evaluation.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/evaluation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation\n",
        "\n",
        "Why do we need robust model evaluation?\n",
        "\n",
        "A forecast is like an experiment -- given a set of conditions, we make a hypothesis that a certain outcome will occur.\n",
        "\n",
        "We wouldn't consider an experiment to be complete until we determined its outcome; i.e., we need to find out whether the forecast was successful.\n",
        "\n",
        "Notebook content below is based on the 2024 AMS Annual Meeting AI for Environmental Science Short Course. Evaluation session taught by Hamid Kamangir (University of California, Davis)."
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import sklearn\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import cross_val_score, learning_curve, validation_curve\n",
        "import sklearn.model_selection as model_selection\n",
        "from sklearn import tree\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns"
      ],
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        "id": "5hN2KLpCSqN5"
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      "source": [
        "#precent auto-scrolling\n",
        "#The next cell prevents output in the notebook from being nested in a scroll box\n",
        "%%javascript\n",
        "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
        "    return false;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "9I3FB47YTWtV",
        "outputId": "f2d16715-74d4-4452-9453-d8096842fb59"
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              "    return false;\n"
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      "cell_type": "markdown",
      "source": [
        "### Let's do a case study application for evaluation\n",
        "\n",
        "We will use a dataset from an American Meteorological Society AI for Environmental Science short course held in January 2024 in Baltimore, MD."
      ],
      "metadata": {
        "id": "xkGuCQQVW1NU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset for nb\n",
        "! wget https://raw.github.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nA_Cw-l-TWqf",
        "outputId": "4d6f9fbf-7013-4cc0-fdaa-0ce6424ccd2b"
      },
      "execution_count": 3,
      "outputs": [
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          "text": [
            "--2024-04-18 16:05:47--  https://raw.github.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv\n",
            "Resolving raw.github.com (raw.github.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.github.com (raw.github.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://raw.githubusercontent.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv [following]\n",
            "--2024-04-18 16:05:47--  https://raw.githubusercontent.com/ekrell/ams_ai_shortcourse_2024/main/data/kdfw_processed_sub_data.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4400996 (4.2M) [text/plain]\n",
            "Saving to: ‘kdfw_processed_sub_data.csv.5’\n",
            "\n",
            "kdfw_processed_sub_ 100%[===================>]   4.20M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2024-04-18 16:05:47 (49.2 MB/s) - ‘kdfw_processed_sub_data.csv.5’ saved [4400996/4400996]\n",
            "\n"
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      "source": [
        "# open csv dataset and do some preprocessing\n",
        "total_dataset = pd.read_csv('kdfw_processed_sub_data.csv', index_col=0).sort_values(by='date') ##kdfw - Dallas-Ft Worth, TX\n",
        "total_dataset = total_dataset.replace('********', np.nan).replace(np.inf, np.nan).dropna(how='any',axis=1)\n",
        "total_dataset.head(5)"
      ],
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        "outputId": "864fb25f-b431-47b4-83ab-2e3b9ee793d7"
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      "execution_count": 4,
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              "         date  OBS_tmpf_max  OBS_tmpf_min  OBS_sknt_max  OBS_phour  \\\n",
              "0  2011-01-01         44.06         30.02          14.0        0.0   \n",
              "1  2011-01-02         48.02         24.98           7.0        0.0   \n",
              "2  2011-01-03         57.92         30.92          18.0        0.0   \n",
              "3  2011-01-04         55.94         41.00           8.0        0.0   \n",
              "4  2011-01-05         57.92         35.96          12.0        0.0   \n",
              "\n",
              "   GFS0_pmsl_max  GFS0_pmsl_min  GFS0_pmsl_avg  GFS0_pres_max  GFS0_pres_min  \\\n",
              "0         1026.3         1015.1    1022.640000         1004.4          993.2   \n",
              "1         1029.5         1024.7    1026.938889         1007.6         1003.1   \n",
              "2         1025.3         1017.1    1021.183333         1003.6          995.6   \n",
              "3         1022.6         1016.0    1018.912500         1001.0          994.5   \n",
              "4         1019.5         1014.8    1016.875000          997.8          993.0   \n",
              "\n",
              "   ...  GFS0_qpf_accum_avg  GFS0_wagl_max  GFS0_wagl_min  GFS0_wagl_avg  \\\n",
              "0  ...            0.000000      15.485419       3.578924      10.642520   \n",
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              "4  ...            0.002297      14.434982       2.635952      10.020610   \n",
              "\n",
              "   GFS0_frz_rain_max  GFS0_frz_rain_min  GFS0_frz_rain_avg  GFS0_sleet_max  \\\n",
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              "      <th>OBS_tmpf_max</th>\n",
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              "      <td>44.06</td>\n",
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              "      <td>1015.1</td>\n",
              "      <td>1022.640000</td>\n",
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              "      <td>1024.7</td>\n",
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              "      <td>2011-01-03</td>\n",
              "      <td>57.92</td>\n",
              "      <td>30.92</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1025.3</td>\n",
              "      <td>1017.1</td>\n",
              "      <td>1021.183333</td>\n",
              "      <td>1003.6</td>\n",
              "      <td>995.6</td>\n",
              "      <td>...</td>\n",
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              "      <td>2011-01-04</td>\n",
              "      <td>55.94</td>\n",
              "      <td>41.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1016.0</td>\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3d2af88e-6dc2-4a03-b96c-7efd4dd42dbd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "total_dataset"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regression\n",
        "\n",
        "Let's set up a regression problem for evaluation."
      ],
      "metadata": {
        "id": "uvuJN_RoXGWb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#first, we seperate the observation (true value) from the features/predictors\n",
        "#Observation data\n",
        "total_label_data = total_dataset.filter(like='OBS')\n",
        "\n",
        "#remove obs data and date from the feature set\n",
        "dropCols = list(total_label_data.columns) # + ['date']\n",
        "total_feature_data = total_dataset.copy(deep=True)\n",
        "total_feature_data = total_feature_data.drop(dropCols,axis=1)\n",
        "\n",
        "#look at the shape of each array again\n",
        "print(total_dataset.shape)\n",
        "print(total_label_data.shape)\n",
        "print(total_label_data.columns)\n",
        "print(total_feature_data.shape)\n",
        "print(total_feature_data.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7LPgp1rT1wl",
        "outputId": "d0b7a4ac-664e-4848-d363-f4089215bcb1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3143, 200)\n",
            "(3143, 4)\n",
            "Index(['OBS_tmpf_max', 'OBS_tmpf_min', 'OBS_sknt_max', 'OBS_phour'], dtype='object')\n",
            "(3143, 196)\n",
            "Index(['date', 'GFS0_pmsl_max', 'GFS0_pmsl_min', 'GFS0_pmsl_avg',\n",
            "       'GFS0_pres_max', 'GFS0_pres_min', 'GFS0_pres_avg', 'GFS0_sktc_max',\n",
            "       'GFS0_sktc_min', 'GFS0_sktc_avg',\n",
            "       ...\n",
            "       'GFS0_qpf_accum_avg', 'GFS0_wagl_max', 'GFS0_wagl_min', 'GFS0_wagl_avg',\n",
            "       'GFS0_frz_rain_max', 'GFS0_frz_rain_min', 'GFS0_frz_rain_avg',\n",
            "       'GFS0_sleet_max', 'GFS0_sleet_min', 'GFS0_sleet_avg'],\n",
            "      dtype='object', length=196)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualization\n",
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "\n",
        "labels = total_dataset['date'].values\n",
        "data_label_pair = [('Max', total_label_data['OBS_tmpf_max'])]\n",
        "\n",
        "for label, temp in data_label_pair:\n",
        "  plt.plot(np.arange(len(labels)), temp, label=label)\n",
        "\n",
        "ax.set_xticks(np.arange(len(labels))[::500])\n",
        "ax.set_xticklabels(labels[::500])\n",
        "plt.ylabel(\"Temperature $\\circ$F\",fontsize=14)\n",
        "plt.xlabel(\"Date\",fontsize=14)\n",
        "plt.legend(loc='upper right',fontsize=18)\n",
        "plt.title('Max Temperature between Jan 2011 to Aug 2019', fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "y2-IbrR2T1uf",
        "outputId": "02694ea2-f63b-4f0b-ecff-734ce174b9f0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For now, let's try with only 50 features from GFS model\n",
        "subsample_feature = total_feature_data.iloc[:,:51] #date + 50 features\n",
        "\n",
        "# we only need maximum temperature from obs\n",
        "subsample_label = total_label_data['OBS_tmpf_max'][:]"
      ],
      "metadata": {
        "id": "KZBDrFnST1r5"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scale_data(train_x, test_x):\n",
        "    \"\"\"\n",
        "    Scales training and testing data using MinMaxScaler.\n",
        "\n",
        "    Parameters:\n",
        "    train_x (array-like): Training data features.\n",
        "    test_x (array-like): Testing data features.\n",
        "\n",
        "    Returns:\n",
        "    tuple: Scaled training and testing data.\n",
        "    \"\"\"\n",
        "\n",
        "    # Fit/find minimum and maximum value for each input variable\n",
        "    min_max_model = MinMaxScaler().fit(train_x)\n",
        "\n",
        "    # This saves the values to apply or 'transform' testing data\n",
        "    train_x_minmax = min_max_model.transform(train_x)\n",
        "    #print('Training data range \\nMax:', np.nanmax(train_x_minmax), ', Min:', np.nanmin(train_x_minmax))\n",
        "\n",
        "    # Apply the min/max values to testing data\n",
        "    test_x_minmax = min_max_model.transform(test_x)\n",
        "    #print('\\nTesting data range \\nMax:', np.nanmax(test_x_minmax), ', Min:', np.nanmin(test_x_minmax))\n",
        "\n",
        "    return train_x_minmax, test_x_minmax\n",
        "\n",
        "def split_data_by_date(features, labels, date_ranges):\n",
        "    \"\"\"\n",
        "    Splits a DataFrame into multiple DataFrames based on specified date ranges.\n",
        "\n",
        "    Args:\n",
        "    df (pd.DataFrame): The original DataFrame.\n",
        "    time_col (str): The name of the column containing time data.\n",
        "    date_ranges (list of tuples): A list of tuples where each tuple contains start and end dates as strings.\n",
        "                                  Example: [('2009-01-01', '2012-12-31'), ('2013-01-01', '2017-12-31')]\n",
        "\n",
        "    Returns:\n",
        "    list: A list containing the split DataFrames.\n",
        "    \"\"\"\n",
        "\n",
        "    features_list = pd.to_datetime(features['date'])\n",
        "    date_mask = (features_list > date_ranges[0]) & (features_list <= date_ranges[1])\n",
        "    out_data = features.loc[date_mask,:].drop(['date'], axis=1)\n",
        "    out_labels = labels.loc[date_mask]\n",
        "\n",
        "    return out_data, out_labels"
      ],
      "metadata": {
        "id": "0hs2_lK-T1pj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "date_ranges = [('2011-01-01','2017-12-31'), ('2018-01-01','2019-12-31')]\n",
        "time_train_x, time_train_y = split_data_by_date(subsample_feature, subsample_label,  date_ranges[0])\n",
        "time_test_x, time_test_y = split_data_by_date(subsample_feature, subsample_label, date_ranges[1])\n",
        "print(f\"train dataset samples: {time_train_x.shape} | test dataset samples: {time_test_x.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRzzXLYCUmU-",
        "outputId": "967a8aa0-4a3e-4e5f-a29f-f3c354734277"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset samples: (2558, 50) | test dataset samples: (583, 50)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_time_train_x, scaled_time_test_x = scale_data(time_train_x, time_test_x)"
      ],
      "metadata": {
        "id": "L-bHEt61UmSX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Mean Absolute Error (MAE)\n",
        "This metric measures the average of the absolute differences between the predicted and actual values. **It gives equal weight to all errors and is less sensitive to outliers.**\n",
        "\n",
        "$$MAE = \\frac{1}{n} \\sum_{i=1}^{n} |y_i - \\hat{y}_i|$$"
      ],
      "metadata": {
        "id": "7ejr_3IASXS8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### R^2\n",
        "\n",
        "This metric measures the proportion of variance in the target variable explained by the model. It ranges from 0 to 1, with higher values indicating better performance. R2 is useful when the goal is to explain the variability in the target variable using the predictors.\n",
        "\n",
        "$$R^2 = 1 - \\frac{\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^{n} (y_i - \\bar{y})^2}$$"
      ],
      "metadata": {
        "id": "gFRH3LusXPVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and evaluate the model to predict max temperature\n",
        "linear_regression_model = LinearRegression()\n",
        "# Train\n",
        "linear_regression_model.fit(scaled_time_train_x, time_train_y.values)\n",
        "\n",
        "# score on training set\n",
        "train_y_pred = linear_regression_model.predict(scaled_time_train_x)\n",
        "R2 = sklearn.metrics.r2_score(time_train_y.values, train_y_pred)\n",
        "MAE = sklearn.metrics.mean_absolute_error(time_train_y.values, train_y_pred)\n",
        "print(f\"Train Evaluation =========>  R^2 = {R2:.2f} | MAE = {MAE:.2f}\")\n",
        "\n",
        "# score on testing set\n",
        "test_y_pred = linear_regression_model.predict(scaled_time_test_x)\n",
        "R2_test = sklearn.metrics.r2_score(time_test_y.values, test_y_pred)\n",
        "MAE_test = sklearn.metrics.mean_absolute_error(time_test_y.values, test_y_pred)\n",
        "print(f\"Test Evaluation  =========>  R^2 = {R2_test:.2f} | MAE = {MAE_test:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcZtAJAoVk5d",
        "outputId": "221d0cfc-8157-4a42-bcd3-50c5781e251f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Evaluation =========>  R^2 = 0.93 | MAE = 3.18\n",
            "Test Evaluation  =========>  R^2 = 0.85 | MAE = 4.47\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def scatter_plot(ytrue, ypred):\n",
        "\n",
        "    R2 = sklearn.metrics.r2_score(ytrue, ypred)\n",
        "    MAE = sklearn.metrics.mean_absolute_error(ytrue, ypred)\n",
        "\n",
        "    g = sns.jointplot(x = ytrue,\n",
        "                        y = ypred,\n",
        "                        height = 6,\n",
        "                        ratio = 3)\n",
        "\n",
        "    g.ax_joint.plot([0, 110], [0, 110], '--r', linewidth=2)\n",
        "\n",
        "    plt.xlabel('Measured (F)')\n",
        "    plt.ylabel('Predicted (F)')\n",
        "    plt.grid(False)\n",
        "\n",
        "    scores = (r'R^2={:.2f}' + '\\n' + r'MAE={:.2f}' + '\\n').format(\n",
        "        R2, MAE)\n",
        "\n",
        "    plt.text(1, 115.7, scores, bbox=dict(facecolor = 'white', edgecolor = 'gray', boxstyle = 'round, pad=0.2'),\n",
        "            fontsize = 12, ha='left', va = 'top')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "FG5Xm8ueUmQC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_plot(time_test_y, test_y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "SBFeqlC8UmNb",
        "outputId": "40a8b75e-9bec-4a15-e06d-a71922213200"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification\n",
        "\n",
        "We can convert our continuous values to binary classes."
      ],
      "metadata": {
        "id": "uTX89-wxWtUk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Common classification evaluation\n",
        "\n",
        "##### Confusion Matrix\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://miro.medium.com/v2/resize:fit:1334/format:webp/1*3yGLac6F4mTENnj5dBNvNQ.jpeg\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
        "        <figcaption>Confusion Matrix. Figure Credit: <a href=\"https://medium.com/analytics-vidhya/what-is-a-confusion-matrix-d1c0f8feda5\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "- A good model is one which has high TP and TN rates, while low FP and FN rates.\n",
        "- If you have an imbalanced dataset to work with, it’s always better to use confusion matrix as your evaluation criteria for your machine learning model.\n",
        "\n",
        "##### Accuracy\n",
        "$$Accuracy = \\frac{TP + TN}{TP + TN + FP + FN}$$\n",
        "\n",
        "##### Precision\n",
        "$$Precision = \\frac{TP}{TP + FP}$$\n",
        "\n",
        "##### Recall\n",
        "$$Recall = \\frac{TP}{TP + FN}$$\n",
        "\n",
        "##### F1-Score (good for class imbalance issues with binary classes)\n",
        "$$F1\\ Score = 2 \\times \\frac{Precision \\times Recall}{Precision + Recall}$$\n",
        "\n",
        "## More specific/common to the weather community\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://learncuriously.files.wordpress.com/2018/10/confusion-matrix-cell-labels1.png?w=1000\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
        "        <figcaption>Confusion Matrix (environmental version). Figure Credit: <a href=\"https://learncuriously.wordpress.com/2018/10/21/confused-by-the-confusion-matrix/\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "\n",
        "Hit (H): True Positives\n",
        "Miss (M): False Negatives\n",
        "False Alarm (FA): False Positives\n",
        "Correct Rejection (CR): True Negatives\n",
        "\n",
        "\n",
        "##### POD\n",
        "$$POD = \\frac{Hit}{Hit + Miss}$$\n",
        "\n",
        "##### False Alarm Rate [0, 1]  (F)\n",
        "$$F = \\frac{FA}{FA + CR}$$\n",
        "\n",
        "##### False Alarm Ratio [0, 1] (FAR)\n",
        "$$FAR = \\frac{FA}{Hit + FA}$$\n",
        "\n",
        "##### Critical Success Index (CSI)\n",
        "\n",
        "$$CSI = \\frac{Hit}{Hit + FA + Miss}$$\n",
        "\n",
        "##### Perice Skill Score (PSS)\n",
        "$$PSS = \\frac{(Hit \\times CR) - (FA \\times Miss)}{(FA + CR) \\times (Hit + Miss)}$$\n",
        "\n",
        "##### Heidke Skill Score (HSS)\n",
        "$$HSS = \\frac{2 \\times ((Hit \\times CR) - (FA \\times Miss))}{((Hit + Miss) \\times (Miss + CR)) + ((Hit + FA) \\times (FA + CR))}$$\n",
        "\n",
        "##### Clayton Skill Score (CSS)\n",
        "$$CSS = \\frac{(Hit \\times CR) - (FA \\times Miss)}{(Hit + FA) \\times (Miss + CR)}$$"
      ],
      "metadata": {
        "id": "jrT415JHYF-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_binary_classification(y_true, y_pred):\n",
        "    \"\"\"\n",
        "    Evaluate binary classification performance by calculating the confusion matrix,\n",
        "    accuracy, precision, recall, and F1 score.\n",
        "\n",
        "    Parameters:\n",
        "    y_true (list or array): Actual true labels.\n",
        "    y_pred (list or array): Predicted labels.\n",
        "\n",
        "    Returns:\n",
        "    dict: A dictionary containing the confusion matrix, accuracy, precision, recall, and F1 score.\n",
        "    \"\"\"\n",
        "    # Calculate the elements of the confusion matrix\n",
        "    TP = sum((y_true == 1) & (y_pred == 1))\n",
        "    TN = sum((y_true == 0) & (y_pred == 0))\n",
        "    FP = sum((y_true == 0) & (y_pred == 1))\n",
        "    FN = sum((y_true == 1) & (y_pred == 0))\n",
        "\n",
        "    # Calculate metrics\n",
        "    accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
        "    precision = TP / (TP + FP) if (TP + FP) != 0 else 0\n",
        "    recall = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
        "    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) != 0 else 0\n",
        "\n",
        "    # Compile results into a dictionary\n",
        "    results = {\n",
        "        'confusion_matrix': {'TP': TP, 'TN': TN, 'FP': FP, 'FN': FN},\n",
        "        'accuracy': accuracy,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'f1_score': f1_score\n",
        "    }\n",
        "\n",
        "    return results\n",
        "\n",
        "\n",
        "def evaluation_skill_metrics(y_true, y_pred):\n",
        "\n",
        "    # Calculate the elements of the confusion matrix\n",
        "    Hit = sum((y_true == 1) & (y_pred == 1))\n",
        "    CR = sum((y_true == 0) & (y_pred == 0))\n",
        "    FA = sum((y_true == 0) & (y_pred == 1))\n",
        "    MISS = sum((y_true == 1) & (y_pred == 0))\n",
        "\n",
        "\n",
        "    POD = Hit/(Hit+MISS)\n",
        "    F   = FA/(FA+CR)\n",
        "    FAR  = FA/(Hit+FA)\n",
        "    CSI = Hit/(Hit+FA+MISS)\n",
        "    PSS = ((Hit*CR)-(FA*MISS))/((FA+CR)*(Hit+MISS))\n",
        "    HSS = (2*((Hit*CR)-(FA*MISS)))/(((Hit+MISS)*(MISS+CR))+((Hit+FA)*(FA+CR)))\n",
        "    CSS = ((Hit*CR)-(FA*MISS))/((Hit+FA)*(MISS+CR))\n",
        "\n",
        "    # Compile results into a dictionary\n",
        "    results = {\n",
        "        'confusion_matrix': {'Hit': Hit, 'Miss': MISS, 'FA': FA, 'CR': CR},\n",
        "        'POD': POD,\n",
        "        'F': F,\n",
        "        'FAR': FAR,\n",
        "        'CSI': CSI,\n",
        "        'PSS': PSS,\n",
        "        'HSS': HSS,\n",
        "        'CSS': CSS,\n",
        "    }\n",
        "\n",
        "    return results"
      ],
      "metadata": {
        "id": "_5oac2VqUmKz"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Probabilistic Classification\n",
        "\n",
        "### Calibration\n",
        "\n",
        "Probabilities can be more informative than labels: <br>\n",
        "    - Tomorrow is rainy or Tomorrow is 80% rainy! <br>\n",
        "\n",
        "Calibration provides information on the trustworthiness of a model.\n",
        "\n",
        "### AUC-ROC Curve:\n",
        "\n",
        "ROC curve is a plot of true positive rate (recall) [it would be FAR in environmental case] against false positive rate (TN / (TN+FP)) [POD]. AUC-ROC stands for Area Under the Receiver Operating Characteristics and the higher the area, the better is the model performance.\n",
        "\n",
        "https://miro.medium.com/v2/resize:fit:1400/format:webp/0*vlaAZ0jKCoihMaI9.png\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://miro.medium.com/v2/resize:fit:1400/format:webp/0*vlaAZ0jKCoihMaI9.png\" alt=\"Alt text for the image\" width=\"500px\"/>\n",
        "        <figcaption> AUC-ROC. Figure Credit: <a href=\"https://learncuriously.wordpress.com/2018/10/21/confused-by-the-confusion-matrix/\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "### Reliability Diagram\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://confluence.ecmwf.int/download/attachments/340763675/Sample%20ROC%20Reliability%20Wk1.png?version=1&modificationDate=1688631863383&api=v2\" alt=\"Alt text for the image\" width=\"1000px\" />\n",
        "        <figcaption>Reliability Diagram. Figure Credit: <a href=\"https://twitter.com/jfd118/status/1540295108138094594/photo/1\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>"
      ],
      "metadata": {
        "id": "wr1Xb5U5ZAzS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calc_pod_far(y_true, y_pred):\n",
        "    # Initialize variables\n",
        "    pods = []\n",
        "    fars = []\n",
        "    thresholds = np.arange(0, 1, 0.001)\n",
        "\n",
        "    # Convert to numpy arrays if not already\n",
        "    y_true = np.array(y_true)\n",
        "    y_pred = np.array(y_pred)\n",
        "\n",
        "    for i in thresholds:\n",
        "        pred_classes = y_pred >= i\n",
        "        # Calculate the elements of the confusion matrix\n",
        "        Hit = np.sum((y_true == 1) & (pred_classes == 1))\n",
        "        CR = np.sum((y_true == 0) & (pred_classes == 0))\n",
        "        FA = np.sum((y_true == 0) & (pred_classes == 1))\n",
        "        MISS = np.sum((y_true == 1) & (pred_classes == 0))\n",
        "\n",
        "        POD = Hit / (Hit + MISS) if (Hit + MISS) != 0 else 0\n",
        "        F = FA / (FA + CR) if (FA + CR) != 0 else 0\n",
        "        pods.append(POD)\n",
        "        fars.append(F)\n",
        "\n",
        "    return pods, fars\n",
        "\n",
        "def roc_curve(y_true, y_pred):\n",
        "\n",
        "    PODs, FARs = calc_pod_far(y_true, y_pred)\n",
        "\n",
        "    roc_data = {\"POD\": PODs, \"FAR\": FARs}\n",
        "    roc_df_logit = pd.DataFrame(roc_data).sort_values(by = [\"POD\", \"FAR\"])\n",
        "\n",
        "    ROC_AUC = np.trapz(roc_df_logit[\"POD\"], roc_df_logit[\"FAR\"])\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(10,6))\n",
        "\n",
        "    ax.plot(roc_df_logit[\"FAR\"], roc_df_logit[\"POD\"], linewidth=3,)\n",
        "    ax.plot([0,1], [0,1],  linestyle='--', color='k')\n",
        "\n",
        "    ax.grid()\n",
        "    ax.legend(loc='lower right')\n",
        "    ax.set_xlim([-0.005, 1.0])\n",
        "    ax.set_ylim([0.0, 1.005])\n",
        "    ax.set_xlabel('Probability of false detection (FAR)',  fontsize=14)\n",
        "    ax.set_ylabel('Probability of detection (POD)', fontsize=14)\n",
        "    ax.set_title(f'AUC: {ROC_AUC:0.3f}')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "wqs2p2q9Saf6"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def reliability_diagram(y_true, y_pred):\n",
        "\n",
        "    # labels and probabilities\n",
        "    observed_labels =y_true\n",
        "    forecast_probabilities = y_pred\n",
        "    # check the format and input size\n",
        "    assert np.all(np.logical_or(\n",
        "        observed_labels == 0, observed_labels == 1\n",
        "    ))\n",
        "\n",
        "    assert np.all(np.logical_and(\n",
        "        forecast_probabilities >= 0, forecast_probabilities <= 1\n",
        "    ))\n",
        "    num_bins = 10\n",
        "    assert num_bins > 1\n",
        "\n",
        "    # inputs_to_bins = _get_histogram(\n",
        "    #     input_values=forecast_probabilities, num_bins=num_bins, min_value=0.,\n",
        "    #     max_value=1.)\n",
        "\n",
        "    bin_cutoffs = np.linspace(0., 1., num=num_bins + 1)\n",
        "\n",
        "    inputs_to_bins = np.digitize(\n",
        "        forecast_probabilities, bin_cutoffs, right=False\n",
        "    ) - 1\n",
        "\n",
        "    inputs_to_bins[inputs_to_bins < 0] = 0\n",
        "    inputs_to_bins[inputs_to_bins > num_bins - 1] = num_bins - 1\n",
        "\n",
        "    mean_forecast_probs = np.full(num_bins, np.nan)\n",
        "    mean_event_frequencies = np.full(num_bins, np.nan)\n",
        "    num_examples_by_bin = np.full(num_bins, -1, dtype=int)\n",
        "\n",
        "    for k in range(num_bins):\n",
        "        these_example_indices = np.where(inputs_to_bins == k)[0]\n",
        "        num_examples_by_bin[k] = len(these_example_indices)\n",
        "\n",
        "        mean_forecast_probs[k] = np.mean(\n",
        "            forecast_probabilities[these_example_indices])\n",
        "\n",
        "        mean_event_frequencies[k] = np.mean(\n",
        "            observed_labels[these_example_indices].astype(float)\n",
        "        )\n",
        "\n",
        "    fig, axes_object = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(6, 6),\n",
        "                        gridspec_kw={\"height_ratios\": [4, 1]})\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.subplots_adjust(hspace=0.03)\n",
        "\n",
        "    perfect_x_coords = np.array([0, 1], dtype=float)\n",
        "    perfect_y_coords = perfect_x_coords + 0.\n",
        "    axes_object[0].plot(\n",
        "        perfect_x_coords, perfect_y_coords, color='gray',\n",
        "        linestyle='dashed', linewidth=3)\n",
        "\n",
        "    real_indices = np.where(np.invert(np.logical_or(\n",
        "        np.isnan(mean_forecast_probs), np.isnan(mean_event_frequencies)\n",
        "    )))[0]\n",
        "\n",
        "    axes_object[0].plot(\n",
        "        mean_forecast_probs[real_indices], mean_event_frequencies[real_indices],\n",
        "        color='b',\n",
        "        linestyle='solid', linewidth=1)\n",
        "\n",
        "    accuracies = mean_event_frequencies\n",
        "    confidences = mean_forecast_probs\n",
        "    counts = num_examples_by_bin\n",
        "    bin_cutoffs = np.linspace(0, 1, num=10 + 1)\n",
        "    bins = bin_cutoffs\n",
        "\n",
        "    bin_size = 1.0 / len(counts)\n",
        "    positions = bins[:-1] + bin_size/2.0\n",
        "\n",
        "\n",
        "    colors = np.zeros((10, 4))\n",
        "    colors[:, 0] = 240 / 255.\n",
        "    colors[:, 1] = 60 / 255.\n",
        "    colors[:, 2] = 60 / 255.\n",
        "    colors[:, 3] = 0.2\n",
        "\n",
        "    gap_plt = axes_object[0].bar(positions, np.abs(accuracies - confidences),\n",
        "                        bottom=np.minimum(accuracies, confidences), width=0.1,\n",
        "                        edgecolor=colors, color=colors, linewidth=1, label=\"Gap\")\n",
        "\n",
        "    acc_plt = axes_object[0].bar(positions, 0, bottom=accuracies, width=0.1,\n",
        "                        edgecolor=\"black\", color=\"black\", alpha=1.0, linewidth=3,\n",
        "                        label=\"Accuracy\")\n",
        "\n",
        "    # axes_object[0].set_xlabel('Forecast probability')\n",
        "    axes_object[0].set_ylabel('Expected frequency')\n",
        "    axes_object[0].set_xlim(0., 1.)\n",
        "    axes_object[0].set_ylim(0., 1.)\n",
        "    axes_object[0].legend(handles=[gap_plt, acc_plt])\n",
        "\n",
        "    orig_counts = counts\n",
        "    bin_data = -orig_counts\n",
        "    axes_object[1].bar(positions, bin_data, width=bin_size * 0.9)\n",
        "\n",
        "    axes_object[1].set_xlim(0, 1)\n",
        "    # axes_object[1].set_yscale('log')\n",
        "    axes_object[1].set_xlabel('Forecast probability')\n",
        "    axes_object[1].set_ylabel('Count')\n",
        "\n",
        "    # Also negate the ticks for the upside-down histogram.\n",
        "    new_ticks = np.abs(axes_object[1].get_yticks())\n",
        "    axes_object[1].set_yticklabels(new_ticks)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "C9Er-tH-SU1L"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Binarize the 'temperature' column\n",
        "subsample_label_class = pd.DataFrame()\n",
        "subsample_label_class['class'] = np.where(subsample_label > 100, 1, 0)\n",
        "\n",
        "# Binarize the 'temperature' column and label classes\n",
        "subsample_label_class['class_name'] = np.where(subsample_label > 100, 'Severe Days', 'Common Days')"
      ],
      "metadata": {
        "id": "Un2BEo-5WilG"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the frequency of each class\n",
        "class_counts = subsample_label_class['class_name'].value_counts()\n",
        "\n",
        "# Plotting\n",
        "plt.bar(class_counts.index, class_counts.values)\n",
        "\n",
        "# Adding count labels on top of each bar\n",
        "for index, value in enumerate(class_counts.values):\n",
        "    plt.text(index, value, str(value), ha='center')\n",
        "plt.title('Temperature Classification')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Da5XJcrsbbT2",
        "outputId": "1b6f59ef-0576-42e7-9471-9e8ba9ce0fa2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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t+PHmxx/lDg4OFhYWFiIlJUX4+voKc3Nz4eDgIGbOnFniUev/WpMQQmzZskU0bdpUmJqaCldXVzFv3jyxatWqEo/Mq9Vq4e/vL6ysrAQArUfw4+PjRatWrYSJiYmoVauWWLRo0VMfu39aHXfv3hVTp04VdevWFSYmJsLW1la0adNGfPnllyI/P7/UfZ60YcMG4evrK6pWrSqMjIxE9erVxTvvvCNiY2OlPqU9dn/27Fnh4+MjLC0tha2trRgxYoQ4efKk1qPnN2/eFCEhIcLd3V1YWFgIa2tr0apVK/Hbb79J4xw/fly8++67olatWkKpVAp7e3vRs2dPcezYMa06UcbH7outWrVKvPbaa0KpVIoqVaqIDh06iOjoaGn7gQMHROvWrYWZmZlwcnISkydPlt764fHzzsnJEQMGDBA2NjYCgPQIfmmP3QshxO7du4W3t7cwMzMTKpVKBAQEiLNnz5Zac/HbQxQr7f8DkS7ws8yIXlGDBw/Ghg0bkJOTo+9SiIgqPc4hIiIiItljICIiIiLZYyAiIiIi2eMcIiIiIpI9XiEiIiIi2WMgIiIiItnjGzM+h6KiIqSnp8PKyuq536KeiIiI9EsIgbt378LJyQkGBs++BsRA9BzS09NLfLo4ERERvRyuXr2KmjVrPrMPA9FzsLKyAvDoC6pSqfRcDRERET0PjUYDZ2dn6ff4szAQPYfi22QqlYqBiIiI6CXzPNNdOKmaiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYheOWFhYXj99ddhZWUFe3t79O7dG0lJSVp9UlJS0KdPH9jZ2UGlUqFfv37IzMyUtsfGxkKhUJS6HD16VOonhMCXX36J+vXrQ6lUokaNGvjss88q7FyJiEg3GIjolbNv3z6EhITg0KFDiI6ORkFBAXx9fZGbmwsAyM3Nha+vLxQKBWJiYnDgwAHk5+cjICAARUVFAIA2bdogIyNDaxk+fDjc3NzQsmVL6VgfffQRfvzxR3z55Zc4f/48tmzZgjfeeEMv501ERGXHT7t/DhqNBtbW1sjOzub7EL2Ebty4AXt7e+zbtw/t27fHrl270L17d9y5c0f6fmZnZ6NKlSrYtWsXfHx8SoxRUFCAGjVqYMyYMfjkk08AAOfOnUPTpk1x5swZNGjQoELPiYiI/t2L/P7W6xWi5cuXo2nTptIbHnp5eWHHjh3S9gcPHiAkJATVqlWDpaUlAgMDtW5rAEBaWhr8/f1hbm4Oe3t7TJo0CQ8fPtTqExsbixYtWkCpVKJu3bqIiIioiNOjSiI7OxsAULVqVQBAXl4eFAoFlEql1MfU1BQGBgb4+++/Sx1jy5YtuHXrFoYMGSK1bd26FbVr18a2bdvg5uYGV1dXDB8+HLdv3y7HsyEiovKg10BUs2ZNfPHFF4iPj8exY8fQuXNn9OrVC4mJiQCAcePGYevWrYiMjMS+ffuQnp6Ovn37SvsXFhbC398f+fn5OHjwIFavXo2IiAjMmDFD6pOamgp/f3906tQJCQkJGDt2LIYPH46oqKgKP1+qeEVFRRg7diy8vb3RuHFjAEDr1q1hYWGBKVOm4N69e8jNzcXEiRNRWFiIjIyMUsdZuXIl/Pz8tD4L59KlS7hy5QoiIyPx008/ISIiAvHx8Xjrrbcq5NyIiEiHRCVTpUoV8eOPP4qsrCxhbGwsIiMjpW3nzp0TAERcXJwQQog///xTGBgYCLVaLfVZvny5UKlUIi8vTwghxOTJk0WjRo20jvHOO+8IPz+/564pOztbABDZ2dn/5dRID0aNGiVcXFzE1atXtdqjoqJE7dq1hUKhEIaGhmLQoEGiRYsWYtSoUSXGuHr1qjAwMBAbNmzQah8xYoQAIJKSkqS2+Ph4AUCcP3++fE6IiIie24v8/q40k6oLCwuxbt065ObmwsvLC/Hx8SgoKNCaz+Hu7o5atWohLi4OABAXF4cmTZrAwcFB6uPn5weNRiNdZYqLiysxJ8TPz08aozR5eXnQaDRaC718QkNDsW3bNuzdu7fEpxz7+voiJSUF169fx82bN/Hzzz/jn3/+Qe3atUuMEx4ejmrVquHNN9/Uaq9evTqMjIxQv359qa1hw4YAHt3KJSKil4feA9Hp06dhaWkJpVKJUaNGYdOmTfDw8IBarYaJiQlsbGy0+js4OECtVgMA1Gq1Vhgq3l687Vl9NBoN7t+/X2pNYWFhsLa2lhZnZ2ddnCpVECEEQkNDsWnTJsTExMDNze2pfW1tbWFjY4OYmBhcv369ROgRQiA8PBxBQUEwNjbW2ubt7Y2HDx8iJSVFartw4QIAwMXFRYdnRERE5U3vgahBgwZISEjA4cOHMXr0aAQHB+Ps2bN6rWnq1KnIzs6WlqtXr+q1HnoxISEhWLNmDdauXQsrKyuo1Wqo1WqtABweHo5Dhw4hJSUFa9aswdtvv41x48aVeFosJiYGqampGD58eInj+Pj4oEWLFhg6dChOnDiB+Ph4jBw5El27dtW6akRERJWfkb4LMDExQd26dQEAnp6eOHr0KJYsWYJ33nkH+fn5yMrK0rpKlJmZCUdHRwCAo6Mjjhw5ojVe8VNoj/d58sm0zMxMqFQqmJmZlVqTUqnUegKJXi7Lly8HAHTs2FGrPTw8HIMHDwYAJCUlYerUqbh9+zZcXV0xbdo0jBs3rsRYK1euRJs2beDu7l5im4GBAbZu3YoxY8agffv2sLCwQPfu3bFw4UKdnxMREZWvSvc+RJ07d0atWrWwZMkS2NnZ4ddff0VgYCCAR7/E3N3dERcXh9atW2PHjh3o2bMnMjIyYG9vDwD4/vvvMWnSJFy/fh1KpRJTpkzBn3/+idOnT0vHGDBgAG7fvo2dO3c+V03l/T5Erh9v1/mYRK+Ky1/467sEInpJvcjvb71eIZo6dSq6d++OWrVq4e7du1i7di1iY2MRFRUFa2trDBs2DOPHj0fVqlWhUqkwZswYeHl5oXXr1gAeTYz18PDAe++9h/nz50OtVmP69OkICQmRrvCMGjUK3377LSZPnoyhQ4ciJiYGv/32G7ZvZwghIiKiR/QaiK5fv46goCBkZGTA2toaTZs2RVRUFLp27QoAWLx4MQwMDBAYGIi8vDz4+flh2bJl0v6GhobYtm0bRo8eDS8vL1hYWCA4OBhz5syR+ri5uWH79u0YN24clixZgpo1a+LHH3+En59fhZ8vERERVU6V7pZZZcRbZkT6w1tmRFRWL81HdxARERFVBgxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7eg1EYWFheP3112FlZQV7e3v07t0bSUlJWn06duwIhUKhtYwaNUqrT1paGvz9/WFubg57e3tMmjQJDx8+1OoTGxuLFi1aQKlUom7duoiIiCjv0yMiIqKXhF4D0b59+xASEoJDhw4hOjoaBQUF8PX1RW5urla/ESNGICMjQ1rmz58vbSssLIS/vz/y8/Nx8OBBrF69GhEREZgxY4bUJzU1Ff7+/ujUqRMSEhIwduxYDB8+HFFRURV2rkRERFR5Genz4Dt37tRaj4iIgL29PeLj49G+fXup3dzcHI6OjqWOsWvXLpw9exa7d++Gg4MDmjdvjrlz52LKlCmYNWsWTExMsGLFCri5uWHhwoUAgIYNG+Lvv//G4sWL4efnV34nSERERC+FSjWHKDs7GwBQtWpVrfZffvkFtra2aNy4MaZOnYp79+5J2+Li4tCkSRM4ODhIbX5+ftBoNEhMTJT6+Pj4aI3p5+eHuLi4UuvIy8uDRqPRWoiIiOjVpdcrRI8rKirC2LFj4e3tjcaNG0vtAwYMgIuLC5ycnHDq1ClMmTIFSUlJ2LhxIwBArVZrhSEA0rparX5mH41Gg/v378PMzExrW1hYGGbPnq3zcyQiIqLKqdIEopCQEJw5cwZ///23Vvv7778v/btJkyaoXr06unTpgpSUFNSpU6dcapk6dSrGjx8vrWs0Gjg7O5fLsYiIiEj/KsUts9DQUGzbtg179+5FzZo1n9m3VatWAICLFy8CABwdHZGZmanVp3i9eN7R0/qoVKoSV4cAQKlUQqVSaS1ERET06tJrIBJCIDQ0FJs2bUJMTAzc3Nz+dZ+EhAQAQPXq1QEAXl5eOH36NK5fvy71iY6OhkqlgoeHh9Rnz549WuNER0fDy8tLR2dCRERELzO9BqKQkBCsWbMGa9euhZWVFdRqNdRqNe7fvw8ASElJwdy5cxEfH4/Lly9jy5YtCAoKQvv27dG0aVMAgK+vLzw8PPDee+/h5MmTiIqKwvTp0xESEgKlUgkAGDVqFC5duoTJkyfj/PnzWLZsGX777TeMGzdOb+dORERElYdeA9Hy5cuRnZ2Njh07onr16tKyfv16AICJiQl2794NX19fuLu7Y8KECQgMDMTWrVulMQwNDbFt2zYYGhrCy8sLgwYNQlBQEObMmSP1cXNzw/bt2xEdHY1mzZph4cKF+PHHH/nIPREREQEAFEIIoe8iKjuNRgNra2tkZ2eXy3wi14+363xMolfF5S/89V0CEb2kXuT3d6WYVE1ERESkTwxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7eg1EYWFheP3112FlZQV7e3v07t0bSUlJWn0ePHiAkJAQVKtWDZaWlggMDERmZqZWn7S0NPj7+8Pc3Bz29vaYNGkSHj58qNUnNjYWLVq0gFKpRN26dREREVHep0dEREQvCb0Gon379iEkJASHDh1CdHQ0CgoK4Ovri9zcXKnPuHHjsHXrVkRGRmLfvn1IT09H3759pe2FhYXw9/dHfn4+Dh48iNWrVyMiIgIzZsyQ+qSmpsLf3x+dOnVCQkICxo4di+HDhyMqKqpCz5eIiIgqJ4UQQui7iGI3btyAvb099u3bh/bt2yM7Oxt2dnZYu3Yt3nrrLQDA+fPn0bBhQ8TFxaF169bYsWMHevbsifT0dDg4OAAAVqxYgSlTpuDGjRswMTHBlClTsH37dpw5c0Y6Vv/+/ZGVlYWdO3f+a10ajQbW1tbIzs6GSqXS+Xm7frxd52MSvSouf+Gv7xKI6CX1Ir+/K9UcouzsbABA1apVAQDx8fEoKCiAj4+P1Mfd3R21atVCXFwcACAuLg5NmjSRwhAA+Pn5QaPRIDExUerz+BjFfYrHeFJeXh40Go3WQkRERK+uShOIioqKMHbsWHh7e6Nx48YAALVaDRMTE9jY2Gj1dXBwgFqtlvo8HoaKtxdve1YfjUaD+/fvl6glLCwM1tbW0uLs7KyTcyQiIqLKqdIEopCQEJw5cwbr1q3TdymYOnUqsrOzpeXq1av6LomIiIjKkZG+CwCA0NBQbNu2Dfv370fNmjWldkdHR+Tn5yMrK0vrKlFmZiYcHR2lPkeOHNEar/gptMf7PPlkWmZmJlQqFczMzErUo1QqoVQqdXJuREREVPnp9QqREAKhoaHYtGkTYmJi4ObmprXd09MTxsbG2LNnj9SWlJSEtLQ0eHl5AQC8vLxw+vRpXL9+XeoTHR0NlUoFDw8Pqc/jYxT3KR6DiIiI5E2vV4hCQkKwdu1a/PHHH7CyspLm/FhbW8PMzAzW1tYYNmwYxo8fj6pVq0KlUmHMmDHw8vJC69atAQC+vr7w8PDAe++9h/nz50OtVmP69OkICQmRrvKMGjUK3377LSZPnoyhQ4ciJiYGv/32G7Zv59NdREREpOcrRMuXL0d2djY6duyI6tWrS8v69eulPosXL0bPnj0RGBiI9u3bw9HRERs3bpS2GxoaYtu2bTA0NISXlxcGDRqEoKAgzJkzR+rj5uaG7du3Izo6Gs2aNcPChQvx448/ws/Pr0LPl4iIiCqnSvU+RJUV34eISH/4PkREVFYv7fsQEREREekDAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyV6ZAtGlS5d0XQcRERGR3pQpENWtWxedOnXCmjVr8ODBA13XRERERFShyhSIjh8/jqZNm2L8+PFwdHTEyJEjceTIEV3XRkRERFQhyhSImjdvjiVLliA9PR2rVq1CRkYG2rZti8aNG2PRokW4ceOGruskIiIiKjf/aVK1kZER+vbti8jISMybNw8XL17ExIkT4ezsjKCgIGRkZOiqTiIiIqJy858C0bFjx/DBBx+gevXqWLRoESZOnIiUlBRER0cjPT0dvXr10lWdREREROXGqCw7LVq0COHh4UhKSkKPHj3w008/oUePHjAweJSv3NzcEBERAVdXV13WSkRERFQuyhSIli9fjqFDh2Lw4MGoXr16qX3s7e2xcuXK/1QcERERUUUoUyBKTk7+1z4mJiYIDg4uy/BEREREFapMc4jCw8MRGRlZoj0yMhKrV6/+z0URERERVaQyBaKwsDDY2tqWaLe3t8fnn3/+n4siIiIiqkhlCkRpaWlwc3Mr0e7i4oK0tLT/XBQRERFRRSpTILK3t8epU6dKtJ88eRLVqlX7z0URERERVaQyBaJ3330XH374Ifbu3YvCwkIUFhYiJiYGH330Efr376/rGomIiIjKVZmeMps7dy4uX76MLl26wMjo0RBFRUUICgriHCIiIiJ66ZQpEJmYmGD9+vWYO3cuTp48CTMzMzRp0gQuLi66ro+IiIio3JUpEBWrX78+6tevr6taiIiIiPSiTIGosLAQERER2LNnD65fv46ioiKt7TExMTopjoiIiKgilCkQffTRR4iIiIC/vz8aN24MhUKh67qIiIiIKkyZAtG6devw22+/oUePHrquh4iIiKjClemxexMTE9StW1fXtRARERHpRZkC0YQJE7BkyRIIIXRdDxEREVGFK9Mts7///ht79+7Fjh070KhRIxgbG2tt37hxo06KIyIiIqoIZQpENjY26NOnj65rISIiItKLMgWi8PBwXddBREREpDdlmkMEAA8fPsTu3bvx3Xff4e7duwCA9PR05OTk6Kw4IiIioopQpitEV65cQbdu3ZCWloa8vDx07doVVlZWmDdvHvLy8rBixQpd10lERERUbsp0heijjz5Cy5YtcefOHZiZmUntffr0wZ49e3RWHBEREVFFKNMVor/++gsHDx6EiYmJVrurqyv++ecfnRRGREREVFHKdIWoqKgIhYWFJdqvXbsGKyur/1wUERERUUUqUyDy9fXFV199Ja0rFArk5ORg5syZ/DgPIiIieumU6ZbZwoUL4efnBw8PDzx48AADBgxAcnIybG1t8euvv+q6RiIiIqJyVaZAVLNmTZw8eRLr1q3DqVOnkJOTg2HDhmHgwIFak6yJiIiIXgZlCkQAYGRkhEGDBumyFiIiIiK9KFMg+umnn565PSgoqEzFEBEREelDmQLRRx99pLVeUFCAe/fuwcTEBObm5gxERERE9FIp01Nmd+7c0VpycnKQlJSEtm3bvtCk6v379yMgIABOTk5QKBTYvHmz1vbBgwdDoVBoLd26ddPqc/v2bQwcOBAqlQo2NjYYNmxYiY8POXXqFNq1awdTU1M4Oztj/vz5ZTltIiIiekWV+bPMnlSvXj188cUXJa4ePUtubi6aNWuGpUuXPrVPt27dkJGRIS1PBq6BAwciMTER0dHR2LZtG/bv34/3339f2q7RaODr6wsXFxfEx8djwYIFmDVrFr7//vsXP0kiIiJ6JZV5UnWpgxkZIT09/bn7d+/eHd27d39mH6VSCUdHx1K3nTt3Djt37sTRo0fRsmVLAMA333yDHj164Msvv4STkxN++eUX5OfnY9WqVTAxMUGjRo2QkJCARYsWaQUnIiIikq8yBaItW7ZorQshkJGRgW+//Rbe3t46KaxYbGws7O3tUaVKFXTu3BmffvopqlWrBgCIi4uDjY2NFIYAwMfHBwYGBjh8+DD69OmDuLg4tG/fXutjRvz8/DBv3jzcuXMHVapUKXHMvLw85OXlSesajUan50RERESVS5kCUe/evbXWFQoF7Ozs0LlzZyxcuFAXdQF4dLusb9++cHNzQ0pKCv73v/+he/fuiIuLg6GhIdRqNezt7bX2MTIyQtWqVaFWqwEAarUabm5uWn0cHBykbaUForCwMMyePVtn50FERESVW5kCUVFRka7rKFX//v2lfzdp0gRNmzZFnTp1EBsbiy5dupTbcadOnYrx48dL6xqNBs7OzuV2PCIiItIvnU2qrgi1a9eGra0tLl68CABwdHTE9evXtfo8fPgQt2/fluYdOTo6IjMzU6tP8frT5iYplUqoVCqthYiIiF5dZbpC9PjVk3+zaNGishyiVNeuXcOtW7dQvXp1AICXlxeysrIQHx8PT09PAEBMTAyKiorQqlUrqc+0adNQUFAAY2NjAEB0dDQaNGhQ6u0yIiIikp8yBaITJ07gxIkTKCgoQIMGDQAAFy5cgKGhIVq0aCH1UygUzxwnJydHutoDAKmpqUhISEDVqlVRtWpVzJ49G4GBgXB0dERKSgomT56MunXrws/PDwDQsGFDdOvWDSNGjMCKFStQUFCA0NBQ9O/fH05OTgCAAQMGYPbs2Rg2bBimTJmCM2fOYMmSJVi8eHFZTp2IiIheQWUKRAEBAbCyssLq1aulqyx37tzBkCFD0K5dO0yYMOG5xjl27Bg6deokrRdfeQoODsby5ctx6tQprF69GllZWXBycoKvry/mzp0LpVIp7fPLL78gNDQUXbp0gYGBAQIDA/H1119L262trbFr1y6EhITA09MTtra2mDFjBh+5JyIiIolCCCFedKcaNWpg165daNSokVb7mTNn4Ovr+0LvRfQy0Gg0sLa2RnZ2drnMJ3L9eLvOxyR6VVz+wl/fJRDRS+pFfn+XaVK1RqPBjRs3SrTfuHEDd+/eLcuQRERERHpTpkDUp08fDBkyBBs3bsS1a9dw7do1/P777xg2bBj69u2r6xqJiIiIylWZ5hCtWLECEydOxIABA1BQUPBoICMjDBs2DAsWLNBpgURERETlrUyByNzcHMuWLcOCBQuQkpICAKhTpw4sLCx0WhwRERFRRfhPb8xY/An09erVg4WFBcowP5uIiIhI78oUiG7duoUuXbqgfv366NGjBzIyMgAAw4YNe+5H7omIiIgqizIFonHjxsHY2BhpaWkwNzeX2t955x3s3LlTZ8URERERVYQyzSHatWsXoqKiULNmTa32evXq4cqVKzopjIiIiKiilOkKUW5urtaVoWK3b9/WehdpIiIiopdBmQJRu3bt8NNPP0nrCoUCRUVFmD9/vtZHcRARERG9DMp0y2z+/Pno0qULjh07hvz8fEyePBmJiYm4ffs2Dhw4oOsaiYiIiMpVma4QNW7cGBcuXEDbtm3Rq1cv5Obmom/fvjhx4gTq1Kmj6xqJiIiIytULXyEqKChAt27dsGLFCkybNq08aiIiIiKqUC98hcjY2BinTp0qj1qIiIiI9KJMt8wGDRqElStX6roWIiIiIr0o06Tqhw8fYtWqVdi9ezc8PT1LfIbZokWLdFIcERERUUV4oUB06dIluLq64syZM2jRogUA4MKFC1p9FAqF7qojIiIiqgAvFIjq1auHjIwM7N27F8Cjj+r4+uuv4eDgUC7FEREREVWEF5pD9OSn2e/YsQO5ubk6LYiIiIioopVpUnWxJwMSERER0cvohQKRQqEoMUeIc4aIiIjoZfdCc4iEEBg8eLD0Aa4PHjzAqFGjSjxltnHjRt1VSERERFTOXigQBQcHa60PGjRIp8UQERER6cMLBaLw8PDyqoOIiIhIb/7TpGoiIiKiVwEDEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJnl4D0f79+xEQEAAnJycoFAps3rxZa7sQAjNmzED16tVhZmYGHx8fJCcna/W5ffs2Bg4cCJVKBRsbGwwbNgw5OTlafU6dOoV27drB1NQUzs7OmD9/fnmfGhEREb1E9BqIcnNz0axZMyxdurTU7fPnz8fXX3+NFStW4PDhw7CwsICfnx8ePHgg9Rk4cCASExMRHR2Nbdu2Yf/+/Xj//fel7RqNBr6+vnBxcUF8fDwWLFiAWbNm4fvvvy/38yMiIqKXg0IIIfRdBAAoFAps2rQJvXv3BvDo6pCTkxMmTJiAiRMnAgCys7Ph4OCAiIgI9O/fH+fOnYOHhweOHj2Kli1bAgB27tyJHj164Nq1a3BycsLy5csxbdo0qNVqmJiYAAA+/vhjbN68GefPn3+u2jQaDaytrZGdnQ2VSqXzc3f9eLvOxyR6VVz+wl/fJRDRS+pFfn9X2jlEqampUKvV8PHxkdqsra3RqlUrxMXFAQDi4uJgY2MjhSEA8PHxgYGBAQ4fPiz1ad++vRSGAMDPzw9JSUm4c+dOqcfOy8uDRqPRWoiIiOjVVWkDkVqtBgA4ODhotTs4OEjb1Go17O3ttbYbGRmhatWqWn1KG+PxYzwpLCwM1tbW0uLs7PzfT4iIiIgqrUobiPRp6tSpyM7OlparV6/quyQiIiIqR5U2EDk6OgIAMjMztdozMzOlbY6Ojrh+/brW9ocPH+L27dtafUob4/FjPEmpVEKlUmktRERE9OqqtIHIzc0Njo6O2LNnj9Sm0Whw+PBheHl5AQC8vLyQlZWF+Ph4qU9MTAyKiorQqlUrqc/+/ftRUFAg9YmOjkaDBg1QpUqVCjobIiIiqsz0GohycnKQkJCAhIQEAI8mUickJCAtLQ0KhQJjx47Fp59+ii1btuD06dMICgqCk5OT9CRaw4YN0a1bN4wYMQJHjhzBgQMHEBoaiv79+8PJyQkAMGDAAJiYmGDYsGFITEzE+vXrsWTJEowfP15PZ01ERESVjZE+D37s2DF06tRJWi8OKcHBwYiIiMDkyZORm5uL999/H1lZWWjbti127twJU1NTaZ9ffvkFoaGh6NKlCwwMDBAYGIivv/5a2m5tbY1du3YhJCQEnp6esLW1xYwZM7Teq4iIiIjkrdK8D1FlxvchItIfvg8REZXVK/E+REREREQVhYGIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSvUgeiWbNmQaFQaC3u7u7S9gcPHiAkJATVqlWDpaUlAgMDkZmZqTVGWloa/P39YW5uDnt7e0yaNAkPHz6s6FMhIiKiSsxI3wX8m0aNGmH37t3SupHR/5c8btw4bN++HZGRkbC2tkZoaCj69u2LAwcOAAAKCwvh7+8PR0dHHDx4EBkZGQgKCoKxsTE+//zzCj8XIiIiqpwqfSAyMjKCo6Njifbs7GysXLkSa9euRefOnQEA4eHhaNiwIQ4dOoTWrVtj165dOHv2LHbv3g0HBwc0b94cc+fOxZQpUzBr1iyYmJhU9OkQERFRJVSpb5kBQHJyMpycnFC7dm0MHDgQaWlpAID4+HgUFBTAx8dH6uvu7o5atWohLi4OABAXF4cmTZrAwcFB6uPn5weNRoPExMSKPREiIiKqtCr1FaJWrVohIiICDRo0QEZGBmbPno127drhzJkzUKvVMDExgY2NjdY+Dg4OUKvVAAC1Wq0Vhoq3F297mry8POTl5UnrGo1GR2dERERElVGlDkTdu3eX/t20aVO0atUKLi4u+O2332BmZlZuxw0LC8Ps2bPLbXwiIiKqXCr9LbPH2djYoH79+rh48SIcHR2Rn5+PrKwsrT6ZmZnSnCNHR8cST50Vr5c2L6nY1KlTkZ2dLS1Xr17V7YkQERFRpfJSBaKcnBykpKSgevXq8PT0hLGxMfbs2SNtT0pKQlpaGry8vAAAXl5eOH36NK5fvy71iY6OhkqlgoeHx1OPo1QqoVKptBYiIiJ6dVXqW2YTJ05EQEAAXFxckJ6ejpkzZ8LQ0BDvvvsurK2tMWzYMIwfPx5Vq1aFSqXCmDFj4OXlhdatWwMAfH194eHhgffeew/z58+HWq3G9OnTERISAqVSqeezIyIiosqiUgeia9eu4d1338WtW7dgZ2eHtm3b4tChQ7CzswMALF68GAYGBggMDEReXh78/PywbNkyaX9DQ0Ns27YNo0ePhpeXFywsLBAcHIw5c+bo65SIiIioElIIIYS+i6jsNBoNrK2tkZ2dXS63z1w/3q7zMYleFZe/8Nd3CUT0knqR398v1RwiIiIiovLAQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BERESvhP379yMgIABOTk5QKBTYvHlziT7nzp3Dm2++CWtra1hYWOD1119HWloaAODy5ctQKBSlLpGRkRV8NlTRGIiIiOiVkJubi2bNmmHp0qWlbk9JSUHbtm3h7u6O2NhYnDp1Cp988glMTU0BAM7OzsjIyNBaZs+eDUtLS3Tv3r0iT4X0wEjfBRAREelC9+7dnxlcpk2bhh49emD+/PlSW506daR/GxoawtHRUWufTZs2oV+/frC0tNR9wVSp8AoRERG98oqKirB9+3bUr18ffn5+sLe3R6tWrUq9rVYsPj4eCQkJGDZsWMUVSnrDQERERK+869evIycnB1988QW6deuGXbt2oU+fPujbty/27dtX6j4rV65Ew4YN0aZNmwqulvSBt8yIiOiVV1RUBADo1asXxo0bBwBo3rw5Dh48iBUrVqBDhw5a/e/fv4+1a9fik08+qfBaST94hYiIiF55tra2MDIygoeHh1Z7w4YNpafMHrdhwwbcu3cPQUFBFVUi6RkDERERvfJMTEzw+uuvIykpSav9woULcHFxKdF/5cqVePPNN2FnZ1dRJZKe8ZYZERG9EnJycnDx4kVpPTU1FQkJCahatSpq1aqFSZMm4Z133kH79u3RqVMn7Ny5E1u3bkVsbKzWOBcvXsT+/fvx559/VvAZkD4xEBER0Svh2LFj6NSpk7Q+fvx4AEBwcDAiIiLQp08frFixAmFhYfjwww/RoEED/P7772jbtq3WOKtWrULNmjXh6+tbofWTfimEEELfRVR2Go0G1tbWyM7Ohkql0vn4rh9v1/mYRK+Ky1/467sEneDrnOjZyuO1/iK/vzmHiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkT1aBaOnSpXB1dYWpqSlatWqFI0eO6LskIiIiqgRkE4jWr1+P8ePHY+bMmTh+/DiaNWsGPz8/XL9+Xd+lERERkZ7JJhAtWrQII0aMwJAhQ+Dh4YEVK1bA3Nwcq1at0ndpREREpGeyCET5+fmIj4+Hj4+P1GZgYAAfHx/ExcXpsTIiIiKqDIz0XUBFuHnzJgoLC+Hg4KDV7uDggPPnz5fon5eXh7y8PGk9OzsbAKDRaMqlvqK8e+UyLtGroLxedxWNr3OiZyuP13rxmEKIf+0ri0D0osLCwjB79uwS7c7OznqohkjerL/SdwVEVBHK87V+9+5dWFtbP7OPLAKRra0tDA0NkZmZqdWemZkJR0fHEv2nTp2K8ePHS+tFRUW4ffs2qlWrBoVCUe71kv5oNBo4Ozvj6tWrUKlU+i6HiMoJX+vyIITA3bt34eTk9K99ZRGITExM4OnpiT179qB3794AHoWcPXv2IDQ0tER/pVIJpVKp1WZjY1MBlVJloVKp+EOSSAb4Wn/1/duVoWKyCEQAMH78eAQHB6Nly5Z444038NVXXyE3NxdDhgzRd2lERESkZ7IJRO+88w5u3LiBGTNmQK1Wo3nz5ti5c2eJidZEREQkP7IJRAAQGhpa6i0yomJKpRIzZ84sccuUiF4tfK3TkxTieZ5FIyIiInqFyeKNGYmIiIiehYGIiIiIZI+BiIiIiGSPgYiIiIhkj4GIdEqtVmPMmDGoXbs2lEolnJ2dERAQgD179ui7tArVsWNHKBQKKBQKKJVK1KhRAwEBAdi4caO+SyMqVzdu3MDo0aNRq1YtKJVKODo6ws/PDwcOHNB3aWUSGxsrvZYNDAxgbW2N1157DZMnT0ZGRoa+yyMdYiAinbl8+TI8PT0RExODBQsW4PTp09i5cyc6deqEkJAQfZdX4UaMGIGMjAykpKTg999/h4eHB/r374/3339f36URlZvAwECcOHECq1evxoULF7BlyxZ07NgRt27d0mtd+fn5/2n/pKQkpKen4+jRo5gyZQp2796Nxo0b4/Tp0zqqkPROEOlI9+7dRY0aNUROTk6JbXfu3JH+feXKFfHmm28KCwsLYWVlJd5++22hVqul7TNnzhTNmjUTK1euFM7OzsLCwkKMHj1aPHz4UMybN084ODgIOzs78emnn2odA4BYsWKF8Pf3F2ZmZsLd3V0cPHhQJCcniw4dOghzc3Ph5eUlLl68qLXfsmXLRO3atYWxsbGoX7+++Omnn0qM+8MPP4jevXsLMzMzUbduXfHHH38882vRoUMH8dFHH5VoX7VqlQAgoqOjpbbJkyeLevXqCTMzM+Hm5iamT58u8vPzhRBCpKamCoVCIY4ePao1zuLFi0WtWrVEYWGhuH37thgwYICwtbUVpqamom7dumLVqlXPrI+oPNy5c0cAELGxsf/ab9iwYcLW1lZYWVmJTp06iYSEBCGEEElJSQKAOHfunNY+ixYtErVr15bWT58+Lbp16yYsLCyEvb29GDRokLhx44a0vUOHDiIkJER89NFHolq1aqJjx47Ptd+T9u7dKwBo/QwTQoh79+6JBg0aCG9vb6ntyJEjwsfHR1SrVk2oVCrRvn17ER8fL20fMmSI8Pf31xonPz9f2NnZiR9//FEIIURkZKRo3LixMDU1FVWrVhVdunQp9Wcq6R4DEenErVu3hEKhEJ9//vkz+xUWFormzZuLtm3bimPHjolDhw4JT09P0aFDB6nPzJkzhaWlpXjrrbdEYmKi2LJlizAxMRF+fn5izJgx4vz581KwOHTokLQfAFGjRg2xfv16kZSUJHr37i1cXV1F586dxc6dO8XZs2dF69atRbdu3aR9Nm7cKIyNjcXSpUtFUlKSWLhwoTA0NBQxMTFa49asWVOsXbtWJCcniw8//FBYWlqKW7duPfU8nxaICgsLRZUqVcTo0aOltrlz54oDBw6I1NRUsWXLFuHg4CDmzZsnbe/atav44IMPtMZp2rSpmDFjhhBCiJCQENG8eXNx9OhRkZqaKqKjo8WWLVue+X0gKg8FBQXC0tJSjB07Vjx48OCp/Xx8fERAQIA4evSouHDhgpgwYYKoVq2a9Jpq2bKlmD59utY+np6eUtudO3eEnZ2dmDp1qjh37pw4fvy46Nq1q+jUqZPUv0OHDsLS0lJMmjRJnD9/Xpw/f/659nvS0wKREI/+MAEgMjMzhRBC7NmzR/z888/i3Llz4uzZs2LYsGHCwcFBaDQaIYQQBw4cEIaGhiI9PV0aY+PGjcLCwkLcvXtXpKenCyMjI7Fo0SKRmpoqTp06JZYuXSru3r37L1950gUGItKJw4cPCwBi48aNz+y3a9cuYWhoKNLS0qS2xMREAUAcOXJECPEoEJmbm0s/RIQQws/PT7i6uorCwkKprUGDBiIsLExaB6D1QzQuLk4AECtXrpTafv31V2Fqaiqtt2nTRowYMUKrxrffflv06NHjqePm5OQIAGLHjh1PPc+nBSIhhGjVqpXo3r37U/ddsGCB8PT0lNbXr18vqlSpIv2CiY+PFwqFQqSmpgohhAgICBBDhgx56nhEFWnDhg2iSpUqwtTUVLRp00ZMnTpVnDx5Utr+119/CZVKVSIw1alTR3z33XdCiEdBo06dOtK2J68azZ07V/j6+mrtf/XqVQFAJCUlCSEevQZfe+01rT7Ps9+TnhWIduzYIQCIw4cPl7pvYWGhsLKyElu3bpXaPDw8tP7gCQgIEIMHDxZCPHptAxCXL18udTwqX5xDRDohnvMNz8+dOwdnZ2c4OztLbR4eHrCxscG5c+ekNldXV1hZWUnrDg4O8PDwgIGBgVbb9evXtcZv2rSp1nYAaNKkiVbbgwcPoNFopHq8vb21xvD29taq5clxLSwsoFKpShz7eQkhoFAopPX169fD29sbjo6OsLS0xPTp05GWliZt7927NwwNDbFp0yYAQEREBDp16gRXV1cAwOjRo7Fu3To0b94ckydPxsGDB8tUF5EuBAYGIj09HVu2bEG3bt0QGxuLFi1aICIiAgBw8uRJ5OTkoFq1arC0tJSW1NRUpKSkAAD69++Py5cv49ChQwCAX375BS1atIC7u7s0xt69e7X2L95WPAYAeHp6atX2vPs9r+Kfe8Wv58zMTIwYMQL16tWDtbU1VCoVcnJytF7Pw4cPR3h4uNR/x44dGDp0KACgWbNm6NKlC5o0aYK3334bP/zwA+7cufPCdVHZMBCRTtSrVw8KhQLnz5/XyXjGxsZa6wqFotS2oqKip+5X/EOqtLYn9ytLPS86BgAUFhYiOTkZbm5uAIC4uDgMHDgQPXr0wLZt23DixAlMmzZNawKoiYkJgoKCEB4ejvz8fKxdu1b6AQoA3bt3x5UrVzBu3Dikp6ejS5cumDhx4gvXRqQrpqam6Nq1Kz755BMcPHgQgwcPxsyZMwEAOTk5qF69OhISErSWpKQkTJo0CQDg6OiIzp07Y+3atQCAtWvXYuDAgdL4OTk5CAgIKDFGcnIy2rdvL/WzsLDQqut593texX84Ff9xEhwcjISEBCxZsgQHDx5EQkICqlWrpvV6DgoKwqVLlxAXF4c1a9bAzc0N7dq1AwAYGhoiOjoaO3bsgIeHB7755hs0aNAAqampL1wbvTgGItKJqlWrws/PD0uXLkVubm6J7VlZWQCAhg0b4urVq7h69aq07ezZs8jKyoKHh0dFlStp2LBhiceBDxw4UG61rF69Gnfu3EFgYCAA4ODBg3BxccG0adPQsmVL1KtXD1euXCmx3/Dhw7F7924sW7YMDx8+RN++fbW229nZITg4GGvWrMFXX32F77//vlzqJyoLDw8P6edCixYtoFarYWRkhLp162ottra20j4DBw7E+vXrERcXh0uXLqF///7SthYtWiAxMRGurq4lxngyBD2urPuV5v79+/j+++/Rvn172NnZAXj0s+PDDz9Ejx490KhRIyiVSty8eVNrv2rVqqF3794IDw9HREQEhgwZorVdoVDA29sbs2fPxokTJ2BiYiJdHabyxUBEOrN06VIUFhbijTfewO+//47k5GScO3cOX3/9Nby8vAAAPj4+aNKkCQYOHIjjx4/jyJEjCAoKQocOHdCyZcsKr3nSpEmIiIjA8uXLkZycjEWLFmHjxo06ucJy7949qNVqXLt2DYcOHcKUKVMwatQojB49Gp06dQLw6MpaWloa1q1bh5SUFHz99del/vBr2LAhWrdujSlTpuDdd9+FmZmZtG3GjBn4448/cPHiRSQmJmLbtm1o2LDhf66f6EXdunULnTt3xpo1a3Dq1CmkpqYiMjIS8+fPR69evQA8+hng5eWF3r17Y9euXbh8+TIOHjyIadOm4dixY9JYffv2xd27d6XXi5OTk7QtJCQEt2/fxrvvvoujR48iJSUFUVFRGDJkCAoLC59aX1n3A4Dr169DrVYjOTkZ69atg7e3N27evInly5dLferVq4eff/4Z586dw+HDhzFw4ECt12qx4cOHY/Xq1Th37hyCg4Ol9sOHD+Pzzz/HsWPHkJaWho0bN+LGjRt8PVcUPc9holdMenq6CAkJES4uLsLExETUqFFDvPnmm2Lv3r1Sn+d97P5xwcHBolevXlptT05cBiA2bdokraempgoA4sSJE1JbaRMkn+ex+8fHFUIIa2trER4e/tSvQ4cOHQQAAUCYmJiI6tWri549e5Y66XzSpEmiWrVqwtLSUrzzzjti8eLFwtraukS/lStXak0+LzZ37lzRsGFDYWZmJqpWrSp69eolLl269NTaiMrLgwcPxMcffyxatGghrK2thbm5uWjQoIGYPn26uHfvntRPo9GIMWPGCCcnJ2FsbCycnZ3FwIEDtR62EEKIfv36CQClvo3EhQsXRJ8+fYSNjY30Nhtjx44VRUVFQoinP9jwb/s9qfhnBgChUCiElZWVaNasmZg0aZLIyMjQ6nv8+HHRsmVLYWpqKurVqyciIyOFi4uLWLx4sVa/oqIi4eLiovXwhhBCnD17Vvj5+Qk7OzuhVCpF/fr1xTfffPPUrzfplkKI55wNS0R6NXfuXERGRuLUqVP6LoWI/oOcnBzUqFED4eHhJW5/k/4Y6bsAInq2nJwcXL58Gd9++y0+/fRTfZdDRGVUVFSEmzdvYuHChbCxscGbb76p75LoMZxDRFTJhYaGwtPTEx07dtR6uoyIXi5paWlwcHDA2rVrsWrVKhgZ8ZpEZcJbZkRERCR7vEJEREREssdARERERLLHQERERESyx0BEREREssdARESyoFAosHnzZn2XQUSVFAMREb0S1Go1xowZg9q1a0OpVMLZ2RkBAQHYs2ePvksjopcA3wSBiF56ly9fhre3N2xsbLBgwQI0adIEBQUFiIqKQkhICM6fP6/vEomokuMVIiJ66X3wwQdQKBQ4cuQIAgMDUb9+fTRq1Ajjx4/HoUOHSt1nypQpqF+/PszNzVG7dm188sknKCgokLafPHkSnTp1gpWVFVQqFTw9PaUPH71y5QoCAgJQpUoVWFhYoFGjRvjzzz8r5FyJqHzwChERvdRu376NnTt34rPPPoOFhUWJ7TY2NqXuZ2VlhYiICDg5OeH06dMYMWIErKysMHnyZADAwIED8dprr2H58uUwNDREQkICjI2NATz61PT8/Hzs378fFhYWOHv2LCwtLcvtHImo/DEQEdFL7eLFixBCwN3d/YX2mz59uvRvV1dXTJw4EevWrZMCUVpaGiZNmiSNW69ePal/WloaAgMD0aRJEwBA7dq1/+tpEJGe8ZYZEb3UyvrpQ+vXr4e3tzccHR1haWmJ6dOnIy0tTdo+fvx4DB8+HD4+Pvjiiy+QkpIibfvwww/x6aefwtvbGzNnzsSpU6f+83kQkX4xEBHRS61evXpQKBQvNHE6Li4OAwcORI8ePbBt2zacOHEC06ZNQ35+vtRn1qxZSExMhL+/P2JiYuDh4YFNmzYBAIYPH45Lly7hvffew+nTp9GyZUt88803Oj83Iqo4/HBXInrpde/eHadPn0ZSUlKJeURZWVmwsbGBQqHApk2b0Lt3byxcuBDLli3TuuozfPhwbNiwAVlZWaUe491330Vubi62bNlSYtvUqVOxfft2XikieonxChERvfSWLl2KwsJCvPHGG/j999+RnJyMc+fO4euvv4aXl1eJ/vXq1UNaWhrWrVuHlJQUfP3119LVHwC4f/8+QkNDERsbiytXruDAgQM4evQoGjZsCAAYO3YsoqKikJqaiuPHj2Pv3r3SNiJ6OXFSNRG99GrXro3jx4/js88+w4QJE5CRkQE7Ozt4enpi+fLlJfq/+eabGDduHEJDQ5GXlwd/f3988sknmDVrFgDA0NAQt27dQlBQEDIzM2Fra4u+ffti9uzZAIDCwkKEhITg2rVrUKlU6NatGxYvXlyRp0xEOsZbZkRERCR7vGVGREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESy9397NHSepwkgkQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subsample_feature_ = subsample_feature.drop(['date'], axis=1)\n",
        "cls_xtrain, cls_xtest, cls_ytrain, cls_ytest = model_selection.train_test_split(subsample_feature_,\n",
        "                                                                 subsample_label_class['class'].values,\n",
        "                                                                 test_size=0.2,\n",
        "                                                                 random_state=4)"
      ],
      "metadata": {
        "id": "MNQvAo6JbbQd"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_cls_xtrain, scaled_cls_xtest = scale_data(cls_xtrain, cls_xtest)"
      ],
      "metadata": {
        "id": "NzLPgEjhbbNF"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the frequency of each class\n",
        "class_counts = np.array([np.count_nonzero(cls_ytest == 0), np.count_nonzero(cls_ytest == 1)])\n",
        "# Plotting\n",
        "\n",
        "# Plotting for Random Over-Sampling data\n",
        "plt.bar(['Class 0', 'Class 1'], class_counts)\n",
        "plt.title('Class Distribution')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Frequency')\n",
        "# Adding text labels on top of each bar\n",
        "for index, value in enumerate(class_counts):\n",
        "    plt.text(index, value, str(value), ha='center')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "NP7c2N2Jba88",
        "outputId": "f4c7b49d-d299-4317-fc50-6a63b6bf1628"
      },
      "execution_count": 21,
      "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": [
        "rf = RandomForestClassifier(n_estimators=50, max_depth=6, random_state=42)"
      ],
      "metadata": {
        "id": "yd7c8VdiZem4"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(scaled_cls_xtrain, cls_ytrain)\n",
        "\n",
        "# Make predictions on the test set\n",
        "cls_y_pred = rf.predict(scaled_cls_xtest)\n",
        "\n",
        "# Compute the confusion matrix\n",
        "conf_matrix = confusion_matrix(cls_ytest, cls_y_pred)\n",
        "\n",
        "results = evaluate_binary_classification(cls_ytest, cls_y_pred)\n",
        "print(f\"Accuracy = {results['accuracy']:.2f} | Precision: {results['precision']:.2f} | Recall = {results['recall']:.2f} | F1_score = {results['f1_score']:.2f}\")\n",
        "\n",
        "skr = evaluation_skill_metrics(cls_ytest, cls_y_pred)\n",
        "print(f\"POD = {skr['POD']:.2f} | F: {skr['F']:.2f} | FAR = {skr['FAR']:.2f} | CSI = {skr['CSI']:.2f} | HSS = {skr['HSS']:.2f} | PSS = {skr['PSS']:.2f}\")\n",
        "\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='g')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix of the Best Random Forest Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "kq4Riuh5wZEh",
        "outputId": "057f2162-882d-474c-cfe3-856deaffa4fd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy = 0.97 | Precision: 0.75 | Recall = 0.50 | F1_score = 0.60\n",
            "POD = 0.50 | F: 0.01 | FAR = 0.25 | CSI = 0.43 | HSS = 0.59 | PSS = 0.49\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cls_ytest_probs = rf.predict_proba(scaled_cls_xtest)[:, 1]\n",
        "reliability_diagram(cls_ytest, cls_ytest_probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "TQMyF7UPwbAa",
        "outputId": "566339f7-2a80-4227-c0aa-6a5d174e92f3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
            "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "<ipython-input-16-a8a29d135972>:107: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  axes_object[1].set_yticklabels(new_ticks)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_curve(cls_ytest, cls_ytest_probs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "g5YuHthtwdFy",
        "outputId": "0388954a-0272-4c6e-8735-2f0782316cef"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How to overcome issues with model learning\n",
        "\n",
        "### Cross-Validation\n",
        "\n",
        "Cross-validation technique works by training with certain parts of the training dataset and holding out other parts as validation.\n",
        "\n",
        "By the end of the process, one has k different results on k different validation sets. This allows you to assess how sensitive your model is to the training data used.\n",
        "\n",
        "There is a ``standard k-fold'' cross validation method (left plot below) and variants of it. One variant is illustrated below on the right.\n",
        "\n",
        "#### concept of window sets (right plot below)\n",
        "\n",
        "A model is trained till a particular date and tested on the future dates iteratively such that the training window keeps increasingly shifting by specific time scale.\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://www.researchgate.net/profile/Rayan-H-Assaad/publication/355889701/figure/fig1/AS:1086138706071552@1635967055039/Classical-k-fold-cross-validation-vs-time-series-split-cross-validation.ppm\" alt=\"Alt text for the image\"/>\n",
        "        <figcaption>Standard K-Fold Cross Validation vs Timeseries. Figure Credit: <a href=\"https://www.researchgate.net/figure/Classical-k-fold-cross-validation-vs-time-series-split-cross-validation_fig1_355889701\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "# What to do if you have high training error and/or high validation / cross validation error?\n",
        "\n",
        "Some suggestions below:\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://zahidhasan.github.io/images/bias_variance7.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
        "        <figcaption> Bias-Variance Trade-off. Figure Credit: <a href=\"https://zahidhasan.github.io/2020/10/13/bias-variance-trade-off-and-learning-curve.html\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "# Some suggestions on interpreting your learning curves\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://zahidhasan.github.io/images/learning_curve2.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "\n",
        "**High Bias**\n",
        "\n",
        "**- When training and testing errors converge and are both high**\n",
        "- No matter how much data we feed the model, the model cannot represent the underlying relationship and has high systematic error\n",
        "- Poor fit\n",
        "\n",
        "**- Possible solutions:**\n",
        "\n",
        "- Use more input features\n",
        "- Increase model parameters (e.g., more hidden layer neurons, more layers)\n",
        "- Decrease regularization (e.g., remove dropout, batch norm)\n",
        "\n",
        "\n",
        "<figure>\n",
        "    <p align=\"center\">\n",
        "        <img src=\"https://zahidhasan.github.io/images/learning_curve4.png\" alt=\"Alt text for the image\" width=\"800px\" />\n",
        "        <figcaption> Bias-Variance Trade-off. Figure Credit: <a href=\"https://zahidhasan.github.io/2020/10/13/bias-variance-trade-off-and-learning-curve.html\">Link</a></figcaption>\n",
        "    </p>\n",
        "</figure>\n",
        "\n",
        "**High Variance**\n",
        "\n",
        "**- When there is a large gap between the training and validation errors**\n",
        "- Poor generalization\n",
        "\n",
        "**- Possible solutions:**\n",
        "\n",
        "- more training data\n",
        "- reduce number or complexity of features, manually or using a model selection algorithm or with dimensionality reduction\n",
        "- increase regularization\n"
      ],
      "metadata": {
        "id": "sT5YmzAHw_2C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Boostrapping section\n",
        "\n",
        "In class, we discussed the concept of bootstrapping for uncertainty quantification and/or hypothesis testing. Below are some code tidbits, but for more information, including types of bootstrapping, please see this [blogpost](https://blogs.sas.com/content/iml/2018/12/12/essential-guide-bootstrapping-sas.html) and this [scipy documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.bootstrap.html)."
      ],
      "metadata": {
        "id": "1k3kfKpP-so6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy"
      ],
      "metadata": {
        "id": "vS_cXPfNyApE"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reading dataset from NOAA online\n",
        "dataset = pd.read_csv(\n",
        "    'http://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt',\n",
        "    delim_whitespace=True)"
      ],
      "metadata": {
        "id": "evQYDFFC_Y30"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KueRWMHl_ZJ7",
        "outputId": "dc6e43f5-b223-43e7-c45e-1cbeb9fffb59"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  SEAS    YR  TOTAL  ANOM\n",
              "0  DJF  1950  24.72 -1.53\n",
              "1  JFM  1950  25.17 -1.34\n",
              "2  FMA  1950  25.75 -1.16\n",
              "3  MAM  1950  26.12 -1.18\n",
              "4  AMJ  1950  26.32 -1.07"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEAS</th>\n",
              "      <th>YR</th>\n",
              "      <th>TOTAL</th>\n",
              "      <th>ANOM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DJF</td>\n",
              "      <td>1950</td>\n",
              "      <td>24.72</td>\n",
              "      <td>-1.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>JFM</td>\n",
              "      <td>1950</td>\n",
              "      <td>25.17</td>\n",
              "      <td>-1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FMA</td>\n",
              "      <td>1950</td>\n",
              "      <td>25.75</td>\n",
              "      <td>-1.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MAM</td>\n",
              "      <td>1950</td>\n",
              "      <td>26.12</td>\n",
              "      <td>-1.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AMJ</td>\n",
              "      <td>1950</td>\n",
              "      <td>26.32</td>\n",
              "      <td>-1.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3a61eb29-f36f-4b17-ba28-bd990742fa1b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3a61eb29-f36f-4b17-ba28-bd990742fa1b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cba1a921-e194-4637-b1c8-4fcca3e46679\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cba1a921-e194-4637-b1c8-4fcca3e46679')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cba1a921-e194-4637-b1c8-4fcca3e46679 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 890,\n  \"fields\": [\n    {\n      \"column\": \"SEAS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"OND\",\n          \"SON\",\n          \"DJF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 1950,\n        \"max\": 2024,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          1954,\n          2013,\n          1960\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TOTAL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9439422178719992,\n        \"min\": 24.38,\n        \"max\": 29.26,\n        \"num_unique_values\": 353,\n        \"samples\": [\n          27.21,\n          26.91,\n          27.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ANOM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8394727578615753,\n        \"min\": -2.03,\n        \"max\": 2.64,\n        \"num_unique_values\": 315,\n        \"samples\": [\n          0.42,\n          0.4,\n          0.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets see how bootstrapping can help us create population statistics\n",
        "future_samples = dataset[\"TOTAL\"][200:]\n",
        "past_samples = dataset[\"TOTAL\"][:200]"
      ],
      "metadata": {
        "id": "3vvjV_Se_cEb"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# the future population\n",
        "plt.hist(future_samples)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "dHURS1yQ_iJd",
        "outputId": "945d8f35-93f3-4133-85a6-015337447ffc"
      },
      "execution_count": 42,
      "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": [
        "# the past population\n",
        "plt.hist(past_samples)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "RxWcl45e_lfY",
        "outputId": "2735bad2-6a8b-4bff-8f89-0729a8234cb8"
      },
      "execution_count": 43,
      "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": [
        "# we can bootstrap the past population's mean to see if it ends up similar to the future population mean\n",
        "boot_res = scipy.stats.bootstrap((past_samples.values,), np.mean, confidence_level=0.9, random_state=0)"
      ],
      "metadata": {
        "id": "mycaFK8M_qHP"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# what is the total population mean?\n",
        "past_samples.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_S8ZOQVo_8WD",
        "outputId": "43287362-7272-410d-9d3a-41ffc98be202"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.74255"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# what is the mean according to our bootstrap?\n",
        "plt.hist(boot_res.bootstrap_distribution)\n",
        "# does the bootstrap means characterize the population mean?\n",
        "plt.vlines(future_samples.mean(), 0, 3000)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-AeScG_X_8Ss",
        "outputId": "2db734fe-472c-4bd4-d3d6-4078a2b911be"
      },
      "execution_count": 47,
      "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": [
        "boot_res.confidence_interval"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BjNGcSD_8QF",
        "outputId": "ed21cb2d-28d0-4c5f-ebd4-fff89a147e35"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ConfidenceInterval(low=26.639905146314355, high=26.845881027024603)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f0MIosoOA22E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}