2_ml_exercise_neural.ipynb

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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "/content/gdrive/MyDrive/University/machine_learning/old_ml/exercise\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "%cd gdrive/MyDrive/University/machine_learning/old_ml/exercise"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
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        },
        "id": "6aIkU7fml3-7",
        "outputId": "65e829e7-c1fc-46c9-faef-7bb060022b0e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0_ml_exercise_preprocess.ipynb\t1_ml_exercise_classical.ipynb  2_ml_exercise_neural.ipynb  data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install shap"
      ],
      "metadata": {
        "id": "IaF_oCrBtsvg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#imports for data exploration\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "#imports for data set balancing\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "#imports for model training and evaluation\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import classification_report, accuracy_score, ConfusionMatrixDisplay\n",
        "\n",
        "#import for feature importance\n",
        "import shap\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "metadata": {
        "id": "F6t7lUa2B0IC"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "depress_kbest = pd.read_csv(\"data/depress_kbest.csv\")\n",
        "depress_kbest.head()"
      ],
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      },
      "execution_count": 6,
      "outputs": [
        {
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\"number\",\n        \"std\": 0.3889685046944408,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CONFLICT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43713128966130216,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SUICIDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2855624499474781,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"THREAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3596265682646422,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4389615314820313,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FINSTR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4859846965478274,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          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1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DEPRESSED\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47501059954983016,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "depress_rfe = pd.read_csv(\"data/depress_rfe.csv\")\n",
        "depress_rfe.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TWNQDKj96Pee",
        "outputId": "4bb93d2a-ce78-4215-dce2-bdc922af6ac5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DEPRI  INFER  ANXI  POSSAT  CHEAT  CONFLICT  LOST  FINSTR  ENVSAT  EDU  \\\n",
              "0    0.0    0.0   1.0     1.0    0.0       1.0   0.0     0.0     1.0  0.0   \n",
              "1    1.0    1.0   1.0     0.0    0.0       0.0   0.0     1.0     1.0  0.0   \n",
              "2    1.0    0.0   1.0     1.0    0.0       0.0   0.0     0.0     1.0  2.0   \n",
              "3    1.0    0.0   1.0     1.0    1.0       0.0   0.0     0.0     0.0  2.0   \n",
              "4    1.0    1.0   1.0     1.0    0.0       1.0   0.0     1.0     0.0  1.0   \n",
              "\n",
              "   WRKPRE  TSSN  AVGSLP  PHYEX  DEPRESSED  \n",
              "0     0.0   1.0     0.0    1.0        0.0  \n",
              "1     2.0   2.0     3.0    2.0        1.0  \n",
              "2     1.0   3.0     1.0    2.0        0.0  \n",
              "3     3.0   1.0     2.0    1.0        1.0  \n",
              "4     1.0   2.0     2.0    2.0        1.0  "
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              "type": "dataframe",
              "variable_name": "depress_rfe",
              "summary": "{\n  \"name\": \"depress_rfe\",\n  \"rows\": 604,\n  \"fields\": [\n    {\n      \"column\": \"DEPRI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4979392587765127,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INFER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43337160183107504,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ANXI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48831805873280476,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"POSSAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5004116785653043,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHEAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4054577796940218,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CONFLICT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43713128966130216,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LOST\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4389615314820313,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FINSTR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4859846965478274,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENVSAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4416460504711227,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EDU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7091404590475628,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WRKPRE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9650564482763412,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TSSN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0852334545141835,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AVGSLP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3485782535643318,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PHYEX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5383265771979078,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DEPRESSED\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47501059954983016,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_kbest = depress_kbest.drop(\"DEPRESSED\", axis=1)\n",
        "y_kbest = depress_kbest[\"DEPRESSED\"]\n",
        "\n",
        "X_rfe = depress_rfe.drop(\"DEPRESSED\", axis=1)\n",
        "y_rfe = depress_rfe[\"DEPRESSED\"]"
      ],
      "metadata": {
        "id": "EcYoClucFQhu"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1 Select K Best data set"
      ],
      "metadata": {
        "id": "L5Hy-HpZeN-1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1.1 Data set balancing"
      ],
      "metadata": {
        "id": "Ur3nipimei6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_kbest, X_test_kbest, y_train_kbest, y_test_kbest = train_test_split(X_kbest, y_kbest, test_size=0.2,\n",
        "                                                                            random_state=42, stratify=y_kbest)\n",
        "\n",
        "X_train_kbest.shape, X_test_kbest.shape, y_train_kbest.shape, y_test_kbest.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHRQ_gd5HHb9",
        "outputId": "ec7b803a-f441-4544-914c-f64ef12f511a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((483, 14), (121, 14), (483,), (121,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sm = SMOTE(random_state=42)\n",
        "X_train_kbest_res, y_train_kbest_res = sm.fit_resample(X_train_kbest, y_train_kbest)"
      ],
      "metadata": {
        "id": "gsyVMDotU3Kq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.bincount(y_train_kbest_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJ05QmoBXpO3",
        "outputId": "71b073e5-5be6-4ef9-da99-2d4f41910ec3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([317, 317])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_kbest_res.shape, y_train_kbest_res.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzOu4XbuWO91",
        "outputId": "99a7e67c-5be9-4784-a88f-6fd32d86d251"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((634, 14), (634,))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1.2 Training"
      ],
      "metadata": {
        "id": "43B0ddZar27x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train_kbest_res = scaler.fit_transform(X_train_kbest_res)\n",
        "X_test_kbest = scaler.transform(X_test_kbest)"
      ],
      "metadata": {
        "id": "dL-ybRWnotov"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_kbest_res = y_train_kbest_res.to_numpy()\n",
        "y_test_kbest = y_test_kbest.to_numpy()"
      ],
      "metadata": {
        "id": "rQdwGZ7oiW5U"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(y_train_kbest_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sq1x8j--pFWe",
        "outputId": "49d0b7c0-d02b-42f0-85c4-4f0178b2b312"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "model_kbest = keras.models.Sequential()\n",
        "\n",
        "early_stopping = EarlyStopping(monitor=\"val_loss\", mode = \"min\", patience=10)\n",
        "num_epochs = 200\n",
        "\n",
        "\n",
        "model_kbest.add(keras.layers.Dense(100, activation='relu', name='hidden1'))\n",
        "model_kbest.add(keras.layers.Dropout(0.5))\n",
        "model_kbest.add(keras.layers.Dense(25, activation='relu', name='hidden2'))\n",
        "model_kbest.add(keras.layers.Dropout(0.5))\n",
        "model_kbest.add(keras.layers.Dense(1, activation='sigmoid', name='affine'))\n",
        "\n",
        "model_kbest.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "               optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "               metrics=[\"acc\"])\n",
        "\n",
        "history = model_kbest.fit(x=X_train_kbest_res,\n",
        "                     y=y_train_kbest_res,\n",
        "                     epochs=num_epochs,\n",
        "                     validation_data=(X_test_kbest, y_test_kbest),\n",
        "                     callbacks=[early_stopping])\n",
        "\n",
        "model_kbest.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3qEhSYEez6u",
        "outputId": "0d31bbc3-ce3c-46af-f19f-7241126320c4"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "20/20 [==============================] - 2s 19ms/step - loss: 0.6953 - acc: 0.5095 - val_loss: 0.6291 - val_acc: 0.7273\n",
            "Epoch 2/200\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.6361 - acc: 0.6278 - val_loss: 0.5623 - val_acc: 0.8678\n",
            "Epoch 3/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5793 - acc: 0.7603 - val_loss: 0.4730 - val_acc: 0.9008\n",
            "Epoch 4/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4978 - acc: 0.8155 - val_loss: 0.3809 - val_acc: 0.8926\n",
            "Epoch 5/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4255 - acc: 0.8265 - val_loss: 0.3077 - val_acc: 0.9174\n",
            "Epoch 6/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3601 - acc: 0.8628 - val_loss: 0.2946 - val_acc: 0.8760\n",
            "Epoch 7/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3252 - acc: 0.8659 - val_loss: 0.2622 - val_acc: 0.8926\n",
            "Epoch 8/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3087 - acc: 0.8785 - val_loss: 0.2571 - val_acc: 0.9091\n",
            "Epoch 9/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.2871 - acc: 0.8849 - val_loss: 0.2621 - val_acc: 0.8843\n",
            "Epoch 10/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2762 - acc: 0.8880 - val_loss: 0.2591 - val_acc: 0.8843\n",
            "Epoch 11/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.2691 - acc: 0.9022 - val_loss: 0.2596 - val_acc: 0.8843\n",
            "Epoch 12/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.2612 - acc: 0.9038 - val_loss: 0.2653 - val_acc: 0.8926\n",
            "Epoch 13/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2623 - acc: 0.8991 - val_loss: 0.2740 - val_acc: 0.8678\n",
            "Epoch 14/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2411 - acc: 0.9069 - val_loss: 0.2710 - val_acc: 0.8678\n",
            "Epoch 15/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2676 - acc: 0.9101 - val_loss: 0.2679 - val_acc: 0.8760\n",
            "Epoch 16/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2749 - acc: 0.8959 - val_loss: 0.2701 - val_acc: 0.8843\n",
            "Epoch 17/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.2363 - acc: 0.9148 - val_loss: 0.2709 - val_acc: 0.8760\n",
            "Epoch 18/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2480 - acc: 0.9085 - val_loss: 0.2661 - val_acc: 0.8843\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hidden1 (Dense)             (None, 100)               1500      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100)               0         \n",
            "                                                                 \n",
            " hidden3 (Dense)             (None, 25)                2525      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 25)                0         \n",
            "                                                                 \n",
            " affine (Dense)              (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4051 (15.82 KB)\n",
            "Trainable params: 4051 (15.82 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss = history.history[\"loss\"]\n",
        "dev_loss = history.history[\"val_loss\"]\n",
        "acc_values = history.history[\"acc\"]\n",
        "\n",
        "plt.plot(train_loss, label=\"train\")\n",
        "plt.plot(dev_loss, label=\"dev\")\n",
        "plt.title(\"Loss curves\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "gF0S8aeKgOew",
        "outputId": "f6f0570f-5b82-45fa-bc3a-0669c5e462ff"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model_kbest.predict(X_test_kbest)\n",
        "predictions = (y_pred > 0.5).astype(int)\n",
        "\n",
        "print(classification_report(y_test_kbest, predictions))\n",
        "ConfusionMatrixDisplay.from_predictions(y_test_kbest, predictions);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "nDWzi-K1gPLA",
        "outputId": "ff6c5482-8f04-4a59-ee4d-24ac75aac6f0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.76      0.95      0.85        41\n",
            "         1.0       0.97      0.85      0.91        80\n",
            "\n",
            "    accuracy                           0.88       121\n",
            "   macro avg       0.87      0.90      0.88       121\n",
            "weighted avg       0.90      0.88      0.89       121\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1.3 Error analysis"
      ],
      "metadata": {
        "id": "Syy0t4pz57eQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Let's plot the value of importance the model had given to the features."
      ],
      "metadata": {
        "id": "FLl9OB-Bwoos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get feature importance using shap\n",
        "explainer = shap.Explainer(model_kbest, X_train_kbest_res)\n",
        "\n",
        "shap_values = explainer.shap_values(X_test_kbest)\n",
        "\n",
        "feat_import = np.abs(shap_values).mean(axis=0)\n",
        "\n",
        "#create a data frame with shap calculations\n",
        "import_feat = pd.DataFrame(index=X_kbest.columns,\n",
        "                           data=feat_import,\n",
        "                           columns=[\"Importance\"])\n",
        "import_feat = import_feat.sort_values(\"Importance\")\n",
        "\n",
        "#create the plot\n",
        "colours = sns.color_palette(\"coolwarm\",\n",
        "                            len(import_feat))\n",
        "\n",
        "sns.barplot(data=import_feat,\n",
        "            x=import_feat.index,\n",
        "            y=\"Importance\",\n",
        "            hue=import_feat.index,\n",
        "            palette=colours,\n",
        "            legend=False)\n",
        "\n",
        "plt.xlabel(\"Feature\")\n",
        "plt.xticks(rotation=45);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "3r62A8pUpkNG",
        "outputId": "8ed2b7f8-7c36-48aa-956c-102788e9cffe"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Academic or professional position satisfaction (POSSAT) had the most impact on the predictions. Struggling with anxiety (ANXI) and feeling deprived (DEPRI) of something, or having lost someone (LOST) were also impactful features."
      ],
      "metadata": {
        "id": "98J1u_5CtcpL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll separate the errors the model made from the correct predictions it performed."
      ],
      "metadata": {
        "id": "8DRK80aS66Z-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def from_categorical(y_pred):\n",
        "  return np.argmax(y_pred, axis=-1)"
      ],
      "metadata": {
        "id": "5FvxUSKaWPEX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = from_categorical(predictions)\n",
        "error_class = np.where(predictions != y_test_kbest)[0]\n",
        "feature_names = X_kbest.columns\n",
        "\n",
        "x_error = X_test_kbest[error_class]\n",
        "\n",
        "x_correct = X_test_kbest[predictions == y_test_kbest]"
      ],
      "metadata": {
        "id": "FJeEbPqlrBoH"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=x_error)\n",
        "plt.xticks(rotation=45,\n",
        "           ticks=np.arange(len(X_kbest.columns)),\n",
        "           labels=X_kbest.columns);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "eU-J2LFevaYZ",
        "outputId": "29dd593d-f08f-4bb4-e670-0115f08bc884"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot shows that DEPRI, ANXI, FINSTR and ENVSAT had the most weight in making incorrect predictions."
      ],
      "metadata": {
        "id": "ZZElI2qfwIf1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=x_correct)\n",
        "plt.xticks(rotation=45,\n",
        "           ticks=np.arange(len(X_kbest.columns)),\n",
        "           labels=X_kbest.columns);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "FwF0SRA3aQ7h",
        "outputId": "49aa24bb-e375-4ad0-a114-f14b35ba555d"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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hIiIi1TGQEBERkeoYSIiIiEh1DCRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHqGEiIiIhIdQwkREREpDoGEiIiIlIdAwkRERGprkiBZMWKFcLd3V1YWFgIb29vcfz48Zd63ubNm4UkSaJHjx5FOSwRERG9pnQOJFu2bBEhISEiPDxcnDp1Snh6egp/f39x9+7dFz7vxo0bYty4caJly5ZFLiwRERG9nnQOJIsWLRJDhw4VwcHBonbt2mLVqlXCyspKrFu3rtDnaDQaMWDAADF9+nRRtWrVYhWYiIiIXj86BZKsrCxx8uRJ4efn988PKFVK+Pn5icjIyEKfN2PGDOHs7CyGDBlS9JISERHRa6u0Ljvfv39faDQa4eLiotju4uIiLl68WOBzDh8+LNauXSuio6Nf+jiZmZkiMzNTfpySkqJLMYmIiOgVY9BRNqmpqWLQoEFizZo1wtHR8aWfFxERIcqWLSt/VapUyYClJCIiIrXpVEPi6OgoTExMRFJSkmJ7UlKScHV1zbf/1atXxY0bN0TXrl3lbbm5uc8OXLq0uHTpknj77bfzPS80NFSEhITIj1NSUhhKiIiIXmM6BRIzMzPh5eUlDhw4IA/dzc3NFQcOHBCjRo3Kt7+Hh4c4c+aMYtvUqVNFamqqWLp0aaEhw9zcXJibm+tSNCIiInqF6RRIhBAiJCREDB48WDRq1Eg0adJELFmyRKSlpYng4GAhhBBBQUGiQoUKIiIiQlhYWIg6deoonm9nZyeEEPm2ExER0ZtL50ASGBgo7t27J8LCwkRiYqKoX7++2LNnj9zRNT4+XpQqxQlgiYiI6OXpHEiEEGLUqFEFNtEIIcShQ4de+NxvvvmmKIckIiKi1xirMoiIiEh1DCRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHqGEiIiIhIdQwkREREpDoGEiIiIlIdAwkRERGpjoGEiIiIVMdAQkRERKorrXYBSL/+t7i9TvtnZEPxeM/KHsLCVHrp53cZs1+n4xERERWENSRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHqGEiIiIhIdZwYjYiIimTJ+js67Z+dlaZ4/OWmRGFqZv3Szx892E2n49GrhTUkREREpDoGEiIiIlIdAwkRERGpjoGEiIiIVMdAQkRERKpjICEiIiLVcdgvERERyQCItLR/hmhbW1sLSZIMflwGEiIiIpKlpaWJ7t27y4937NghypQpY/DjssmGiIiIVMdAQkRERKpjICEiIiLVMZAQERGR6hhIiIiISHUMJERERKQ6BhIiIiJSHQMJERERqY6BhIiIiFTHQEJERESqYyAhIiIi1TGQEBERkeoYSIiIiEh1DCRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHqGEiIiIhIdQwkREREpDoGEiIiIlIdAwkRERGpjoGEiIiIVMdAQkRERKpjICEiIiLVMZAQERGR6ooUSFasWCHc3d2FhYWF8Pb2FsePHy903zVr1oiWLVsKe3t7YW9vL/z8/F64PxEREb15dA4kW7ZsESEhISI8PFycOnVKeHp6Cn9/f3H37t0C9z906JDo16+fOHjwoIiMjBSVKlUSHTp0ELdu3Sp24YmIiOj1oHMgWbRokRg6dKgIDg4WtWvXFqtWrRJWVlZi3bp1Be7//fffi48++kjUr19feHh4iK+//lrk5uaKAwcOFLvwRERE9HrQKZBkZWWJkydPCj8/v39+QKlSws/PT0RGRr7Uz3j69KnIzs4W5cqVK3SfzMxMkZKSovgiIiKi15dOgeT+/ftCo9EIFxcXxXYXFxeRmJj4Uj9j4sSJonz58opQ87yIiAhRtmxZ+atSpUq6FJOIDAyAePLkifwFQO0iUQH4OtGrpLQxDzZnzhyxefNmcejQIWFhYVHofqGhoSIkJER+nJKSwlBCVIKkpaWJ7t27y4937NghypQpo2KJqCB8nehVolMgcXR0FCYmJiIpKUmxPSkpSbi6ur7wuQsWLBBz5swRv/76q6hXr94L9zU3Nxfm5ua6FI2IiIheYTo12ZiZmQkvLy9Fh1RtB1UfH59Cnzdv3jwxc+ZMsWfPHtGoUaOil5aIiIheSzo32YSEhIjBgweLRo0aiSZNmoglS5aItLQ0ERwcLIQQIigoSFSoUEFEREQIIYSYO3euCAsLExs3bhTu7u5yX5MyZcqw6rAEMC8txAh/SfGYiMgQSptaiY7vfaV4TKSl8+UnMDBQ3Lt3T4SFhYnExERRv359sWfPHrmja3x8vChV6p+Kly+//FJkZWWJ3r17K35OeHi4+Oyzz4pXeio2SZKEhanapSCiN4EkScLUzFrtYlAJVaT74VGjRolRo0YV+L1Dhw4pHt+4caMohyAiIqI3CNeyISIiItWxxwAREb1xAIi0tDT5sbW1tZAk6QXPIENjICEiojcO52gpeRhIiIiIXmO3pu7Vaf+0nAzF49ufHxDWpQufzPR5FT731+l4WuxDQkRERKpjICEiIiLVMZAQERGR6hhIiIiISHUMJERERKQ6BhIiIiJS3Rsx7JcT4BAREZVsb0Qg4QQ4REREJRubbIiIiEh1DCRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItW9EfOQEL0OOMEfEb3OGEiIXhGc4I+IXmdssiEiIiLVMZAQERGR6hhIiIiISHUMJERERKQ6BhIiIiJSHQMJERERqY7DfomI9IjzxRAVDQMJEZEecb4YoqJhICEi4bd+lk77Iytb8bj7pgVCMjN96ef/OniKTsejZ7p/E6nT/shKVzzuv/G4kMwsX/r5O9730el4RMXBPiRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHqGEiIiIhIda/kxGh3ZizXaf+0nCzF48R5q4V1aTOdfoZb2Cid9iciIuM5tPiOTvtnZKcpHh9emSgsTK1f+vm+Y9x0Oh79O9aQEBERkepeyRoSIiIiMgwrE3PxZeNPFI+NgYGEiIiIZJIkCevSFkY/LptsiIiISHUMJERERKQ6BhIiIiJSHQMJERERqY6dWolU0O7b93R/Umau4mH3zR8IYf7y9xQHBm3W/ZhEREbCGhIiIiJSHQMJERERqY6BhIiIiFTHQEJERESqYyAhIiIi1TGQEBERkeoYSIiIiEh1DCRERESkOk6MRkT0Ap3+s02n/ZGVqXjc+/ufhGT28su3/xLcR6fjEb0uWENCREREqmMNCRHpzrS0EO+1VD6mksfUQlgHTlE8JiqpeBYhIp1JkiSEmanaxaB/8ex1slS7GEQvpUhNNitWrBDu7u7CwsJCeHt7i+PHj79w/23btgkPDw9hYWEh6tatK3bv3l2kwhIREdHrSedAsmXLFhESEiLCw8PFqVOnhKenp/D39xd3794tcP8jR46Ifv36iSFDhoioqCjRo0cP0aNHD3H27NliF56IiIheDzoHkkWLFomhQ4eK4OBgUbt2bbFq1SphZWUl1q1bV+D+S5cuFR07dhTjx48XtWrVEjNnzhQNGzYUy5cvL3bhiYiI6PWgUyDJysoSJ0+eFH5+fv/8gFKlhJ+fn4iMjCzwOZGRkYr9hRDC39+/0P2JiIjozaNTp9b79+8LjUYjXFxcFNtdXFzExYsXC3xOYmJigfsnJiYWepzMzEyRmfnPWP7k5GQhhBApKSlCCCFSM9J1KbZI02SLnJwc+XFqZobIzdHo9DOs//+xS7qnGTn/vpMepfzL3yU9vWSVp6TISc/W/UmZuULkeR+L9Gwhcl/+nuJFf5uc9Azdy1MMr8rrJIQQOelPddofWVmK842Uni4kzcufb170t8lOT9OpLMX1b69TRnqqkUryTEqKdaHfS8vQrSwZ2WmK1yktI1VoNLl6KUtqpnH/LkK8+LVKzVTvfaP9P4B/fyJ0cOvWLQghcOTIEcX28ePHo0mTJgU+x9TUFBs3blRsW7FiBZydnQs9Tnh4OIQQ/OIXv/jFL37x6zX4SkhI+NeMoVMNiaOjozAxMRFJSUmK7UlJScLV1bXA57i6uuq0vxBChIaGipCQEPlxbm6uePjwoXBwcHg2jK0IUlJSRKVKlURCQoKwtbUt0s/Qp5JUnpJUlpJWnpJUlpJWHpbl1ShPSSpLSStPSSpLSSuPvsoCQKSmpory5cv/6746BRIzMzPh5eUlDhw4IHr06CGEeBYWDhw4IEaNGlXgc3x8fMSBAwfE6NGj5W379+8XPj4+hR7H3NxcmJsrp1q2s7PTpaiFsrW1Vf2FzqsklacklUWIklWeklQWIUpWeViWwpWk8pSksghRsspTksoiRMkqjz7KUrZs2ZfaT+eJ0UJCQsTgwYNFo0aNRJMmTcSSJUtEWlqaCA4OFkIIERQUJCpUqCAiIiKEEEJ8+umnonXr1mLhwoWic+fOYvPmzeLEiRNi9erVuh6aiIiIXlM6B5LAwEBx7949ERYWJhITE0X9+vXFnj175I6r8fHxolSpfzraNWvWTGzcuFFMnTpVTJ48WVSvXl389NNPok6dOvr7LYiIiOiVVqSp40eNGlVoE82hQ4fybevTp4/o00fdFSzNzc1FeHh4vqYgtZSk8pSksghRsspTksoiRMkqD8tSuJJUnpJUFiFKVnlKUlmEKFnlUaMsEvAyY3GIiIiIDKdIa9kQERER6RMDCREREamOgYTIANgSSkWVlZWldhGIVMFAQq+d3NyXn/5Z365duyauXbtW5An86M128uRJ0bx5c3H//n21i0JkdAwk9Nq4fPmyuHXrlmLYuTFFR0eLhg0bij///FOV47/KTpw4IYYOHarKsbOzi7CukAHExMQIX19f4ePjIxwdHY123Nu3b4urV68a7XhkGHnXfzOWp091W+fp3zCQ5BEbGysWLlyo6h12QdSo/o+NjRXLly83+nGLKiYmRnh4eIidO3eqdvxmzZqJjz76SAwePFiVMryqYmJiROvWrVUZ6njixAnRokUL8fjxY6MfO68LFy6IFi1aiLFjx4ovvvjCaJ/5qKgoUaNGDREbG2uU473KSkpwLcjp06dFSEhIvmVaDH3MgQMHivj4eP390H9d7eYN8t1330GSJMyaNQsajUbVsmRnZ7/wsaGtWrUKkiRhwYIFRj1uUcTExMDS0hJTp05V5fgXL16Evb09QkJC5G25ubmqlOV5T548QUJCAvbv348TJ04ovlcSynj27FlYWVnhs88+M/qxo6OjUaZMGXzyySeK7cb+u8TExKBcuXJwcnLCgQMH5O2GPgdFR0fD2tpa8b4taXJycvKd+9R43168eBHBwcE4efKk0Y/9b6KjoyFJEsLCwox6TBMTE0ybNk2vP5eB5Dnr1q1DqVKlMHPmTNVCyaVLlzBs2DD06dMHH330kVyOnJwcgx/70aNHyMzMREZGBpYtW4ZSpUph3rx5Bj9uUWnDwLBhw+RtxjxhaU/qkiRhwIABePz4sdGO/W8uXbqEPn36oG7durCwsIAkSejRowd27dol76NmKDlz5gwcHBxQt25d3L1716hlio6OhqWlJSZPnqzYbuzPfFRUFKysrDBo0CD4+/ujU6dO2Llzp/x9Q/0tLly4oAjRxji36OrSpUv46KOP0KFDB8yaNQsXL15UpRwpKSnw8vKCiYkJBg0ahL///luVchTk/PnzsLS0xKxZs4x2zAsXLsDS0hIzZszQ+89mICnA119/rVooiY6OhoODA/r374/OnTvD2dkZfn5+RjlJ37x5E/Xq1cP169cBABkZGVi6dGmJDSVRUVFyGOjduzeuXLli1ONHR0fD3Nwcn332Gc6dOwcHBwcEBASUiFASExMDNzc3jBgxAtu3b0dUVBTWr18PR0dHeHp6YuvWraqWTxsIfH194e7ujvHjx+Pq1atGOba2VmbmzJmK7bNmzUJQUJDRQtq1a9cgSRImTpwIADh58iR8fX3RqVMn/Pzzz/J++i5PdHQ0bGxsYG1tjb59+yIlJQVAyQol0dHRcHJyQo8ePdC3b19IkoSBAwciOTnZ6CE6IyMD3bp1g42NDXr27Il+/fqViJqS06dPw8HBAfb29vK2rKwsgx4zJiYGDg4OsLS0lLfp833zRgeSuLg4fP3111i9ejUOHz6s+J4aoeTMmTOwtLTE9OnTAQBpaWn48MMPYWNjo0jlhipPcnIynJ2dcejQIXlbSQ0lUVFRMDMzw7x585CQkICyZcuiS5cuRgsl9+/fR/Xq1RV32MeOHSsRoeT06dOwtLTEtGnT8r1XTp06BXd3dzRp0gQXLlxQpXznz5+HJEmYMmUKAOCrr75C+fLlMX78eFy7ds2gx87MzERgYCAkSUJmZqa8PSIiAg4ODtizZ49Bjw/8EzD+/PNPrF+/XrHt5MmTaNOmjcFCycmTJ2FjY4OJEydi27ZtaNq0Kbp164bk5GQAJSOUnD59Gra2toomiDFjxqBUqVKIiYkB8M/fw9DhRPv3OHv2LFq2bIkRI0agVatWCAwMxKlTpwx67BfRBvoePXrA09MT7dq1k79nqOZ97TEHDx4MT09PNG7cGGlpaQD0d016YwNJTEwMKleuDB8fH1hYWMDT0xMbNmxQ7GPMUPLo0SM0btwY7u7uiu3/93//B0mSsG3bNty5c8dgH8Ds7Gw8efIEVapUwdq1axXfK2mh5OHDh/D29kZoaKi87cKFC0YPJX/99Zf8f+3rcvz4cVVDSVxcnHznq6XRaJCbmyu/h48dOwZJklR7LZctW4ZVq1YpthkrlOTm5iIqKgpeXl6oW7cuAGDx4sUoV64c9u3bZ7Dj5qU9iWvl5OQoXh9DhZLk5GSULVsWn376KYBnn/n169ejadOm6N69e4moKUlLS4OZmRk8PT2RkZEhbx82bBgkScKhQ4fkGty89H1ezFvToNFocPv2bbz33nvYtGkT9u/fDx8fH9VCyalTpyBJktzvateuXfDw8DBoKDlz5gxMTU3lfnrR0dHw8PBA48aN8fTpUwD6CSVvZCCJiYmBlZUVpkyZgrS0NPz1119wdXVFz549kZWVpXhzf/311zA3N8ekSZMMGkoeP36MWbNmoWHDhhgxYgQAYOHChTA3N0fnzp3RpUsX+Pj4oF69elizZg1++eUXvRz3+Sq+fv36YfTo0fm+pw0l2iYKteWtMdKW8+LFiwYPJTdv3lTUIAH5T+B5Q4n2ztNY4uPjUbVqVXTp0gW//fabvF37ntb+rXr16oVu3bopagkMLSEhAXv27EFERIR8wsz7tzNmKDl79izq1KkDOzs72Nvb56shBYClS5fi999/1+uxz507h/Lly2PBggWKvjzAsxP686Gka9eu2L59e7GPm5KSgkePHsnNYnnfDxs2bCgRoSQuLg6pqan46quvYGpqKveLmD9/PszMzNCgQQMMGzYMFhYWCAgIwJQpUxAZGZkv4BXXpUuXMGTIkHzNmsuXL4eLiwsePXqEHTt2oHnz5ggMDERUVJRej/9vfvrpJ0yYMEF+nJmZafBQ8vPPP+Pzzz9XbIuOjkatWrX0GkreuEBy48YNODo6ok+fPortzZo1Q5UqVZCYmJjvOUuXLkW5cuVw7949vZfn9OnT8oXj8ePHWLx4MerVq4eGDRuiXLly+Ouvv5CVlYWsrCzExsZi5MiR8Pb2hpOTEx48eFCsY1+8eBF9+/bFiBEjsHnzZly9ehU9e/aEv79/gfs/efIEERERKFeuXLGPXRRxcXH57mK1Hzrtv3lDib77JNy9exd2dnaws7ND//79sW/fvny1INoT+fHjx+Hq6or27dsbJZRcv34dly9flv9fv359+Pn5FTpqo23btujRo4fBy6V15swZNGjQAIGBgZgwYQKePHlSYLm0oWTSpEl6C5VZWVl48uQJLl++rPh8nzt3Dn5+fqhYsaJ8N64tS3h4OCRJwtmzZ/VSBq3FixdDkiT069cPXbp0QevWrXHkyBHcvn0bgPJO/9SpU2jQoAH69OmD1NTUIh/z4sWLCAwMxOeff464uDh5u/Z3zc7OVj2UnDp1Cg4ODnIIWLZsGSRJQps2beDi4oLffvtNLu+ff/6J+fPnw83NDfXr18edO3f0Vo5Hjx6hfv36kCQJJiYmGDZsGKZPny5fcN9//318/fXXAIBvv/0Wvr6+ePfdd+WmJEPKzs7Od8HXvkZZWVkGCSV5a6m08pZB36HkjQskR44cQY0aNdCvXz/57iciIgKSJMHLywudO3fG0KFDERERgQcPHsgXk0ePHum9LDExMZAkSe4zAjwLJYsWLUKNGjXQqVOnAp+XmJiol3D0yy+/YNSoUWjZsiXq168POzs71K5dG6VLl8agQYPw7bff4uDBg/KxtCdLNcLIvXv35A6sQ4cOxYYNG/Ld3eetKXF0dESrVq0KrN4tqvj4eHTu3Bnbtm3DyJEj0aVLF9SuXRu7d+9GbGxsvv2PHDmCqlWrIiEhQW9lKEhUVBTKlSuH7777Tn6Nrl+/Dk9PT7Rv315RU5KTk4Nbt26hS5cuctOcodvhtSM6Jk+erBhNk1fek9iaNWtgYWGBsLCwYp9QL1++jOHDh8PT0xN2dnZwcHDArFmz5LB6/vx51K1bF3Xr1pXD5dSpU2FhYWGQjos3b97Ehx9+iO3bt+Pu3bvo2bMnunXrhgYNGmD79u35QvSpU6dw48aNIh8vJiYGrq6uCA4Oxt69e/N9X3tB04aSFi1awNfXt1gBSFfR0dGwsrLC+PHjFdtXr14NSZIwaNAgpKen53tedna2HOT0JTc3F59//jn8/f3RuXNnjB07Fn369IG7uzuWLl0KX19fBAQEyPt/88036NSpE27evKnXcjzv3LlzeO+997BkyZJ8Nzjaz07eUFLYTaUuLly4gMGDB6NNmzaYOnVqoaOctKHEx8en2LVVb1wgAYD9+/ejadOmGDBgAD788EM4ODhg586duHz5Mn799VeEhYXB3d0dLi4uePfdd5GdnW2Qnu6FzZ3x4MEDLFq0CPXq1cPw4cPl7fqqXk9OTs6XfHNzcxEdHY0NGzbA1NQUrVq1Qp06dWBqaoqaNWuiZs2aGDt2rGrty6mpqfjggw+wcuVKTJw4ER07dkS1atWwadOmAu9Ozp49iypVqiA+Pl6v5RgyZAg6duwIALh16xamT5+ONm3awNPTE0uWLFHcgQIo8ESqT9r30aRJk+Rt2vfq1atX5VCSt6YkNDQUderUyVdWQ0hLS0PXrl3xwQcfKLYX9HnKG0rWr19fYMjThbaf2ODBg7F06VJs2rRJ7pPVu3dvnDt3DsCzk72npycaNWqE8ePHw9LSMt+cLfqSmZmJvn37YvDgwfK227dvw83NDY6Ojmjfvj2mTJmCq1evFnh3qosbN26gcuXKmDhx4guDXd5axtWrV6N9+/YGD9Fa2mH72g6sGo1GcY758ssvIUkSPv/8c0VtpCE6bmrffxqNRg4lH374IZ48eYI1a9bg448/hp2dHSRJQnR0tPw8Q9eAZmdnY9iwYahcuTKGDx+O8uXLY+XKlYiMjMy3b1ZWFnbv3g0XFxd07969yMeMjo6GnZ0dPvjgAwwcOBBeXl5yU31BHYpjYmLg4uKCtm3bFvmYwBsSSOLi4rBhwwZ8/vnncso7dOgQmjRpAktLywI792VmZmL9+vUGGYqonbsi70UEANauXStXu2ubb+rWrYuRI0fq7di3b9+Gn58fli9fLp/w8r6xTp8+DScnJ/n3PnfuHH777TeEhobi0qVLeitHUYwaNQrdunUD8OxCP3PmTAQGBqJSpUpYunRpvouIPofAaU+ADx48QKtWrbBt2zb5e61atULlypXh7OwsV+EmJSXp7diF0d5Z5u3cCwCRkZFyTYS2+aZ9+/Y4evQowsLCYG1trTihGtL9+/dRq1YtfP/99wV+//lgoq/XTDvSaPLkyflC4YYNGyBJEoYMGYL09HTk5ubi3LlzeOeddyBJkl5rRm7duiW/Ftrf9eLFi6hYsaLc/BgUFISqVati69at+OKLL2BjYwNvb+9i18quWrUK7du3l6vSgWfvh127dmH69OnYuHGjvD1vKDFWvyftxHTaWhCtvH1pgH+abyIiIvDw4UO9lyMzMxM5OTlITk6Wa4Y0Gg3mzJmDxo0b4+OPP5bv/A8fPix3NDbmlBDbt2+Hk5MT7t69i3Xr1mHQoEGoWLEiJkyYkK9PW3p6Ovbu3VvkJs+YmBhYW1vLo+AA4JNPPkFgYCDS09MLnTPozJkzxW5mfe0DSUxMDNzd3eHh4QFLS0u5ahsAfvvtN3h7e6Nv376KF9WQHf2SkpJgaWmJ/v37A/jnBZ0zZw7KlCmjSL2PHz/G0qVLUbFiRb3NppiRkQF/f3/4+Phg7dq1+drOc3Nz0ahRI3zzzTd6OZ4+aMuWnp4OT09PrFixQv6en58fnJ2dUbNmTfj4+MDX11cOdcWt1YqJick3E2FaWhree+89eaRCUFAQXFxcEB8fj6SkJCxevBjNmzeXy2Ao169fh42NDT766CMA//yuM2bMgKenJ65fvy7/3a5fv45GjRrBzs4OZcqUMdjdf0HOnDkDMzMzRQ3N87KzszFv3jy91SZdunQJkiQpZmDNO4oFAFasWAFJkuRQoA0l+qxRi4qKgiRJ+N///qcox9OnTxEcHIxFixahX79+cHV1VYSgtLQ0vdRezZo1C82bN5ebWL///nt069YNbm5u8PDwgCRJis6RxpzfQzshXGhoKA4fPgwHBwdFv77nX6+VK1dCkiQsXLhQr+W8cOECBgwYAC8vL5QrVw5NmzaVR4Dl5uZi/vz5aNKkCf7v//7PIH0IdTFw4EAsXLgQwLPz+OnTpyFJEqpWrQo/Pz9ERkYWq3kPeHbDKkkSgoODFdtHjhyJWrVqoWbNmqhRowbWrVsnf0+fr8drHUi0o2mmTZuG27dvIy4uDr1794ajo6N8B7tv3z74+PggICBA7z3qC3L//n306tULjo6O8oVh9uzZimGHubm58ov86NEjrFy5Ui81Ndq7z4yMDPTp0weNGzcuMJS0a9cuXxW7saWmpiruhjQaDbKzszF27FhFGHB1dUV8fDxu3bqFjRs36i0MaJtC8gYS7Wty4sQJODk5wcvLC66uroqhfxqNxigjV9atW4e3334bQ4cOldvRZ8+eDQcHB8UILG3195UrV9CuXTucPn3a4GXLe0ceGxsLCwsLzJkzB0DBd5V79uxB79699dZvITIyEpIk4dNPPy2wj0FOTg7u37+PGjVqYPLkyQa5EGvv/rWTngHK333jxo2QJAlubm6Kpil9Nolu2LABtra2+PDDD9GrVy/Y2dlh9OjR+OOPP6DRaLBixQrY29vLTVfG8uDBA9jY2Mg1xLm5udi7dy8cHBwUw9WfDyVr1qzRa1lPnz4NOzs7DB06FMuWLcOCBQvg5+cHSZLw0UcfyZ1I58yZAx8fH4wYMcLo/efyvjfnzJmDJk2ayI9HjhyJ8uXLY/v27ejQoQNcXV3Rtm1bZGRkFPk9nZqaimbNmsHDwwNnzpwBAMydOxeWlpZYvXo1vvrqKwQHB0OSpAL7JBXXaxtICkt6v/32G6ytrRUd/fbv34+WLVuiffv2irkl9Ony5cvYsWMHgGe1JH379oWdnR1GjBgBFxeXAofxau+ainvC1Paazys9PR0BAQFo1KiRIpQAzy5s2n4SatCO/lm8eHG+zmKRkZEoU6YMGjZsiPLly+erXtfHCV3bFPL8tOJaWVlZCAoKgru7u9GH/OX9/ZYvX47mzZtjxIgRmDRpEpycnAqc2Et7UTbGekg3btxAQECAohxBQUEoW7asPFRbWw7t+3rixIkYNGiQIsgURUZGhvyz9+7dCxMTE0Vgy3tMAKhatSrGjBlTrGMWRLu20vPvn+ffq4MGDUJwcLBBX5e5c+eia9euaNu2Lfbv369oBtq4cSNq165d4MhCQ9E2Bz0/KV9ubi727dv3r6FEX+7cuYN33nlHERiBZ01ss2bNQunSpeXXT6PRYMGCBahVqxbGjBlj8KaahIQE/PTTT/Jj7Xs2Ozsb77zzDpYtW4YPPvgArq6uivPPjh07ilxDEh8fL/+s1NRUtGnTBjVq1MCnn34KJycnRfg4f/48XFxcDLJ2zmsbSPImvdOnT8sn8kOHDsHa2jrfyWHXrl3o0KGDQTpzRUVFoXTp0vjqq6/kbUlJSXj//fchSRK++OILAMqLzYQJE1CuXDncv3+/WMc+f/48ypYti8DAQISGhuLq1au4desWgGcn8IEDB6JBgwZYs2aNXGW+cuVKlC9fHmlpaaosNObi4oL3339fMSkU8M8H85NPPkGlSpUMEh5Pnz4NKyurfB+2//znP4p5CdavXw9bW1t5FI8x2pNv3LiBlStX4tixY/K2ZcuWoVGjRihdurTcTyPv+2jq1Klo0qQJ0tPTjVLGuLg4VK5cGZ06dcKvv/4K4NlIEU9PT9jb2+PAgQNy8Lh586YcpM6fP1+s416/fh0tWrTA4cOH5Qv8nj175KGbeYeG5uTk4MKFC2jZsqXe5vPRunz5MiRJytc/bObMmShVqpTi/DJ37ly88847hY480sWVK1cwY8YMdO7cGb169cInn3yiOHcU1Ddn/Pjx6NChg9Em8Lt48SJ69eqFsWPHFljT8KJQom8HDx5EvXr15JrnvJ+Nhw8fYsKECShdurTclK/RaPDFF1/oddReQc6cOYP69eujefPmihCgLd/ChQthY2ODWrVqyTXsxb0JO3XqFGxsbBTz3aSmpuLdd9+FJElyE5a2DCkpKWjcuDG+/PLLYh23IK9dICko6VWpUgW3b99GfHw8XFxcMG7cOHn/vG92fU+wAxTegRV4loQHDRoEW1tbxUVm2rRpKFOmjGJbUX3xxReQJAmurq5o1qwZXF1dUadOHYwbNw6//vorkpOT0bNnT3Ts2BFff/01MjIykJCQYJQRGM/TjgqYNGnSC09C3377LSpUqCB3UNbXhfb+/fuoXbs26tWrp9g+a9Ys2NnZ4ejRo4rtHTp0QO/evY3SRHPmzBnUqFEDAwYMwNatWxV/ny+//BLe3t4IDg5W1ChNmzYNFhYWRlsMLG+flfr166Ndu3b4448/ADy7ALRs2RKSJMHb2xve3t5o3rw53N3d9TLbZXZ2tjwvxbFjxwoMJdogDgCTJk1CkyZN9D5s9LfffpP7ZmhDQEREBJydneXwk/f9Wq5cObkfUFFpQ3yXLl0wYMAAtGvXDnZ2dnj77bexc+fOfO/PpKQkTJgwAXZ2dkZpwgOeBX1XV1cMHz48X5+avLShxNXV1aC1tAsXLoSLi0uh5fj7779hbW2Nb7/91mBleN65c+dgb2+PCRMmFNo5VDsj9fz58/VyTG3TYt6+RFrJycno0KEDqlatqugEP3XqVFSuXNkg4ey1CiQFJb2UlBS0atUKbm5ucHFxUYxYef5Cpu8kfubMGVhZWSnmGQGgCBpJSUkIDAyEjY0Nzp07h3nz5sHCwkKvHQ/nzJmDUqVK4YcffsChQ4ewcuVKNG3aFBUrVkSjRo3QuXNnWFpawt3dHZs3b9bbcXW1YsUK+Pn5KSbNiouLw969ezF37ly5MzIAtG7dGu3atdPra3bz5k2MGzcOdevWlV+z+fPnw8HBQXG3oj1mWFgYvLy8DH6HmfdEVVgN3tKlS9GsWTMEBQUhOTkZc+bM0fv76GVo79auXbuG+vXro23btvIsqI8fP8by5csxbNgwDBgwAGvWrCn2SU3bSRR4VgtQp04d1KlTp9BQkpKSgvDwcNjY2Oh9Mivt+2LHjh0wMzPD1KlTMWvWrEKnpb916xaWLVtWrNFrN27cQMWKFREaGqqYU+TkyZNo1KgRKlWqhCNHjsjlGzduHHr37o2aNWsabaTVjRs3UKlSJYSGhr7UKKrc3Fz873//Q9WqVQ02v8fPP/+M0qVL5+tsnfd8UrlyZYSHhxvk+M9LTk5Gy5Yt5Vmy89KeD7V/u0mTJqFZs2bF/ttomxbzjqYBnvWR0zbzp6SkoE2bNnB3d8fVq1cxffp0g83RA7xGgeRFSS81NRUBAQH5xo8bUkZGBrp06QITExPF9pkzZ8LU1FTR1peUlIT+/ftDkiSULl1abxeRvFV548aNg6WlpTzULz09HQ8ePMC8efMwYcIElC1bFi4uLgYfHfIioaGhaNOmjfzB27hxI7p37w43Nze4u7vDysoKo0aNAvDsAty0aVO9dDK7efOm3IE2Pj4e4eHhqF27Nlq3bg1HR0ccPHgw33O0zRH6nufkeWlpaQgICFCMGAGevb+SkpLkjmfAs+Yb7eq5ZmZmRg0jeU/kz4eSdu3aKf6G+qrRunr1KqZOnYo+ffrIF5YXhRILCwtUqlQJtra2Bv/b/Pe//5Un8tu9e3e+70+aNEkOSMXxxRdfwN/fXw5leV+HK1euoHbt2mjZsqW8bcOGDZgxY4bBFzHMa82aNejcubOis+XVq1fx888/49NPP8XPP/+cb6hxbm6uXmus79y5o7ipOHnyJMqVK4fg4GBFbbC2z8qtW7fg5eWlqM0xpFu3bsHT01MOjwDwxx9/YNq0aahQoQJ8fHwwd+5cpKen4/fff4e9vb3cJ7EoLl++jDJlymDYsGGK7dOnT5cHCmilpKTInX2trKwMutLxaxFIXibpJScny0kv70ncUDIzM3HkyBHUrFkTLVq0AAAsWrRIUXWb182bNzFhwoRil+3ChQuYPHkybty4ke/EP27cOJiamhZYDXn16lW9tGXrKu9Qz02bNkGSJIwYMQK9e/eGnZ0dxowZgz/++AOZmZmYP38+3N3dcfPmTdy7d08vYSAlJQWdOnVCs2bN5HATFxeH8PBwVKhQQR6eDfxzoQ0LC4MkSUaZPOrp06do0KABli1bJm/bt28fxowZAwcHBzg4OChGRM2bNw8+Pj5Gmco6Pj6+0Op37d+qsGnsiysmJgZvvfUWPvroIyxfvhypqamK9VkKCiW//PILnJyc9Log2o0bN7Bs2TIsXLgw36iDPXv2wNLSEmPHjlW8z8PDw1GqVCkcP3682McPDg6Gn58fgPw1vBqNBl999VW+mjJjdG7Oa+bMmahZs6bcofb777+XbzRq164NSZIwc+ZMAIbpL3L27Fk0adIkX2fruXPnQpIkjB49Ol8n22nTpqFatWoG/4zfunULd+7cQUJCAuzs7OTRaEuWLIGXlxfatWuHzz77DH369EGtWrXkJtABAwYUOnPqy/jf//4HExMTjB8/Xr4JnTNnDpycnAoM0CkpKRg8eLDBb+hf+UCia9Jr3749bG1tDTbULTY2Vn5zZ2dn4++//5ZnfXVwcJDfUAUpbuekrKwsNG7cGJIkoXr16hg3bhy2bNmi2CckJASmpqaFTlRlTJcvX8a4ceOwdu1aOTwtW7YMbdq0Qdu2bbFv3z7FqIANGzagRo0air4AxaXRaLBu3Tq0atUKnTp1kjsCamtKatWqpai2nTp1KqysrIxW+3Dnzh34+vpi7NixOHv2LObPn49atWqhT58+mDt3Lr7//ntYWlrKC5EBhlnm4HkZGRl4//33Ua9ePfz3v/+VtxdWU9KoUSM0atTohe//l3XlyhW4urpiwoQJitCdm5srX2zzhpLjx4/LtW7FHcmTV0xMDCpUqIDWrVvDxsYGHh4eWL58uWKfn376CWZmZvJQ9bCwMJibm+vtLvODDz5A7dq15cfPX9CvXr2qmG/FWPK+B7ds2QJvb2/06dMH/fv3R9myZTFmzBg5oC5atAhmZmbFnkOjIGfOnIGDg0OBoQN4ViurXTZkxowZmD17NoYMGQI7OzuDr+SblpYGNzc3zJgxAwDw2WefoUyZMqhatSrMzc0xb948xQ2qk5NTvpvu4vj2229Rvnx5hIaGYty4cXBwcCjwfaKveZ1exisfSHRNeqmpqejcubNBmiays7MxcOBAxaJc2dnZOH78OJo1a4aaNWvK+xpqxMO8efOwaNEi7Nu3D+Hh4bC3t8eAAQOwYsUK+Q0VHh4OS0tLeS0TNZw+fRqVK1dGUFAQfvjhB8X3nj59WmBb87hx49CxY0e9zSSpvWBmZmZi48aN8PX1RdeuXeXmG21NiYeHB+bOnYvZs2cbpV/G1atX0aNHD/n1WrJkCdzd3VGxYkXY2triyy+/VLx/O3fujH79+hltRJS2k+Sff/6JwMBAtGrVStFvq6BQEh8fD19fX71cdCZMmIAuXboUWqWvfe9kZWXB09MTFSpUkF8zff2NtKOxpk2bhoyMDJw9exZvvfUW/Pz88k3w9tNPP8Ha2hpVq1bV28R02t/jP//5D8qVK6eYLFAbyjQaDY4dO4Z33nnHqE2xDx8+hIODAyIiIuRt06dPR2BgIFq3bo39+/cr+l3t2LED77zzjt47GD98+BBNmjRRDGLQytvRd/PmzejcuTOcnZ3RqFEjvP/++0abm2XMmDFo0KAB7t27h3v37uHPP//EmjVrFPNO5eTk4MGDB2jXrh02bNhQ5GOlpaXh3r17+PXXX+X+Jzt37oSjoyNMTEzwn//8R94377Xi3XffLXbT4st65QMJoHvSM6TLly8jICAANjY2crrNzMzE33//japVq6J58+byCdMQoeTgwYOwtbWVR1bcvn0bn332GSwtLeHt7Y3Vq1fj0qVLmDVrFhwdHY02TXResbGxcHJywqRJkwrtEJq3tujBgweYOHEi7O3t9TIqIG+Vdd4TU2BgIMzNzdGlSxe5piQuLg4zZsyAnZ0dSpUqZZSakRMnTqBMmTKKVTtPnDiBY8eO5ZuSPjMzE507d5bvsgzt77//RtOmTeW/z7FjxxAQEFBoKMnMzMTChQtx6tQpvb3fW7RoIfclKmiUBvBPTUhmZiaaNWum1yUg4uPj4eTkJC9joNW+fXs4OzsXGLq2bduGKlWqFOuuO2/Q0f6ed+/eRb169VC1atUCaz21o4mMOaFXSkoKpk2bBlNTUyxYsEDxvYJqgceNGwc/Pz+9n4u0ywHkbb6MjIzErFmzULt2bbRo0UK+ac3JyZEDrj6XnPg3O3fuhIuLi9wnrTBhYWGoVq1akQP9pUuXEBQUBA8PD1hYWMDGxgb9+/dHfHw8Dh8+DGdnZ4wePVrRwTosLAylSpUyaJ+R572SgaSkJ70rV66ge/fuilCibb55++234evra9A3/bhx4zBgwAD5BBYYGAgPDw8EBQWhVatWMDU1xbZt21RZtTcnJwfjxo3D+++/D+Cf1+f+/fuIiorCpk2bFH1ZJk6ciB49eqBGjRp6mYTsypUrCA0NzVd9u2DBAtjb22PixIlo0aKFovnm+vXrmDNnjlHX8jl27BiqVKmC1q1bF7pPdnY2pkyZgooVKxZ7DYmXoe04/nwH28jIyAJDSXp6OkaMGAErKyu9/e2ys7PRsGFDxaKTBQkODs63xoe+xMTEwNPTEz179sSuXbsAPKuVlSQJ9evXR8eOHfHee+9hwoQJuHnzpjzxWN7RY7q6efMm+vTpo5jQURusr127hooVK6JSpUoYPnw4zp49ix07dmDs2LEoU6aM0SfvA56NqNKuor5o0SJ5e94bgFu3bmHSpEkoW7asQfr1aWutvv76awCQRxe2atUKo0aNgr+/PxwcHOTPTkGLxulTYT+/U6dO8Pb2Vizup7V//36MHj0adnZ2RX4dY2Ji4ObmhuHDh+Obb77BhQsXMHHiRLz11luoWbMmrl69ij179sDNzQ0ff/wxbt++jZkzZ+q1afFlvXKBpKQlvfj4ePz000/5Fn2Ki4tD586dYWNjI9/VZ2dn48SJE7C3t0enTp30Xhatbdu2wcfHBxqNBkOGDIGLi4vchHTx4kUsXrxYfqyGvn37ol+/fvLjH3/8EUFBQShTpgwcHBzg4uIi10Rs3LgRYWFhehsVsH37drlNP28Tn729vXwB+/7779GsWTN07dpVXr/CGB0B8y4ZAABHjx5FlSpV0KZNm3y1C7t27cKIESP03kmzMIUt5Je3rM+Hko8//hjW1tbFrlW6e/eufOORk5ODDh06oF69eoXOwHrt2jX06tVL7xe5hw8fynfRx44dQ+vWrdGrVy8EBQXBwcEBe/bswY0bNxAVFYW5c+eiXr16cHBwQOvWrYs1nTfwrBnPx8cHnTt3lodRA//czcfFxeG9996Dq6srTExMUK1aNbRt29YonZuBZ4MGnq+9e/DgAWbPng1JkrB48WLF9xYtWoS+ffvCw8NDr4EpNjZW7uypnVKhevXqePvtt2FhYYGIiAhFLau9vX2Bi6sawvNNjNpwtnPnTtSuXVuu1dd+1n/88Uf06NED7dq1K/J7Wbt8SmhoaL5z2JYtW+Dp6YkmTZrgyZMn2Lp1K9zd3VGrVi2j9pPL65UKJCUt6d2+fRuOjo6QJAmVK1fGBx98gEWLFuHGjRvIzc3F7du3MWDAAFhbW8sBICsrC1FRUQZvPmrVqhVKlSqF8uXLG22o87/Rrkfz8ccfo127dlixYgXGjx+PChUqYOjQodi6dSvu3LmDDh06oHHjxoopk4vr7t27+Pvvv5GYmIi9e/eiQoUKmDJlSoFNfBqNBps2bULt2rXRp08faDQag/bPuHXrlhygCwslHTp0kLft27cP7du3R0BAQLFnOH0ZN27ckJciz2vZsmWKfgLa5ps2bdqgdevWehki+ODBA/j6+mLYsGFydfXvv/8Oc3NzDBkyBKmpqfnuLMPCwtCqVSu9jhpLTExE+/btMX36dLmm4+jRo/LvWViT2fbt2/U2gVRsbCw6duwIf39/RSjRXtiePn2KR48eYfPmzUhLSzPaDKyXL19GtWrVUKtWLcybNy9fR/pZs2ahVKlS8sJwwLNh+wsWLNDr8OO8ffi0F/CrV69iy5YtWLRokWJUSm5uLuLj49GoUSPFNO2GEhcXh4YNG2Lq1Kn5amcfPnwIDw+PfJ+vW7duISoqqsiL+sXHx8PR0THfooV5z6erV6+GtbU1Vq9eDeDZivNvvfWW0YLs816ZQFLSkl5mZiYePHiAzp07o27dumjRogVCQ0Ph5uaGGjVqoGHDhliyZAm+/vprdOzYEW5ubsUapvWytBezXbt2oUaNGvIICGNPAf8iV69ehZ+fHzw9PeXJ2PKOnAkPD4ePj4/emrXOnTuH5s2bo3379ujZsycA4JtvvoGzszNMTU0VTXzaNu6cnBxs27bN4FNFp6SkoHv37mjXrp18ono+lPz+++9wc3NTzOh55coVo11wtmzZglq1amHAgAHyQnCzZs1C2bJl8y1Iefz4cfj5+eVbZ6M4pkyZgsaNGyMkJESeM2LevHkwNTVFYGAg9u/fD41Gg8jISISEhKBs2bJ6n4E0MzMT7733Hpo1a4b58+fLoeTkyZPw9fVFly5dFEOgDTV7b2GhJCcnBxkZGRg/fjwCAgKM2g9i/vz5sLCwgL29PerUqYO6devirbfeQr9+/fDzzz/j6NGjWLZsGSRJki98gGFqHfP24fu390BYWBhq165t8LmENBoNLl++jE8//RSOjo7w9PRE3759ERsbKzcLb926Fa6urgXOeVRU169fR+PGjdGtWzf8+eefiu/lPb+0atUKPXr0kB/ra5HLonglAklJS3oJCQno3bs3oqKicOfOHQwcOBDt27fH5s2b5b4iH3zwATp27IjSpUvLY+1r1qyJrKwso4SDxMREVKtWDVOnTjX4sV4kPj4e27Ztw/jx4xERESFfwDIzM5GcnFxgn56hQ4ciKChILyf1s2fPws7ODpMnT0ZcXJziPfPDDz/A1dUVISEhBltx9WWsWbMGfn5+6NWrl1zjkTeUaDQazJ07F82bNzfqEuh5+zz85z//QevWrTFo0CCMGTOm0Pl0gGfNO8WdvyE+Pl4xa/Ds2bNRv359hISE4M6dO8jJycHatWvh5OQECwsLmJmZoUaNGmjcuLHeawS174e0tDQMHToU3t7eWLBggaKmxNfXF++++26BI/v0raBQkpmZiVGjRsHExMQoTXgA5AU5U1NTER4ejq5du2L48OGIj4/H6tWrMXDgQDg7O6NGjRpo2rQp3N3dIUmSwaccyNuHL2/NtNaxY8cwevRolC1b1uD9a+Lj4xEYGCj3U7lx4wYWLFiABg0ayB2jd+3ahZiYGDRt2hRLliwBoL9zUN73St5Qkvca5Ovrq5hvSU2vRCApaUnv6tWr8Pb2xrvvvouzZ8/i5s2b6N+/Pxo3bizPhAo8O0kcO3YMq1atQt++fY3euezbb7+FtbW1XtbEKYqYmBhUrVoVrVu3xttvv41KlSpBkiQMGzaswDbR5ORkTJ48GQ4ODnppinjw4AFatGiRrxNm3lCiXRfnk08+UXWW2g0bNqBNmzbo2bOnPORQo9HIzRDr1q1D3bp1jVYrkpCQgL59+2Lbtm3ytrVr16JZs2YwNTWVOwo+Pw+IPmRmZmLo0KF45513FJP45Q0l2hq1xMRE/Pzzz1i7di1OnTplsMn98objhg0bwsPDQ1FTcvToUfj5+aFFixYGWZb9eXkvNAcPHsSECRNgaWlptDCSkJCAgQMHyueWlJQUhIaGonHjxggLC5MvqBcuXMBff/2FAQMGwNfXF5Ik6bX26mX68GnPNTk5OVi+fDm6deuGVq1aGWWCzJ9++gkNGzZE586dFZ9r4Fkn28DAQHlVeicnJzg7O+v9pqOwWjWNRoOEhAR06tQJ33zzDQD1a9JfiUAClLykFxsbC39/f3To0AFnz57F7du30b9/f/j4+Kg6v0deN2/ehK+vr1FmFH1ebGwsHBwcMGXKFPkDlpCQgAULFkCSJPTt21fRfrxy5Up88MEHqFy5st5OqufOncPbb7+N33//vcB1i7Tvne+++w6VK1dGcHCwXoeHFiYuLg5r167F+vXrFbN1fvfdd/D19UWvXr3kmj1tuUePHo1evXrpdWKvFzl69CiaNm2KLl26YOfOnfL2DRs2oEWLFggMDFT0e9G3EydOYODAgWjRogXWr18vb88bSgz5Wl26dEmeHyfv77dkyRLY2dnB398fTZo0UYSSw4cPo0uXLgZvAtCKjY1Fly5dYG9vDzMzM6ONiHj69Cl+/vlnNGzYEAEBAfLnNSUlRW5eGzt2rFyDktfzwaE4XrYPX5kyZeQwcOPGDRw4cCBfB1xD2rZtG9q1awd/f/8Cm+3379+P999/H1WqVIEkSQZZv6ew6+fEiRPh6empyjWiIK9MIAFKXtJ7USgpzgQ2+vT8JE3GkJOTgzFjxmDQoEEAlBM1AcBXX32l6Hl/9+5dfPDBB/j444/1OrT2+++/R+nSpRVNH89LS0vDzZs38d///he1atWSh2gaSkxMDCpWrAgfHx+UK1cOXbt2VVxINmzYAD8/P/j4+ODXX39FZGSkXGtkrJVZtf766y/4+/ujY8eOilCydu1atG7dGr1795abuvT1ecv7c6Kjo9GvX798oSQiIgINGjTA+PHjDbImS05ODubPn5+veUE7Guuvv/4C8KxpsUmTJli4cKFcG2vsz9vFixfRrVs3o42a+/vvv1GzZk3cv38fW7duRdu2bdG9e3c5lKSmpmLKlCnw9vbGuHHj5JolfTeDFqUPnzE6gBdm48aNaNeuHTp27Cif4/LWgD59+hRJSUkGma1WK+/189SpU5g7dy7KlClTYgY9AK9YIAFKXtIrKJQEBQWhVq1a2LRpk1HLUpK0bNnyhcuqBwUFwc3NTe7UlZ6erve7/7/++gsWFhb5ZoLNa+nSpWjfvj0AGLw5JDo6GpaWlpg0aRLS09Px559/wsnJSZ7LQmvnzp1477335HktmjdvbpQwUtBF49ChQ/D394e/v3++UKK969PHHCjp6enyybmgUNK8eXNFONCuazRlyhS9do48e/YsZsyYgfT0dISGhsLU1BR79+7FvHnz8q3am5GRgeHDh6N69epYtmxZvs7IxmKsDqzR0dGwsbGRJ6UDIM9wXFAoad68OUaMGKH3Dr4luQ/f2bNnMXDgQMyfPx9nzpyRz2/AsxlpW7duragp0X7mjPW+0daqaTv0qzG090VeuUAClLyklzeUnDt3Djdv3sTQoUMNPkKjpMrMzESdOnUwZswYAMoLnfaDt3btWpQrV85gy4sDz5qsnJ2d0a1bN8WdR94P/9ixYzF+/HiDX0zOnz8PU1NTTJs2TbG9ffv2GDVqFD7++GPFfAgajQbnz5/H7du39VrNXZjTp0+jUaNGmDZtGr777jvFrJnHjh1D+/bt4e/vr1i3ZsWKFejSpUuxX8O4uDh4eXnh4MGDBU4OFR0djT59+sDf3x9Hjx6Vty9dulSvNSTR0dGQJAmzZ8+Wt40fP15ehTvvbJra93RGRgY+/fRTo66eqwbtKMfJkycDUH6Gdu3ahTZt2uQLJaNHj4afn5/em0dKah++nJwcNGrUCJIkwc3NDba2tmjVqhWGDx+OX3/9FTk5OdiyZQsCAgIUQd5Qy4gUxti1arp4JQMJUPKSXmxsLN599114e3vjwoULRh+pobabN29i8+bN+O6773Du3Dl89tlncHZ2VoRE7dLewLM7qzp16hh8Ibjt27fD3NwcgwYNUqxPkZaWhtDQUFSpUsUoM7AuXboUkiThxx9/lLdpJ40aOHCgvLz383MRGEtAQAAkScI777wjLzPQsmVLbN26FQkJCTh27Bi6deuGnj17Koa36qtW6Z133kHNmjVx+PDhAkPJ8ePHUaVKFaxatUovx3veuXPnYGlpqVhIUUv7OuXt4AsYf9VctWhHOfbt21exfcGCBfJEeZs2bZKbb7QX/tTUVIN1Mi5pffguXbqE06dPIyYmBjVr1sSAAQMwe/ZsrF27Fo0aNUL16tXh7u6OoUOHokuXLqhTpw5atmwpD2M3NmMOC9fFKxtIgJKX9C5cuICAgADV3mRq0Y6mqV27NkxMTFCnTh3069cPderUQa9evQpcqGrEiBHo1q2bwTtpajQarFq1CqVLl4aHhweCg4PlYzs7OxttVAIAuQlgz549mDt3LsqVKyc316SlpWHGjBmwsLCQ1yEyptzcXLRt2xbVq1fHli1bsHHjRgwYMACenp6wsrJCcHAwvL294eHhgQYNGsgrtRbH9evXsXz5crl2wdvbG2+//Tb+/PPPAkNJ165dDdJp/cyZM3B0dEStWrXkbc+fsCdMmABTU1PFHfibIu8oR23fvYiICNja2ipqjTZv3owOHTqgTZs2RplYq6T04YuKioKVlRW++OILAM8WnXzrrbcwaNAgXLlyBTk5Obh37x7mz5+PkSNHwtnZGZIkwdLS0mgdoF8Vr3QgAUpe0jPUhEgllbYqd8KECbh16xZ+/vln+Pv7o1WrVhg4cCDKlSuH5s2b45dffsH9+/dx8eJFTJo0STEczxiOHTuG3r17o379+mjZsiUmTpyomHvEkPLWlhXUBKCt/tZO6GeMkT4Fyc3NRZ06ddCwYUN59M/jx4+xf/9+TJgwAa1bt4YkSbC3ty92c+Tp06dRo0YN9OzZU9EMpA0lhw8flj/bOTk5yMrKQo8ePRQzw+qDdkp8X19flC9fXjFE/PlazokTJ8La2hrr1q3TaxleBdpm8m7dumHo0KFwdnaWhzfnDY3r169Ht27djNaXT+0+fNr3z6RJkwD881mOjIxE1apV0bt373yjnxITE3HgwAGDdmB9Vb3ygYTUU9CEdcCzIbzlypXD7du3sXLlSnh5eUGSJDg5OaFu3bqoV6+eKgt+GbMZ7cKFC5g8eTJu3LiRr4141qxZBTYBTJgwAT4+PoqOcIby6NEjXLp0CXv27MHly5cVVeuenp6oVq0ajh49mi/wHzlyRDGrblFcuHAB9vb2mDRpUoE/q1mzZnjrrbfw3//+F8nJyXj69CnCwsJQuXJlvYbIv//+G6ampvjss8+Qk5ODr776Co6Oji8MJdo7XDVWyVbbpUuX0L59e1haWuZbxTfve9xYS9VrqdWH7/l+NVq//PILnjx5giNHjqBq1ap47733FMP7qXAMJFRkhU1Yt2/fPtjZ2clToV+/fh179+7FmjVr8Mcffxh8aG1h8nbEM2QH1qysLDRu3BiSJKF69eoYN25cvvU9QkJCYGpqKo8cCQsLg5WVlVE6Zp85cwYtW7ZEzZo1YWNjA0tLS/To0QM7duyQ96lfvz5q1qyJI0eO6LXTXXp6Ovr06YORI0cqtmdlZeHatWtyMOrfvz+qVauGKlWqoFOnTqhSpYreQ+zvv/+uCB+PHz9+qVBizDksSporV66gQ4cO6NSpU775oNScVMvYffgK61czc+ZMVKxYUe5G8Oeff6Jq1aoYMGCAokM2FYyBhIpFW5XboUMHnD9/HqmpqXBycsKECRPULpqq5s2bh0WLFmHfvn0IDw+Hvb09BgwYgBUrVsgn7vDwcFhaWqJ9+/Z6WRX3ZZw9exa2trYICQnB77//jtjYWMybNw/Vq1dHxYoVFX0kGjRogDp16uCPP/7Q28UmOzsbLVu2xLJly+Rte/bswejRo2Fra4uKFSsiICAAAHDgwAEsWLAAmzdvNnj1tvb3S05OLjCUvCkdWF9GYfNBqc2YffgK61fj6OgoL6mgDUWHDx+GnZ0d3n///QIni6N/MJBQscXGxqJTp05o3bo17O3tMXr0aPl7xh7SVlIcPHgQtra2cgfV27dv47PPPpNHsKxevRqxsbFYuHAhSpcubZTOtSkpKWjbti0+/vjjfN/bvXs3vL29Ubt2bcWdXNWqVdG4cWO9TfiVnJwMDw8PDB06FBcvXsTs2bNRs2ZNBAQEYOnSpVi7di3c3d3l0RtqyBtKtEPXSUk7yrFp06aIjIxUuzgyY/bhe75fjZOTU4HLBiQnJ+PKlSuqLk3xqmAgIb2IjY1F27ZtUaVKFcUKsGqvjaCmcePGYcCAAfLFPDAwEB4eHggKCkKrVq1gamqKTZs2GW19mlu3bqFu3bry5F4ajUZRtb19+3ZYW1vLM+hq6XuOjQMHDqB06dKoUqUKbGxssGrVKvlknZWVhQ4dOiAoKEivx9RVcnIy1qxZA0mS5A6LpHThwgX07t37jRtVmFdB/WryNl9NmTIFbm5uqq6g+yphICG9uXz5comsylXLtm3b4OPjA41GgyFDhsDFxUVuW7548SIWL15s1CHrp06dgomJiWKoJqAMjV27dkWHDh0AGPZuMz4+HidOnMi3kJhGo0GfPn0wdepU1fslPH78GN98841R5ql5Vb1powoLkrdfzR9//CFvnzZtmmrD+F9VEgAIIj25fPmyCAkJEffv3xeLFy8WTZs2VbtIqmrdurU4fPiwcHV1Fbt37xaenp5GPf69e/dEXFyckCRJVKtWTbi6uoqwsDARGhoqcnNzRalSpRT7BwQECI1GI3766SejllMIIbKyssTMmTPFunXrxKFDh0T16tWNXobnARCSJKldDCrhLl++LD755BMBQERERIj9+/eL8PBwcfjwYeHl5aV28V4ZpdUuAL1eqlevLubPny+mTZsmypcvr3ZxVKO9kE2cOFEkJiaKuXPnCk9PT6Ne4M6fPy+GDRsmypQpI6ysrMSPP/4oBgwYICIiIkS7du1EkyZNhEajESYmJkKj0QhJkoSJiYmoV6+e4ncwhu+++078/fffYsuWLeKXX34pEWFECMEwQi+levXq4osvvhAhISGiY8eO4tGjRyIyMpJhREel/n0XIt14eHiI77//XlSuXFntoqhGeyHz8vISubm54uTJk4rthnbu3DnRvHlz0bp1a7F69WqxdetWIYQQw4YNEzVq1BAdOnQQ+/btExkZGUKIZ7UTM2bMEIcOHRL9+/c3alkvXbok1q5dKxISEsTBgwdFgwYNjHJcIn2qXr26WLBggWjatKmIiopiGCkCNtkQGdh3330nhg8fLn777TfRpEkTgx/v4cOHonv37qJhw4Zi6dKl+b7/66+/ioiICHHw4EHRpEkTYWVlJSwsLERUVJTYtWuXaNiwocHL+Ly7d+8Kc3NzUbZsWaMfm0ifsrOzhampqdrFeCWxyYbIwNq0aSMaN25stCasxMREcefOHREQEKDoJ6JtgvHz8xNeXl5i27Zt4tixYyIlJUW0bNlSLFu2TLz99ttGKePznJ2dVTkukb4xjBQda0iIjCAjI0NYWFgY5VgbN24UgwcPFllZWUKSJEUo0f7/6dOnIiEhQdSsWdMoZSIi+jfsQ0JkBMYKI0II4e7uLkqXLi1+/PFHIYRQjKTR/n/t2rXi448/FpmZmUYrFxHRizCQEL1mqlSpImxtbcWGDRtEXFycvD1vZWhcXJzw8vISZmZmahSRiCgfBhKi10yFChXEl19+Kfbu3SumTZsmzp8/L4R4Nmrm6dOnYvLkyeKHH34QwcHBHNZKRCUG+5AQvYZyc3PFmjVrxKhRo0S1atWEj4+PsLCwELdu3RJHjx4Ve/bs4fBaIipRGEiIXmPHjx8X8+fPF1euXBE2NjaiWbNmYsiQISVm4jEiIi0GEqLXnHY2ViKikox9SIhec3lH2fD+g4hKKtaQEBERkepYQ0JERESqYyAhIiIi1TGQEBERkeoYSIiIiEh1DCRERESkOgYSIiIiUh0DCREREamOgYSIiIhUx0BCREREqmMgISIiItUxkBAREZHq/h/YOzgRMDZ5sAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "POSSAT and ENVSAT had the most weight in making correct predictions."
      ],
      "metadata": {
        "id": "Ni_Jd4jIuIU7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2 RFE data set"
      ],
      "metadata": {
        "id": "5ixvEi2I7xEF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.1 Data set balancing"
      ],
      "metadata": {
        "id": "rHdjmWCr8R0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rfe, X_test_rfe, y_train_rfe, y_test_rfe = train_test_split(X_rfe, y_rfe, test_size=0.2,\n",
        "                                                                    random_state=42, stratify=y_rfe)\n",
        "\n",
        "X_train_rfe.shape, X_test_rfe.shape, y_train_rfe.shape, y_test_rfe.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VrbqLGsoVI7V",
        "outputId": "d488f3bc-f3a9-41fe-a6cc-bb6d4e1764fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((483, 14), (121, 14), (483,), (121,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=\"DEPRESSED\", data=depress_rfe);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "mxd2RnEh8QK4",
        "outputId": "09b65edd-2886-4694-9ad8-6a08ccebfdc4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rfe_res, y_train_rfe_res = sm.fit_resample(X_train_rfe, y_train_rfe)"
      ],
      "metadata": {
        "id": "gJow2Cw_8ZsM"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.bincount(y_train_rfe_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFMRSotF8Zny",
        "outputId": "9ecdd244-bc9e-490d-fdb4-bb3b22c50460"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([317, 317])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rfe_res.shape, y_train_rfe_res.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9_-HPdz8ZcU",
        "outputId": "301fbe50-74d1-4547-bbf3-96f698af8ef8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((634, 14), (634,))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.2 Training"
      ],
      "metadata": {
        "id": "8m4u2Kd78xTM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "\n",
        "X_train_rfe_res = scaler.fit_transform(X_train_rfe_res)\n",
        "X_test_rfe = scaler.transform(X_test_rfe)"
      ],
      "metadata": {
        "id": "TWRZSnts8spk"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_rfe_res = y_train_rfe_res.to_numpy()\n",
        "y_test_rfe = y_test_rfe.to_numpy()"
      ],
      "metadata": {
        "id": "KMphgwJD8sjc"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()\n",
        "\n",
        "model_rfe = keras.models.Sequential()\n",
        "\n",
        "early_stopping = EarlyStopping(monitor=\"val_loss\", mode = \"min\", patience=10)\n",
        "num_epochs = 200\n",
        "\n",
        "\n",
        "model_rfe.add(keras.layers.Dense(100, activation='relu', name='hidden1'))\n",
        "model_rfe.add(keras.layers.Dropout(0.5))\n",
        "model_rfe.add(keras.layers.Dense(25, activation='relu', name='hidden2'))\n",
        "model_rfe.add(keras.layers.Dropout(0.5))\n",
        "model_rfe.add(keras.layers.Dense(1, activation='sigmoid', name='affine'))\n",
        "\n",
        "model_rfe.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "               optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "               metrics=[\"acc\"])\n",
        "\n",
        "history = model_rfe.fit(x=X_train_rfe_res,\n",
        "                     y=y_train_rfe_res,\n",
        "                     epochs=num_epochs,\n",
        "                     validation_data=(X_test_rfe, y_test_rfe),\n",
        "                     callbacks=[early_stopping])\n",
        "\n",
        "model_rfe.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHj-nfyz9Vzu",
        "outputId": "ec45ad2e-b28e-4d8e-953d-c96db7b48f96"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "20/20 [==============================] - 3s 28ms/step - loss: 0.6658 - acc: 0.5773 - val_loss: 0.5999 - val_acc: 0.8512\n",
            "Epoch 2/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6068 - acc: 0.7066 - val_loss: 0.5148 - val_acc: 0.8843\n",
            "Epoch 3/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5407 - acc: 0.7650 - val_loss: 0.4131 - val_acc: 0.8926\n",
            "Epoch 4/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4681 - acc: 0.8013 - val_loss: 0.3523 - val_acc: 0.8843\n",
            "Epoch 5/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4171 - acc: 0.8186 - val_loss: 0.3008 - val_acc: 0.8843\n",
            "Epoch 6/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3689 - acc: 0.8470 - val_loss: 0.2869 - val_acc: 0.8843\n",
            "Epoch 7/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3386 - acc: 0.8596 - val_loss: 0.2510 - val_acc: 0.9008\n",
            "Epoch 8/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3645 - acc: 0.8454 - val_loss: 0.2529 - val_acc: 0.8926\n",
            "Epoch 9/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3451 - acc: 0.8517 - val_loss: 0.2645 - val_acc: 0.8926\n",
            "Epoch 10/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3118 - acc: 0.8801 - val_loss: 0.2651 - val_acc: 0.8926\n",
            "Epoch 11/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3245 - acc: 0.8596 - val_loss: 0.2733 - val_acc: 0.8678\n",
            "Epoch 12/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3253 - acc: 0.8644 - val_loss: 0.2686 - val_acc: 0.8843\n",
            "Epoch 13/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3350 - acc: 0.8659 - val_loss: 0.2729 - val_acc: 0.8760\n",
            "Epoch 14/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3053 - acc: 0.8770 - val_loss: 0.2780 - val_acc: 0.8678\n",
            "Epoch 15/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3107 - acc: 0.8754 - val_loss: 0.2818 - val_acc: 0.8678\n",
            "Epoch 16/200\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2977 - acc: 0.8722 - val_loss: 0.2724 - val_acc: 0.8760\n",
            "Epoch 17/200\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3009 - acc: 0.8943 - val_loss: 0.2802 - val_acc: 0.8760\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hidden1 (Dense)             (None, 100)               1500      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100)               0         \n",
            "                                                                 \n",
            " hidden3 (Dense)             (None, 25)                2525      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 25)                0         \n",
            "                                                                 \n",
            " affine (Dense)              (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4051 (15.82 KB)\n",
            "Trainable params: 4051 (15.82 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss = history.history[\"loss\"]\n",
        "dev_loss = history.history[\"val_loss\"]\n",
        "acc_values = history.history[\"acc\"]\n",
        "\n",
        "plt.plot(train_loss, label=\"train\")\n",
        "plt.plot(dev_loss, label=\"dev\")\n",
        "plt.title(\"Loss curves\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "TOkxGUPQ9Vwz",
        "outputId": "964eb2c4-ea29-4ed2-ae8f-9f6f89650242"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model_rfe.predict(X_test_rfe)\n",
        "predictions = (y_pred > 0.5).astype(int)\n",
        "\n",
        "print(classification_report(y_test_rfe, predictions))\n",
        "ConfusionMatrixDisplay.from_predictions(y_test_rfe, predictions);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "Rq12gcrs4Lkc",
        "outputId": "c14a421a-fd1f-4306-ad17-05735a2d7096"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.75      0.95      0.84        41\n",
            "         1.0       0.97      0.84      0.90        80\n",
            "\n",
            "    accuracy                           0.88       121\n",
            "   macro avg       0.86      0.89      0.87       121\n",
            "weighted avg       0.90      0.88      0.88       121\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.3 Error analysis"
      ],
      "metadata": {
        "id": "z1bF91199mb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#get feature importance using shap\n",
        "explainer = shap.Explainer(model_kbest, X_train_rfe_res)\n",
        "\n",
        "shap_values = explainer.shap_values(X_test_rfe)\n",
        "\n",
        "feat_import = np.abs(shap_values).mean(axis=0)\n",
        "\n",
        "#create a data frame with shap calculations\n",
        "import_feat = pd.DataFrame(index=X_rfe.columns,\n",
        "                           data=feat_import,\n",
        "                           columns=[\"Importance\"])\n",
        "import_feat = import_feat.sort_values(\"Importance\")\n",
        "\n",
        "#create the plot\n",
        "colours = sns.color_palette(\"coolwarm\",\n",
        "                            len(import_feat))\n",
        "\n",
        "sns.barplot(data=import_feat,\n",
        "            x=import_feat.index,\n",
        "            y=\"Importance\",\n",
        "            hue=import_feat.index,\n",
        "            palette=colours,\n",
        "            legend=False)\n",
        "\n",
        "plt.xlabel(\"Feature\")\n",
        "plt.xticks(rotation=45);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "f8anHcOz9vA9",
        "outputId": "f0c4dce6-8ad8-4bc4-92a6-01d23e026933"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = from_categorical(predictions)\n",
        "error_class = np.where(predictions != y_test_rfe)[0]\n",
        "feature_names = X_rfe.columns\n",
        "\n",
        "x_error = X_test_rfe[error_class]\n",
        "\n",
        "x_correct = X_test_rfe[predictions == y_test_rfe]"
      ],
      "metadata": {
        "id": "Snc_AD7P99Ex"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=x_error)\n",
        "plt.xticks(rotation=45,\n",
        "           ticks=np.arange(len(X_rfe.columns)),\n",
        "           labels=X_rfe.columns);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "_Sxg0Ast4gP3",
        "outputId": "9de6720a-d19e-4ede-848b-43602703345c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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i2LFjokGDBiI4OFjcuHHjsa+7du2aCA8PF61bty51YYmIiOjZp3M4WbJkiRg5cqQICwsTfn5+YtWqVcLW1lasW7euxNcUFBSIwYMHi/fee094e3s/VYGJiIjo2aZTOMnNzRVHjx4VQUFB/76BmZkICgoSBw4cKPF1s2fPFm5ubuLVV199ouPk5OSIjIwMxQ8RERE9H3QKJ7du3RIFBQXC3d1dsd3d3V2kpqYW+5p9+/aJtWvXijVr1jzxcaKiokT58uXlH09PT12KSURERGWYQYcSZ2ZmiqFDh4o1a9YIV1fXJ35dRESESE9Pl3+SkpIMWEoiIiJSE52GEru6ugpzc3ORlpam2J6WliY8PDyK7H/58mVx7do10aNHD3lbYWHhwwOXKycuXLggXnzxxSKvs7KyElZWVroUjYiIiJ4ROtWcWFpaiiZNmojdu3fL2woLC8Xu3btFYGBgkf19fX3FqVOnxPHjx+Wfnj17ivbt24vjx4+zuYaIiIiK0HkStokTJ4rhw4cLf39/0axZM7Fs2TKRlZUlwsLChBBCDBs2TFSpUkVERUUJa2trUbduXcXrnZychBCiyHYiIiIiIUoRTkJDQ8XNmzfFrFmzRGpqqmjYsKHYuXOn3Ek2MTFRmJlxVnwiIiIqnVJNXz9+/Hgxfvz4Yp+LiYl57Gs///zz0hySiIiInhNcW4eIiJ47AERWVpb82M7OTkiSZMISkTaGEyIieu5kZWWJXr16yY+3bdsm7O3tTVgi0sbOIURERKQqDCdERESkKgwnREREpCrsc0JEVEb0+rzkBVaLg9wHiseDNsYKydLmiV+/bUTRyTWJjIE1J0RERKQqDCdERESkKgwnREREpCrsc6IHF+Z002n/+3lQPL64sL+wtXjyyX98Zm7X6XikPh2/GKD7i3IKFQ97bXpFCKsnv7/YPXST7sckIjIB1pwQERGRqjCcEBERkaownBAREZGqMJwQERGRqrBDLBGJoPXzdNofuXmKx72+XiwkS4snfv1vw6frdDwier6w5oSIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIWTsBERUZkXs/RvnfbPzstSPN4XnSqsLeye+PXtJlTS6XikG4YTem7MWtNep/3zc4XQrlycv76HKGf55K+fPXKPTscjIqKH2KxDREREqsJwQkRERKrCcEJERESqwj4nRETPKgtrYRc6XfGYqCxgOCEiekZJkiSEpY2pi0GkMzbrEBERkaqw5oRUxcJCiJe6FioeExHR84XhhFRFkoSw1GEuESIievawWYeIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIXhhIiIiFSF4YSIiIhUheGEiIiIVIXhhIiIiFSlnKkLQEREZGxW5WzFpOBPFI9JPRhOiIjouSNJkrC2sDN1MagEbNYhIiIiVWHNiQnYlBNicUsrxWMiIiJ6iF+LJiBJkrC1MHUpiIiI1InNOkRERKQqDCdERESkKgwnREREpCoMJ0RERKQq7BBLVFZYSkIMdFE+NhWLckIMaK18TESkJ7yiEJUVkiSElQkDiRZJkoSw5JAzIjIMNusQERGRqjCcEBERkaownBAREZGqMJwQERGRqjCcEBERkaownBAREZGqMJwQERGRqjCcEBERkapwEjaiEphbCBHwcqHiMRERGR7DCVEJJEmIcpamLgUR0fOHzTpERESkKqUKJytXrhReXl7C2tpaBAQEiNjY2BL3XbNmjWjdurVwdnYWzs7OIigo6LH7ExER0fNN53CyefNmMXHiRBEZGSmOHTsmGjRoIIKDg8WNGzeK3T8mJkYMHDhQ7NmzRxw4cEB4enqKzp07i+vXrz914YmIiOjZo3M4WbJkiRg5cqQICwsTfn5+YtWqVcLW1lasW7eu2P2/+uorMXbsWNGwYUPh6+srPv30U1FYWCh279791IUnIiKiZ49O4SQ3N1ccPXpUBAUF/fsGZmYiKChIHDhw4Ine4/79+yIvL09UqFBBt5ISERHRc0Gn0Tq3bt0SBQUFwt3dXbHd3d1dnD9//oneY8qUKaJy5cqKgPOonJwckZOTIz/OyMjQpZhERERUhhl1tM77778vNm3aJP73v/8Ja2vrEveLiooS5cuXl388PT2NWEoiIiIyJZ3CiaurqzA3NxdpaWmK7WlpacLDw+Oxr128eLF4//33xa5du0T9+vUfu29ERIRIT0+Xf5KSknQpJhEREZVhOoUTS0tL0aRJE0VnVk3n1sDAwBJft3DhQjFnzhyxc+dO4e/v/5/HsbKyEo6OjoofIiIiej7oPEPsxIkTxfDhw4W/v79o1qyZWLZsmcjKyhJhYWFCCCGGDRsmqlSpIqKiooQQQixYsEDMmjVLbNy4UXh5eYnU1FQhhBD29vbC3t5ej/8UIiIiehboHE5CQ0PFzZs3xaxZs0Rqaqpo2LCh2Llzp9xJNjExUZiZ/Vsh8/HHH4vc3FzRt29fxftERkaKd9999+lKT0RERM+cUq2tM378eDF+/Phin4uJiVE8vnbtWmkOQURERM8prq1DREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKrCcEJERESqwnBCREREqsJwQkRERKpSqnCycuVK4eXlJaytrUVAQICIjY197P5bt24Vvr6+wtraWtSrV0/s2LGjVIUlIiKiZ5/O4WTz5s1i4sSJIjIyUhw7dkw0aNBABAcHixs3bhS7//79+8XAgQPFq6++KuLi4kTv3r1F7969xenTp5+68ERERPTs0TmcLFmyRIwcOVKEhYUJPz8/sWrVKmFrayvWrVtX7P7Lly8XISEhYtKkSaJ27dpizpw5onHjxuKjjz566sITERHRs0encJKbmyuOHj0qgoKC/n0DMzMRFBQkDhw4UOxrDhw4oNhfCCGCg4NL3J+IiIieb+V02fnWrVuioKBAuLu7K7a7u7uL8+fPF/ua1NTUYvdPTU0t8Tg5OTkiJydHfpyeni6EECIjI0MIIURm9gNdiq0Xdv9/7OLcy84zYkn+/T0U5352vhFL8viyCCHEgwfqKU+OisqS/8C454wQ/1WebCOW5L/KYvzP9+PLc9+IJXl8WfIeZBmxJP/9+c5+kGmkkjyUkWFX4nNZ2eopS2aOccsixOP/Vpk5pjtvNP8PQLc3gQ6uX78OIQT279+v2D5p0iQ0a9as2NdYWFhg48aNim0rV66Em5tbiceJjIyEEII//OEPf/jDH/48Az9JSUm6xA3oVHPi6uoqzM3NRVpammJ7Wlqa8PDwKPY1Hh4eOu0vhBARERFi4sSJ8uPCwkLxzz//CBcXFyFJki5FlmVkZAhPT0+RlJQkHB0dS/Ue+qSm8qipLGorj5rKorbysCxlozxqKovayqOmsqitPPoqCwCRmZkpKleurNPrdAonlpaWokmTJmL37t2id+/eQoiHwWH37t1i/Pjxxb4mMDBQ7N69W7z99tvytl9//VUEBgaWeBwrKythZWWl2Obk5KRLUUvk6Oho8j+6NjWVR01lEUJd5VFTWYRQV3lYlpKpqTxqKosQ6iqPmsoihLrKo4+ylC9fXufX6BROhBBi4sSJYvjw4cLf3180a9ZMLFu2TGRlZYmwsDAhhBDDhg0TVapUEVFRUUIIId566y3Rtm1b8cEHH4hu3bqJTZs2iSNHjojVq1frXFgiIiJ69ukcTkJDQ8XNmzfFrFmzRGpqqmjYsKHYuXOn3Ok1MTFRmJn9OwioRYsWYuPGjWLGjBli2rRpombNmuL7778XdevW1d+/goiIiJ4ZOocTIYQYP358ic04MTExRbb169dP9OvXrzSH0hsrKysRGRlZpLnIVNRUHjWVRQh1lUdNZRFCXeVhWUqmpvKoqSxCqKs8aiqLEOoqj6nLIgG6ju8hIiIiMhwu/EdERESqwnBCREREqsJwQmQAbC0lIio9hhN65hQWFprs2FeuXBFXrlwp9WSBRKQupryePM8YTuiZcfHiRXH9+nXFUHZjOn78uGjcuLHYu3evSY6vqyNHjoiRI0ca7P1TUlLE5cuXDfb+zyrtdcWMoSzV8t27d89ox7p8+bJITk4WZmZmZSagmKqcBQUFen9PhhMt8fHx4oMPPlDdiWiKi0d8fLz46KOPjH7c0jpx4oTw9fUVP/zwg8mO36JFCzF27FgxfPhwk5RBFydOnBBt27Y12DDBuLg4UatWLREfH2+Q939WnTx5UkycOLHIkh/6lpKSIo4dOyaEEEKSpDIRUI4ePSpeeOEFowTevLw8MWXKFFGtWjV57i5DfAE/rcTERPHZZ5+JefPmiVOnTpnkxiw+Pl6sXLlSCKHnkKLTSjzPuC+//BKSJGHevHkoKCgwaVny8vIe+9jQVq1aBUmSsHjxYqMetzROnDgBGxsbzJgxwyTHP3/+PJydnTFx4kR5W2FhoUnK8iROnz4NW1tbvPvuuwZ5/+PHj8POzk7x+1CT/Pz8Ip8nNfy9jh8/DkmSMGvWLIMeJzs7G02bNkVQUBBiY2Pl7Wr4HZTk+PHjcHBwwIQJE+Rthi7vmTNn0KVLF1SsWBHXrl0D8PDcUYsTJ07ghRdegJ+fHxwdHWFnZ4fNmzcDgFG/vyZNmgQ/Pz+9vy/DySPWrVsHMzMzzJkzx2QB5cKFCxg1ahT69euHsWPHyuUwxgfjzp07yMnJQXZ2NlasWAEzMzMsXLjQ4MctLU0wGDVqlLzNmBdZzRexJEkYPHgw7t69a7Rjl8apU6fg4uKCevXq4caNG/J2ff3Ozp07pwhqarqYAw8/W2PHjkXnzp0xb948nD9/3tRFAgCcPXsWNjY2mDdvnlGOt3//ftSuXRt9+vTBoUOH5O1qDCjHjx+HjY0Npk2bpth++/Ztgx/73Llz6NSpk+oCyvHjx2Fra4uIiAjcuHEDZ86cwdChQ+Hg4IDExESjluWrr75CvXr19P6+DCfF+PTTT00WUI4fPw4XFxcMGjQI3bp1g5ubG4KCgoxy0UhOTkb9+vVx9epVAA/vsJYvX67agBIXFycHg759++LSpUtGPf7x48dhZWWFd999F2fOnIGLiwv69Omj2oCiuci3a9cOXl5emDRpEi5fvqzX93dwcICdnR369++PjIwMAOq4mAMPy1exYkX07t0b/fv3hyRJGDJkCNLT0036pXzy5Em4uLjA2dlZ3pabm2uw42muabGxsahRo0aRgKImZ8+ehYWFBebPn6/YPnfuXLRq1QpZWVl6O1ZSUhJ27NhR5D0vXryI9u3bw9XVVb42mvKcTktLg5WVFUaMGKHY/vPPP8Pe3h5//vmnUctz9uxZlC9fXlELpw/PdThJSEjAp59+itWrV2Pfvn2K50wRUE6dOgUbGxu89957AICsrCy89tprcHBwwOHDh+X9DFWe9PR0uLm5ISYmRt6m1oASFxcHS0tLLFy4EElJSShfvjy6d+9utIBy69Yt1KxZU3E3d+jQIdUGlLNnz0KSJEyfPh0A8Mknn6By5cqYNGkSrly58tTvf/ToUTg4OGDKlCnYunUrmjdvjp49eyI9PR2A6QPKyZMn4ejoqGgymTBhAszMzHDixAkA/9YaGLvmzcbGBr1790aDBg3QsWNH+Tl9NuVq/k2aa4d2QKlZsyb69OmDY8eO6e14+pCfn49p06ZBkiT5bwQAUVFRcHFxwc6dO/V2rOvXr8PV1RWSJKFWrVoYPXo0Vq9ejevXrwMArl27ht69e6NChQpISEiQy2cKiYmJ6NKlCypXrowzZ87I2/fv3w97e3scOXLEoMe/du0a/ve//yE1NRV3795FZmYmqlSpgl27dgH49/fytJ+j5zacnDhxAtWqVUNgYCCsra3RoEEDbNiwQbGPMQPKnTt30LRpU3h5eSm2v/7665AkCVu3bsXff/9tsAtnXl4e7t27h+rVq2Pt2rWK59QWUP755x8EBAQgIiJC3nbu3DmjB5S//vpL/n/N3yU2NlaVAWXFihVYtWqVYpu+Akp6ejrKly+Pt956C8DDc2n9+vVo3rw5evXqZfIalKysLFhaWqJBgwbIzs6Wt48aNQqSJCEmJka+I9Zm6JBy7NgxSJIk9/3Zvn07fH199R5Qrl69inbt2slBsbiAUq1aNQwZMgSZmZlPfTx9un79OoYMGQJ7e3tcuXIFK1asQIUKFfDLL78U2fdpzq9Tp06hU6dOqFevHlq0aIF33nkHLi4u8PX1RatWrRAdHY3169ejbdu28Pb2RnJy8tP8s57a9evX0b17d7i4uODGjRu4c+cO3NzcEB4ebrBjFhYWIjs7G61bt0aVKlXg5uYGLy8vDB8+HPb29ggLC8PVq1cVzcVP47kMJydOnICtrS2mT5+OrKws/PXXX/Dw8MBLL72E3NxcxUXp008/hZWVFaZOnWrQgHL37l3MmzcPjRs3xpgxYwAAH3zwAaysrNCtWzd0794dgYGBqF+/PtasWYOff/5ZL8d9tPp44MCBePvtt4s8pwkommYMU9OuSdKU8/z58wYPKMnJyYqaJaDoRVE7oGi+EEwlKSkJO3fuRFRUlPxFp13epw0oGRkZuHPnjtw8pPns5ObmYsOGDSYPKAkJCcjMzMQnn3wCCwsLuU/HokWLYGlpiUaNGmHUqFGwtrZGnz59MH36dBw4cECvzQUl+f777zF58mT5cU5OjkECytmzZ1GlShX4+/vL4ePRgLJv3z6Ym5tj06ZNT3UsQ0hNTUVoaCjMzMxgY2MjN0FpX6dnz56Nzz//XOf3fvDggfw+R44cwcCBAxESEoLvv/8e9+/fx59//okBAwagY8eOMDc3R506dSBJEho1aoT8/HyTNgcmJyeja9eucHJygqurq6KzsCG+qx48eADgYa1xfn4+fv/9d6xfvx7h4eHw9fWFJElwcXFBrVq10K1bN0yYMAHr16/HyZMnS3W85y6cXLt2Da6urujXr59ie4sWLVC9enWkpqYWec3y5ctRoUIF3Lx5U+/lOXnyJH7//XcADwPK0qVLUb9+fTRu3BgVKlTAX3/9hdzcXOTm5iI+Ph7jxo1DQEAAKlas+NQdws6fP4/+/ftjzJgx2LRpEy5fvoyXXnoJwcHBxe5/7949REVFoUKFCkbpjPaohIQEuepQQ3Ph1vxXO6Dosz8FANy4cQNOTk5wcnLCoEGDsGvXriK1I5ov39jYWHh4eKBTp04mCyinTp1Co0aNEBoaismTJ+PevXvyc9oXL01AmTp1qk6h7vz58wgNDcXcuXPlqm7t987LyzNpQDl27BhcXFywZcsWAA9rjyRJQvv27eHu7o7ff/9dLuvevXuxaNEiVKpUCQ0bNsTff/9tsHLl5eUV+fLQ/E5yc3MNElBOnz6NevXqoUGDBvLfoaCgAIWFhfIXbJs2bRS1kaaQlpaGP/74AwcPHkRaWpq8PSUlBePHj4elpaXc/KT5Hb777ruQJEnnZqmLFy8iPDwcn376qaIWqX///ggMDMT//vc/ed8HDx4gJiYGy5cvR/fu3REXF/d0/1A9SU5OxogRIyBJEk6fPg3AMJ+vuLg4+Pv7y81c2vLz8zFhwgQMHToUx48fx/fff4/XXnsNHTp0QN26dUtdK/vchZP9+/ejVq1aGDhwIP744w8AD9swJUlCkyZN0K1bN4wcORJRUVG4ffu2/MVy584dvZflxIkTkCRJ7mMCPAwoS5YsQa1atdClS5diX5eamqqXoPTzzz9j/PjxaN26NRo2bAgnJyf4+fmhXLlyGDp0KL744gvs2bNHPpbmImaKYHLz5k258+vIkSOxYcMG5OTkKPbRrkFxdXVFmzZtiq2uL63ExER069YNW7duxbhx49C9e3f4+flhx44diI+PL7L//v374e3tjaSkJL2V4UlpRs1MmzatxGpW7S/INWvWwNraGrNmzXqiL8ITJ07Aw8MDYWFhj61i1wSUVq1aoV27dkZrNtCMZpg0aZJi++rVqyFJEoYOHSrfCWrLy8tDSkqKwcp15swZDBgwAMuWLSsSWjV/D+2AUtKNwpPS/qI6c+YM6tSpowgoGg8ePEBQUBA+/vjjpzre0zh58iRq166N2rVry6PftENAamoqBg4cCHt7exw8eBAAMGPGDFhZWeHo0aM6H6tatWoYNmwYvv32W8VzR44cQf/+/dG6dWts3Ljxqf9dT+PKlSs4cODAY2tCEhMT0b17d7i6uuLs2bMA9FtzoukXNXXqVHmbdqgFHk7DUaVKlSLfS4+eZ7p47sIJAPz6669o3rw5Bg8ejNdeew0uLi744YcfcPHiRfz222+YNWsWvLy84O7ujq5duyIvL0/v1XeaP3hxc3Pcvn0bS5YsQf369TF69Gh5+6NfxqWVnp6uaHsHHp5sx48fx4YNG2BhYYE2bdqgbt26sLCwgI+PD3x8fPDOO++YrN9AZmYmXnnlFURHR2PKlCkICQlBjRo18PXXXys6y2mcPn0a1atX1/uwuldffRUhISEAHrb7vvfee2jfvj0aNGiAZcuWKWoQABT7BWhoWVlZ6NGjB1555RXF9uLOYe2L2Pr164sNWY+6du0aqlWrhilTpjw2yGjXaq1evRqdOnUySlDTDC/XdH4tKChQnLcff/wxJEnC3LlzFTVfhp5LKC8vD6NGjUK1atUwevRoVK5cGdHR0Thw4ECRfXNzc7Fjxw64u7ujV69eOh3n+vXr2Lx5M8LCwjBs2DB88cUXch+Js2fPok6dOvKoPE0tzowZM/Diiy/qNczr4sSJE7Czs5ObF5csWYJy5cphwYIFiv00AaVChQoYPHgwbG1tde4AGh8fj4oVK2Lq1Kkl9gs7dOgQ+vXrh9atW2Pr1q2l/nc9rZCQEJQvXx779u17bOBITk5G7969IUmSXofHx8XFFTuM+9HQ8ddff8HNzU0+zzRlfZrvzecinCQkJGDDhg2YO3eu/IeLiYlBs2bNYGNjU2wnz5ycHKxfv17vTQPAv3NjaCdRAFi7di0uXrwI4N8mnnr16mHcuHF6O3ZKSgqCgoLw0UcfyQFF+wQ6efIkKlasKP+7z5w5g99//x0RERG4cOGC3spRGuPHj0fPnj0BPPzSnzNnDkJDQ+Hp6Ynly5cXuUjpczim5ovr9u3baNOmjeKC1aZNG1SrVg1ubm5o164dunbtqqiSNrZbt26hdu3a+Oqrr4p9/tELhq6/p1WrVqFTp064f/++vO3q1avYvn073nvvPcXdpnZAMUbz1vHjx2Fvby/XjmgUFBQoLu6aJp6oqCj8888/Bi+XxrfffouKFSvixo0bWLduHYYOHYqqVati8uTJRfoyPXjwAL/88otOTW2nT59G48aN0alTJzRq1AitWrWCJEkIDg7Gnj17ADz8cg4ICICrqys6dOiA3r17o2rVqiYbrXPhwgU4ODhg/Pjx8ra8vDxUqlQJnTt3LhIa09LS0Ldv31LVmOTn5yM8PFwehqv5LNy6dQtxcXHYuHGj/NnV9EGpV6+eoonH2Fq3bg1vb2/8+eefjw0oCQkJGDRokN6u02fPnoWlpSXef/99xfalS5diwoQJRW5wa9WqhW+++UYvxwaeg3By4sQJeHl5wdfXFzY2NqhQoQK+/PJLAMDvv/+OgIAA9O/fX3Fh0FcNRXHS0tJgY2ODQYMGAfj3w/H+++/D3t5ecRd19+5dLF++HFWrVtXbbJvZ2dkIDg5GYGAg1q5dK59g2knX39+/VJ3LDEVTtgcPHqBBgwZYuXKl/FxQUBDc3Nzg4+ODwMBAtGvXTg54T1vbdeLECcycOVOxLSsrCwMGDJBHpgwbNgzu7u5ITExEWloali5dipYtW8plMIVTp07B0tISu3fvLnGfvLw8LFy4sFQ1O/PmzUPLli3l5r2vvvoKPXv2RKVKleSOcdodPY3VaTAuLk6emGrfvn1wcXFR9C0rLCxUXNyjo6MhSRI++OADo3ZsHDJkCD744AMADz+PJ0+ehCRJ8Pb2RlBQEA4cOCBP+KWL48ePo3z58kU6N2/duhWurq5o27atYj6ThQsXYurUqViyZIlBbsKelGY26kWLFsl9febOnQtJktCpUyeMHj0a69atw2+//Sa/Jj09vdj+gU+if//+GDhwoPz4u+++w7Bhw2Bvbw8XFxdUrFhR7nC/b98+hIWFlervUVqpqak4duyYovk8MDCwxICSnZ2NpUuX4tixY3qr/Xvw4AF69+4NW1tbRe1zVFQU7Ozs5KAL/Pv59vb2xpw5c/RyfOAZDyeaUTkzZ85ESkoKEhIS0LdvX7i6usrpeNeuXQgMDESfPn3kPiiGdOvWLbz88stwdXWV7/Tnz5+PChUqyJ09tdvz7ty5g+joaL1cPDR3yNnZ2ejXrx+aNm1abEDp2LFjkSYBY8vMzFTc0RYUFCAvLw/vvPOOIhh4eHggMTER169fx8aNG/UWDDTNbtrhRLtXf8WKFdGkSRN4eHgo7jgLCgoMGm5Lol2LER8fD2tra/mOp7i7rZ07d6Jv376l6gOyYcMGODo64rXXXsPLL78MJycnvP322/KFc+XKlXB2dlbMwWBot2/fhoODg1wbWVhYiF9++QUuLi7o37+/vN+jAWXNmjVGKad2+Hn//ffRrFkz+fG4ceNQuXJlfPvtt+jcuTM8PDzQoUMHZGdnP3FoOn36NOzt7eXzVdOMpfnvtm3bYGNjo9daWH2aPXs2qlSpguXLlyMiIgIVKlTA6tWrsWvXLsyaNQs9e/aEo6Mj/P39S/UFqPmb5+Tk4M0330THjh2xcuVKTJo0CVWqVMHIkSOxZcsW/P333+jcuTP8/f3l3/2jNQSGdPr0aTRt2hQDBw4sEjaKCyjZ2dl44403IEmS3mpMkpKSkJeXh59++gldu3ZFy5YtkZ6ejo8++qjEYdwAMGfOHLlTrj48s+EkJSUFkiQhLCxMsf3333+HnZ2dPEIGeNgHpXXr1ujUqZNi7gp9unjxIrZt2wbgYe1J//794eTkhDFjxsDd3b3YocGaKsunvasrrlPSgwcP0KdPH/j7+ysCCvAwLGn6VZiCZhTR0qVLi8wncODAAdjb26Nx48aoXLlykWpdffSJ0XSmfLSdVSM3NxfDhg2Dl5eXKnrtX7t2DX369FFMSjVs2DCUL19evgPUXOQ059KUKVMwdOhQRajRxYIFC9CjRw906NABv/76q6LD+MaNG+Hn51fqO1tdaZqLzp07p9heWFiIXbt2/WdAMZSkpCR8//33iuMCD/8WderUwYoVK/DKK6/Aw8NDcR5t27ZNpzv1/Px8OShr/w4e7bQ4e/ZsWFlZFRk9YYrhsKmpqdi/fz++++47educOXPg7OwMa2trfP3114r97927h8OHD2PcuHFP1C9K29WrVzFv3jz5puXatWsICgpCo0aN4OXlhU2bNilGoURGRiIwMNDoNxmnTp2Ck5MTwsPD5Q6/gPKa1rx5czmg3L9/H2+++Sbs7Oz01iR3584d1KtXT76W/Pjjj+jcuTM8PT1hZ2cnzwKrfc7MnTsXx48f18vxtT2z4SQzMxMtWrSAr68vTp48Kf+BY2JiYGdnV+RLbfv27ejcubNBOuzFxcWhXLly+OSTT+RtaWlp8hCwDz/8EIDyJJw8eTIqVKiAW7duPdWxNVMLh4aGIiIiApcvX5Y/iNnZ2RgyZAgaNWqENWvWyFX80dHRqFy5MrKysox+4Tpx4gTc3d0xYsQI/Pjjj4rnNGV588034enpaZAgefLkSdja2hZZfO2zzz6Th6QCDzuPOjo6yh0ITblQZEJCAqpVq4YuXbrIVd/Hjh1DgwYN4OzsjN27d8shJDk5GVOnTkXFihXlnv2Pc+nSJcyePRvdunXDyy+/jDfffFNxThbXX2XSpEno3LmzUSahO3/+PF5++WW88847xY4ie1xAMaRTp06hYcOGaNmypeJOU3OefPDBB3BwcEDt2rXlGtTSBOsLFy7g5MmTOHz4MGrVqoUBAwYo5gAqLCxUXPvs7e31Ps24rk6ePIm6devC09MTkiShVatW8jVp0aJFcHNzw/vvv68IDKWddfTkyZPw8fFBr169sHr1avn3n5mZiTt37iiG12uMHDkSw4YNM2o4uXnzJpo2bVrsUO7s7GzFvDsBAQGoWbMmevbsWex32dO4f/8+qlevjhUrVsjbNN+Nfn5+RdYX0gzjNsRN2jMXThITE+VfVGZmJtq3b4/q1asjJSUFiYmJcHd3V8yip32yG2LipZI6vwIP76yGDh0KR0dHRVvwzJkzYW9vr5f1Lj788ENIkgQPDw+0aNECHh4eqFu3LsLDw/Hbb78hPT0dL730EkJCQvDpp58iOzsbSUlJRUadGINmFMjUqVMfexH64osvUKVKFblzs76Cwa1bt+Dn54f69esrts+bNw9OTk6KuxkA6Ny5M/r27WuSZhwNzb/96tWraNiwITp27CivrbFnzx60bt0akiQhICAAAQEBaNmyJby8vJ7oTksTFLt3747BgwejY8eOcHJywosvvogffvihyL87LS0NkydPhpOTU6knXtLFyZMn4eHhgdGjR+Onn36Stz967mgCioeHh1FqBM+cOQNnZ2dMnjy5xM6smhmNFy1aVOrjaPrYaG5u9u7dC29vb4SGhio6h2vOkQ0bNqBOnTp6m8GzNDTNpVOmTMH+/fvxzTffoHr16ggKCpL3mTNnDqpWrYq5c+c+1XwzZ8+ehaurKyZPnlzsTd6jNWjp6emYNm0aXFxcnii469O5c+fQsGFDRdA4dOgQli9fjsaNG2PQoEGKGcwbN24MSZL0WmOhCRxhYWGYMmWK4rmffvoJnTt3RosWLeTmoxkzZsDa2lqv4UjbMxVOjh07BgcHB8W49YyMDLRp0waVKlWCu7u7os310S81fd9NnTp1Cra2top5TAAoQkdaWhpCQ0Ph4OCAM2fOYOHChbC2ttbr+gjvv/8+zMzM8M033yAmJgbR0dFo3rw5qlatCn9/f3Tr1g02NjZyFaeprFy5EkFBQYq7mYSEBPzyyy9YsGCB3JEZANq2bYuOHTvq9W+WnJyM8PBw1KtXT/6bLVq0CC4uLoq7X80xZ82ahSZNmph8mnrNReXKlSto2LAhOnToIK8VdffuXXz00UcYNWoUBg8ejDVr1jzRcNFr166hatWqiIiIUMxZcvToUfj7+8PT0xP79+8H8PD3ER4ejr59+8LHx8cgVbzFlc/T0xMRERFPNNqosLAQP/30k8GnHk9PT0fr1q3lWZa1ac5rTXmnTp2KFi1alKo8mqZH7T42AHDw4MFiA0peXh5ef/11DBs2rNRNeU/r6tWrkCQJkZGR8rbCwkI5/Gv3FZszZw5eeOEFTJs2rVTNg9nZ2QgNDVVMxQA8bM5OTU3F5cuXFeF65cqVeOWVV1CtWjWTjFravXs3LCws5BvrNWvWoEWLFvKAjeDgYPj6+ioGbuh7mgTNOTR79mw0bNhQ7uenoQkoHTp0wMiRI2FjY2PQdXyemXCiGUKoPUpAIzMzE3369NF70nyc7OxsdO/eHebm5ortc+bMgYWFhaJNOS0tDYMGDYIkSShXrpze/uDa1cTh4eGwsbGRh3k+ePAAt2/fxsKFCzF58mSUL18e7u7uJh1lEhERgfbt28sX740bN6JXr16oVKkSvLy8YGtrKw83XL58OZo3b66XCeGSk5PlzreJiYmIjIyEn58f2rZtC1dXV0XPdA1N84mxlyfXph3MHg0oHTt2VJRb19qlDz/8EMHBwfIXmfaxLl26BD8/P7Ru3VretmHDBsyePVsviwg+iTVr1qBbt26KTqOXL1/Gjz/+iLfeegs//vhjkaHLhYWFBp+W/vr162jQoIEc3ADgzz//xMyZM1GlShUEBgZiwYIFePDgAf744w84OzvLfdGelKaj/6N9on7++Wfcu3dPnvxPO6BERkYW6ZNibDExMbC1tcWwYcMU2z///HNUq1YNSUlJimtWREQE6tSpo3PTtmbY+KMj+3bu3ImxY8fK/VpGjBiB8+fP459//sErr7yCN954w6TTJfTq1QuSJKFhw4awtLTE3Llz5aB09OhReHh44IsvvtDrMc+dO4c5c+bgiy++wIkTJ5Cfn4+vvvoKbdu2VfSR0tixYwcCAgL03pxUnGcinJw4cQI2NjbyiqsaR44ckTuDpqeno3379vDy8sKpU6cMXqacnBzs378fPj4+aNWqFQBgyZIlcHNzK7bza3JyMiZPnvzUZTt37hymTZuGa9euFflCCg8Ph4WFRbEn+OXLl01S3as9lPXrr7+GJEkYM2YM+vbtCycnJ0yYMAF//vkncnJysGjRInh5eSE5ORk3b97USzDIyMhAly5d0KJFCznoJCQkIDIyElWqVJGHfAP/BoBZs2ZBkiSTzPyamJhYYhOGpnyaJp6goKDHDid+nLCwMLmq/dHaqYKCAnzyySdFavgMPYmZtjlz5sDHx0fuiPvVV1/JQdbPzw+SJMmjOozRb+r69ev4+++/kZSUBCcnJ3mk1LJly9CkSRN07NgR7777Lvr164fatWvLTW+DBw/WadKsxMREuLq6KvrPAP82hWhGS+zbtw/e3t4YMWIEwsLCDFr9/l8uXLiAn3/+GYWFhfj111/ldaeAh7+3ChUqKEbFaV+3dJ0J+/Tp05g9ezZu3bqFXr164aWXXsKFCxcwf/58+Pj4IDQ0FGvWrMGWLVtgb28vN6vdv3/fqDVKCQkJWLFiBebOnSv3EcrOzkZ0dDQ++OCDIudESkoK/P399T7fyuLFi9GuXTu4ubnB29sbNWvWRGBgoLzYrIb2Z3vXrl1GafYv8+Hk4sWLsLe3x6hRoxTb33vvPXmYqUZGRgY6deoER0dHgw0fjI+Pl+9O8vLycPjwYXm2WRcXF/miVJynHWmSm5uLpk2bQpIk1KxZE+Hh4di8ebNin4kTJ8LCwqLECbqMSbO2xdq1a+UL0ooVK9C+fXt06NABu3btUowC2bBhA2rVqlXs+g6lVVBQgHXr1qFNmzbo0qWLfJemqUGpXbu2ohp6xowZpZqVUh+ys7MxYsQI1K9fX3GRKqkGxd/fH/7+/o8950ryyiuvwM/Pr9hjAA/DrCRJRdY6MiTtc2Hz5s0ICAhAv379MGjQIJQvXx4TJkyQw9iSJUtgaWlplPkpsrKyUKlSJcyePRvAw06C9vb28Pb2hpWVFRYuXKi46ahYsWKRG6kndfXqVTRt2hQ9e/aUm+6ioqLg6uoq3/RozoG9e/fCyclJsR6NMRUWFuLevXvw9fWVO1hq+v84OTkhJCQE1atXVzS1a86z0swwevz4cXliPeBhjUzTpk3h6uoqD03W7gPUvXt39O7d2+izXp86dQq1a9fGa6+9Ji9Eqa248kybNg21a9fWW5PklStX8OWXXyoW80tJScEXX3yB6OhoNG7cGH369FEMSjDmzQfwDISTn376Cebm5pg0aZLcJPH++++jYsWK2LFjR5H9MzMz0a1bN4M0X+Tl5WHIkCGKRZjy8vIQGxuLFi1awMfHR97XUKM7Fi5ciCVLlmDXrl2IjIyEs7MzBg8ejJUrV8of9MjISNjY2GDt2rUGKcOT0F7b4tFZBe/fv19sP4Lw8HCEhITobaZRzUUgJycHGzduRLt27dCjRw+5iUdTg+Lr64sFCxZg/vz5eu8P9KQ07eN79+5FaGgo2rRpo+hbVVxASUxMRLt27XT6gta8z2effYYKFSooqsU1F6eCggIcOnQIderUMVoz4D///AMXFxf5iwd4eAMSGhqKtm3b4tdff1X0/dm2bRvq1Klj0HVytE2YMAGNGjXCzZs3cfPmTezduxdr1qxRzE+Un5+P27dvo2PHjorOjbqKj49HSEgIevbsiZEjR6JixYrFzj1x584dJCUlmbSpFgB8fX0VN0magOLj44NKlSrJ258mJJw5cwY2NjaKGwngYZiLjY1V9FspLCzEgwcP0LNnT6OvsH727Fk4OztjxowZilrjjRs3KkbIaBw7dgzh4eFwdnbWa5eEN954A87Ozvj000+LXTcuLi4Obdu2RdeuXYv9HjWGMh9OgIejNypXroyIiAiEh4fDxcWl2Ds6Y3xIL168iD59+sDBwUG+W8rJycHhw4fh7e2Nli1byl+8hggoe/bsgaOjo1xVmJKSgnfffRc2NjYICAjA6tWrceHCBcybNw+urq4mWTH3Sda20L5Q3b59G1OmTIGzs7NeRoFo3wFod4oLDQ2FlZUVunfvLtegJCQkYPbs2XBycoKZmZlJgsnhw4fRvHlzuUyHDh1Cnz59SgwoOTk5+OCDD3Ds2LEnOse0L5Ka97hx4wbq168Pb2/vYmvZpk6dimbNmhltEciMjAzMnDkTFhYWWLx4seK54r7UwsPDERQUZLTz+4cffoC7u7tiFtPizJo1CzVq1HjqGp0LFy6gU6dOsLGxkX8f2vOaTJ8+HZUqVTLaQovFycvLQ05ODl544QXFHDya5zRNPE87xPvUqVNwdXVF7dq15W2PmzhNs5aQp6enznOmPI309HR07doVr7/+uuKcjYqKgrm5OerXry+PvAIe1hQHBASgRYsWBhn99uqrr6JmzZpYvXq1YqVqzTXj8OHDCAoKQqtWrUqceM2QymQ4ycrKws2bN/Hbb7/J1Vw//PADXF1dYW5ujs8++0zeV7u2oGvXrk+1SuKTunTpEnr16qUIKJomnhdffBHt2rXT67ovjwoPD8fgwYPlL53Q0FD4+vpi2LBhaNOmDSwsLLB161aTrC78X2tbfP3114q+L1OmTEHv3r1Rq1YtvYylv3TpEiIiIop0DFy8eDGcnZ0xZcoUtGrVStHEc/XqVbz//vsm6Syn6ej95ptvKrYfOHCg2IDy4MEDjBkzBra2tk9U3uTkZPTr108xKaEmvF25cgVVq1aFp6cnRo8ejdOnT2Pbtm145513YG9vb/QJ6O7evSuvIL5kyRJ5u3bAvH79OqZOnYry5csbrG+Z5px99Iu0S5cuCAgIkC/u2sHw119/xdtvvw0nJye9/d4uXbqEzp07o0uXLoqmu5kzZ8La2lox34kxXblyRf4Mp6amokKFCsV2Ki8oKMCuXbvg7u5e6tWXNaOW2rVrh8qVKys+J8WF1m+//RajRo1CxYoVjd7UlZycjBdeeEEx8dzmzZvh4uKC9evXY+zYsWjRogWWL18O4GFt4a+//vpUw6mLo31z9sYbb6BWrVpFAorm3D5w4AC6d+9uko7/ZS6cXLhwAcOGDYOvry+sra3h4OCAQYMGITExEfv27YObmxvefvttxYV51qxZMDMzM0iHsMTERHz//fdFFg9LSEhAt27d4ODgIKfevLw8HDlyBM7OzujSpYvey6KxdetWBAYGoqCgAK+++irc3d3lZqbz589j6dKlep1mWFf/tbaFu7u7XEOxceNGzJo1S2+jQL799ltYWlrirbfeUjQDOjs7y8P0vvrqK7Ro0QI9evSQO+QZu70V+PfCW9zETMDDYaOPBpQ33ngDdnZ2T1zDc/nyZQQGBqJbt25yHwbg3+GuCQkJGDBgADw8PGBubo4aNWqgQ4cOxa4ErW/p6elFFlC8ffs25s+fD0mSsHTpUsVzS5YsQf/+/eHr62vQ4PToiB9NOPrhhx/g5+cn19pqwsl3332H3r17o2PHjnoPTJomnuDgYBw7dgwLFiwwWdMj8PBzEhwcDGdnZzmguLm5KUYvPeqnn35CjRo1dO5gfvjwYVhYWODdd99Ffn4+PvnkE7i6upYYUH777TeMGDEC/fv3N/o8JsDDkS6SJCk6+d6+fVv+WyUlJeH1119HtWrVig1zT0P7pjw/P18xGaAkSY8NKMacvl9bmQonJ06cQKVKlTB69Gh8/vnnOHfuHKZMmYIXXngBPj4+uHz5Mnbu3IlKlSrhjTfeQEpKCubMmVOq1SufREpKClxdXSFJEqpVq4ZXXnkFS5YswbVr11BYWIiUlBQMHjwYdnZ2chjIzc1FXFycwZuY2rRpAzMzM1SuXNlow6f/i2bc/BtvvPGfa1s0bdq02KFspXXjxg0cPnwYqamp+OWXX1ClShVMnz692GbAgoICfP311/Dz80O/fv0UH1RjuXbtGpycnIqscbRixQpFvwtNE0/79u3Rtm1b2Nra6nyua3/BaQcUzZfu/fv3cefOHWzatAlZWVlGmdfl4sWLqFGjBmrXro2FCxcW6dg9b948mJmZyQvoAQ+Hly9evNigw5kTEhLQuHFjzJgxo0jt2z///ANfX98if7Pr168jLi5O55EnTyo+Ph7du3eHm5sbLCwsTBZMNM6dO4dWrVqhZs2aOH36NJo1a4YvvvgCqampOHfuHM6cOYOEhARcuXIFe/bswfXr10s1xPuPP/5QBJG7d+/+Z0BJSEgwalP2pUuXEB0dDeDhjYC9vT0WLlxYZD9NWPjhhx/QrFkzvTY33bx5E66urooZygHITfsxMTEYOXJkkSaekmoIjaXMhBPN2P6IiIgiX1abN29GgwYN0KxZM9y7dw9btmyBl5cXateubbCRFTk5Obh9+za6deuGevXqoVWrVoiIiEClSpVQq1YtNG7cGMuWLcOnn36KkJAQVKpUSachg6WlOZG2b9+OWrVqyaM6THWCFefy5csICgpCgwYNHru2hb6avs6cOYOWLVuiU6dOeOmllwA87MmvuZhrNwNqL5i2devWJ5qwzBA2b96M2rVrY/DgwfKFat68eShfvnyRBSpjY2MRFBRUZI0WXZQUUPLz85GdnY1JkyahT58+Bm2O1LZo0SJYW1vD2dkZdevWRb169fDCCy9g4MCB+PHHH3Hw4EGsWLECkiRh9erV8usMWcNVUFCAixcv4q233oKrqysaNGiA/v37Iz4+Xm4C3LJlCzw8PPR+5/tfzp8/j549e5q0RlTbhQsXEBAQAFtbW0iShIoVK8LV1RU2NjawsrKCq6srHBwc4OTkpJch+ZrrW3p6erEBxRSzOOfl5WHcuHFo1KgRsrKycOvWLTRu3BgNGzbE3r17i33NlClTEBISUqQm/mkUFhZi0qRJsLKyklebj4qKQoUKFRSdXcPCwuDn54cPP/zQpH2VNMpEONGM7X90+XPtC9Hq1athZ2cnX6jWrl2LF154wSDVz0lJSejbty/i4uLw999/Y8iQIejUqRM2bdok9y155ZVXEBISgnLlysnzLvj4+CA3N9coQSE1NRU1atTAjBkzDH6sx0lMTMTWrVsxadIkREVFyV+sOTk5SE9PL7YPkD7Xtjh9+jScnJwwbdo0JCQkKM6Zb775Bh4eHpg4caLiTsXYQwu1ac+O+9lnn6Ft27YYOnQoJkyYUOIcOcDDJqCnvcgXF1BycnIwfvx4mJubG6WNXlOFnJmZicjISPTo0QOjR49GYmIiVq9ejSFDhsDNzQ21atVC8+bN4eXlBUmSDD40PjExEaGhofJQ1GvXrmHx4sVo1KgRKlasiJ49e2L79u04ceIEmjdvjmXLlgEw7rlkrOD4qNu3byMuLg6XLl2Sv9QKCwvldY/MzMywfft2XLhwAUeOHEFcXBzi4uJw/PhxvU4LoKEdUCZMmKD399fFzz//DDMzM7lD6ZEjR+Dg4ICWLVsqgkFaWhomTpwIR0dHvXV+TU5OxubNm7Fp0ybExMRg+fLlkCQJPXv2hLu7u9xJWfua2LdvXzRp0qTYETzGVibCifbY/kcTp/YXfZs2bdC7d2/5saHS3+XLlxEQEICuXbvi9OnTSE5OxqBBg9C0aVN5Blbg4YX90KFDWLVqFfr372/0DoRffPEF7Ozs9LJGT2mcOHEC3t7eaNu2LV588UV5oa9Ro0YV2/au77Utbt++jVatWhXpTKr9YdSs0/Pmm2+afMhlUlIS+vfvr5j8aO3atWjRogUsLCzw6aefAlB2tNR30NUOKHv27MHkyZNhY2NjlGCSlJSEIUOGyOdrRkYGIiIi0LRpU8yaNUv+oj937hz++usvDB48GO3atYMkSQZfy+f7779H48aN0a1bN3mOJM3fITo6GqGhofIq6BUrVoSbm5vBmnHU5NSpU2jUqBFq1qwJR0dHhIeHKzpPnj9/Hi1btkTdunWL9B8ypPT0dKxZswaSJBW7rpkx9enTByEhIXLt2p49e+S+dR07dsRLL72EoKAgvU6dr7n2+vr6yjfIa9euxcqVK2Fubo6JEycCQLFN58Yafv9fykQ4AZQXTe2Aon1xbteunWJGT0OXJzg4GJ07d8bp06eRkpKCQYMGITAw0KTzh2hLTk5Gu3btTDKTaXx8PFxcXDB9+nT5Ip2UlITFixdDkiT0799f0TcgOjpa72tbnDlzBi+++CL++OOPYtdR0pw7X375JapVq4awsDDFvBTGdvDgQTRv3hzdu3fHDz/8IG/fsGEDWrVqhdDQULmjtyFr3zR9GJydnWFpaWmUmUXv37+PH3/8UZ78SXMOZGRkYPr06WjatCneeeedYjvn6bMK/HG2bt2Kjh07Ijg4uNgm2l9//RUjRoxA9erVIUmSQdfwUQPNSLIJEybg2LFjGDt2LKysrIrM4RIfH4/AwEDUqlXLqF98d+/exeeff26SUXban89Vq1ahevXqiia3a9euYerUqQgJCUH37t2xYMECvfWV0nSBmDx5Mq5fv44ff/wRHTt2RJMmTXD48GF5JeH169crymrKGuPilJlwApTcLl5QUICkpCR06dJFblMzRtPJ4wLK00yypE/ac1gYS35+PiZMmIChQ4cCUE7eBQCffPKJYrTFjRs3DLK2xVdffYVy5coVmXVSW1ZWFpKTk/G///0PtWvXLtUiY/r0119/ITg4GCEhIYqAsnbtWrRt2xZ9+/aVm6AMeY4bsw/D4cOH4ePjg1u3bmHLli3o0KEDevXqJQeUzMxMTJ8+HQEBAQgPD5eb+0xxMd24cSM6duyIkJAQ+VzVnhvi/v37SEtLM8rMtKZ07tw5ODo6KppNbt68CVtbW3maAI3CwkJcvHgRPj4+aNy4sVH/bsbsa3fjxg1F+NK+3jRo0AChoaEGL0NxXSCAh9dce3t7XLhwAXl5eZg5cyYkSdL7Wj36VKbCCVByDcqUKVPQoEEDo9cSFBdQhg0bhtq1a+Prr782alnUpHXr1hg7dmyJzw8bNgyVKlWSqzofPHig97Ut/vrrL1hbWxeZgVbb8uXL0alTJwAwyerCxV2oY2JiEBwcjODg4CIBRXPnrj0Nt6EYow/D8ePH4eDgIC/oCECerbe4gNKyZUuMGTPG4B0cT58+jSFDhmDRokU4deqUYvG5bdu2oW3btooaFM3fUU0dzw0lJydHvvvesGGD3G9s9uzZkCQJAwYMwIwZM/DLL78olglJSkoy2sKQxnb37l24ubmhWbNmmDJlCjIyMhTn6KpVq1CnTh057GvX3Goe60NJXSB27doFFxcXufnz3r17iIyMhCRJJl2J/nHKXDgBih/bb29vb7Ihs9oB5cyZM0hOTsbIkSNNNtLD1HJyclC3bl35rkr7C1jzIVy7di0qVKhg0Krv5ORkuLm5oWfPnoo7We0LwTvvvINJkyYVuVgYw8mTJ+Hv74+ZM2fiyy+/VAxxPHToEDp16oTg4GDFOjorV65E9+7dn4kmg0dX19X+/W/fvh3t27cvElDefvttBAUFGbT/Qn5+Pvz9/SFJEipVqgRHR0e0adMGo0ePxm+//Yb8/Hxs3rwZffr0UQRFQy1JoSZJSUnw9/fHrVu3MHnyZHh7e2PLli3yUhlRUVHYunUrBgwYgKCgIDg4OKBHjx6qaeo2pB07dmDKlClwd3dHzZo1MWrUKDmMpKWlwc3Nrdi1dPRN8/3YuXNnnD17FpmZmahYsSImT56s2C8zMxPz5s0zyZwvT6JMhhNAfWP74+Pj0bVrVwQEBODcuXOqa78ztOTkZGzatAlffvklzpw5g3fffRdubm6KwFhYWChfwDdu3Ii6desavFf4t99+CysrKwwdOlRxF5eVlYWIiAhUr17dZMuk9+nTB5IkoU6dOvLyAq1bt8aWLVuQlJSEQ4cOoWfPnnjppZcUKxGbooZH30paXXfx4sXypHNff/213MSj6UyemZlp0NWzL1y4gJMnT+LEiRPw8fHB4MGDMX/+fKxduxb+/v6oWbMmvLy8MHLkSHTv3h1169ZF69atjbJKqxokJibC09MTY8aMAQCMGjUKzs7OsLe3L7Ji7q1bt7B582YMGDDAZJ8xQ9OsAq9dI5SZmYlJkyahdevWKFeuHF577TX89ttvWLlyJXx8fIxS6xkfH48uXbqgbdu2cHZ2xttvvy0/V9zNohqV2XACqG9s/7lz59CnT5/n5kKloekZ7ufnB3Nzc9StWxcDBw5E3bp18fLLLxe7AvSYMWPQs2dPgy9TXlBQgFWrVqFcuXLw9fVFWFiYfGw3NzeTrNaqUVhYiA4dOqBmzZrYvHkzNm7ciMGDB6NBgwawtbVFWFgYAgIC4Ovri0aNGskr7j4LSlpd19HRUbE+zaZNm9C5c2e0b9/e4LPSxsXFwdbWVl7fZO/evXjhhRcwdOhQXLp0Cfn5+bh58yYWLVqEcePGwc3NDZIkwcbGxiTTe5tCfn4+FixYAD8/P3lagPHjx6Ny5cr47LPPFLV/+pxEUY20V4GvUaMG3nnnnSJN+dHR0QgODoaDg4N8vmzZssUo5YuPj0eHDh1QvXp1xdxIag4k2sp0OAFMN7a/JKaY7MeUiusZHhwcjDZt2mDIkCGoUKECWrZsiZ9//hm3bt3C+fPnMXXqVMW6Q8Zw6NAh9O3bFw0bNkTr1q0xZcoUoy76VZLCwkLUrVsXjRs3RmxsLICHNSO//vorJk+ejLZt20KSJDg7Oz9zzYSPrq7r5uYmzweh3USyfv169OzZ06D9yTRLBWiGnWqvLeLt7Y2+ffsWGbWUmpqK3bt3P/OdXx8dDXX37l3Ur18fQUFB8rZhw4apboZRYyhuFfhBgwYhOjpaPoczMjJw+PBhdO/eHTVq1DDqdefixYvFDiIpC8p8OCHTKalneHR0NCpUqICUlBRER0ejSZMm8iyR9erVQ/369Y0+5wtg+qFyd+7cwYULF7Bz505cvHhR0TzRoEED1KhRAwcPHiwSuPfv32+QyarUoLjVdTW0A4ohF+x8tO+Lxs8//4x79+5h//798Pb2xoABA+QA+by4dOkSXF1d0atXL6SlpcnTzB86dAjW1taYP3++vO+IESNUNcOoMTxuFfhmzZph9erV8jIH2dnZJmmS1XSBaN68OQ4cOGD045cWwwmV2uN6hjs5OckfyqtXr+KXX37BmjVr8Oeff5psuK4hesc/qVOnTqF169bw8fGBg4MDbGxs0Lt3b2zbtk3ep2HDhvDx8cH+/fufi86VGtqr6z46h5Gh/04l9X2ZM2cOqlatKjcZ7927F97e3hg8eDAOHjxo0DKpSXx8PJycnCBJEjp37oxly5bJNZ4TJ06Ev7+/4o5cTTOMGktJq8APHz5cXgV+0aJFJi3juXPn0Ldv3zLV5YDhhJ7Kk/YMf56dPn0ajo6OmDhxIv744w/Ex8dj4cKFqFmzJqpWraqYVbhRo0aoW7cu/vzzz2e6OvxRJc1hZGgl9X1xdXWVlwrQ1Ljt27cPTk5OGDFihMlWajWGR/uKLF++HBMmTMD06dMxevRoNG3aFD///DNiY2Ph4+OD9957TxGm1TLDqLH81yrwy5cvV0W/yLLW5YDhhJ7a43qGP081AMXJyMhAhw4d8MYbbxR5bseOHQgICICfn5/ibtzb2xtNmzY1yQR6pmSq6udH+75UrFhR7vuiLT09HZcuXTL5MgeG9miTTExMDEJCQrBjxw7cv38fK1asgJOTE5YsWYKQkBA4OTkZtf+YGqlxFfiyjuGE9KKs9ww3lOvXr6NevXrYtWsXgIdhTbvvy7fffgs7Ozt5tlyNZ3Wyqv9iqurn4vq+aDcrTZ8+HZUqVXrm+1L8/fff8PT0lBfK1JgzZw5cXV3l+XX27t2LV155Bd26dYMkSejRo4fJ+3SZQllYBb6sYjghvSnLPcMN5dixYzA3N1cMjwWUF68ePXqgc+fOAMpe1ashmOp3oN335c8//5S3z5w5E9bW1nKnx2fZnTt38N5776F8+fLo0KGDIjQPHz4cw4cPlzt1pqam4vfff0e3bt0MvvCi2qllFfhniZkg0pMaNWqIDz/8UFhYWIjw8HBx8OBBUxfJJG7evCmOHDkijh49Kry9vYWFhYWIjY0VQghRWFgohBBCkiR5fwsLC2FjYyOEEMLS0tL4BVYZU/0OXnzxRfHRRx8JAGLevHkiLi5OLFy4UCxatEjs27dP+Pv7m6RcxuTk5CRmzZol9u/fLypUqCBWrlwp2rdvLy5cuCC6desmhBDi8OHDQggh3N3dRfv27cWPP/4o6tWrZ8pim5y7u7uIjIwUS5culT/r9HQYTkivatasKRYtWiSqVq0qKleubOriGN3Zs2fFSy+9JGbMmCHmzZsnypcvLwYPHiyioqJEbGysMDMzEwUFBUIIIQoKCkRhYaEwNzcXjRo1EkIIAcCUxX/u1axZUw7YISEhYsaMGWLfvn2iSZMmpi6aUfn5+YlPPvlELFu2TKSnp4uuXbuKY8eOidOnT4utW7cq9tUO2s+z9u3bi6ZNmz6X1z1DkMCrIRlAbm7uc1cLcObMGdGqVSsxduxY8frrr4vKlSuLcuXKidjYWDF27Fhx6dIlsWXLFtGyZUthZ2cnHjx4IBYsWCCio6PFX3/9JWrWrGnqfwL9vwsXLojJkyeL+fPnizp16pi6OCY3YcIEcf78eXHq1CmRkpIiVq9eLV577TVTF0t1srOzhbW1tamL8UxgOCHSg3/++Uf06tVLNG7cWCxfvrzI87/99puIiooSe/bsEc2aNRO2trbC2tpaxMXFie3bt4vGjRuboNT0OHl5ecLCwsLUxTApAHLNSExMjNi5c6eIjo4WsbGxwtfX18Slo2cZwwmRHpw9e1b07NlTrFu3TrRq1UqYmT1sMdW+uN+5c0ds3bpVHDp0SGRkZIjWrVuLbt26iRdffNGURSd6LO1zWAghMjIyhKOjowlLRM8DhhMiPdi4caMYPny4yM3NFZIkicLCQjmgaP7//v37IikpSfj4+Ji4tERE6sYOsUR64OXlJcqVKye+++47IYSQg4n2/69du1a88cYbIicnxyRlJCIqKxhOiPSgevXqwtHRUWzYsEEkJCTI27UrJhMSEkSTJk2eu47CRES6Yjgh0oMqVaqIjz/+WPzyyy9i5syZ4uzZs0KIh8Ms79+/L6ZNmya++eYbERYWxqGXRET/gX1OiPSksLBQrFmzRowfP17UqFFDBAYGCmtra3H9+nVx8OBBsXPnTnk+EyIiKhnDCZGexcbGikWLFolLly4JBwcH0aJFC/Hqq69yHhMioifEcEJkAAUFBcLc3NzUxSAiKpPY54TIALRH6zD/ExHphjUnREREpCqsOSEiIiJVYTghIiIiVWE4ISIiIlVhOCEiIiJVYTghIiIiVWE4ISIiIlVhOCEiIiJVYTghIiIiVWE4ISIiIlVhOCEiIiJVYTghIiIiVfk/UCzoTZjxu7UAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=x_correct)\n",
        "plt.xticks(rotation=45,\n",
        "           ticks=np.arange(len(X_rfe.columns)),\n",
        "           labels=X_rfe.columns);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "0XEA1IO44poq",
        "outputId": "a1376923-a2d5-4540-bd44-a81c42f92583"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Contrary to the classical classifiers, the weight of the ordinal features in decision making is not as apparent. However, due to the complexity of neural models, it's not as straightforward to extract reliable or highly interpretable feature importance analysis as it is with decision based algorithms. Although thse neural models performed slightly better than the tree-based models, I trust the latter's error analysis a bit more."
      ],
      "metadata": {
        "id": "eMIN_pe1-YuJ"
      }
    }
  ]
}