdFZpfATjGzGu8xHgblNo7ExhrWEKK/1R+UBLJ83Ca/HxZzJ\nw9j9dTX5hyqpawyR4naSNSyZVYumkRxv/Cu6Bo1i1NL/JnXGZTjFCCU+u7MpUj3am2grHKtm4ecL\nzyDF7WSkN5GczHR+vlAXOJ0KnY4+xCI6+hBbdOTpP/vB16nwBYhzCOMHeygqq2N0egK19X7qtjzM\nMXcGqd++GjDSlAclu6nyNTJn8nDeLSxj+bxJTByRYkxpEmHlwim28rn+ie28V1Q+4NKXO0uXRR9E\n5Dml1JWmU7CF9lBKnXmSMmr6Ie15+p/ZXow/EMLjcnL9t8by1IcHAThyvJbSl37dlLqMkenmD4Y5\nbGY2vrmnlBp/kC27S9iyu8SePG0NmIWWMyE0J09HjsZl5s+F3S2Ipn9h5SVY3Zk/Ka7AFwjj9bgo\nKKnGFwihQkFqt/wmapSbK04IhlRUpWNjMIzH5aCkqoEbzxtPdX0AJDoZ6bmdh6jwBXhu5yEdgjxF\n2vUpROQJ3K6UOhj5QIcMNSathSGtKMQvNxfwXlE5aZ54vB4XV+VmUlJZT7xDOP7Kv3Ns99+jGqT4\ng9EKwYGV+myMhtuyu4QNd57PyoVTWLe1sOkzlXYudhWddTR+r5VtF3elIJrYp60chNbqG6z05hFp\nRrrykGQ3nzxwEW/u+Yaisjoawwr3+NwWHZM8LgeRIx0TXE77uctpZD5aVsh7ReUseXIHeQcrdPek\nLqRdpSAiPzH9CZNE5LOIx5fArp4RURMrswbaKm5qrb7BigysvvxMcjLTQSnyDlZw+Fgt/qP7AEiZ\nNrdFtWP28BRWLZpmRyZ+vnAKcWavxUDIqJxctamAksp6Ozqxbmuh/Xm6+vHU6cin8AzwKkbm4YqI\n7TVKqePdJpUmiliZNWBd9M3C4G3oAAAegElEQVQLi9pqZ2bd0VGK/MNVrN1SQNnGX3N873Yyb3kM\nUpryD1xOYdzgJPtOf/YYr+2PWHL+eB577wBg5DSgFEVlRhNXr8elC526mHaVglKqCqgSkXXAcaVU\nDYA5A2KmUmp7Twg50GnrYuxpOupluHrzHta//yVLzh/PigWTo5qnnDkymZKXfmX6EJYgEQoBjBFu\n+8tquW79R4zyeigqrbXnSgL8fz+YZoc7rbBkXWOIpHhna6JoToHOtmP7BDjHTEVGRBzATqXUOd0s\nX7voPIXepXnl4+k/20wwrHCKMaOh+Hg9jaEw4VCQik1rqNn7AYMuXELKjEXtnjfF7YyyFCKHzEai\ncxM6z4nkKXR67oOK0B5m1yQ9cm6A09zHsPBMI0dgUHI8RWV1+ENGR+XaXVup2fsB3guXMOWia9o9\np8tp9Faobgjy3I7iNhUCDIxpTb1BZy/sAyJyF0bfRDDCkQe6RyRNXyFyNPwz24vZ+OnXAJTXGI1N\nXA7Bm+QiMXc+GaMzyZgyE5TC5RRz4rOBlbAEkOw28hjyD1UCsGrj52y48/xWP1+3Zu8eOqsUbgMe\nAX6Okdn4JnBLdwmliX3yDlaw/v0v7dHwgJ1foDDGwZdtfYLh8xdTH0whJ+fbAHY2YiSjvIkkxDk4\nWu23fQafFFcaFZWiB5H1NJ1SCmZz1au7WRZNH8DyI5RUNdj+g+XzJlF8rM6OEETOdtw7dBzJZ82j\nrjHE0aqGVs85OCmeDXeeH1U78aebzrX9FZqepaPah/uUUr8Wkf+g9dqHu7pNMk1MsuKFzygqrSXO\n9EYNSornjx98yYEyo99BpELwXriU5LPmYY10rPEHcQi4HA5SPXHUNQQZ5fXYYcjIvo2fPHCRXhr0\nEh05GveYP3cS3bC1w8atmr5NWwlTR6uMIiWrdUFZbSNFpbWElKEQqjb/e1QtA5hTnpXC63ERVkba\ncl1DEF8gTFK803YkXpWbSZxDuCo3s8d+T01LOspT2Gj+fLJnxNHECm0lTF07cyzr3/8SULZiEEw/\nQtBPoKqUkfNvxXXWpcYdRwylUB8M4/MH7fM0hoyD6xqb2rkXlFQTDCsKSqpbhDs1PUdHy4eNtLJs\nsOjM2DhN36R5KXLewQpWbSpgz9dVBJtNYwqHgqDCxLmTmPXT/2R/hRF9CAO3fWcCv//7AY5U1Ef9\nI5k6ISr5KDJJK1ayOAciHS0f/h2jaeuXQD1GQ9XfA7XA/u4VTdNTtLZU2LK7hApfgC27S8g7WMGS\nJ3eQf6gSfyhaIVg+hNIXVqFUiP0VjSSbF3rO6DTWv/+lsbRo9plxTiFndBorLz3D/nxoUgDV9QFy\nMtO1o7EX6Gj58C6AiPymWTbURhHRqYT9BOuuXN0QJDUhjvlTMyiprCfFbTxft7Ww1RkLjnCQso1r\n8BVuY/CcpSgxlIHHHcfpw4yR8c2tCotASHHwuC/q88FQCuu2FpJ/uIrZWUP00qEX6GyeQpKITFBK\nHQAQkfFAUveJpekp8g5WUFLVQIrbaQ92tQaxAvzx/QM0BMMtjlOhIN+YUYbBc5aSnNtU7Vhe46e0\npvVZxOmJcQxNdnOgvI4KX4AlT+5g+bxJQMsaD20l9A6dVQr3AO+IyAEMv9JY4NZuk0rT7djj3+sD\nFJnt00+Ld9qTlxymg/Cr476o7EOL42/8rinsmGs0WR2c7Ka0xs+QFDf1jUFq/SF72WCdLxSGpAQX\nIYVd+vzcjuKorEWdqdi7dKr2QSm1BWNE/DKMbs4TlVKvdadgmu7FNtlFyPQm4gDKa/1clZtJituJ\ny+5h0Lr5P3zWZQy66HY77BhS2NZBaY2fGn+IRJcTj8tB1rBkHlo0Da/HZc99mJ01hAlDk42T6azF\nmKJTSkFEPMBy4E6l1KfAGHOArKYDYqVBSnOsYqIrczMpqWogDByubODZnYeo8YdaOBTBWDLUFbyL\noAimjyHl7AXtfoYvEGKU18Mb936XxTPH2K3er5wxhmVzs0mKd5KTmc7KhVO66bfUnAydXT78ESNZ\n6Vvm6yPA34BN3SFUf6I3Q2ttxfojt6/aVBDlDKz1B3E7pc0og69wG87UYSSMntwpGYpKa1m9eQ8f\nfXmcwqPV+AJhnttRTGqiSzsTY5TOKoXTlFJXicg1AEopnzkxWtMBvek0a0shRUYb9pfWRB0TCCmE\npoQkaJm63JpCiKx0bI5VOGVR1xjiyhljdEv2GKWz/RQaRSQR8/9ERE4DWncva6Lo6d6BkcuV+VMz\n8HpcURde3sEKOwcApajxG5OaXBHdUhVtK4TmPRUtXHEt/5XcTgcpbidLzh9Pirvp/pMU74zKg9DE\nFp21FH4BbMEYQf80cB7w4+4SSnPyRFoHgH3hWbMQInMA5k/N4ODxvXw3eygv5X/d6vn8R/bgK/qo\nXYUAgDJGwqclxuEPhBnl9bD68jNtZfi9M0awalMBKMWVM8bw3I5inZwUo3SoFMxlwl7gn4FZGJbl\nMqVUebsHanqFyOXKF0drbBM9cjhLSVUDO786bk9p3tCGQgBIGDONkTc/imvw6HY/1/JBVPiM+oaM\ntIQW1lFqQpydwqz9CbFLh0pBKaVEZLNSahrwSg/IpDkFrOVK3sGKqFLksYM85B+uYteRKoLmcJW2\nUKEg5ZvXkjT5u3hOP7eFQmjeOSkSj8tJ9vDoqdNWmrSVFamTk2Kbzi4f/iEiM5RSO7pVGs0pE5mU\nVOEL2AlCtf5qwHie6U0kFPa3qhgifQjujIkt3k9xO6nxh1pst977002G/8TybViWQYUvQIrbSXWD\nYUno5KTYpbNKYSbwIxH5CqjDdE7rAbO9Q3tlxZZPISczndlZQyipaqCotDbqzv51ZT2t3egjFULm\ngttwTDNSUSIjEXWNIYalxHOstpFLzxrJV+V1dvlzUoQzMdK3YVkEJVUN5B+qbLfvoqb36Wz0YR4w\nAbgQuBRj4OylnTlQRJwi8omIbGq2/RERqY147RaRZ0Vkn4hsF5FxnZRtwNHWpCZoSkpauXCKcTEq\nhTvOYWcoAq0rhHDIVghTL7+L5HOanIoCOM3/lLCCSSNSWbVoGm/uKaXOH2T15WfSEAiRf6iSxb//\niLyDFVGdlq0lja00dDQ7pulobFyCiNyNkc04HzjSbMhsZ1hGUwcn67y5QHMP081AhVLqdGAt8KtO\nnn9AERlSbG1NHlk3sOTJHUar9WCYQBvVijbiwJk8iB/d/QC7nl/HqPQE+60wTf0PPC4HJZX1/HzD\nLmr8QYrK6ljx/KccMsfG+4NhVm0qaNWSWblwiq2wNLFLR5bCk0AuxtzIizF6K3QaERkNXAKsj9jm\nBNYA9zXb/TLz8wCeB+boBKmWWJ771IS4dj33K57/tNVy5+aoUJBgdRkiwqC5tzJ45iK+95t3OHCs\nrsW+Xo+L7BGpFJXVEaljjlY3paxkehOpawjwXlE5qzZ+HnW8nvfYN+jIpzDFjDogIk8AH5/g+X+L\ncfGnRGy7E3hZKVXS7JofBRwCUEoFRaQKGAxEhT5F5BbM9vJjxow5QXH6Pm157iNDjlt2l/CV2avA\nwuWA5n5FFQpSvnEN/q+/YOTNj+Jwe9oMTzrEmBw9a/wg9pfWkBjvpNZsvHrjeeN5bkcxiLBy4RQj\nHwH0MqGP0pFSsG815oXa6RObBVOlSqk8EbnA3DYS+CFwwQlL2iTH48DjYIyNO9nz9FWalxVHtlwv\nKq1tmpcQgdC2QvB98QGD5yzF4fa0+CwBhqTEU1bTSFgZdQxHq+qp8YeIczrwBcJkpCWweOYYtuwu\nsf0cKxdO0e3Z+zAdKYWzRKTafC5Aovnaij6ktnPsecD3RWQBkACkAp9jpEfvMxWMR0T2mX6EI0Am\ncFhE4oA04NhJ/l4DhlUbPyf/cJW9DqxtphCgZSu0SIVg9UOIxIo2OB3C5WeP5s29pewvqyWsYERa\nImenJUTNeYRoC0b3Q+jbdNSO7aRH+iql7gfuBzAthX9RSkWVW4tIrakQAF4GbgA+BK4A3oqcX6lp\nA9N6swyBznxhVR8+ayuEyNRlSxkkupz4gyF7+tO0UWkUldbi9bi48bzxbNldwsQRKXbqNOjGKP2J\nWBoS+wTwZxHZBxxHT6RqQd7BCsN5Z67dAVCKrGHJFB/34W+lbVprpM74Aa5Bo0ma8t2o7ZZC8QVC\ndqVkUrzTLqjSXZYHBj2iFJRS7wDvtLI9OeJ5A4a/QdMGVuTBel7dECT/cBU5mek4O3D3qFCQqu3P\nk5q7CIfb00IhtNjf/Hm4soE1r+1l/Q0zmD7Wq1OUBwCdTV7S9ALNuzYtm5tN1tAkPC4nX5bXUXjU\ncPfsOlxJUkLb+t3KVKz6+/9Qf6DjJtxxjmgNU+EL2BEFSzGs21oYc92kNF1DLC0fNM1obqpPH+sl\nIz2RorI6fGayEBgZisdrG6OOtac2NeuHkDSp4/RiqyGK0wHhsHGeXYcrWb15DwUl1YaFYo6K10uI\n/oe2FGKUyNbrkU1S5k/NIMXtZFiKm8j7efPU5dYUQqRT0R2x3kj3tH5vCIWblhEhZXRQeq+o3G68\nqpcQ/RNtKcQY1ni2/aU1djViZJOU58zGqpHt05uTNTSJL4/5aKg6RsORArwXLmXorEUEQsqOUkT2\nYKw0eyA4BFTENKesoUkcKK8jNdFFMBTm2pljKSipjkpf1jMf+x9aKcQY67YW2qa52ykEwop/FFdw\n2v2vcOlZIyk8avRUbC/0WPhNNYgDd/pwzrz7D1SF4vGbvRct4hzRlgBAQpwjqpy6vK6RkDJ8Cjmj\n01ooBEteHY3oX2ilEGMsm5tNdUOQuoYAB8qNGoNa02JoLQXZKo8+UuEj3uWgoqaB8pd/TVz6CLz/\ndBMVwXh730gFEAwTpSTSPXFU+oL20JYUt5PvZg9l46dfk5GeCCJRo+Us5aCjEf0P7VOIMaaP9bLh\njvOA1kucm/NeUbnZLyFsKwRf4TacyYMZlhLf7rGRp6+pN5YQpw1JYnbWEP5000zeLSwjpKDOH7Qr\nHFEqqmy7eZFTrM650HQerRRihGe2F3P2g6/zzPZi8g5WsL+8ZZUiYOcjNM9LaAy0dCqWN4tItIVD\nICM9kayhSSQluOyhslflZuL1uFg+b5J98a+89Ix2nYzt9XrQ9A308iFGsPopPrTpcxpDKqo02TLp\noW3rofyVh/EVbmPI3KUkTTeiDB21ULBRcLiinjiHEAwrdh+pssOSnzxwUdSuHaUz6+VE30dbCjHC\nVbmZOAR8gbB9QVqzGE4bmtxi/wlDood+J2V/m/ELb2forH8+4c+2XIvBsMIpxk+vx3VSF7bumdD3\n0ZZCjFBQUt3izh62NijFsBRjorPTASPTjAQmFQriLykiYfRkPGZSki9gOCWHpsRzvLYRh8PovOwU\nGJmeaHdIisQpTRbIhCFJZKQn6hDjAEZbCjHCsrnZeFxNRalxDmHpdyYwO2sISQkuSmv85GSmk5rg\n4lBFvZ2Y9M1fVhCoMKYsWXd8p0ClGU4Mm3f/QcnxJLicuM1mi26ng6yhSeRkprNq0TRyMtNtn4JW\nCAMbbSnEENnDkymva+R4bSOj0hMYMziJgpJqZo0fxMFjdZTX+qnwBVpkKrq8TRmPgnHXDzXzQZTV\nNFJW02iMiwNQipWXnmFf/ItnjuH6J7bbTkKdczBw0UohRljx/KcUldXZTsWisjrb+bht/zGCYUUw\nFI5SCJFORTDMvrZ8i0NT4hmV7uHK3Ex+ubmAGn+IFS98RkZags450EShlw8xgtX8NKwMB2PO6DSW\nz5uE1+MiGFbEOYRrZ44lVPR3fIXbGHHRLVEKQTCWD20phckjUtlwx3ls2V1ip08frapvN+dAMzDR\nlkKMcP+CyfzsxV2A4QewhqVMHJFij1wrKKlmw29/xt3jxpE4ZhrFx+rsGgZFU2XkaG8idf6gOZUp\njtOGJkVZAdX1ARDhytzMqJZqGg1oSyFmmDgihdHpCTgFln5ngp0ZCPDY4hzcHz3BZeMgd9wgJkyb\nQVFpLZNHppE1NKnlyZRi/Q0zzMzEc9lw5/n23X/6WC8rLz2D1IQ4Jo5I0ZaBpgXaUogR1m0t5HBl\nA7OzhrBiwWTb6RcOBml4Yy2F775I2dUX8UxyBju/qsDjcjBr/CCe3t40k8daOlTVB9tNMtJFTJr2\n0EohRmitK3I4GKTkpV/x1qsbefjhh7nttts4+8HX7VyEx947EHUOp8OY+nz/gskn9FnNZ0bokOTA\nRvpyw+Tc3Fy1c2fH7cX6IoFAgGuuuYYXXniBhx9+mHvuuQcwaiR+ubmAOn/IzktwSFPZ8+ysISd8\n97esEq/HRYUvcFLn0MQ2IpKnlMrtzL7apxAjNK8ubGxspKysLEohgJFPsOtf5/PQD6aR4o7D43IQ\nVpA9IrVFoVJnKxatYbDL503SHZU02lKIFay79fnj03ns2rNITk4mGAwSF9f+Cq+1zkfWNquXor7z\na7Sl0AdZNjeb88enU/LSr7j44os7pRCg9dwC25GoeylqTgLtaIwRzhyZTMMba22nYmcUQls0H+Gm\n0ZwIWinEAG05FU8WPcJNcyro5UMMsGzZsi5TCBrNqaIthRjg7rvv5qyzzuLWW2/tbVE0Gm0p9BaB\nQIA//elPKKXIzs7WCkETM2il0AtYPoQbb7yRd999t7fF0Wii0Eqhh4l0Kq5du5YLLrigt0XSaKLQ\nSqEHaa4Q7r777t4WSaNpgVYKPUheXh4vv/yyVgiamKbbow8i4gR2AkeUUgtF5GkgFwgAHwO3KqUC\nIiLAOmAB4AN+rJT6R3fL1xMopRARZs2axRdffMH48eN7WySNpk16wlJYBuyJeP00MAmYBiQCS8zt\nFwNZ5uMW4Hc9IFu3Yy0Znn32WQCtEDQxT7cqBREZDVwCrLe2KaU2KxMMS2G0+dZlwFPmWx8B6SKS\n0eKkfYhIhXD06NHeFkej6RTdbSn8FriPppEENiLiAq4DtpibRgGHInY5bG7rkzR3Ki5btqy3RdJo\nOkW3KQURWQiUKqXy2tjlUeA9pdTfT/C8t4jIThHZWVZWdspydgehUEhHGTR9lu60FM4Dvi8iXwF/\nBS4Ukf8BEJFfAEOBeyP2PwJkRrwebW6LQin1uFIqVymVO3To0O6S/ZRwOBxkZ2drhaDpk/RIkxUR\nuQD4FzP6sAS4CZijlKqP2OcS4E6M6MNM4BGl1LntnTfWmqwEAgEOHTrEhAkTelsUjSaKWG+y8hgw\nHPhQRPJF5AFz+2bgALAP+D1wey/IdtJYPoRZs2Zx/Pjx3hZHozlpeqRKUin1DvCO+bzVzzSjEXf0\nhDxdTXOn4qBBg3pbJI3mpNEZjaeITl3W9De0UjhF1qxZoxWCpl+hm6ycInfffTfZ2dlcccUVvS2K\nRtMlaEvhJAgEAjzwwANUVVXh8Xi0QtD0K7RSOEECgQCLFy9m1apVvPLKK70tjkbT5WilcAJYCuH5\n559n7dq1LF68uLdF0mi6HK0UOklzhaCdipr+ilYKnaS0tJSPP/5YKwRNv0dHHzogGAzicDgYNWoU\nu3fvJiUlpbdF0mi6FW0ptIOVmHT77bejlNIKQTMg0EqhDSJ9CJMmTcLoFqfR9H+0UmgF7VTUDGS0\nUmiFG2+8USsEzYBFOxpb4dprr+Xcc8/lrrvu6m1RNJoeR1sKJoFAgLfffhuAiy++WCsEzYBFKwWa\nogxz585l7969vS2ORtOrDPjlQ/N+CJMmTeptkTSaXmVAWwq6QYpG05IBrRQ2bNigFYJG04wBvXz4\n4Q9/yIcffsisWbN6WxSNJmYYcJZCIBBg6dKl5OfnA2iFoNE0Y0ApBcuHsH79erZt29bb4mg0McmA\nUQrNnYq3396nxkpoND3GgFAKOsqg0XSeAaEUQqEQPp9PKwSNphP06+hDIBCgrq6O9PR0Nm3ahMMx\nIHSgRnNK9NurxFoyzJkzh8bGRq0QNJpO0i+vlEgfwnXXXUd8fHxvi6TR9Bn6nVLQTkWN5tTod0ph\n+fLlWiFoNKdAv3M03nvvvUybNo2bb765t0XRaPok/cJSCAQCPPbYY4TDYcaMGaMVgkZzCnS7UhAR\np4h8IiKbzNfjRWS7iOwTkWdFJN7c7jZf7zPfH9eZ81s+hJ/85Ce88cYb3feLaDQDhJ6wFJYBeyJe\n/wpYq5Q6HagArNv6zUCFuX2tuV+7KKVsp+LDDz/MvHnzulh0jWbg0a1KQURGA5cA683XAlwIPG/u\n8iSwyHx+mfka8/050sGwhQMHDtgK4Z577ulq8TWaAUl3Wwq/Be4DwubrwUClUipovj4MjDKfjwIO\nAZjvV5n7t0lVVZVWCBpNF9Nt0QcRWQiUKqXyROSCLjzvLcAt5kv/vffeu/vee+/tqtN3B0OA8t4W\nohP0BTm1jCfP2M7u2J0hyfOA74vIAiABSAXWAekiEmdaA6OBI+b+R4BM4LCIxAFpwLHmJ1VKPQ48\nDiAiO5VSud34O5wyfUFG6Btyahl7hm5bPiil7ldKjVZKjQOuBt5SSl0LvA1cYe52A/CS+fxl8zXm\n+28ppVR3yafRaFqnN/IU/g9wr4jsw/AZPGFufwIYbG6/F1jRC7JpNAOeHsloVEq9A7xjPj8AnNvK\nPg3AD0/w1I+fqmw9QF+QEfqGnFrGHkC0ha7RaCLpF2nOGo2m64h5pdDdadLdJOPTIvKFiOwWkT+I\niMvcLiLyiCnjZyJyTk/J2JqcEdsfEZHaiNex9F2KiPybiBSKyB4RuStie698l63IOEdE/iEi+SLy\nvoicbm7vte/xVIh5pUA3pkl3o4xPA5OAaUAisMTcfjGQZT5uAX7XgzJCSzkRkVzA22y/WPouf4wR\nqp6klJoM/NXc3pvfZXMZfwdcq5TKAZ4Bfm5u783v8aSJaaXQ3WnS3SEjgFJqszIBPsbIx7BkfMp8\n6yOMnI2M7paxLTlFxAmswcg6jSRmvkvgJ8CDSqkwgFKqNELGHv8u25BRYeThgJFf83WEjD3+PZ4q\nMa0U6OY06W6S0cZcNlwHbGkuo0mk/N1Na3LeCbyslCpptm8sfZenAVeJyE4ReVVEsprLaNJT32Vr\nMi4BNovIYYy/9+rmMvbw93hKxKxSiEyT7m1Z2qITMj4KvKeU+nsPitWC1uQUkZEYIeD/6DXBImjn\nu3QDDWaW4O+BP/S4cCbtyHgPsEApNRr4I/BwjwvXhcRy56VuSZPubhlF5H+UUj8SkV8AQ4FbI/a3\nZLSIlL9H5QQ+B/zAPtOi9YjIPnP9GzPfJYYF8L/mPi9iXHTQO99lazK+guHv2G7u8yxNlmFvfI+n\njlIq5h/ABcAm8/nfgKvN548Bt5vP7wAeM59fDTzXizIuAbYBic32uQR4FRBgFvBxb36XzbbXRjyP\npe9yNXBTxPYdsfBdWjJi3FjLgWxz+83AC7HwPZ7sI5Ythbb4P8BfReQh4BOi06T/bKZJH8f4I/QW\njwEHgQ/Nu/D/KqUeBDYDC4B9gA+4sdckbJ9Y+i5XA0+LyD1ALU2RnJj4LpVSQRFZCrwgImGMiNhN\n5tux9D12Gp3RqNFooohZR6NGo+kdtFLQaDRRaKWg0Wii0EpBo9FEoZWCRqOJQiuFfoyIDDYr9/JF\n5KiIHIl43SWjuEUkRUSOiUhys+2bROTydo6bKyIbukIGTdfSF/MUNJ1EKXUMyAEQkf+LkaD075H7\nmAU6osyCo5P4jBoReROj+Odp85xejISiK9o7VhObaEthACIip4tIgYg8jZHunCkilRHvXy0iVmXq\ncBH5X7Mg6WMRmdXKKf9CdGLO5cArSqkGEZklIh+a/Qc+iChoipTnIRG5O+L1XrMaERG5wfzcfBF5\nVEQcIhInIn8WkV1i9Ky4q2u+GQ1oS2EgMwm4Xim108zLb4tHgF8rpT4ym4RsAqY222cz8N8i4lVK\nVWAoCMsi2QN8x8z8mw88BFzVGQFFZCrwA+Db5vGPm+feDwxRSk0z90vvzPk0nUMrhYHLfqXUzk7s\nNxeYGNEGwCsiiUqpemuDUspvFgb9s9mN6Axgq/l2OvCUiJx2EjLOBWYAO83PT8QoRX7NlOkR4BXg\n9ZM4t6YNtFIYuNRFPA9jFBZZJEQ8F+BcpVRjB+f7C7Ac48J9UTX1vPg34DWl1KNmm7ItrRwbJHop\na32+AH9QSq1sfoCInInRfekOjOXKLc330Zwc2qegwXQyVohIlog4MEx2i60YFx4AIpLTxmnexLAQ\nbsNQEBZpNJU0/7iNY78CppvnP5emkuitwJUiMsR8b7CIjBGRoRjO0b8BDwA92uuyv6OVgsbi/2CY\n5dswehhY3AGcJ0Zz1AJgaWsHK6VCGH0PUoH3I976FbBGRP5BtDUSyd+A4SKyG+OOf8A85y7gX4Gt\nIvIZxjJhOIbSeE9E8jH6K/zsxH9dTVvoKkmNRhOFthQ0Gk0UWiloNJootFLQaDRRaKWg0Wii0EpB\no9FEoZWCRqOJQisFjUYThVYKGo0miv8fvAYfaJPw0FwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXLgLglsFqFr",
        "colab_type": "code",
        "outputId": "28c0bb73-b40b-4a15-fef6-b8b7eb87cef5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Check the MAE as well\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "print(\"MAE on test set is: {0:.2f}\".format(mean_absolute_error(y_test.array, test_predictions)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAE on test set is: 3.44\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}