PCA_work.ipynb

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      "name": "PCA_work.ipynb",
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        "1wwiF_DXYlPV",
        "oyK5_KvMZrnT",
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        "eRSWshVFjnyT"
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      "toc_visible": true,
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/george-zakharov/ml_notebooks/blob/master/PCA_work.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svglh-o7Pz5c",
        "colab_type": "text"
      },
      "source": [
        "### Homework 7\n",
        "\n",
        "\n",
        "\n",
        "#### Light: \n",
        "\n",
        "Датасет: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009\n",
        "(из Homework 6)\n",
        "\n",
        "С помощью алгоритма PCA найти размерность пространства признаков до которой можно понизить исходное пространство без потери точности.\n",
        "\n",
        "\n",
        "#### Pro: \n",
        "\n",
        "Датасет: https://www1.nyc.gov/html/gbee/html/plan/ll84_scores.shtml\n",
        "(из Homework 4)\n",
        "\n",
        "С помощью алгоритма PCA (t-SNE) найти размерность пространства признаков до которой можно понизить исходное пространство без потери точности."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wwiF_DXYlPV",
        "colab_type": "text"
      },
      "source": [
        "# Load Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBvln67wYnAl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compression libs\n",
        "from sklearn import decomposition\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "\n",
        "# Normalization tests\n",
        "from scipy.stats import kurtosis\n",
        "from scipy.stats import skew\n",
        "from scipy.stats import shapiro\n",
        "from scipy.stats import normaltest\n",
        "\n",
        "# Data normalization / standartization\n",
        "from sklearn.preprocessing import PowerTransformer, MinMaxScaler, StandardScaler\n",
        "\n",
        "# libs for train tuning\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
        "\n",
        "# Data resampling\n",
        "from sklearn.utils import resample\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Boosting\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "\n",
        "# Model parameters fitting\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score, auc, f1_score, confusion_matrix,precision_score, recall_score, roc_auc_score, roc_curve, mean_absolute_error\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
        "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV, LinearRegression, LassoCV, Lasso, Ridge, SGDRegressor\n",
        "from sklearn.model_selection import cross_val_score, cross_val_predict, StratifiedKFold, GridSearchCV, train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.feature_selection import SelectKBest, chi2, SelectFromModel\n",
        "from sklearn.pipeline import Pipeline, FeatureUnion\n",
        "from sklearn.svm import LinearSVR\n",
        "import sys"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FavEUQ3AZmgG",
        "colab_type": "text"
      },
      "source": [
        "# Light"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyK5_KvMZrnT",
        "colab_type": "text"
      },
      "source": [
        "## Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vNz4BQIZuFl",
        "colab_type": "code",
        "outputId": "b9391050-d6da-4672-f6b0-c2c4751f3aad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpTOmaYuaSkM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine = pd.read_csv('/content/drive/My Drive/Colab Notebooks/ML/6/data/winequality-red.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQuli5AgajvE",
        "colab_type": "code",
        "outputId": "20d4b82e-211f-454a-a5c8-32d947e2ed80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "wine.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "US7DDhxag85P",
        "colab_type": "text"
      },
      "source": [
        "## User defined functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa08jH6Decmo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_upsampled_wine_data(wine_preprocessed):\n",
        "  # Get data classes for further balancing\n",
        "  wine_3 = wine_preprocessed[wine_preprocessed.quality == 3]\n",
        "  wine_4 = wine_preprocessed[wine_preprocessed.quality == 4]\n",
        "  wine_5 = wine_preprocessed[wine_preprocessed.quality == 5]\n",
        "  wine_6 = wine_preprocessed[wine_preprocessed.quality == 6]\n",
        "  wine_7 = wine_preprocessed[wine_preprocessed.quality == 7]\n",
        "  wine_8 = wine_preprocessed[wine_preprocessed.quality == 8]\n",
        "\n",
        "  # Use upsampling (based on the previous work best results)\n",
        "\n",
        "  # Transform classes\n",
        "  wine_3_upsampled = resample(wine_3, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "  wine_4_upsampled = resample(wine_4, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "  wine_6_upsampled = resample(wine_6, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "  wine_7_upsampled = resample(wine_7, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "  wine_8_upsampled = resample(wine_8, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "\n",
        "  wine_upsampled = pd.concat([wine_5, wine_3_upsampled, wine_4_upsampled, wine_6_upsampled, wine_7_upsampled, wine_8_upsampled])\n",
        "\n",
        "  X_ups = wine_upsampled.drop('quality', axis = 1)\n",
        "  y_ups = wine_upsampled.quality\n",
        "\n",
        "  return X_ups, y_ups"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK8Rulf1pJ5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train model and get accuracy score\n",
        "def train_and_get_accuracy(X_train, y_train, X_test, y_test):\n",
        "  model = XGBClassifier(\n",
        "    base_score = 0.5, booster = 'gbtree', colsample_bylevel = 1, colsample_bynode = 1,\n",
        "    colsample_bytree = 1, gamma = 0, learning_rate = 0.1, max_delta_step = 0, max_depth = 3,\n",
        "    min_child_weight = 1, missing = None, n_estimators = 100, n_jobs = 1, nthread = None,\n",
        "    objective = 'binary:logistic', random_state = 0, reg_alpha = 0, reg_lambda = 1,\n",
        "    scale_pos_weight = 1, seed = None, silent = None, subsample = 1, verbosity = 1\n",
        "  )\n",
        "\n",
        "  model.fit(X_train, y_train)\n",
        "\n",
        "  # make predictions for test data\n",
        "  y_pred = model.predict(X_test)\n",
        "  predictions = [round(value) for value in y_pred]\n",
        "\n",
        "  # evaluate predictions\n",
        "  accuracy = accuracy_score(y_test, predictions)\n",
        "  result = \"Accuracy: %.2f%%\" % (accuracy * 100.0)\n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L72NI2j6h7l0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Functions for normalization test\n",
        "def draw_hist_for_feature(data):\n",
        "  plt.style.use('ggplot')\n",
        "  data.hist(bins = 60)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def chech_skew(feature):\n",
        "  method_name = '\\nSKEW TEST: '\n",
        "  skew_ = np.abs(skew(feature))\n",
        "  if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "    print(method_name + 'Данные рекомендуется логарифмировать\\n')\n",
        "  elif skew_ >= 1:\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def check_shapiro(feature):\n",
        "  method_name = '\\nSHAPIRO TEST: '\n",
        "  shapiro_ = np.abs(shapiro(feature))\n",
        "  if (shapiro_[1] < 0.50):\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def print_stats(data, need_hist = True):\n",
        "  if (need_hist == True):\n",
        "    draw_hist_for_feature(data)\n",
        "\n",
        "  print(\"mean : \", np.mean(data))\n",
        "  print(\"var  : \", np.var(data))\n",
        "  print(\"skew : \", skew(data))\n",
        "  print(\"kurt : \", kurtosis(data))\n",
        "  print(\"shapiro : \", shapiro(data))\n",
        "  print(\"normaltest : \", normaltest(data))\n",
        "\n",
        "\n",
        "def print_stats_all(df, need_hist = True):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    print_stats(df[feature_name], need_hist)\n",
        "    chech_skew(df[feature_name])\n",
        "    check_shapiro(df[feature_name])\n",
        "    n += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DN-t-dH4g_Yw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data preprocessing according to skew metrics\n",
        "def data_preprocessing(df):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    feature = df[feature_name]\n",
        "\n",
        "    print_stats(feature)\n",
        "    \n",
        "    skew_ = np.abs(skew(feature))\n",
        "\n",
        "    if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "      print('\\nДанные рекомендуется логарифмировать\\n')\n",
        "      feature = np.log(feature)\n",
        "    elif skew_ >= 1:\n",
        "      if feature.min() > 0:\n",
        "        print('\\nДанные рекомендуется нормализовать методом boxcox\\n')\n",
        "        box_cox_transform = PowerTransformer(method = 'box-cox', standardize = False)\n",
        "        feature = box_cox_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "      else:\n",
        "        print('\\nДанные рекомендуется нормализовать методом yeo-johnson\\n')\n",
        "        yeo_johnson_transform = PowerTransformer(method = 'yeo-johnson', standardize = False)\n",
        "        feature = yeo_johnson_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "    else:\n",
        "      print('\\nДанные достаточно стандартизировать\\n')\n",
        "\n",
        "    print_stats(pd.DataFrame(feature, columns = [feature_name]))\n",
        "    chech_skew(feature)\n",
        "\n",
        "    df[feature_name] = feature\n",
        "\n",
        "    n += 1\n",
        "\n",
        "  return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkqcfkkJhC1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data standartization\n",
        "def scale_std_minmax(df):\n",
        "  print(df.shape)\n",
        "\n",
        "  scaler = StandardScaler()\n",
        "  print(scaler.fit(df.copy()))\n",
        "  StandardScaler()\n",
        "  result = scaler.transform(df.copy())\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "\n",
        "\n",
        "  scaler = MinMaxScaler()\n",
        "  print(scaler.fit(result))\n",
        "  MinMaxScaler(copy = True, feature_range = (0, 1))\n",
        "  print(scaler.data_max_)\n",
        "  result = scaler.transform(result)\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "  \n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1617ynSguXY",
        "colab_type": "text"
      },
      "source": [
        "## Dataset transformations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrfYXcpdgw-W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Divide features and labels\n",
        "X = wine.drop(['quality'], axis = 1)\n",
        "y = wine.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvrq8qZlhZdh",
        "colab_type": "code",
        "outputId": "b3ac34a0-bded-443e-bd18-b9fd84fc680d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Normalization\n",
        "X_normalized = data_preprocessing(X)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1. fixed acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  8.319637273295838\n",
            "var  :  3.0295205688671296\n",
            "skew :  0.9818292953262073\n",
            "kurt :  1.1248563352037486\n",
            "shapiro :  (0.942030668258667, 1.5255951637239901e-24)\n",
            "normaltest :  NormaltestResult(statistic=224.53087840457746, pvalue=1.7528277735470436e-49)\n",
            "\n",
            "Данные рекомендуется логарифмировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  fixed acidity    2.098226\n",
            "dtype: float64\n",
            "var  :  fixed acidity    0.03974\n",
            "dtype: float64\n",
            "skew :  [0.39354537]\n",
            "kurt :  [0.09695618]\n",
            "shapiro :  (0.9848697185516357, 6.456312047486978e-12)\n",
            "normaltest :  NormaltestResult(statistic=array([39.54165981]), pvalue=array([2.59201595e-09]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "2. volatile acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yD4VC2rt3r5599lkVFxdry5YtKisr0+zZs5M9FIBxiPWviImYFTP7viKVj0PS19yPHz+u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\nUe4AYCDKHQAM9L993jHqC3jLkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.5278205128205131\n",
            "var  :  0.03204232613332036\n",
            "skew :  0.6709623963499574\n",
            "kurt :  1.2179633893005306\n",
            "shapiro :  (0.9743340611457825, 2.686806772838566e-16)\n",
            "normaltest :  NormaltestResult(statistic=143.4193435598286, pvalue=7.192589039756692e-32)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EWO5ERBJiuRMRSYjlTkQkIZY7EZGEWO5ERBJiuRMRSYjlTkQkIZY7EZGEWO5ERBLi9dzJ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\niOVORCQhljsRkYRY7kREEvpf8w4yzRVi0I0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  volatile acidity    0.527821\n",
            "dtype: float64\n",
            "var  :  volatile acidity    0.032042\n",
            "dtype: float64\n",
            "skew :  [0.6709624]\n",
            "kurt :  [1.21796339]\n",
            "shapiro :  (0.9743340611457825, 2.686806772838566e-16)\n",
            "normaltest :  NormaltestResult(statistic=array([143.41934356]), pvalue=array([7.19258904e-32]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "3. citric acid\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.2709756097560964\n",
            "var  :  0.037923751124940605\n",
            "skew :  0.3180385895475358\n",
            "kurt :  -0.7902825153036646\n",
            "shapiro :  (0.9552900195121765, 1.0208196357632134e-21)\n",
            "normaltest :  NormaltestResult(statistic=152.039214793795, pvalue=9.662822259281018e-34)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fX6SkpLi8ASIiep7NcF+6dKnV7evXr39umyRJmDt3rvNVERGRU/gOVSIiATHciYgExHAn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\nTkQkIIY7EZGAGO5ERAJiuBMRCej/ABvvioyem4oRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  citric acid    0.270976\n",
            "dtype: float64\n",
            "var  :  citric acid    0.037924\n",
            "dtype: float64\n",
            "skew :  [0.31803859]\n",
            "kurt :  [-0.79028252]\n",
            "shapiro :  (0.9552900195121765, 1.0208196357632134e-21)\n",
            "normaltest :  NormaltestResult(statistic=array([152.03921479]), pvalue=array([9.66282226e-34]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "4. residual sugar\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  2.5388055034396517\n",
            "var  :  1.9866539202699018\n",
            "skew :  4.536394788805638\n",
            "kurt :  28.524437824252235\n",
            "shapiro :  (0.5660765171051025, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1520.3239698236891, pvalue=0.0)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bm2EymTBr1izcfffdzi1CJru0t7fLzz77rHzu3Dm5ra1Nfv755+UzZ85YjPn8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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  residual sugar    0.513556\n",
            "dtype: float64\n",
            "var  :  residual sugar    0.01211\n",
            "dtype: float64\n",
            "skew :  [-0.09584002]\n",
            "kurt :  [1.60404381]\n",
            "shapiro :  (0.9759386777877808, 1.011885569943366e-15)\n",
            "normaltest :  NormaltestResult(statistic=array([62.41154248]), pvalue=array([2.80224271e-14]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "5. chlorides\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.08746654158849257\n",
            "var  :  0.0022137573233114395\n",
            "skew :  5.675016527504259\n",
            "kurt :  41.581708043675775\n",
            "shapiro :  (0.4842468500137329, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1783.1059225626427, pvalue=0.0)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  chlorides   -4.763474\n",
            "dtype: float64\n",
            "var  :  chlorides    0.926346\n",
            "dtype: float64\n",
            "skew :  [-0.49917442]\n",
            "kurt :  [14.5513695]\n",
            "shapiro :  (0.8716785907745361, 2.4586373518860107e-34)\n",
            "normaltest :  NormaltestResult(statistic=array([412.77325778]), pvalue=array([2.33037565e-90]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "6. free sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  15.874921826141339\n",
            "var  :  109.3464567637451\n",
            "skew :  1.249393847434253\n",
            "kurt :  2.01348999733378\n",
            "shapiro :  (0.9018407464027405, 7.6973984391279615e-31)\n",
            "normaltest :  NormaltestResult(statistic=342.2591484251238, pvalue=4.779365332171477e-75)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DnYhIQgx3IiIJMdyJiCTEcCcikhDDnYhIQgx3IiIJMdyJiCTEcCcikhDDnYhIQgx3IiIJ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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  free sulfur dioxide    3.129049\n",
            "dtype: float64\n",
            "var  :  free sulfur dioxide    0.971227\n",
            "dtype: float64\n",
            "skew :  [-0.01975919]\n",
            "kurt :  [-0.59777277]\n",
            "shapiro :  (0.9874722361564636, 1.5522860774552782e-10)\n",
            "normaltest :  NormaltestResult(statistic=array([49.9466963]), pvalue=array([1.42630598e-11]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "7. total sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  46.46779237023139\n",
            "var  :  1081.4256355891553\n",
            "skew :  1.5141091878506638\n",
            "kurt :  3.7941717664905665\n",
            "shapiro :  (0.8732253909111023, 3.5741436086741964e-34)\n",
            "normaltest :  NormaltestResult(statistic=487.42725648953467, pvalue=1.433890834343538e-106)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  total sulfur dioxide    3.965719\n",
            "dtype: float64\n",
            "var  :  total sulfur dioxide    0.715565\n",
            "dtype: float64\n",
            "skew :  [-0.00802061]\n",
            "kurt :  [-0.67038533]\n",
            "shapiro :  (0.990358293056488, 8.859659672566522e-09)\n",
            "normaltest :  NormaltestResult(statistic=array([71.36209297]), pvalue=array([3.19093827e-16]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "8. density\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.9967466791744831\n",
            "var  :  3.559801792630711e-06\n",
            "skew :  0.07122077153539946\n",
            "kurt :  0.9274107990478333\n",
            "shapiro :  (0.9908668398857117, 1.9400959061499634e-08)\n",
            "normaltest :  NormaltestResult(statistic=30.70774994095191, pvalue=2.1473202738102222e-07)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hzJs3j5EjRwLnj93m5ubS0NDAqFGjWLhwIf369fPrzD/5yU8YNGgQAwYMAGDq\n1Kncd999fp35QrNnz/bqKZu+yNvY2EheXh41NTUMGDDgkq/FnzJ/+OGHvP766wQFBREWFkZGRobX\nDj/k5uZy4MAB6uvriYyMZObMmbS2tgJw2223YRgGmzZtYt++fYSEhJCZmUlCQgIAxcXFFBYWAud/\nS/nmB9ihQ4dYt24dLS0tTJ48mYcfftirp2x6O/PJkyfJyMhg6NChrlOkv//973Prrbf6beYLVVdX\n8/zzz3fplE2/KX0REfE9vzi8IyIiPUOlLyJiIip9ERETUemLiJiISl9ExERU+iIiJqLSFxExkf8P\nR27z3YvADIkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  density    0.996747\n",
            "dtype: float64\n",
            "var  :  density    0.000004\n",
            "dtype: float64\n",
            "skew :  [0.07122077]\n",
            "kurt :  [0.9274108]\n",
            "shapiro :  (0.9908668398857117, 1.9400959061499634e-08)\n",
            "normaltest :  NormaltestResult(statistic=array([30.70774994]), pvalue=array([2.14732027e-07]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "9. pH\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  3.311113195747343\n",
            "var  :  0.023820274241131745\n",
            "skew :  0.19350175891005525\n",
            "kurt :  0.8006714441211544\n",
            "shapiro :  (0.993486762046814, 1.7136950418716879e-06)\n",
            "normaltest :  NormaltestResult(statistic=33.684697471483915, pvalue=4.8468645347727716e-08)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  pH    3.311113\n",
            "dtype: float64\n",
            "var  :  pH    0.02382\n",
            "dtype: float64\n",
            "skew :  [0.19350176]\n",
            "kurt :  [0.80067144]\n",
            "shapiro :  (0.993486762046814, 1.7136950418716879e-06)\n",
            "normaltest :  NormaltestResult(statistic=array([33.68469747]), pvalue=array([4.84686453e-08]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "10. sulphates\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.6581488430268921\n",
            "var  :  0.028714647013983425\n",
            "skew :  2.426393455449087\n",
            "kurt :  11.6798841115311\n",
            "shapiro :  (0.8330425024032593, 5.821617678881608e-38)\n",
            "normaltest :  NormaltestResult(statistic=906.8944479227036, pvalue=1.1759065222978855e-197)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  sulphates   -0.609663\n",
            "dtype: float64\n",
            "var  :  sulphates    0.115999\n",
            "dtype: float64\n",
            "skew :  [-0.02475606]\n",
            "kurt :  [0.39890141]\n",
            "shapiro :  (0.9964831471443176, 0.0010049439733847976)\n",
            "normaltest :  NormaltestResult(statistic=array([8.24567945]), pvalue=array([0.01619845]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "11. alcohol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mj3NftWqV3n77bW3cuFFXrlxRZWVlrIcAAIwjJgegz58/X/Pnz5ckFRYWas+ePbG4WwBA\nhPh0EdIWZ7IExsbpBwDAQJQ7ABiIcgcAA1HuAGAg3lDFmDhvDJC+KHcYZaI/kDiqBqZjWQYADES5\nA4CBWJaZoNt/3b91mlp+tU9fYy3f8DWFKZi5A4CBmLkDEeDUB0h1lDvigsMogeRiWQYADES5A4CB\nKHcAMBBr7kCK4k1bRIOZOwAYiJl7FDgiBECqotyBcfBDHOko4nL3+XxqaGjQ5cuXZVmWXC6Xvvvd\n7+rKlSuqq6tTT0+PpkyZoi1btig7OzuWmQEA44i43G02m370ox+ppKRE169f144dO3T33Xfr6NGj\nKisrU1VVlVpbW9Xa2qrVq1fHMjMAYBwRl3t+fr7y8/MlSV//+tc1ffp0+f1+eb1e7dq1S5JUUVGh\nXbt2Ue74Shhr+ebSly5Hc8QLR9BgomJytEx3d7c++ugjzZkzR/39/cHSz8vLU39/fyyGAACEIeo3\nVL/44gvV1tbq8ccfV2Zm5ojrLMuSZVkh9/N4PPJ4PJIkt9stp9MZbZS4+vLsC2YK9TqM9dd+oq/1\niY4bz+8du92e8t+bY0nX7LHKHVW5Dw4Oqra2VkuXLtX9998vScrNzVVfX5/y8/PV19ennJyckPu6\nXC65XK7gZZ/PF00UICYS8TqM9RjxzOx0OtP2ezNds4eTu6ioaMzrIl6WCQQCevHFFzV9+nR973vf\nC24vLy9Xe3u7JKm9vV0LFy6MdAgAQIQinrmfPXtWx44d06xZs7Rt2zZJ0sqVK1VVVaW6ujq1tbUF\nD4UEACRWxOX+zW9+U2+88UbI63bu3BlxIADxwZE2Xy2cWwYADES5A4CBOLcMcBvOIwNTMHMHAANR\n7gBgIJZlgATjqBUkAuUOpADW+hFrLMsAgIEodwAwEOUOAAai3AHAQLyhChhoom/QcuSOuZi5A4CB\nKHcAMBDLMgCM8OUlpkv6ai8xUe5Amov1B6Buv7/b/45rrIuS9f74YlkGAAxEuQOAgSh3ADBQ2q+5\ns24HpBZOgpYamLkDgIHiNnM/deqUDhw4oOHhYS1fvlxVVVXxGmrCJjqjYOYPjMZvyeklLuU+PDys\nl19+Wc8++6wKCgr0s5/9TOXl5ZoxY0Y8hgOQJCzBhCeRPyDjsixz/vx5TZs2TYWFhbLb7Vq8eLG8\nXm88hgIAhBCXmbvf71dBQUHwckFBgc6dOzfiNh6PRx6PR5LkdrtVVFQU2WDvnIjPbWO5L4D4S4fv\n0QlmjLgPb5O0N1RdLpfcbrfcbneyIkRsx44dyY4QkXTNLZE9WcieeLHKHZdydzgc6u3tDV7u7e2V\nw+GIx1AAgBDiUu6zZ8/WxYsX1d3drcHBQR0/flzl5eXxGAoAEIJt165du2J9pxkZGZo2bZr279+v\nI0eOaOnSpVq0aFGsh0mqkpKSZEeISLrmlsieLGRPvFjktgKBQCAGWQAAKYRPqAKAgSh3ADBQ2p84\nLJHeffddvffeewoEAlq+fLlWrFiR7EhjamxsVGdnp3Jzc1VbWytJunLliurq6tTT06MpU6Zoy5Yt\nys7OTnLS0UJl/+tf/6o//elPunDhgnbv3q3Zs2cnOWVoobI3Nzero6NDdrtdhYWFqqmpUVZWVpKT\njhYq+8GDB3XixAlZlqXc3FzV1NSk3JFvoXLfcvjwYTU3N6upqUk5OTlJSji2UNnfeOMNvffee8G8\nK1eu1H333Rf+nQcwIR9//HFg69atgS+++CIwODgY+MUvfhG4ePFismONqaurK/Dhhx8Gtm7dGtzW\n3NwcaGlpCQQCgUBLS0ugubk5WfHuKFT2Tz75JHDhwoXAc889Fzh//nwS091ZqOynTp0KDA4OBgKB\n/30N0ul5v3r1avD/77zzTuC3v/1tMqLdUajcgUAg0NPTE3j++ecD69evD/T39ycp3Z2Fyv7HP/4x\ncOjQoajvm2WZCbpw4YLmzJmjyZMny2az6Vvf+pb+9re/JTvWmObNmzdqVu71elVRUSFJqqioSNlT\nQoTKPmPGjJh8ai/eQmVfsGCBbDabJGnu3Lny+/3JiDauUNkzMzOD/79x44Ysy0p0rHGFyi1Jr732\nmlatWpWSmW8ZK3sssCwzQTNnztTBgwc1MDCgSZMm6eTJkym7NDCW/v5+5efnS5Ly8vLU39+f5ERf\nPW1tbVq8eHGyY4TlD3/4g44dO6bMzEw999xzyY4zIV6vVw6HQ8XFxcmOEpG//OUvOnbsmEpKSvTY\nY49F9AOAmfsEzZgxQz/4wQ/0/PPPa/fu3SouLlZGRvo+fZZlpfSMxkRvvvmmbDabli5dmuwoYVm5\ncqV+85vf6IEHHtCRI0eSHWdcN27cUEtLi374wx8mO0pEHnroIe3fv1+/+tWvlJ+fr9///vcR3U/6\ntlMSVFZW6oUXXtDPf/5zZWVl6a677kp2pLDk5uaqr69PktTX15eSbzCZ6ujRo+ro6NCmTZvS9ofq\n0qVLU3op8pZLly6pu7tb27Zt04YNG9Tb26vt27fr8uXLyY42IXl5ecrIyFBGRoaWL1+uDz/8MKL7\nodzDcGsZw+fz6YMPPtADDzyQ5EThKS8vV3t7uySpvb1dCxcuTHKir4ZTp07p0KFD2r59uyZPnpzs\nOGG5ePFi8P9erzct3veYNWuWmpqa1NDQoIaGBhUUFOiFF15QXl5esqNNyK0JmCR98MEHmjlzZkT3\nwydUw7Bz504NDAzIbrfrscceU1lZWbIjjam+vl5nzpzRwMCAcnNzVV1drYULF6qurk4+ny+lD4UM\nlT07O1uvvPKKPv/8c2VlZam4uFjPPPNMsqOOEip7S0uLBgcHg891aWmp1q5dm+Sko4XK3tnZqYsX\nL8qyLDmdTq1duzblDoUMlbuysjJ4/YYNG7Rnz56U/E01VPauri79+9//lmVZmjJlitauXRt8rywc\nlDsAGIhlGQAwEOUOAAai3AHAQJQ7ABiIcgcAA1HuAGAgyh0ADPT/Pfcu2RASJB4AAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  10.422983114446502\n",
            "var  :  1.1349371714888994\n",
            "skew :  0.8600210646566755\n",
            "kurt :  0.1956543762123122\n",
            "shapiro :  (0.9288389682769775, 6.643664824998876e-27)\n",
            "normaltest :  NormaltestResult(statistic=154.17806951912516, pvalue=3.3163288473185496e-34)\n",
            "\n",
            "Данные рекомендуется логарифмировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  alcohol    2.339021\n",
            "dtype: float64\n",
            "var  :  alcohol    0.009775\n",
            "dtype: float64\n",
            "skew :  [0.66123544]\n",
            "kurt :  [-0.28947897]\n",
            "shapiro :  (0.9464339017868042, 1.154532723556871e-23)\n",
            "normaltest :  NormaltestResult(statistic=array([105.75210886]), pvalue=array([1.08697946e-23]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYME_QMrhhWS",
        "colab_type": "code",
        "outputId": "6aed43b0-b96d-4db8-8c60-f554c194c46f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# Standartization\n",
        "X_scaled = scale_std_minmax(X_normalized)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1599, 11)\n",
            "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
            "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
            "[3.35136224 5.87797605 3.74357393 2.72895662 4.37508071 2.83835204\n",
            " 3.07267011 3.68005512 4.52828197 3.23051437 3.66487349]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9fM_QKkibEd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Prepare train / test data\n",
        "wine_preprocessed = pd.concat([X_scaled, y], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJeYOpWxijZ8",
        "colab_type": "code",
        "outputId": "5c372153-3e1e-47f3-80ac-ed8b7f3e7911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "wine_preprocessed.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.520548</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.739835</td>\n",
              "      <td>0.739956</td>\n",
              "      <td>0.692260</td>\n",
              "      <td>0.599486</td>\n",
              "      <td>0.494126</td>\n",
              "      <td>0.362205</td>\n",
              "      <td>0.628770</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.693719</td>\n",
              "      <td>0.726509</td>\n",
              "      <td>0.559731</td>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.508811</td>\n",
              "      <td>0.409449</td>\n",
              "      <td>0.602052</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.717475</td>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.680266</td>\n",
              "      <td>0.591301</td>\n",
              "      <td>0.570440</td>\n",
              "      <td>0.582232</td>\n",
              "      <td>0.330709</td>\n",
              "      <td>0.528593</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates   alcohol  quality\n",
              "0       0.383325          0.397260         0.00  ...   0.504120  0.196252        5\n",
              "1       0.425771          0.520548         0.00  ...   0.628770  0.268963        5\n",
              "2       0.425771          0.438356         0.04  ...   0.602052  0.268963        5\n",
              "3       0.717475          0.109589         0.56  ...   0.528593  0.268963        6\n",
              "4       0.383325          0.397260         0.00  ...   0.504120  0.196252        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8uNJ8EPi3QG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split features and labels (after normalization & standartization)\n",
        "X = wine_preprocessed.drop('quality', axis = 1)\n",
        "y = wine_preprocessed.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWxbY-AUi6QQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Disbalanced dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tnfyM0Dki-FN",
        "colab_type": "code",
        "outputId": "2983913a-79dd-4e56-8c2f-d6fca72bc897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# Check labels count\n",
        "wine_preprocessed.quality.value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    681\n",
              "6    638\n",
              "7    199\n",
              "4     53\n",
              "8     18\n",
              "3     10\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0gwMU_hffWI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_ups, y_ups = get_upsampled_wine_data(wine_preprocessed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGxZGnYSjk7Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_ups, X_test_ups, y_train_ups, y_test_ups = train_test_split(X_ups, y_ups, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aq3dZWVWlxlO",
        "colab_type": "text"
      },
      "source": [
        "## Train and PCA use"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vObBHWeBj3BC",
        "colab_type": "code",
        "outputId": "c2dabcc5-43f7-4e0e-ef1c-d55f1754f2dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Use model based on previous work best results\n",
        "train_and_get_accuracy(X_train_ups, y_train_ups, X_test_ups, y_test_ups)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Accuracy: 82.89%'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtOJ4HCiouuZ",
        "colab_type": "code",
        "outputId": "d7190f4a-5ffc-4791-f003-082696eaefe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6Fsm81Gm3Wl",
        "colab_type": "code",
        "outputId": "38a7d8d9-b456-4352-bc75-7e4ff445a4d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "pca = decomposition.PCA().fit(X)\n",
        "\n",
        "plt.figure(figsize = (10, 7))\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_), color = 'k', lw = 2)\n",
        "plt.xlabel('Number of components')\n",
        "plt.ylabel('Total explained variance')\n",
        "plt.xlim(0, 15)\n",
        "plt.yticks(np.arange(0, 1.1, 0.1))\n",
        "plt.axvline(10, c = 'b')\n",
        "plt.axvline(7, c = 'g', ls = '-')\n",
        "plt.axhline(0.95, c = 'r')\n",
        "plt.show();"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vr18/RUREKDk5WZGRkYqOjlZKSoo2btyowMBABQcHa9y4cef+U1Wj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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlcZHnICocEc",
        "colab_type": "code",
        "outputId": "676bc31a-2054-475f-8759-e0026aa7bc58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "np.cumsum(pca.explained_variance_ratio_)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3136648 , 0.55171673, 0.71459223, 0.79165751, 0.85461648,\n",
              "       0.89921754, 0.93163379, 0.96023803, 0.98147133, 0.99481206,\n",
              "       1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqBwScL5qgtV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# With dispersion of 0.95 features can be compressed to 7 from 11\n",
        "pca = decomposition.PCA(n_components = 7)\n",
        "X_reduced = pca.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Nd4e9gfq9fy",
        "colab_type": "code",
        "outputId": "494b18ff-9363-4eaf-80f7-e88874a2c9ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_reduced.shape"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ms1FnLF2cumG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_reduced_df = pd.DataFrame(X_reduced)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMBvwJ1JciC9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "62c06ef1-f915-4fa2-fd0b-fe13469735a6"
      },
      "source": [
        "X_reduced_df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.268228</td>\n",
              "      <td>-0.008343</td>\n",
              "      <td>-0.274758</td>\n",
              "      <td>-0.019410</td>\n",
              "      <td>-0.069423</td>\n",
              "      <td>0.010938</td>\n",
              "      <td>-0.045702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.225280</td>\n",
              "      <td>-0.255354</td>\n",
              "      <td>-0.137577</td>\n",
              "      <td>-0.122590</td>\n",
              "      <td>-0.032140</td>\n",
              "      <td>-0.208872</td>\n",
              "      <td>0.038397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.179989</td>\n",
              "      <td>-0.130177</td>\n",
              "      <td>-0.159575</td>\n",
              "      <td>-0.067744</td>\n",
              "      <td>-0.042668</td>\n",
              "      <td>-0.127054</td>\n",
              "      <td>0.035465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.387149</td>\n",
              "      <td>-0.181626</td>\n",
              "      <td>0.029808</td>\n",
              "      <td>0.163405</td>\n",
              "      <td>0.118911</td>\n",
              "      <td>0.025121</td>\n",
              "      <td>-0.057808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.268228</td>\n",
              "      <td>-0.008343</td>\n",
              "      <td>-0.274758</td>\n",
              "      <td>-0.019410</td>\n",
              "      <td>-0.069423</td>\n",
              "      <td>0.010938</td>\n",
              "      <td>-0.045702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2         3         4         5         6\n",
              "0 -0.268228 -0.008343 -0.274758 -0.019410 -0.069423  0.010938 -0.045702\n",
              "1 -0.225280 -0.255354 -0.137577 -0.122590 -0.032140 -0.208872  0.038397\n",
              "2 -0.179989 -0.130177 -0.159575 -0.067744 -0.042668 -0.127054  0.035465\n",
              "3  0.387149 -0.181626  0.029808  0.163405  0.118911  0.025121 -0.057808\n",
              "4 -0.268228 -0.008343 -0.274758 -0.019410 -0.069423  0.010938 -0.045702"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-6BFJHReF0b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine_reduced = pd.concat([X_reduced_df, y], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntKAXejmeLJQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b9922ef1-ab87-496d-bb0f-553fd11887fa"
      },
      "source": [
        "wine_reduced.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.268228</td>\n",
              "      <td>-0.008343</td>\n",
              "      <td>-0.274758</td>\n",
              "      <td>-0.019410</td>\n",
              "      <td>-0.069423</td>\n",
              "      <td>0.010938</td>\n",
              "      <td>-0.045702</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.225280</td>\n",
              "      <td>-0.255354</td>\n",
              "      <td>-0.137577</td>\n",
              "      <td>-0.122590</td>\n",
              "      <td>-0.032140</td>\n",
              "      <td>-0.208872</td>\n",
              "      <td>0.038397</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.179989</td>\n",
              "      <td>-0.130177</td>\n",
              "      <td>-0.159575</td>\n",
              "      <td>-0.067744</td>\n",
              "      <td>-0.042668</td>\n",
              "      <td>-0.127054</td>\n",
              "      <td>0.035465</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.387149</td>\n",
              "      <td>-0.181626</td>\n",
              "      <td>0.029808</td>\n",
              "      <td>0.163405</td>\n",
              "      <td>0.118911</td>\n",
              "      <td>0.025121</td>\n",
              "      <td>-0.057808</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.268228</td>\n",
              "      <td>-0.008343</td>\n",
              "      <td>-0.274758</td>\n",
              "      <td>-0.019410</td>\n",
              "      <td>-0.069423</td>\n",
              "      <td>0.010938</td>\n",
              "      <td>-0.045702</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2  ...         5         6  quality\n",
              "0 -0.268228 -0.008343 -0.274758  ...  0.010938 -0.045702        5\n",
              "1 -0.225280 -0.255354 -0.137577  ... -0.208872  0.038397        5\n",
              "2 -0.179989 -0.130177 -0.159575  ... -0.127054  0.035465        5\n",
              "3  0.387149 -0.181626  0.029808  ...  0.025121 -0.057808        6\n",
              "4 -0.268228 -0.008343 -0.274758  ...  0.010938 -0.045702        5\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwlHF6Cwf2g7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_ups_reduced, y_ups_reduced = get_upsampled_wine_data(wine_reduced)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fa9SR2IvgBmy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_ups_reduced, X_test_ups_reduced, y_train_ups_reduced, y_test_ups_reduced = train_test_split(X_ups_reduced, y_ups_reduced, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IWqJVT0gLFg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ecac66aa-e03c-4c6d-8e39-3e24c75a1b1d"
      },
      "source": [
        "train_and_get_accuracy(X_train_ups_reduced, y_train_ups_reduced, X_test_ups_reduced, y_test_ups_reduced)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Accuracy: 83.62%'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GO4W-wIEgY-H",
        "colab_type": "text"
      },
      "source": [
        "As we can see, accuracy on reduced dataset is even better than on the full one"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LaN0Sc_Xtl5K",
        "colab_type": "text"
      },
      "source": [
        "# Pro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CXSvSOsuFJ5",
        "colab_type": "text"
      },
      "source": [
        "## Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENc1kvOBtnK0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHaxDGpMuHpd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read in data into a dataframe \n",
        "data = pd.read_csv('/content/drive/My Drive/Colab Notebooks/ML/4/Блокноты/Homework 4/Energy_and_Water_Data_Disclosure_for_Local_Law_84_2017__Data_for_Calendar_Year_2016_.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTg_62ZtuMGe",
        "colab_type": "code",
        "outputId": "6dcd638c-b36b-4931-ebf1-df38591dfbfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order</th>\n",
              "      <th>Property Id</th>\n",
              "      <th>Property Name</th>\n",
              "      <th>Parent Property Id</th>\n",
              "      <th>Parent Property Name</th>\n",
              "      <th>BBL - 10 digits</th>\n",
              "      <th>NYC Borough, Block and Lot (BBL) self-reported</th>\n",
              "      <th>NYC Building Identification Number (BIN)</th>\n",
              "      <th>Address 1 (self-reported)</th>\n",
              "      <th>Address 2</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Street Number</th>\n",
              "      <th>Street Name</th>\n",
              "      <th>Borough</th>\n",
              "      <th>DOF Gross Floor Area</th>\n",
              "      <th>Primary Property Type - Self Selected</th>\n",
              "      <th>List of All Property Use Types at Property</th>\n",
              "      <th>Largest Property Use Type</th>\n",
              "      <th>Largest Property Use Type - Gross Floor Area (ft²)</th>\n",
              "      <th>2nd Largest Property Use Type</th>\n",
              "      <th>2nd Largest Property Use - Gross Floor Area (ft²)</th>\n",
              "      <th>3rd Largest Property Use Type</th>\n",
              "      <th>3rd Largest Property Use Type - Gross Floor Area (ft²)</th>\n",
              "      <th>Year Built</th>\n",
              "      <th>Number of Buildings - Self-reported</th>\n",
              "      <th>Occupancy</th>\n",
              "      <th>Metered Areas (Energy)</th>\n",
              "      <th>Metered Areas  (Water)</th>\n",
              "      <th>ENERGY STAR Score</th>\n",
              "      <th>Site EUI (kBtu/ft²)</th>\n",
              "      <th>Weather Normalized Site EUI (kBtu/ft²)</th>\n",
              "      <th>Weather Normalized Site Electricity Intensity (kWh/ft²)</th>\n",
              "      <th>Weather Normalized Site Natural Gas Intensity (therms/ft²)</th>\n",
              "      <th>Weather Normalized Source EUI (kBtu/ft²)</th>\n",
              "      <th>Fuel Oil #1 Use (kBtu)</th>\n",
              "      <th>Fuel Oil #2 Use (kBtu)</th>\n",
              "      <th>Fuel Oil #4 Use (kBtu)</th>\n",
              "      <th>Fuel Oil #5 &amp; 6 Use (kBtu)</th>\n",
              "      <th>Diesel #2 Use (kBtu)</th>\n",
              "      <th>District Steam Use (kBtu)</th>\n",
              "      <th>Natural Gas Use (kBtu)</th>\n",
              "      <th>Weather Normalized Site Natural Gas Use (therms)</th>\n",
              "      <th>Electricity Use - Grid Purchase (kBtu)</th>\n",
              "      <th>Weather Normalized Site Electricity (kWh)</th>\n",
              "      <th>Total GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <th>Direct GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <th>Indirect GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <th>Property GFA - Self-Reported (ft²)</th>\n",
              "      <th>Water Use (All Water Sources) (kgal)</th>\n",
              "      <th>Water Intensity (All Water Sources) (gal/ft²)</th>\n",
              "      <th>Source EUI (kBtu/ft²)</th>\n",
              "      <th>Release Date</th>\n",
              "      <th>Water Required?</th>\n",
              "      <th>DOF Benchmarking Submission Status</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Community Board</th>\n",
              "      <th>Council District</th>\n",
              "      <th>Census Tract</th>\n",
              "      <th>NTA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>13286</td>\n",
              "      <td>201/205</td>\n",
              "      <td>13286</td>\n",
              "      <td>201/205</td>\n",
              "      <td>1013160001</td>\n",
              "      <td>1013160001</td>\n",
              "      <td>1037549</td>\n",
              "      <td>201/205  East  42nd  st.</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>10017</td>\n",
              "      <td>675</td>\n",
              "      <td>3 AVENUE</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>289356.0</td>\n",
              "      <td>Office</td>\n",
              "      <td>Office</td>\n",
              "      <td>Office</td>\n",
              "      <td>293447</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1963</td>\n",
              "      <td>2</td>\n",
              "      <td>100</td>\n",
              "      <td>Whole Building</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>305.6</td>\n",
              "      <td>303.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>614.2</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>5.15506751E7</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>38139374.2</td>\n",
              "      <td>1.10827705E7</td>\n",
              "      <td>6962.2</td>\n",
              "      <td>0</td>\n",
              "      <td>6962.2</td>\n",
              "      <td>762051</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>619.4</td>\n",
              "      <td>05/01/2017 05:32:03 PM</td>\n",
              "      <td>No</td>\n",
              "      <td>In Compliance</td>\n",
              "      <td>40.750791</td>\n",
              "      <td>-73.973963</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Turtle Bay-East Midtown                       ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>28400</td>\n",
              "      <td>NYP Columbia (West Campus)</td>\n",
              "      <td>28400</td>\n",
              "      <td>NYP Columbia (West Campus)</td>\n",
              "      <td>1021380040</td>\n",
              "      <td>1-02138-0040</td>\n",
              "      <td>1084198; 1084387;1084385; 1084386; 1084388; 10...</td>\n",
              "      <td>622 168th Street</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>10032</td>\n",
              "      <td>180</td>\n",
              "      <td>FT WASHINGTON AVENUE</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>3693539.0</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>3889181</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1969</td>\n",
              "      <td>12</td>\n",
              "      <td>100</td>\n",
              "      <td>Whole Building</td>\n",
              "      <td>Whole Building</td>\n",
              "      <td>55</td>\n",
              "      <td>229.8</td>\n",
              "      <td>228.8</td>\n",
              "      <td>24.8</td>\n",
              "      <td>2.4</td>\n",
              "      <td>401.1</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1.96248472E7</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>-3.914148026E8</td>\n",
              "      <td>933073441</td>\n",
              "      <td>9330734.4</td>\n",
              "      <td>332365924</td>\n",
              "      <td>9.62613121E7</td>\n",
              "      <td>55870.4</td>\n",
              "      <td>51016.4</td>\n",
              "      <td>4854.1</td>\n",
              "      <td>3889181</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>404.3</td>\n",
              "      <td>04/27/2017 11:23:27 AM</td>\n",
              "      <td>No</td>\n",
              "      <td>In Compliance</td>\n",
              "      <td>40.841402</td>\n",
              "      <td>-73.942568</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>Washington Heights South                      ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4778226</td>\n",
              "      <td>MSCHoNY North</td>\n",
              "      <td>28400</td>\n",
              "      <td>NYP Columbia (West Campus)</td>\n",
              "      <td>1021380030</td>\n",
              "      <td>1-02138-0030</td>\n",
              "      <td>1063380</td>\n",
              "      <td>3975 Broadway</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>10032</td>\n",
              "      <td>3975</td>\n",
              "      <td>BROADWAY</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>152765.0</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>231342</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1924</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>231342</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>04/27/2017 11:23:27 AM</td>\n",
              "      <td>No</td>\n",
              "      <td>In Compliance</td>\n",
              "      <td>40.840427</td>\n",
              "      <td>-73.940249</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>Washington Heights South                      ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4778267</td>\n",
              "      <td>Herbert Irving Pavilion &amp; Millstein Hospital</td>\n",
              "      <td>28400</td>\n",
              "      <td>NYP Columbia (West Campus)</td>\n",
              "      <td>1021390001</td>\n",
              "      <td>1-02139-0001</td>\n",
              "      <td>1087281; 1076746</td>\n",
              "      <td>161 Fort Washington Ave</td>\n",
              "      <td>177 Fort Washington Ave</td>\n",
              "      <td>10032</td>\n",
              "      <td>161</td>\n",
              "      <td>FT WASHINGTON AVENUE</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>891040.0</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>1305748</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1971</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1305748</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>04/27/2017 11:23:27 AM</td>\n",
              "      <td>No</td>\n",
              "      <td>In Compliance</td>\n",
              "      <td>40.840746</td>\n",
              "      <td>-73.942854</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>Washington Heights South                      ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4778288</td>\n",
              "      <td>Neuro Institute</td>\n",
              "      <td>28400</td>\n",
              "      <td>NYP Columbia (West Campus)</td>\n",
              "      <td>1021390085</td>\n",
              "      <td>1-02139-0085</td>\n",
              "      <td>1063403</td>\n",
              "      <td>710 West 168th Street</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>10032</td>\n",
              "      <td>193</td>\n",
              "      <td>FT WASHINGTON AVENUE</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>211400.0</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>Hospital (General Medical &amp; Surgical)</td>\n",
              "      <td>179694</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>1932</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>179694</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>04/27/2017 11:23:27 AM</td>\n",
              "      <td>No</td>\n",
              "      <td>In Compliance</td>\n",
              "      <td>40.841559</td>\n",
              "      <td>-73.942528</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>Washington Heights South                      ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Order  ...                                                NTA\n",
              "0      1  ...  Turtle Bay-East Midtown                       ...\n",
              "1      2  ...  Washington Heights South                      ...\n",
              "2      3  ...  Washington Heights South                      ...\n",
              "3      4  ...  Washington Heights South                      ...\n",
              "4      5  ...  Washington Heights South                      ...\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRSWshVFjnyT",
        "colab_type": "text"
      },
      "source": [
        "## Data transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUc2IG0hjnby",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Replace all occurrences of Not Available with numpy not a number\n",
        "data = data.replace({'Not Available': np.nan})\n",
        "\n",
        "# Iterate through the columns\n",
        "for col in list(data.columns):\n",
        "    # Select columns that should be numeric\n",
        "    if ('ft²' in col or 'kBtu' in col or 'Metric Tons CO2e' in col or 'kWh' in col or 'therms' in col or 'gal' in col or 'Score' in col):\n",
        "        # Convert the data type to float\n",
        "        data[col] = data[col].astype(float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1MWtQqxjvMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function to calculate missing values by column\n",
        "def missing_values_table(df):\n",
        "        # Total missing values\n",
        "        mis_val = df.isnull().sum()\n",
        "        \n",
        "        # Percentage of missing values\n",
        "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
        "        \n",
        "        # Make a table with the results\n",
        "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "        \n",
        "        # Rename the columns\n",
        "        mis_val_table_ren_columns = mis_val_table.rename(\n",
        "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
        "        \n",
        "        # Sort the table by percentage of missing descending\n",
        "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
        "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
        "        '% of Total Values', ascending=False).round(1)\n",
        "        \n",
        "        # Print some summary information\n",
        "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
        "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
        "              \" columns that have missing values.\")\n",
        "        \n",
        "        # Return the dataframe with missing information\n",
        "        return mis_val_table_ren_columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPxdrF0ojx4q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "36b44c67-1bf1-4b43-be1b-834074ee3ac0"
      },
      "source": [
        "missing_values_table(data)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 60 columns.\n",
            "There are 46 columns that have missing values.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fuel Oil #1 Use (kBtu)</th>\n",
              "      <td>11737</td>\n",
              "      <td>99.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diesel #2 Use (kBtu)</th>\n",
              "      <td>11730</td>\n",
              "      <td>99.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Address 2</th>\n",
              "      <td>11539</td>\n",
              "      <td>98.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Oil #5 &amp; 6 Use (kBtu)</th>\n",
              "      <td>11152</td>\n",
              "      <td>94.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>District Steam Use (kBtu)</th>\n",
              "      <td>10810</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Oil #4 Use (kBtu)</th>\n",
              "      <td>10425</td>\n",
              "      <td>88.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3rd Largest Property Use Type - Gross Floor Area (ft²)</th>\n",
              "      <td>10262</td>\n",
              "      <td>87.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3rd Largest Property Use Type</th>\n",
              "      <td>10262</td>\n",
              "      <td>87.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Oil #2 Use (kBtu)</th>\n",
              "      <td>9165</td>\n",
              "      <td>78.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2nd Largest Property Use Type</th>\n",
              "      <td>8005</td>\n",
              "      <td>68.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2nd Largest Property Use - Gross Floor Area (ft²)</th>\n",
              "      <td>8005</td>\n",
              "      <td>68.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Metered Areas  (Water)</th>\n",
              "      <td>4609</td>\n",
              "      <td>39.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water Intensity (All Water Sources) (gal/ft²)</th>\n",
              "      <td>3984</td>\n",
              "      <td>33.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water Use (All Water Sources) (kgal)</th>\n",
              "      <td>3984</td>\n",
              "      <td>33.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Longitude</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Community Board</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Council District</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Census Tract</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NTA</th>\n",
              "      <td>2263</td>\n",
              "      <td>19.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENERGY STAR Score</th>\n",
              "      <td>2104</td>\n",
              "      <td>17.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Site Natural Gas Intensity (therms/ft²)</th>\n",
              "      <td>1963</td>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Site Natural Gas Use (therms)</th>\n",
              "      <td>1962</td>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Source EUI (kBtu/ft²)</th>\n",
              "      <td>1465</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Site EUI (kBtu/ft²)</th>\n",
              "      <td>1465</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Natural Gas Use (kBtu)</th>\n",
              "      <td>1442</td>\n",
              "      <td>12.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Site Electricity Intensity (kWh/ft²)</th>\n",
              "      <td>787</td>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather Normalized Site Electricity (kWh)</th>\n",
              "      <td>786</td>\n",
              "      <td>6.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electricity Use - Grid Purchase (kBtu)</th>\n",
              "      <td>244</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Site EUI (kBtu/ft²)</th>\n",
              "      <td>163</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source EUI (kBtu/ft²)</th>\n",
              "      <td>163</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NYC Building Identification Number (BIN)</th>\n",
              "      <td>162</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Street Number</th>\n",
              "      <td>124</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Street Name</th>\n",
              "      <td>122</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Borough</th>\n",
              "      <td>118</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOF Gross Floor Area</th>\n",
              "      <td>118</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Water Required?</th>\n",
              "      <td>118</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Direct GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <td>83</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <td>74</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Indirect GHG Emissions (Metric Tons CO2e)</th>\n",
              "      <td>65</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Metered Areas (Energy)</th>\n",
              "      <td>57</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOF Benchmarking Submission Status</th>\n",
              "      <td>30</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NYC Borough, Block and Lot (BBL) self-reported</th>\n",
              "      <td>11</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BBL - 10 digits</th>\n",
              "      <td>11</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Largest Property Use Type</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Largest Property Use Type - Gross Floor Area (ft²)</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    Missing Values  % of Total Values\n",
              "Fuel Oil #1 Use (kBtu)                                       11737               99.9\n",
              "Diesel #2 Use (kBtu)                                         11730               99.9\n",
              "Address 2                                                    11539               98.2\n",
              "Fuel Oil #5 & 6 Use (kBtu)                                   11152               94.9\n",
              "District Steam Use (kBtu)                                    10810               92.0\n",
              "Fuel Oil #4 Use (kBtu)                                       10425               88.8\n",
              "3rd Largest Property Use Type - Gross Floor Are...           10262               87.4\n",
              "3rd Largest Property Use Type                                10262               87.4\n",
              "Fuel Oil #2 Use (kBtu)                                        9165               78.0\n",
              "2nd Largest Property Use Type                                 8005               68.2\n",
              "2nd Largest Property Use - Gross Floor Area (ft²)             8005               68.2\n",
              "Metered Areas  (Water)                                        4609               39.2\n",
              "Water Intensity (All Water Sources) (gal/ft²)                 3984               33.9\n",
              "Water Use (All Water Sources) (kgal)                          3984               33.9\n",
              "Latitude                                                      2263               19.3\n",
              "Longitude                                                     2263               19.3\n",
              "Community Board                                               2263               19.3\n",
              "Council District                                              2263               19.3\n",
              "Census Tract                                                  2263               19.3\n",
              "NTA                                                           2263               19.3\n",
              "ENERGY STAR Score                                             2104               17.9\n",
              "Weather Normalized Site Natural Gas Intensity (...            1963               16.7\n",
              "Weather Normalized Site Natural Gas Use (therms)              1962               16.7\n",
              "Weather Normalized Source EUI (kBtu/ft²)                      1465               12.5\n",
              "Weather Normalized Site EUI (kBtu/ft²)                        1465               12.5\n",
              "Natural Gas Use (kBtu)                                        1442               12.3\n",
              "Weather Normalized Site Electricity Intensity (...             787                6.7\n",
              "Weather Normalized Site Electricity (kWh)                      786                6.7\n",
              "Electricity Use - Grid Purchase (kBtu)                         244                2.1\n",
              "Site EUI (kBtu/ft²)                                            163                1.4\n",
              "Source EUI (kBtu/ft²)                                          163                1.4\n",
              "NYC Building Identification Number (BIN)                       162                1.4\n",
              "Street Number                                                  124                1.1\n",
              "Street Name                                                    122                1.0\n",
              "Borough                                                        118                1.0\n",
              "DOF Gross Floor Area                                           118                1.0\n",
              "Water Required?                                                118                1.0\n",
              "Direct GHG Emissions (Metric Tons CO2e)                         83                0.7\n",
              "Total GHG Emissions (Metric Tons CO2e)                          74                0.6\n",
              "Indirect GHG Emissions (Metric Tons CO2e)                       65                0.6\n",
              "Metered Areas (Energy)                                          57                0.5\n",
              "DOF Benchmarking Submission Status                              30                0.3\n",
              "NYC Borough, Block and Lot (BBL) self-reported                  11                0.1\n",
              "BBL - 10 digits                                                 11                0.1\n",
              "Largest Property Use Type                                        2                0.0\n",
              "Largest Property Use Type - Gross Floor Area (ft²)               2                0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mj2IPdKGj1nk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0d02c7ad-a5f6-4bc8-ce21-60a8ed5c5fbe"
      },
      "source": [
        "missing_df = missing_values_table(data);\n",
        "missing_columns = list(missing_df[missing_df['% of Total Values'] > 50].index)\n",
        "print('We will remove %d columns.' % len(missing_columns))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 60 columns.\n",
            "There are 46 columns that have missing values.\n",
            "We will remove 11 columns.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MpUqwj4j4ss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop the columns\n",
        "data = data.drop(columns = list(missing_columns))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsbBajrTkSnf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b1e4965b-d6af-41eb-d8b8-bbe58d2c93d4"
      },
      "source": [
        "# Copy the original data\n",
        "features = data.copy()\n",
        "\n",
        "# Select the numeric columns\n",
        "numeric_subset = data.select_dtypes('number')\n",
        "\n",
        "# Create columns with log of numeric columns\n",
        "for col in numeric_subset.columns:\n",
        "    # Skip the Energy Star Score column\n",
        "    if col == 'score':\n",
        "        next\n",
        "    else:\n",
        "        numeric_subset['log_' + col] = np.log(numeric_subset[col])\n",
        "        \n",
        "# Select the categorical columns\n",
        "categorical_subset = data[['Borough', 'Largest Property Use Type']]\n",
        "\n",
        "# One hot encode\n",
        "categorical_subset = pd.get_dummies(categorical_subset)\n",
        "\n",
        "# Join the two dataframes using concat\n",
        "# Make sure to use axis = 1 to perform a column bind\n",
        "features = pd.concat([numeric_subset, categorical_subset], axis = 1)\n",
        "\n",
        "features.shape"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:856: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:856: RuntimeWarning: invalid value encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11746, 116)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiX-q_DGkX3B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b738c331-7a4d-4aaf-afe8-474018b0ab1e"
      },
      "source": [
        "# Extract the buildings with no score and the buildings with a score\n",
        "no_score = features[features['ENERGY STAR Score'].isna()]\n",
        "score = features[features['ENERGY STAR Score'].notnull()]\n",
        "\n",
        "print(no_score.shape)\n",
        "print(score.shape)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2104, 116)\n",
            "(9642, 116)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3dTuHQWkbMp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "19c1eab6-5c76-4fa2-e2dd-b8b23adada83"
      },
      "source": [
        "# Separate out the features and targets\n",
        "features = score.drop(columns = 'ENERGY STAR Score')\n",
        "targets = pd.DataFrame(score['ENERGY STAR Score'])\n",
        "\n",
        "# Replace the inf and -inf with nan (required for later imputation)\n",
        "features = features.replace({np.inf: np.nan, -np.inf: np.nan})\n",
        "\n",
        "# Split into 70% training and 30% testing set\n",
        "X, X_test, y, y_test = train_test_split(features, targets, test_size = 0.2, random_state = 42)\n",
        "\n",
        "print(X.shape)\n",
        "print(X_test.shape)\n",
        "print(y.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7713, 115)\n",
            "(1929, 115)\n",
            "(7713, 1)\n",
            "(1929, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0Ld-oVAlFqm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Replace all occurrences of NaN with python integer max size (for further feature selection)\n",
        "features = features.replace({np.nan: sys.maxsize})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNgcxypLkueT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "outputId": "c80f253e-3265-4306-b71c-4b056404a0bc"
      },
      "source": [
        "# ExtraTreesClassifier (feature importance)\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(features, targets)\n",
        "\n",
        "feat_importances = pd.Series(model.feature_importances_, index=features.columns)\n",
        "\n",
        "et_clf = feat_importances.nlargest(10).index\n",
        "\n",
        "feat_importances.nlargest(10).plot(kind = 'bar')\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j+vTpWLFiBfR6PQIDA+Hm5oY9e/bA09MT/fr1w7BhwxAREYFPP/0Utra2CAoKAgCcOHEC\n9+/fx/79+7F//34AwKJFi9CqVav/OWBCCCF/z2P10fv5+cHPz89o24QJE/ifraysEBwcXOvfvf76\n63j99df/YYiEEEL+CZoZSwghEkeJnhBCJI4SPSGESBwlekIIkThK9IQQInGU6AkhROIo0RNCiMRR\noieEEImjRE8IIRJHiZ4QQiSOEj0hhEgcJXpCCJE4SvSEECJxlOgJIUTiKNETQojEUaInhBCJo0RP\nCCESR4meEEIkjhI9IYRIHCV6QgiROEr0hBAicZToCSFE4ijRE0KIxFGiJ4QQiaNETwghEkeJnhBC\nJI4SPSGESBwlekIIkThK9IQQInGU6AkhROIo0RNCiMRRoieEEImjRE8IIRJHiZ4QQiSOEj0hhEgc\nJXpCCJE4SvSEECJxFo9zUHJyMiIjI6HX6zF8+HCMGzfOaL9Go0FERASysrJgZ2eHoKAgODs7AwAO\nHjyImJgYmJmZYdq0afD19W36V0EIIaRejZ7R6/V6bN++HQsXLsS6desQHx+Pe/fuGR0TExMDGxsb\nhIeHY8yYMYiKigIA3Lt3DwkJCVi7di0+//xzbN++HXq9XphXQgghpE6NJvrMzEy4urrCxcUFFhYW\nGDhwIC5evGh0TFJSEgICAgAA/v7+SElJAcdxuHjxIgYOHAhLS0s4OzvD1dUVmZmZgrwQQgghdWu0\n60apVMLR0ZF/7OjoiIyMjHqPMTc3h1wuR1lZGZRKJZ5++mn+OAcHByiVylptREdHIzo6GgAQFhaG\ndu3aNRzU0aTGwhaeGGIAxBGHGGIAxBGHGGIAxBGHGGIAxBGHiWMQxc3YESNGICwsDGFhYU3yfKGh\noU3yPP+UGOIQQwyAOOKgGP5LDHGIIQZAHHEIHUOjid7BwQGFhYX848LCQjg4ONR7jE6nQ2VlJezs\n7Gr9W6VSWevfEkIIEVajid7T0xN5eXkoKCiAVqtFQkIC+vXrZ3RM3759cfr0aQDA+fPn4eXlBZlM\nhn79+iEhIQEajQYFBQXIy8tDly5dBHkhhBBC6mb+1VdffdXQAWZmZnB1dUV4eDhOnDiBIUOGwN/f\nH3v27IFKpUK7du3g7u6Os2fPYvfu3cjOzsYHH3wAW1tbtGrVCuXl5di8eTPOnj2L6dOnN97/3kQ8\nPDyYtNMYMcQhhhgAccRBMfyXGOIQQwyAOOIQMgYZx3GcYM9OCCHE5ERxM5YQQohwKNETQojEUaIn\nhDyRnqRZ+o9V66Y54DgOcXFxKCgowBtvvAGFQoHi4mKTjPJRqVSwsrKCmRn7v6OFhYWIj49Hamoq\nioqKYGVlBTc3N/j5+cHX15dJTGKIAQBu3bqFmzdvGsXQq1cv2NraMmkfALRaLS5duoTU1FQolUpY\nWVnB3d0dfn5+zAYmGJSXl/O/izZt2jD9fJaXlze4n9V7cuHCBcTGxiI9PR06nQ56vR7Ozs7o06cP\nRowYAScnJyZxqNVqXLp0qdbn08/PD25ubk3enmRuxm7duhUymQzXr1/HunXrUF5ejhUrVmDlypWC\nt63X65GQkIC4uDjcunULlpaW0Gg0sLOzg5+fH55//nm4uroKHsfGjRuhVCrh5+cHT09PtGrVCmq1\nGnl5ebh+/TqysrLwzjvvoGfPnpKO4dSpUzhx4gTatGkDDw8PtGrVChqNBrm5uUhLS4Obmxveeust\nwb/U+/fvx4ULF9CjRw8+jpq/C47jMHnyZLi7uwsWQ2VlJU6cOIH4+HhotVrY29tDo9GgpKQETz/9\nNF544QV4e3sL1r7BzJkzIZPJwHEcFAoFbG1twXEcKioq4OTkhA0bNggew1dffYVu3bqhf//+6NSp\nEywsqs9zlUolrl+/jvj4eAwePBiDBw8WNI69e/fizz//hJeXFzw8PPj3JC8vDykpKdBoNJgyZQo6\nduzYdI1yEjF//nyO4zhu3rx5/La5c+cyaXvx4sXcvn37uOzsbE6n0/Hby8rKuHPnznFff/01d+bM\nGcHjyMnJaXC/RqPh8vLyJB/D8ePHuYcPH9a7//bt29zVq1cFjYHjOO7ChQsN7lcqlVxGRoagMSxd\nupQ7c+YMV15eXmvfrVu3uMjISO7kyZOCxlDTpk2buD///JN/fOnSJW7z5s1M2m7oM/F3jvmnar7+\nuhQXF3OZmZlN2qZkEv2CBQs4nU7HJ/ySkhKjpC8kjUbTJMf8UzqdjlOr1RzHVX9YTEEMMYiV4ffy\nJAsODn6sbULLzMzk4uLiOI6rPiFTKBTMY+A4zujEUEiSuRn74osv4uuvv0ZJSQl++uknfPHFF3j1\n1VeZtF1YWIisrCwAwM2bN+s8xnCZKKS1a9di3bp1uH37Nn7++WfB2xNrDD/99BPWr1+PiooK7Nq1\nyyQx1JSRkYE5c+Zg1qxZAIDs7Gzs2LGDWdsXLlwAAP7/puTg4IADBw6goKAABQUF+OWXX5iXRTl0\n6BB++ukn7Nu3D0D1PbX169cza3/Tpk0ICwvDgwcPsHXrViZtSibRDxkyBJMmTcK4cePQunVrzJs3\nDwMGDGDS9rZt23Du3DmcOXMGsbGxTNqsi729PebOnYuYmBgoFIonNobCwkK8++672LlzJ4qLi00S\nQ03ff/89QkNDYWdnBwDo1KkTrl+/zqTtffv2oaioCL/++isuX77MpM2GfPbZZygtLcWaNWuwZs0a\nlJSU4LPPPmMaQ3x8PBYuXIgWLVoAAJycnFBZWcmsfZlMhuDgYBw4cABlZWVM2pREotfr9QgKCkL7\n9u0xatQojBo1Ch06dGDWftu2bTFx4kSUl5fXKuHMkpubG8zMzDBlyhRYW1s/sTHI5XK0atUK48eP\nZ5ZQG6LX69GmTRujbaxGvLRt2xYjR46Ek5MTrl27xqTNhtja2mLatGlYvXo1Vq9ejWnTpjEdBQUA\nlpaWMDMzg0wmA1A9AoYlBwcHWFlZYdKkScxOhiQxvNLMzAzt2rWDQqFgNjyqpj59+gAAxowZA3Nz\nc+btG7z44osAgD/++ANz5swx2nfs2DGMHj36iYhh+vTpAACFQoGNGzca7UtNTUX37t0Fj6EmR0dH\nZGZmQiaTQa/X4/jx42jbti2Ttg0jSAYNGsSfwZpCWFgYn1jrEhISwiyWfv36YceOHaisrERsbCxO\nnjyJ5557jln748ePBwBcu3atVmn2c+fOCdITIYkzegCoqKhAcHAwli5dilWrVvH/sWBI9OHh4Rg1\napTRvvDwcCYx1HTmzJla2wzVRZ+kGCIjI2udOUdGRjKNAQDef/99HDlyBAqFAu+//z4yMjLwr3/9\ni0nbhoV/li5dWqvq7NKlS5nEAABjx47Fyy+/XO9/LL322mvo1asX+vTpg4yMDJPEAFTfK3icbU1B\nEmf0ADBhwgRTh1BrLV29Xs/fpGXh7NmzOHv2LAoKCoz+yKlUKmaXx2KIIT09HWlpaSgtLcWRI0f4\n7ZWVlSaZDdmqVSsEBQUxbxeo7pZQq9UoKyszmrRUWVlZ52pvQhFy3sT/ol+/frX+8LFy+fJlXL58\nGUql0uimfFVVlWBdepJJ9D179kRxcTFu3boFAOjSpQtatWrFpO2DBw/i4MGDUKvVmDp1KoDqmboW\nFhYYMWIEkxiA6jKnrVu3RllZmdEZirW1ddNOvhB5DFqtFiqVCjqdDlVVVfx2uVyO4OBgJjEYXLt2\nDYWFhfDx8TFakvP06dP8OstCio6OxtGjR1FUVITQ0FBw/39+pFwur3X1KXVFRUXYvXs3lEol+vTp\ng9GjR/OJde3atcw+Gw4ODvDw8EBSUpJRaeKWLVvy+aOpSSbRJyQk4Mcff+TPHHbs2IHJkyfD399f\n8LZ79OiBV199FVFRUZg4caLg7dVn/fr1WLVqFVxcXEx2BiWGGE6ePIlPP/0UcrkcY8aMMUkMAPDz\nzz/j+vXr6NSpE/bv34+xY8fihRdeAAAcP36cSaJv3bo1NmzYwOz+iJht3LgRvr6+eP755xETE4Ol\nS5di/vz5kMvluH//PrM4du7cicWLFyMnJ4fJZwCQUKI/ePAgVq5cyZ/Fl5aWYtmyZUwSfWRkJFat\nWoWrV6+aNNFrtVqcPXsWGRkZSExMrLX/2WeffSJiyMrKglKpxOnTp+u8ycaqC+nixYtYtWoVLCws\nUF5ejvXr1yM/Px+TJ0/mz6yFdujQIQwYMABnzpwRRaK/c+eOoCUfGlJcXMz/4e/atStiYmKwePFi\nhIaGNnijuKkVFRUhLS0NycnJGDp0aK3PghALkEgm0ev1eqOuGltbW2b9sRYWFti8eXOtPjcDwygQ\nob3//vuIi4tDRUUF/vzzz1r7WSRZMcTw/PPPY9myZcjPzzfqrgCqxzBHREQIHgNQ/Zk0TJSztbVF\naGgoNm3ahG+//RY6nY5JDLa2tli+fHmteyYGLEe7ANVzTjQaDQICAjBkyBDI5XJmbWu1Wmi1Wv49\nGTZsGJ566iksXboUDx8+ZBbH+PHjceDAARQWFuKHH36otf/LL79s8jYlU9Rs165duHPnDgYNGgSg\nuivH3d0dkyZNErzt0tJSXLt2DVFRUfzQqZpYXZ4ZFl+PiYnBsGHDmLQpxhgMtm7divfff99k7a9c\nuRLjxo1Djx49jLZHRUXh8OHD2LNnj+AxaLVaZGVlISIiAh9++GGt/aboXsvLy8OpU6dw7tw5dOnS\nBYGBgejVq5fg7R46dAhPP/00vLy8jLZnZmbihx9+YDYKyfDHZv/+/XjjjTeYtCmZRA8AiYmJSE1N\nBVDdb/7MM88waffs2bPo3bs3lEolsxuOdVm5ciXKy8vRs2dP+Pr6onv37szH9YshhpCQEHTv3h19\n+vRBz549YWVlxbR9A5VKBQB1Thx78OBBrUlUQjh48CB8fX3h6OgIe3t7wdt7XHq9HhcuXEBkZCTk\ncjk4jsPbb7/N5IrP1EJDQ+Hg4ABfX1/4+vrC2dlZ8DYlk+gLCgrw1FNP8V9qtVqN4uJiJr/EQ4cO\n4cqVK9DpdPD29kafPn3QpUsXpv1+Bmq1Gjdu3MDly5eRlpYGJycn/gPFsta2KWPQ6XRITU1FcnIy\nrl+/Djs7O/Tu3Ru+vr7Ma8BrtVokJCTg7t27AKpnDg8cOJBJ7SOg+sr28uXLyMnJQceOHdGnTx/m\nNflrysnJwalTp3D58mX4+Phg2LBh8PDwgFKpxKJFi2pNcGtqKSkpOHToED8U2s3NDa+88gqTUs01\nFRQUIDk5GcnJySgqKkK3bt34ExNLS8smb08yiT40NBTLly/nv0BarRZffPEFk3r0BlVVVbh27RqS\nk5ORmZmJ9u3bw9fXF71798ZTTz3FLI6aCgoKcPnyZSQnJ6O4uJjp70MsMSiVSv5LlZ+fj6effprJ\nhKW//voLq1atQpcuXfgbbFlZWcjMzMT8+fOZlukAgNu3byM5ORlXr16FXq+Hj48PfH19mS7O8+WX\nX2L48OHw9/evdaUVGxuLoUOHCtb26dOncfToUbz99tvw9PQEUL04zc8//4zRo0cz62J9lFarNTox\nsbe3x4IFC5q2ESY1Mhmoq/Y8q3r09bl79y53+PBhbvny5SaNw4BFqWSxx6DT6bibN28yaWvp0qXc\n5cuXa22/fPkyt2TJEiYx1KeiooI7d+4c99133zFt98iRI7W2HT16lEnbs2fPrrN0dnFxMRcUFMQk\nhsdRWFjY5M8pmVE39vb2SEpK4me7Xbx4ka8WyMqNGzdqbfP09GQ+vXrKlCn8aj46nQ5arRbW1tbY\nuXPnExXDkiVL6twuxKiGuhQWFsLX17fWdl9fX3z//fdMYqgpLS0NDx48MBrxM2PGDKYxxMbG1prb\ncPr0aSZDPzmOq3MSJauJlY8yrLpVk0wmE6RsimQS/fvvv4/w8HBs374dQHUhqU8++YRpDIcPH+Z/\n1mg0yMzMhIeHB7PEYlBzyBbHcbh48SLzqppiiGHy5Mn8z2q1GomJiUxvDOv1eqPhfAYajYZ5KYbw\n8HDk5+ejU6dORtPsWRXzMpTGyM/PN1lpDGtra9y9e7fWmqx37941ScG3mgXNNBoNzp071+jauv+z\nJr9GMLGqqiquqqrK1GFwHMdxDx484L7++mtTh8FxHMdstS2xxxAaGsqsrb1793JhYWFGqxcpFApu\n9erV3J49e5jFwXEcFxQUxOn1eqZt1lRQUMClpKRwCxcu5K5fv87/d+vWLU6r1TKJ4dq1a9xHH33E\nHTx4kLty5Qp35coV7pdfflZZZNkAACAASURBVOE+/vhj7tq1a0xiaIxhhbym1uzP6JOSktCxY0d+\nqNqRI0eQmJgIJycnTJs2jcmom/o4Ojrir7/+Yt5uzRmpHMfxC5Y/aTHUPDsyFJhjucDEm2++iaNH\nj2LhwoX8GbyZmRlefvll5qUZ3NzcUFxcjNatWzNt16BNmzZwdHSElZWVyUpjeHt7Y9myZTh+/DiO\nHj0KAOjQoQOWLFlikvLmNQseGr4jQl3pNftE//PPP2PFihUAgD///BNxcXH47LPPcPv2bWzduhWf\nf/45s1hqzorlOA7Z2dno3Lkzs/YNas5INTMzg7OzM+bPn//ExRASEsLfJzA3N4ezszM++ugjpjGM\nGTMGY8aM4f/osB7WaKgDr1KpEBwcjC5duhh1JbGcGWtY7KOyspLpjFiD8vJyqNXqWpMo8/LyUF5e\nzvy9qbnMpeE7Mnv2bEHaavaJXiaT8f1riYmJCAwMhIeHBzw8PPD7778zjaVmjQpzc3MMGjSI+SIX\nQPXU7kfbTU1NZXrTSQwxrFu3rtYQPo1Gw6z9Y8eOwdraGsOGDTNKIjExMXj48CG/SIuQxo4dK3gb\nf4e1tTXmzJmDXr16GfWLsygTsmPHDgQGBtZa9KWwsBD79+/Hp59+KngMNX344YdwcXEx2lZQUCBI\nW81+4RGO46BSqaDX65GSkgIfHx9+H+slwiorKxEQEMDX8ejevTuOHTvGNAag7sU1WC+4IYYYvvji\ni1rbFi1axKz9+saFDxkyBCdPnmQSw+3bt2FlZYVu3bqhZ8+etf5j7ZlnnsGECRPQo0cP/oRMiCJe\ndcnNzTXKDwbe3t7Izs5mEkNNa9eurbXtm2++EaStZn9GP3r0aMybNw9yuRzt27fnJ0Lcvn2beX9k\nXRUCWQ0dA8Sx4IYYYiguLoZSqYRarcbt27f5omZVVVVMi1fpdLo6Z8BaWloyq15ZWFiI77//Hn/9\n9Rfc3d3RrVs3/j9TzI4NCAiAWq2GQqFgPku55toEj2JVZA6onkh39+5dVFZWGt3LqqqqEuyKs9kn\n+mHDhsHX1xclJSVGdWaeeuopfPzxx0xiEMOqSoA4FtwQQwzJyck4c+ZMreqA1tbWePvtt5nEAFTf\nAC4tLa1VY6akpIRZDFOmTAFQ/b7cunULaWlpOHXqFLZs2QK5XI5169YxiwWoHjyxa9cuaLVabNiw\nAdnZ2dizZw+TewXOzs64evVqrQJq165dY3ozNjc3F5cuXapV4dXa2lqweQ3NPtED1Su2ODg4GG1j\neTbfrVs3k6+qBIC/HA8ICGBSMEusMRi6z86fP89kPYL6vPzyy1i5ciWmTp1qVAJh165deOmll5jG\nolarUVVVhcrKSlRVVaF169YmqQu/b98+rFy5El999RUAoFOnToL1Sz9q6tSpWLVqFXx8fIzej2vX\nrjG9Kd2/f3/0798f6enp6Nq1K5M2JZHoTa1NmzZo06YNP/oHqC5dbGdnx7SwWWlpKX777TfY2Nhg\n2LBh2LVrF1JTU+Hi4oIpU6bA1dX1iYgBqJ6lbLjplpCQgJs3b8LFxQUjR45kNswzICAAdnZ2iIqK\nwt27dyGTydChQwe8+uqrzNYr3bx5M+7duwdra2s8/fTT6NatG1566SWTFTWzsLCoNeKG1XekQ4cO\nWLNmDc6cOcP3yXfs2BFTpkyps8KoUNRqNRISEmBrawuO43D48GH+8/n6668LUmVUMkXNTCk9PR27\nd++Gra0tXn/9dURERKC0tBQcx+GTTz6pcxq8EJYvXw4PDw+oVCpcu3YNgYGB6Nu3L27evImzZ8/y\nZ1FSj2Hbtm24c+cONBoN2rZtC5VKBV9fX6SlpYHjOMyaNUvwGMRixYoVKCsrg5ubG7p164auXbvC\nzc3NJJVVAWDTpk3w8fHBoUOHMGfOHBw/fhxarRYffPCBSeIxhbVr18LCwgIqlQoVFRVwc3NDv379\nkJqaiuzsbISGhjZ9o4JMwxKB8vJy7sCBA0zaCgkJ4ZKTk7mEhATu3Xff5dLS0jiO47h79+4xnQ1q\nKOKm1+u5Dz/8sM59T0IMhgJVDx8+5KZNm8bpdDo+puDgYCYxiIler+dycnK4P/74g4uIiOBCQkK4\nZcuWMZ+dy3Ecp1KpuN27d3OhoaFcSEgIt3v3bu7hw4fM4zAlw2dQq9Vy//rXv4z2CfUdafZdNwqF\nAgcOHEBRURH69++PQYMGYe/evYiNjeVXmxKaTqdD7969AQB79+7l+93at2/PpH0DQw0TmUxW6/Kv\nZn0TqcdgGDtvZWWFNm3aGMXEqg68mMhkMri7u8PGxgZyuRxyuRyXLl1CZmZmnSuiCalFixZ4++23\n8corr0Amk6Fly5ZM2xcDw2fQ3Ny81r1Fob4jzf5Tv2HDBvTo0QPPPvsskpOTsWDBAnTs2BFr1qxh\nVgO+5pvz6AQdlpfIhoJRHMcZFY/iOI7ZDS8xxFBSUoIjR46A4zj+Z0MMpaWlTGIQi2PHjvFDXi0s\nLNC1a1d069YNgYGBJrkZm5mZiU2bNvGrb8nlcnz00UfMxtI/SqVSQalUMh3qWVhYyM+ir/kzUL12\nghCafR/9vHnz8PXXX/OPP/zwQ2zcuJHZ2SMATJgwAdbW1uA4Dmq1mp/xx3EcNBoNfvrpJyZx1FUm\nuSYWE2TEEMO+ffsa3P/mm28KHkNNJ06cwNChQyGXy7Flyxbcvn0b77zzTp2Td5razp07+XHzpqpz\nU9PcuXPx3nvv8evopqamYtu2bVizZg2zGJYvX47g4GBwHIeQkBC0bNkSvr6+mDhxIpP2T58+3eB+\nIRZAafZn9IBx8So7OzujwlUsRhewWOT5cRiSaFZWVq0zpJrjdaUegyGR11W/hNVVRU3R0dEYNWoU\nrly5guLiYsyYMQObNm0ymnMhlKlTpwJAnYu1R0VFMUtuBmZmZkaLpZtiTeHS0lLI5XLExMRg8ODB\neOuttzB37lxmvwtDIi8oKKhVdDEzM1OQNpt9CYTKykqEhoYiJCQEISEhqKys5H8W5O51A2JiYmpt\ni4qKYhoDUD2k7s6dO/zjs2fP4sCBA09cDKtWrTL6o3/v3j0myfVRhu67y5cvY+jQoejUqROzmbEG\niYmJiIuL4x9v27bNJN1YPXv2xJYtW3D9+nXcuHED27ZtQ8+ePZGVlWVUzVFIOp0O5eXlSExMRN++\nfZm0WZdvvvnGqKvmxo0b2LRpkyBtNfsz+g0bNpg6BF5iYiIsLS0xZMgQANVfJtb1dgAgODgYa9eu\nxaxZs3Dz5k3ExsYyrfEilhheffVVrFq1CgsWLEBubi4iIiJMMrSyY8eOWLlyJXJzc/H2229DpVIx\nH944Z84crFq1CjKZDMnJybCxsWGybu6jcnJyAAD79+832m4Y185ikZ5x48bh888/R7du3fD0008j\nPz/fJGWK33//fXz99dcICQlBVlYWfvrpp6ZfK/b/a/Z99HW5f/8+zp49i4SEhDoLBwlFrVZj1apV\nCAwM5L9M06ZNY9Z+Tbm5ufj666/h5OSEefPm1bpJ/KTEcOHCBRw+fBhVVVWYM2cO8/oqwH9r4bu6\nusLW1halpaUoLCxkUsK6ZrdmVVUVvv76a3Tr1g0TJkwAwL5sMjGWnp6OLVu2wNLSEgsWLBBkshQg\noUSvVCqRkJCA+Ph43LlzB+PGjcOzzz7LZGSBWL5Mc+bMMTpTLCkpgVwu52eCsrjhJYYYao5iAICU\nlBS4uLjwJRlYlMR9VHFxMRQKhVHxrG7dugnermFdUo7j+P8byGQyRERECB5DTZWVldi3bx9u3rwJ\noLor54033mBan768vBynT59GQUGBUaE9Vlc4hjUCDO7du4fWrVvDxsYGgDBrBDT7RB8dHY34+Hgo\nlUoMGDAAAwYMwOrVq5l26Yjly/TgwYMG97OoPSOGGEwxqqEhu3fvRlxcHNq3b280pl+oy3QxW7Nm\nDdzd3fm1amNjY5GTk4O5c+cyi2Hx4sXo3LkzPDw8jEbnGbpchWaKkWnNvo9++/bt6Nq1K2bNmsWX\nKGbd/ymW+wR2dnaN1uxQqVSC1vUQQwysE3ljEhMTsX79epN0XaWmpja4+E1lZSUUCgWzMfX5+flG\nSf3NN9/EvHnzmLRtUFVVZbIuVQDo0aNHoznKcNLYVMy/YlF8REAjR46ETCbDkSNHcOjQIZSUlOD2\n7dtMqwOmpqY2eDOnsrISeXl5gq+u9O9//xs5OTmwtLSEnZ0dPwMvPz8fFy5cwO7duyGXy9GhQwdJ\nxxAWFgZra2s4OzvXmk+Rn5+Po0ePoqCggNkyj5cuXcKAAQNMMiv3yJEj2L9/P5RKJV9b5f79+7h2\n7RqOHDmCP/74A97e3rVmaArlzJkzaN++Pf99SU1NxfXr1zF8+HAm7QPVwxoNS/eZwpIlS/gKojW7\nrLRaLW7cuIG9e/eiqqoKnTp1arI2m33XTU2FhYV8P/3Dhw/Rv39/vPPOO4K3+/333yMzMxO+vr7w\n8PCAvb091Go17t+/j+vXr0OhUGDy5Mno0qWL4LFcunQJZ8+eRVpaGsrLy2Fubo527drBz88Pw4YN\nYzJb2NQxFBcX84vE29ra8u9HQUEBXF1dMWrUKPTv31/QGGpau3YtcnJy4OPjY1Q50zDGXWjl5eU4\nf/480tLSUFxcDEtLS3To0AF+fn7Ml7rMzs7Ghg0b+GGvNjY2mDlzJtNy3u+99x7Ky8thbW3NLwIj\nk8mwbds2Ju2r1WqcOnWKX8NCLpdDrVaD4zj06tULI0eObPKTkGaf6Our6Zybm4uEhAS88cYbTOIQ\n05eJ/FdBQQGKi4thZWWFtm3bGq1Tykpd8ysA1JrAJHV6vR7nz5/HwIED+URvikXC61vFiVX56pq0\nWi3KyspgZWXF34wVQrNP9CEhISaZBEPI36HT6XD//n0AgKurK/PZoGIRGhqKsLAwU4eBK1euGI38\neXTVKalp9jdjCRG7mzdvIiIiAg4ODuA4DsXFxfjkk0+eyCs9Hx8fHD58GAMHDjS6Ic9yPP+ePXtw\n/fp1vrrtgQMHkJqayrySJ0vNPtHXrJBYF5ZLhBFSl507d2LBggX8Deh79+4hIiJCFGe2rCUkJAAA\nfvvtN34b6/H8Fy9eRFhYGH9zfPjw4QgNDaVEL2b29vZG67Q+yWpO3KoLi7MmMcQgNlqt1miUUYcO\nHaDVapnGEBISgsDAQAwePNik74EYhiIbqswaEr3hRqiUNftEb21tzaT0bUMSExMb3P/ss88yiSMk\nJKTWhC0DVmdNYojh0dm5BobRFSxL4gJA586dsWXLFgwdOhQAEBcX16RD5x7H7NmzcerUKSxYsACe\nnp4ICAhA7969mc05ycvLw65du5Cfnw83NzdMmTKF2ZDOR7388suYP38+3y9/7do1pqWrp0yZ0uDn\nc+fOnU3eZrO/GbtmzRqms+rqsnHjxgb3f/zxx4wiIYA4ZufWpFarcfz4caSmpgKonjAzatQok0yg\n0uv1uHTpErZu3QozMzMEBgZi9OjRgp/lL168GEOHDkXPnj2RlJSE9PR05t9bpVLJ/3FRKBRIT08H\nAHTt2tUkRc1YavaJPjMzE05OTvzY7DNnziAxMRFOTk4YP378E9VV8GiZV5lMBjs7O6YfYjHEIBYb\nNmzAzJkzTR0GLycnB6dOncLly5fRu3dvDBkyBKmpqYiNjTVavEcIjy4QZIrRcmIZoVdX96aNjY2g\nV1fNvutm69at+OKLLwBU15DYvXs3pk2bhuzsbGzevBlz5swRPAbDUnU12dvbo3v37kxn3+3atavW\ntvLycmi1Wnz22WdMugvEEENdl8b29vbw8vLCxIkTYWdnJ3gMAIzq8ZtaSEgIbGxsMGzYMEycOJEf\nM/70008jLS1N8PY1Gg1u377Nd+mp1WqjxyyWEhTLOW1d3ZsqlQqdOnXCjBkzBMkZzf6MvuaZwrZt\n22Bvb8/fPX/0LEIodS1dV15ejitXruDNN99ktkh5fW7duoUffvgBS5YseWJjMFQsTE9PR3BwMJM2\ng4KC8Nlnn9WbYFiuk5qfnw8XFxejbXWtcCSUxt53FnXo//WvfzVYuIzVTOX6JCYmIjo6Gp9//nmT\nP3ezP6PX6/XQ6XQwNzdHSkoKPvjgA6N9LNR3I6e8vBzLli0zeaL39PTkF2N+UmOwtbXFSy+9xHS4\nrVKpxA8//FDvfhbJzWDt2rW1ui2++eYbZl0Zj/tar169KtjkJSsrK7i5uQny3E3h2WefxS+//CLI\nczf7RD9o0CB89dVXsLOzg5WVFb8e5f37900yvbomW1tbUVwuFhcXmzoEUcSg1WqN6sELzdXVlWky\nr8tff/2Fu3fvorKy0mh0WFVVVb2lAEwpKipKsERvZ2cn6rITKpVKsJPTZp/oX3vtNXh7e6O4uBi9\nevXi+2b1er1JS5EC1QteCFm/4lGPLrgBVF9VpKen4913331iYqhruGtFRQUSEhLg7+/PJAaxyM3N\nxaVLl1BRUWG0OLu1tTVmzJhhwsjqJuSJ0aOVTE2lrnt65eXl+PPPPzFy5EhB2mz2ffRiUNe47fLy\ncrRu3RozZ84UtCRvTY8uuGEY8eLp6Sl4iWQxxVDXcFc7Ozt4eXnBz8+PSQxAdT2V3r17M2uvIfUV\n/xMbsYyMEdKj9/RkMhlsbW3Rs2dPwdYFoETfBB4dt21444RcXKMulZWV9XZXKRQKJkMcxRAD+a9f\nf/0Vr7zySp1XWoBpllVsyJOQ6BtiuN/Y1MRxLdPM5efno02bNmjTpg04joOTkxOf5BubNduUao5s\nWLp0qdE+FqOPxBJDzQXhf/zxR6N9y5cvZxKDWLRv3x5A9Qifuv4TG9aT2UzBMBwcAMLDw432LVy4\nUJA2m30f/datWzFx4kST3njdtWsXfxby6EiGX375hVkJhJoXZ49OymB14SaGGAzlgIHq6e01lZaW\nMolBLPr16wfAeHlFvV4PlUplku9MXSc+crkc7u7uaNWqFZPZspmZmejQoQN/MlZVVYXc3Fx+KVKh\nPXz4kP/57t27RvuE+o40+zN6FxcXhIaG4uzZsyaLoeab8+gbxbJnrOZ9gkfvGbCqaSK2GP7OPqEs\nW7YMFRUV/OPy8nKsWLGCaQzr169HZWUlVCoV5syZg+DgYBw+fJhpDED1Iizfffcd4uLiEBcXh82b\nN+PXX3/FF198gdjYWCYxbNmyxaj8RIsWLbBlyxYmbQOm+Y40+zP6sWPHYvDgwdi5cydiYmLwwgsv\nGP2yWJxNiyG5AUBJSQmOHDkCjuP4n4HqPzaszmTFEMPDhw/5WZePzsBUq9VMYqiprKzMaPSVra0t\nSkpKmMZw7949yOVyxMXFoU+fPnjnnXcQGhqKsWPHMo1Dr9dj3bp1fMmS4uJibNiwAf/+97/x5Zdf\n8oXfhMRxnNEIHDMzM6bDbisqKnDhwgXo9XpUVFTwVzkcx/ErbzW1Zp/oAcDBwQF+fn74+eefkZSU\nZPQmskj0hpr4HMcZ1cfnOA4FBQWCt28wfPhwVFVV1foZYLdsnRhiaN26NT9R6amnnjKatMRizdxH\nyWQyoxvRDx48YH5lodPpoNVqcfHiRYwaNQoWFhYmubpRKBRG70GrVq2gUChga2vLbNWtNm3a4Pff\nf8eIESMAAL///jvTewOGwm6Gn2sOezXMA2pqzX7Uzd27d7Ft2za0bt0aU6dORevWrZnHcOPGjQb3\nm7qMMjGt5ORkbN68GT179gTHcUhNTcUHH3wAX19fZjEcO3YMv/76Kzp16oTQ0FAoFAqEh4fXumEu\ntG3btkGhUPDzGc6fPw8nJydMmjQJq1atYjLBrLi4GNu3b+e/t15eXpg+fbpJTgJYafaJfvbs2Zg6\ndSrTLw0hf1dpaSkyMjIAVBcSs7e3N3FEwg3lawjHcUhMTORLNnfv3h3PPvusSa4uniTNPtFrNBqT\nrN5OSGP++usvtG/fvlbpZgOWwxs1Gg0SExNRUFBgNM3+jTfeYBaDQXFxMTIzMyGTydClSxdmE+n+\n85//4OWXX8b3339f5x8WUxc1E1Kz76OPi4tDeXk5f1NpxowZUKlU4DgOkyZNwgsvvGDiCMmT6siR\nI5gxY0adpZsBtkXNVq9eDblcDg8PD5OeGCUkJODHH3/kuzN37NiByZMnMylNYajeKdTsU1HjmrnQ\n0FCutLSUfzxv3jyO4zju4cOH3OLFi00Sk0qlMkm7BkVFRdzGjRu5FStWcBzHcXfv3uVOnjz5xMWg\n1+u5M2fOcPv27eM4juMePHjAZWRkMI1Bp9NxN2/eZNpmXYKDg00dAsdxHDd37lyuuLiYf1xSUsLN\nnTuXWfs6nY778ccfmbXXEJVKxe3bt4/btGkTx3Ecl5ubyyUlJQnSVrMfR89xnNFCEoYzAysrK+ZD\n6dLS0jB79mwEBQUBALKzs7Ft2zamMQDVtV569+6NoqIiAEDbtm1x9OjRJy6Gbdu2IT09HfHx8QCq\nC3lt376daQxmZmb1lh9gqWvXrqJYCEWv1xt11dja2jIrJw5Uvx+NDZ5gZePGjbC0tOTv3Tg4OODn\nn38WpK1m33VTcyIKUF3NEqj+QJWVlTGNZefOnfj888+xevVqAECnTp1w8+ZNpjEA1eO2Bw4ciEOH\nDgEAzM3NmVfuE0MMmZmZWLVqFebPnw+gOqlotVqmMQCAt7c3zp8/b9KbjqmpqTh9+jScnZ1haWlp\nsoXSfX19sWLFCn6NhoSEBPTp04dpDJ07d8aaNWvg7+9vVI/KMIuYlfz8fMyePZs/EWnRooVgbTX7\nRN+7d2/8/PPPeOutt4y27927V7C61g15tGiXKUqjtmjRAmVlZXxSSU9PZz7dXQwxmJubQ6/X8zGU\nlpaaJNFGR0fj6NGjMDMzg5WVFZ9kd+7cySwGoWqo/F2TJ0/G+fPn+eULR4wYgWeeeYZpDFVVVWjR\nogUuX77Mb5PJZMwTvYWFBdRqNf+ZvH//PiwshEnJzX7UjUqlwnfffYdbt26hY8eOAKoXQfb09MSM\nGTPQsmVLZrF88803eOmll7Bjxw6sWLECx44dQ1ZWFt+Vw0pWVhYiIyNx584duLu7o7S0FMHBwfzv\nx1QxzJ49m8masQZxcXFISEjA7du38dxzz+H8+fOYMGECBg4cyCwGMUlNTUVeXh4CAwNRWloKlUrF\ndE1jsairZLMpyjhfvXoVBw4cwL1799C7d2+kpaXh448/hpeXV5O31ewTvUF+fj5fIKhDhw5wdXVl\nHkNpaSm+//57XLt2DRzHoVevXpg2bRqzxahr0ul0yM3NBcdxaNeunWBnCvXRaDQwMzMzioHjOOYj\nPv766y++sJm3tzeztQFqWrp0KRYvXtzoNiHt27cPt27dQl5eHtavXw+lUol169Zh2bJlTNqva8F2\nACa5uqmrFLKpyiOXlZUhIyMDHMcJOr+i2XfdGLi4uBgtfpybm4vDhw/jww8/ZBaDvb09Zs2axay9\n+pw4cQJDhgzh18csLy9HfHy8YKvX1GXRokVYtWqV0RqdrL9M4eHh+PTTT/lSvTW3saBWq6FWq1FW\nVmZUybOyshJKpZJJDAYXLlzA6tWr+TVzHRwcjMpTCK2htXNZyczMRHp6OkpLS3Hs2DF+e1VVFdNa\nNwYXLlyAt7c3vxiOoQaOEF1ZzT7R5+TkYNeuXSgqKkL//v0xcuRIbN++HZmZmXjppZeYxhIREYFp\n06bxBazKy8vxww8/4OOPP2Yax8mTJzFq1Cj+sa2tLU6ePMkk0RcXF0OpVNYqJlZVVWVUnpWFe/fu\nGT3W6/X1Tl4SgqFvvqioyGhRcrlcbvT+sGCobWM4qzb1YvGmoFKpUFpaCp1OZ1Rgr2XLlggODmYe\nz759+4ySuo2NDfbv30+Jvi6bN2/GCy+8gK5du+Ly5cuYP38+nnvuOcyaNcuoFCkLd+7cqVWlMDs7\nm2kMQHVCM1wSGx6zGm2SnJyMM2fOoLCw0OgsztraGm+//TaTGA4ePIiDBw9CrVZj6tSp/B8bCwsL\nvpAVC6NHj8bo0aNx/PhxvPjii8zarcuAAQOwZcsWVFRUIDo6GqdOnRL1QtlC8Pb2hre3NwIDA+Hi\n4mLyWfV19ZoLdWXR7BO9RqPhF1Vo164djh8/jkmTJpkkFo7jUF5eDltbWwDVZ/SmuCTs3bs31q1b\nh+effx4A8McffzCrBRQQEICAgACcP3/eZAtxv/rqq3j11Vexe/duvPPOOyaJoabAwEAcOHAACoUC\nM2bMQF5eHnJzc9G3b19mMYwdOxZXr15Fy5YtkZubiwkTJphkVJoYlJWVYfXq1aisrMSmTZuQnZ2N\nmJgY5ssqenh4YOfOnfyV9m+//SZYWQxJJPqaXQSWlpZGj1nWE3nppZewaNEio8p8hnH9LE2aNAnR\n0dH4/fffAQC9evXC8OHDmbQdGxuLoUOH4sGDB3Wuds+iO81QY8bf37/OrhrWS+ht2rQJHh4eSE9P\nB1DdP7527Vqmif7HH3/EpEmTjJK7YduTJjIyEqGhofzSlp06dcL169eZxzF9+nQcOHAA3377LWQy\nGXx8fPDee+8J0lazT/R11Ruv+ZhlPZHnnnsOnp6eSElJAQDMnTuX+SgPvV6PiIgIzJo1yyR1fgz9\n8KbsA/7Pf/6DDz/8UBQ1ZgC2E2Pq8+iSikB1N9uTmOj1en2t+vOs57vo9Xrs3bsXU6ZMYdJes0/0\nX331lalDAFD9xgUHB+Pbb781yRA+AzMzMzx48ABarZb5kEoAfHfRm2++ybxtA8NIK9YJvT4sJ8Y8\n6vfff8dvv/2GgoICo/VYq6qq0K1bNyYxiI2joyNfPVOv1+P48eNo27Yt0xjMzMz4SWMsNPtx9L/+\n+iteeeUVAMC5c+cwYMAAfh/rPtrVq1dj+vTptWbHshYREYG//voLffv2NZrizaLbJDo6Gl5eXmjb\nti04jsOmTZuQmJiINm3aYObMmejcubPgMWRmZsLJyYlfSOLMmTNITEyEk5MTxo8fz99DYYXlxJhH\nVVZWory8HLt378bEXCShQgAAIABJREFUiRP57S1btmT+exCLkpISREZG8lc5Pj4+mD59OvM1ArZu\n3QqlUokBAwYYXeUJsSpesz+jT0hI4BP9oUOHjBL9lStXmCb6iooKBAcHo0uXLkZvXM2hdSwY5hRw\nHMd0rDQAHD9+nL85Hh8fj5ycHEREROD27duIjIxksqLR1q1b8cUXXwCoXv1r9+7dmDZtGrKzs7F5\n82bMmTNH8Bhq6tWrFzp37sxPjHn33XeZJRW5XA65XI6goCDo9XoUFxdDr9dDpVJBpVKZ/KTEFFq1\nasV8tnpdNBoN7Ozs+K5eA0r0dah5QfLoxQnri5UJEyYwba8+puw2MTMz47sl/vzzTzz33HOws7ND\nr169EBUVxSQGvV7Pn60mJCRg+PDh8Pf3h7+/P+bNm8ckBgC1bgQbrjAUCgUUCgXTm8InTpzAvn37\n0KpVK74LyRRFzUypsdm3rBceYTm/ptkn+prTqh+dYs26gJVY1oZdsmRJndtZ9FmbmZmhqKgINjY2\nSElJMRp1xKpstF6v55fJS0lJwQcffGC0j5UFCxbAzc2t3hIYLO8hHD16FN9++61JynGIxYkTJ+Du\n7g5/f39mq1o1ZOPGjXVuF+IPQLNP9NnZ2fykGMMEGaD6bF6j0TCNpWY9D61WC61WC2tra6Z1PIDq\nCoEGarUaiYmJzNYGHT9+PEJDQ6HX69G3b1++BMKNGzeYFdAaNGgQvvrqK9jZ2cHKygo9evQAUH0T\nlGUFzSlTpuD8+fOwsrLCoEGD8MwzzxjdM2HJycmJefVQsfnuu+9w7tw5nDt3zug9MdXvxVD6AKju\nxrlw4QJat24tSFvN/masWHEch4sXLyIjI8PoJpipLFiwACtXrmTSlk6nQ1VVldHNPsNwS0Oiu3r1\nqqATdtLT01FcXIxevXrxbebm5kKlUjEfR5+fn4/4+HgkJSXByckJr732GtMqnkD1WP7c3Fz4+fkZ\nzQZlXSZELB48eID4+Hh+guWQIUNMHRL0ej0WL16M5cuXN/lzN/szerGSyWR45plnsH//fuaJvmYB\nLUN9l8rKSmbtm5ub1xrR8eiZbFRUlKCJvq6Ss+3atROsvYa4uLigf//+UKvViIuLQ25uLvNE7+Tk\nBCcnJ/5K80mWk5OD+Ph4JCcnw8fHh2n57obcv38fJSUlgjx3s0/0hu6SmhcmMpkMOp0OWq1WsKW5\n6pKYmMj/zHEcbt26ZZJaGiEhIfzvxNzcHM7Ozvjoo4+Yx9GQJ+FCsuaZvKOjIwYNGoTXXnuNeQ0m\nwLQ36MVi//79+PPPP+Hi4oKBAwdi/PjxJplrYlAzd8lkMjz11FOCnRRKrutGpVLhxIkTiI6OxjPP\nPMNs5hlgfHPFzMwMzs7OGD58uChu/IiNqep/szRhwgS4u7ujf//+aNmyZa3BASy6TcLCwhoclMB6\n6K8pTZgwAS4uLvzV5aO/Fyl/Hpv9Gb1BRUUFjh49itjYWAwePBgrV65kPsKAdTni+mi1Wvz+++/8\nerVeXl4YMWKESc9enkSvv/66ycsCjx071iTtitH69etNHUItSUlJ/GLlXl5egtU/avZn9KWlpThy\n5AgSEhIQGBiIF1980WR30QsLC7Fjxw5+anP37t0xbdo0ODo6Mo3ju+++g1ar5ScuxcbGwszMjOki\nLI1Zs2aN0ZR8Qp40UVFRuHXrFgYPHgygeoKhp6enIJM8m32inzx5Muzt7REQEFDn+rAsRxUsW7YM\ngwcPxtChQwFUr1kaFxfHz9JkZd68eXxlvoa2Canm/QoDuVwOd3f3J6Yr68CBAxg5cmS9pQZSUlLw\n8OFDplUsn2RLlizBgAED0K9fPzg4OPDbdTod0tLScObMGXTv3h2BgYFM4pk7dy5Wr17NF1TT6/WY\nP3++IJPYmv21/Msvv8xfHrOe7v+o0tJSow9JQEAAjh49yjwOMzMz3L9/n183Nz8/n3l1vpiYGKSn\np/P1XG7cuAEPDw8UFBTgjTfe4P8YSpm7uztWrVoFS0tLdO7cGfb29tBoNMjLy0N2djZ8fHxMUsb6\nSRUaGoqTJ09izZo1UCqVsLW1hVqthkajgY+PD0aOHAlPT0+mMVVWVvInAkKOjGv2Z/RisnTpUgQE\nBPCXYmfPnsXp06eZLgINVJek3bhxI1/vRqFQ4KOPPoK3tzezGFasWIGZM2fy0/6Li4uxYcMGfPbZ\nZ/jyyy/xzTffMIvF1PLy8pCWloaioiJYWVmhffv26NmzJ9PRN3fu3IG7uzuz9sROo9GgpKQEVlZW\nzIuZGZw9exa7d++Gl5cXOI7DzZs3MXHiRAwcOLDJ22r2Z/Rr167l13t8dCGF5cuXY9GiRcxi+eij\nj7Bjxw5+Jmy3bt1McoPWx8cH//d//4fc3FwA1ePHWQ/zVCgUfJIHqgtJKRQK2NraMpulKxZt27Zl\nXgb3Udu2beNXYxsyZMgTP0vW0tLS5AXdBg8eDC8vL9y6dQtA9YJBNb8zTanZJ/r79+/zPz+6uELN\nBYBZaNOmjUmHq9Usz2tpaYns7GyTlef18vJCWFiY0WpbXl5eUKlURuvqEjaWLl2KvLw8nDp1CiEh\nIejSpQsCAwOf2OUETenBgwewsbGBXC5H69atYW1tjYsXL+L+/fsYNWqUIKPj2HbcCqChMcKsippF\nR0cjLy8PQPVEoI0bN2Lq1KmYO3dunUvZCWXr1q38h8RQnnfo0KGQy+XYvHkzszgA4L333kNAQACy\ns7ORnZ2NgIAAvPfee7C2thbNgiBPmrZt2+Ktt97CxIkTcePGDURGRiIoKKjOG+dEOOvWreOH22Zn\nZ2PdunVwcnJCdnY2tm3bJkibzf6M/uHDh/wasWq12mi9WFbVEhuqwf79998zqcEOiKc8L1D9R7Z7\n9+6wsLCATCZDly5dmFcTJf+Vk5ODU6dO4fLly/Dx8UFISAg8PDygVCqxaNEiQWqgi5VarYalpSVk\nMhnu37+P3Nxc9O7dm1mXolqt5kf9xMbGIjAwEC+//DI/6kYIzT7Rt27dml8jtq71Y1kQQw12QDzl\neYHqPzQ//vgjX7p5x44dmDx5Mt+V8yT58ccf+dIH//73v5GTk4OpU6cyHXm0Y8cODB8+HO+8847R\nTWAHBwe89dZbzOIQg8WLF2PJkiWorKzE0qVL0blzZyQkJOCTTz5h0n7N8S/Xr1/H22+/DUDYdWub\nfaJ/3G4AIasliqEGOyCe8rwAcPDgQaxcuZIfM19aWoply5Y9kYn+ypUrmDRpEi5cuIA2bdpg7ty5\n+PLLL5km+meeeaZWe8eOHcPo0aOfiKGuNXEchxYtWuDUqVN44YUXMG7cOKZXvN7e3li7di1at26N\n8vJyfjRcUVGRYLPXm32if1xCVksUQw12AHjttdfg7e3Nl+c1dJXo9XpMmzaNWRyGNmtOjLK1tWV+\nVSEWhtd96dIlDBgwwCQjXmJjYzFmzBijbadPn8bo0aOZx2Jqer0emZmZiIuLw4wZM/htrLz77rtI\nSEhAUVERli1bxif34v/X3r1HRVWufwD/zgwIIoiokKKCEg6INxLSKI94K7UijyvUNPN2sqV1uoFJ\n6TFUVORSVoqmeTnKSTNP3jLSSl2KKXoUb6EwIKjchwGR24wzw+zfH/zYhwnSfuvH++6R/XzWYi1m\nT6v3Wc7mmXe/l+etrGT2dCWbRM9yu0BQUBA2bNjQrAa7j48P3n//ffE16xrsgO2U5w0MDMSqVavw\nzDPPAGgYynniiSe4x2ELhgwZgvfeew/t2rXD66+/jqqqKm7LXU+fPo3Tp09Dq9VaFe0yGAyyPRx8\n1qxZ2Lt3L4KCguDl5YXS0lLx6ZcHhUIh/l001adPH3ZtymXDlC1US7SFGHhKS0sT6/7069cPQ4cO\nlTgi6dTU1MDJyQlKpRL379+HXq/nModUVlYGrVaLXbt2WZXAdXR0hLe3t+z2NMiVbHr0tkAm36mi\nxhU/BCgsLERZWRnq6+vFa6GhoczbdXd3h7u7O1atWsW8rUdFSUkJDh8+3Ozz4Lm5kjfZJHp3d3ep\nQ5DF8sKm5+Y21Xi4Au/zc23BunXrUFpait69e1utrOCR6JcuXYqYmJhmn4ucP49PPvkEo0ePxl/+\n8hfuNaCk0mYS/cOqJVJJXD6aLm8lDXJzc/Hpp59K8kUfExMDgD6XppRKJSZMmCBZ+5GRkQ+8F6h6\n5QM8CtUSbeGpgvDXq1cvVFZWws3NTbIYNBoNevXqJZby1uv1KCgoQN++fSWLSSrBwcHiCXRNlzPy\nWg314YcfAgCOHj0KAFZlzVlpM4neYrFg7dq1zaolrl69mtuaZXqqIC2prq5GREQEfH19rRILz7pI\nW7ZssVoI4ODg0OyaXBw/fhxAw3kBTW3cuJFL+40dvqtXryI+Pl68/uqrryIqKorJubFtJtHbQrXE\nR+GpgvBnCwdzN47JN1IqlVYTkXLCK6E/jCAIyMzMhL+/PwAgKyuL2Xr+NpPobaFaoi08VRDbExAQ\ngMrKSrEcra+vL/dTth577DGkpKTgueeeAwD89NNPXDfz2ZL6+nr88ssv4pnKAQEBGDNmDPelpgsW\nLMDGjRvFA0ecnJywYMECJm21mUT/t7/9DefOnUNmZiaAhtOdhg0bBoVCwa1aoi08VRDbYwt1f+bN\nm4ft27dj3759UCgUGDBggLgrVG62bt0Kg8EgngaXmpqKvLw87v8ePj4+SEhIsEr0rLSZRG8L1RJt\n4amC2B5bqPvj6uqK9957j1t7tiw7O9vq/OTBgwdzr+4KNDzx7969G3fv3sXixYtRUFAAjUaD0aNH\nt3pbbSbR20KvyRaeKojtkbLuz8GDBzFx4kRs27atxffnzp3LJQ5bolAooNVqxaErrVYrydLXDRs2\nYOTIkdi/fz+AhvMC1q5dS4n+QWyh12QLTxXE9khZ96dHjx4AGoYJSINXX30V0dHR8PT0hCAIKCkp\nwfz587nHUV1djaeffhoHDhwAAKhUKmYbuNpMoreFaom28FRBbM9rr71mVfdn7Nix3Or+BAcHw2Kx\n4M6dO5g5cyaXNm3d4MGD8fnnn6OgoAAA0LNnT64HtTdycHBAdXW12BnUaDTMxunbTKK3hWqJtvBU\nQWyTlHV/lEql+CUjZ9evX0dAQAAuXLhgdb2iogJAw5ciTzNnzkR8fDxKSkqwdOlSVFVVWVW7bU1t\nJtFL2WtqZAtPFcR22FKdmd69eyMuLg4hISFwcHAQr8vpCMGrV68iICAAZ8+ebfaeQqHgnuh9fHyw\nbNkyFBUVQRAEeHp6Mjt4RDZlinlITk7GnTt3rJ4qvLy8MGPGDIkjI3K3YcOGFq+/+eabnCMhjd5+\n+22EhYWJexsAYM2aNWKJhNb0yPfobalaoi08VRDbU1JSgi5dusDe3h4ZGRm4ffs2QkNDuS65HT16\ntLgDs1Hj6jC5OXLkCEaMGAEnJyds3rwZeXl5mD59OgYOHMg1DpVKhYyMDOTk5OCNN96AnZ2dOIzU\n6gRCCFMLFy4UzGazUFxcLLzzzjvCzp07hdWrV3ONYdGiRX/qmhxERkYKgiAIly9fFuLi4oS8vDxJ\n/i0a2zxw4IDw0UcfCWVlZczieOR79LbAlp4qiO1RKpVQqVQ4f/48xo8fjwkTJmDRokVc2tZoNMjK\nykJVVRUOHz4sXq+rq5Pt/FHj3+qlS5cwYsQI9O7dW5JDgRrbnDhxIvr06YOVK1eipqaGSVuU6FsB\n1fomD6JSqXD69GmcPHlSrFjJq6CY2WyGwWBAfX099Hq9eN3JyQkRERFcYrA13t7eiI2NRVFREaZN\nmwaDwSDJfpcpU6aIvw8aNAhLlizByZMnmbRFk7GEMFZQUICffvoJarUaw4cPh1arxZkzZ/DXv/6V\nWwxlZWV0HsL/slgsyM3NRbdu3eDs7Izq6mrodDqmh3M3VVhYiB49eiA3N7fF91lsbqNETwhHNTU1\nKC8vh7e3N9d2Y2JiEBERIU4A19TU4PPPP8eSJUu4xmELNBoNvL294eDggNOnT+PWrVsYP348unbt\nyqX9L7/8EvPnz8fy5ctbfJ9FuRQauiGEsWXLlmHRokWwWCyIioqCq6sr/Pz8MGvWLG4xVFdXW63y\ncXZ2xr1797i1b0s2b96MhIQE3L59GwcPHsSoUaOwfv16LFu2jEv7jeUWeNa/okRPCGN1dXVwcnLC\nsWPHEBoaiilTpnA/bUyhUECn04m91rKyMtnWYVKpVFAoFPjPf/6D8ePHY8yYMczGxlvS0kl0TbHY\nxEaJnhDG6uvrcffuXZw9exavvPKKJDFMmzYNS5cuRUBAgHiy0RtvvCFJLFJzcHDAoUOHkJqaimXL\nlsFiscBsNnNr/+LFiw98n0WipzF6Qhg7e/YsvvvuO/j7++P1119HaWkpkpOTuffqq6qqkJ2dDQDo\n27cvOnbsyLV9W1FRUYHU1FQ8/vjjGDBgAHQ6Ha5duyYeRNIWUaInRAYEQUBqaqp4frFOp0NlZSV8\nfX2lDk3W0tPTkZ+fD5PJJF4LDw9v9XZo6IYQRmzp0I8tW7ZAoVAgIyMD4eHhcHR0xNatWxEbG8st\nBqlFR0dj+fLlmDNnTovvb9++nWs8mzdvhtFoREZGBkaPHo20tDRmX7yU6AlhxJYO/cjJyUFcXJy4\nI9fZ2ZnruLQtaFzlsnXrVokjaaDRaJCYmIiFCxdi8uTJCAsLw+rVq5m0RYmeEEYay96OHDlS2kDQ\nsNLEYrGIK22qqqpkt+qm8fQmpVKJuro6VFRUWO1Q5r23ofGwEwcHB1RUVMDFxQV3795l0hYlekIY\nu3nzJvbt2wedTmeVWBITE7nFMGHCBCQkJODevXvYvXs30tLSJFsBJLW9e/fil19+gYeHh/hlp1Ao\n/nADEytDhgxBbW0twsLCEBUVBYVCweS8WIAmYwlh7t1338Vrr70GLy8vq14075IEhYWFuHbtGgBg\nwIAB6NmzJ9f2bcW7776LxMRE2NvbSx2KyGQywWQy0VGChDyqOnbsyP30okZNqyG6urpi+PDhVu85\nOztLEZakevXqBb1eL3mit1gsSE9Ph1artaok+uKLL7Z6W9SjJ4Sxa9eu4ddff8WAAQOskguPY/ze\neustKBQKsSRu4xNFYwnt9evXM4/B1uTm5iIhIQFeXl5WnwfvfQ2xsbGwt7dv9qQ3efLkVm+LevSE\nMHbixAkUFRXBbDaLE4IAn0SflJTEvI1HTVJSEl544YVmCZa38vJybvM0lOgJYezmzZv4/PPPJWn7\n1KlTGDFiBICGowObHid45MgRjB8/XpK4pNSuXTsmwyP/V4GBgbhy5QoGDx7MvC1K9IQw5ufnh4KC\nAkkmP3/44Qcx0W/fvh1xcXHieydOnJBlou/Xrx+++eYbBAcHWw3d8F5eqVarkZiYCIvFAjs7O6Yn\n0lGiJ4Sx7OxsfPDBB/Dw8IC9vb34B83jsb3pFNzvp+PkOj2Xk5MDALh+/bp4TYrllTt27MDKlSu5\nDCFRoieEscWLF0vWdtME8vtkIrcNU41WrFghdQgAgK5du6JXr15cPgdadUMII7/99hsGDBgAANBq\ntfDw8BDfO3fuHJfJ2BkzZqBbt24QBAGlpaXo1q0bgIbevFarRXJyMvMYbMXOnTsxc+ZMAM3nJzZu\n3IgFCxZwjScpKQlarRaBgYFWQ0gs5g+oR08II8nJyeKY+CeffGI1Pr5v3z4uiX7t2rXM23hUZGRk\niL//fn7i1q1b3OPx8PCAh4cHzGYz87pDlOgJYcQWxsfpQPD/etDnwZvFYoFerxefMFijRE8IIzQ+\nblsEQYBer4cgCFa/A7DamcqDUqlEVlYWt/ZojJ4QRmbPno1+/fqJR/f169cPQEPCycrK4l7/XO7m\nz5//wC/YjRs3cowG+Oqrr1BRUYGQkBA4ODiI1+koQUIeIU2X77UkICCAUyQNjEYjdDodPD09ubZL\nWrZhw4YWr7/55put3hYlekJk4MKFC0hOTobZbEZSUhJu3bqFPXv2ICoqSurQCAc0Rk+IDOzduxex\nsbFYtmwZAKB3797QarXSBiVz5eXl2LZtmzhW7+/vjzlz5qBLly6t3pby4f8JIeRRZ2dn16zWOU0I\nS2vDhg0IDg7Gpk2bsGnTJgQHB//hcM7/FyV6QmSgZ8+eOH36NCwWC4qLi7Ft2zao1Wqpw5JUZWUl\nKioqUFFRwX3VDdBwnOOoUaOgUqmgUqkwcuRIVFVVMWmLhm4IYSQzMxOlpaUIDQ0F0LBpqvEgkJdf\nflncNcvD3LlzsW/fPtjb2+OLL77A4MGD8fLLL3Nr3xYcPHgQJpMJ4eHhAICPPvoIjo6OqK+vx5gx\nYzBx4kSu8bi4uODUqVPiYTCnT5+Gi4sLk7Yo0RPCyLfffou5c+eKr4uKivDWW2/BYDBg//79XBN9\neno6pk2bhmnTponXzp49i5CQEG4xSO3MmTNWhctcXFwQHx+P+vp6LF++nHuiX7BgAbZt24YdO3ZA\noVBArVYzWXEDUKInhBm9Xm9Vmrh79+7w8fEBAOzevZtrLAcOHGiW1Fu61tY5OjqKvzeWQFCpVLh/\n/z73WNzd3bmteqJETwgjtbW1Vq+bHlVXWVnJJYZLly7h0qVLqKiowLZt28Trer3e6rQrOdDr9aiv\nr4dKpQIAjB49GgBgNptRV1fHLY5///vfD3y/cWipNcnrkyaEI09PT6Snpze7fvHiRW6bltzc3ODj\n4wN7e3v4+PiIP8HBwViyZAmXGGzFsGHD8NVXX8FoNIrXjEYjtmzZwqXAXCMHB4dmPwBw/PhxHDx4\nkEmbtGGKEEZKSkoQGxsLPz8/9OnTB0DDwdQajQZRUVFcd6g27cnKVX19Pb7++mucOnUKHh4eEAQB\nZWVlGDFiBKZPnw47O/4DHHq9HikpKTh+/DhCQkIQFhYGV1fXVm+HEj0hDJlMJqSmpqKgoABAwzLH\n4cOHo127dlza//TTTxEREYHIyEirdfM8T7myNQaDAcXFxQAa5k2ajtvzUlNTg8OHDyM1NRWhoaF4\n/vnn4ezszKw9SvSEtGF3796Fm5sbysrKWnxfTmWML1y48MD3g4ODucSRnJyM8+fPY8yYMRg/fjyX\nLxpK9IQwMnPmzBZ3n7I8BPphqqurcePGDXTt2lVcASQX69atE3+/fPkyAgMDxdcKhQJ///vfucQx\ndepU2NnZQaVStfiUxeK+oERPCAeLFi1CfHw893bXrFmD6dOnw8vLC3fv3kVUVBR8fHxQWlqKsWPH\n4oUXXuAeky2Q6vOQCq26IYQDqerKaLVaeHl5AWg4Pm/QoEH48MMPsXr1apw4cUKSmGyB3Or8UKIn\npA1rutLmt99+wxNPPAEAaN++veySnZzRhilCGDl37pz4e21trdVrgM1JQr/XpUsX/Pjjj+jSpQvy\n8vLEcWmj0Yj6+nrm7duShIQE8cuttLS02Yqjphva2hoaoyeEkYeVnGVV16Spe/fuYc+ePaisrMS4\nceMwePBgAA29+9zcXLz00kvMY7AV165de+D7AwcO5BRJwxm1MTExiI6O5tIeJXpCCJHAihUrsHDh\nwmbnBLBAQzeEMFJeXo6ysjL4+/sDAA4fPgyDwQAAGD58OLp16yZleLJz8eJFlJeX47nnngMALF26\nVKz//uqrr2Lo0KFc43F0dERkZCQGDRpkdTh404qnrYUmYwlhJDk52aqw2c8//yz+QX/77bdShSVb\nBw4cECejgYYdssuXL8fixYtx5MgR7vEMHToUU6dORb9+/azqELFAPXpCGCkuLkZQUJD42sHBAWFh\nYQCAjz/+WKqwZMtsNlvtBFar1ejUqRMASFKmeOTIkTAajdDpdMzrHlGiJ4SRplUSAevkXl1dzSWG\npqWJW8JimMBWNZ7u1WjevHni76yO8HuQCxcuIDk5GWazGUlJSbh16xb27NnDpEY9Dd0Qwkj79u1R\nVFQkvm4sWlVYWMitkFbjcIDJZEJeXh66d++O7t274/bt2zCbzVxisBWPP/44jh8/3uz68ePHJSkH\nsXfvXsTGxqJDhw4AgN69e0Or1TJpi3r0hDAyZcoUxMXFYdKkSWIiyc3Nxf79+zF79mwuMYwcORJA\nw/zAihUrxA1Uzz77LLelfbZi9uzZiI+Px6+//iqWjc7Ly0NdXR0WLVrEPR47O7tmK25YbWKjRE8I\nI4GBgYiMjMShQ4fw448/AgB69eqFyMhIsSwBLzU1NdDr9eJThcFgaDaU0dZ16tQJq1evxpUrV5Cf\nnw8AeOmll8S9Bbz17NkTp0+fhsViQXFxMX788Ueo1WombdE6ekJk4MSJE9i7dy/69+8PQRBw48YN\nTJ48WezxE/7u37+Pffv24erVqxAEAYMHD8bLL7/M5KwCSvSEyERlZSWys7MBAH379hVXnJC2jxI9\nITIgCAJSU1Oh1WoRHh4OnU6HyspK+Pr6Sh2abBUVFeH7779HWVmZVd0hFnMnNEZPCGOZmZni7tgH\nXWNpy5YtUCgUyMjIQHh4OBwdHbF161bExsZyi4FYW7t2LZ599lmMGTMGSiXbBZCU6AlhbPv27YiL\ni3voNZZycnIQFxcnri5xdnaW3fLKOXPmPPD97du3c4qkgVKpFMsxsEaJnhBGNBoNsrKyUFVVhcOH\nD4vX6+rqYLFYuMaiUqlgsVjE5XtVVVWyq0e/detWqUMA8N+NW0FBQTh69CiGDh0Ke3t78X0Wh4RT\noieEEbPZDIPBgPr6euj1evG6k5MTIiIiuMYyYcIEJCQk4N69e9i9ezfS0tLwyiuvcI1Bar8fHqmp\nqbHavdy5c2cucURFRUGhUKBxevTQoUPiewqFAuvXr2/1NmkylhDGysrKrGqsSKWwsFCsyT5gwAD0\n7NlT4oikkZ6ejh07dqC8vBwuLi6oqKhA9+7d8dlnn3GNw2g0NltK2dK11kAlEAhh7Msvv7SqYllT\nU4NVq1ZxjWHjxo0wmUwYP348xo8fj549e8q2gubu3bsRExMDT09PbNy4EUuWLOE6Md5o6dKlf+pa\na6BETwhj1dWL1t1SAAAOHUlEQVTVYj0ToGEM9t69e1xjuHLlCpKSknDy5Enx2sWLF7nGYCtUKhU6\nduwIQRAgCAIGDRqEmzdvcmu/srISubm5MBqNyMvLQ25uLnJzc5GRkcGsiiaN0RPCmEKhgE6nQ9eu\nXQE0DOXwngh1dXVFdHQ0vvjiC2RnZ2POnDmQ66itk5MTDAYD/Pz8sH79eri6ujIZLvkjly9fxsmT\nJ1FeXo6dO3eK1x0dHTFt2jQmbdIYPSGMXb58GZs2bUJAQAAEQUBmZibeeOMN8aBuHhYtWoT4+HgA\nDYeeXLt2DXfv3mUy8Wfr9Ho9HBwcYLFYcOrUKdTV1WHEiBHo2LEj1zjS0tLw1FNPcWmLEj0hHFRV\nVVmVH+CdVL799ltMmTJFfH3hwgX88MMPsqtgCQC7du3C9OnTH3qNh/T0dOTn58NkMonXwsPDW70d\nGqMnhDFBEHD58mXk5eUhKCgIRqMROTk5XGNomuQBIDg4WJZJHmiYr/i9S5cucY9j8+bNOHPmDI4c\nOQJBEHD27FmUlZUxaYvG6AlhTMryA0uXLkVMTAxmzpxpNS8gCAIUCgV27NjBPAZb8fPPP+Pnn39G\ncXGx1SlOer1ekoNHNBoNEhMTsXDhQkyePBlhYWFYvXo1k7Yo0RPCmJTlB2JiYgDAatJPrkJCQjBw\n4MBmwzTt27eHq6sr93gaJ4AdHBxQUVEBFxcX3L17l0lblOgJYUzK8gMPO1yExXZ7W+Xs7AxnZ2dE\nREQgPz8fN27cAAD069dPkkQ/ZMgQ1NbWIiwsTNwtO3r0aCZt0WQsIYylpqbizJkzyMvLQ2hoqFh+\nICQkhHnbb731ltV2+6ZYbbe3dUeOHMHRo0fx5JNPAmiYmB43bhzGjRsnWUwmkwkmk6nZ0YKthRI9\nIRxQ+QHbsXDhQqxcuVI8oN1gMOAf//gHEhMTucZx//59fP/999DpdJg/fz6Ki4tRVFSEoKCgVm+L\nhm4IYchisSAiIgKfffYZevToIWksNTU1KCkpsSrkFRAQIGFE0hAEAXZ2/019KpVKks1jGzZsgI+P\nj7jstnPnzvj0008p0RPyqFEqlfD09LTaGSuFY8eOISUlBRUVFejduzc0Gg3UarWslljW19dDpVJh\nxIgRWLx4MYYNGwYAOH/+PEJDQ7nHU1paivfffx+//vorgIZJWVYo0RPCWG1tLSIiIuDr62v1x9x0\niR9rKSkpiI2NxZIlSxAdHY3CwkLs3r2bW/u2YPHixYiLi8PEiRPRv39/ZGZmAgDmzZsnyZGKdnZ2\nMBqN4sR8SUmJ1ZNGq7bF5P9KCBFNnTpV6hDQrl07cTmfyWRCjx49UFRUJHFUfDUdnvH19ZX8vNwp\nU6Zg1apV0Ol0+OKLL5CVlYU333yTSVuU6AlhzBbGwTt37oza2lo8+eSTWLlyJTp06GATNfJ5+v1J\nX7/34osvcowGGDRoEPr06YPs7GwIgoDZs2czK41BiZ4QRmxpV+oHH3wAoKEXef36ddTV1XEtqmYL\nLBYLDAaD5FU74+Pj4efnBz8/P/j6+mLIkCHM26TllYTIRE1NDcrLy1FfXy9ek2Lrv1SioqK4Hsj+\nRy5evIisrCxoNBrcvn0bPXr0gFqthr+/P9RqNTp16tTqbVKPnhDG7ty5g8LCQgBAz5490atXL+4x\nfPPNNzh58iQ8PDyszk6V06obW+nTBgUFiUsoLRYL8vLykJGRgeTkZGi1WuzZs6fV26RETwgjdXV1\niI+Ph06ng7e3NwRBQH5+Prp27YoPPviA2S7Ilpw9exbr1q1jtqrjUfDxxx9LHYKoqqoKGo0GWVlZ\nyM7OhslkwsCBA6FWq5m0R0M3hDCybds22NnZYcaMGWIv2mKxYNeuXTAajZg7dy63WBITEzFv3jxJ\naroQa++88w6cnJwwbNgw9O3bF76+vuIuXVbk+/VOCGPXrl1DYmKi1VCJUqnEtGnTsHDhQq6xTJo0\nCYsWLYKXl5dVr57nWn7SYNSoUcjOzsa5c+dw584d5OfnQ61Wo0+fPlb3SmuiRE8II3Z2dlCpVM2u\nq1Qq7kMoSUlJmDhxIry8vJglE/LnTJo0Sfy9qKgIGo0Gx44dQ2ZmJlxcXLB8+fJWb5MSPSGMmEwm\n5OXltTgJyKsefSMHBwc8//zzXNskD1ZaWoqcnBxkZ2cjOzsb9+7dg4eHB5O2KNETwoibm9sfHvjB\nYgndg/j7+2PXrl0IDg62epqQ0/JKW5GQkICcnBy0b98earUafn5+mDBhAtOKpjQZS4jErl69ikGD\nBjFt44+GA+S0vNJWXLhwAWq1+qG7YFvzvqAePSES+/rrr5kmeovFgmeffRZPP/00szbInxccHPyn\n/rvWvC9oVoYQibF+qFYqlTh06BDTNkjra837ghI9IRLjcX7swIEDcejQIeh0OtTU1Ig/xHa15n1B\nQzeEyMCZM2cAAEePHhWvyfXMWDmiRE+IxHiUC05KSmLeBmldrXlf0KobQhg7d+5cs2tOTk7w8vLi\nVpLAbDbjp59+wo0bNwAA/fv3x9ixY2Vd+0ZqPO8L+pQJYez48ePQaDTo378/AOD69evw8fGBVqtF\neHg4RowYwTyGLVu2wGw2Y9y4cQCAU6dOYcuWLZg/fz7ztknLeN4XlOgJYcxisWDt2rXiJqnKykok\nJSVh9erViI6O5pLob968iYSEBPH1gAEDxMNIiDR43he06oYQxnQ6ndVOWFdXV+h0Ojg7O7dYC4cF\npVKJkpIS8XVpaSnVvJEYz/uCevSEMNa/f3+sWbMGTz31FAAgLS0N/fv3h8FgQIcOHbjEMGPGDCxf\nvhyPPfYYBEGATqfDggULuLRNWsbzvqDJWEIYEwQB586dQ2ZmJoCGujPDhg3jsn6+KZPJhKKiIgCA\np6cn7O3tubZPrPG8L6hHTwhjCoUC/v7+sLOzg0KhgK+vL7ckf/369RavZ2dnAwACAgK4xEGa43lf\nUI+eEMbOnDmDf/3rX2JSvXHjBl577TXxkZ2lNWvWNLumUChw584d6HQ6JueTkj+H531BPXpCGNu/\nfz9iY2PFtdFVVVWIiYnhkug//PBDq9eZmZnYt28fOnXqhDlz5jBvn/wxnvcFJXpCGLNYLFYbYJyd\nnWGxWLjGcO3aNXz33XdQKBSYNGkS87LI5OF43heU6AlhLDAwEKtWrcIzzzwDoOGR/YknnuDSdnp6\nOvbt2wcnJye88sor8Pf359IueTie9wWN0RPCQVpaGrKysgAA/fr1w9ChQ7m0O3XqVHTu3Bne3t4t\nTvTR4eDS4nVfUKInpA37o1U3jWjVjTxQoieEkZkzZ7bYixYEAQqFAjt27JAgKiI1Ke4LSvSEENLG\nUbELQghp4yjRE9LGWSwW7Ny5U+owiIQo0RPSximVSnFlB5EnGqMnRAa++uorVFRUICQkBA4ODuL1\nYcOGSRgV4YU2TBEiAyaTCS4uLvjtt9+srlOilwfq0RNCSBtHY/SEyEBRURFWrFiByMhIAMDt27fx\n3XffSRwV4YUSPSEysGnTJkyfPl08os7b2xtnzpyROCrCCyV6QmTAaDTC19fX6hqdGSsf9EkTIgMu\nLi4oKSkRt96npaXBzc1N4qgILzQZS4gMlJaWYvPmzcjKykKHDh3g4eGBt99+Gx4eHlKHRjigRE+I\njBgMBgiCgPbt20sdCuGI1tETIgMmkwnnzp2DVqu1OsUoPDxcwqgIL5ToCZGB+Ph4ODk5wcfHB/b2\n9lKHQzijRE+IDFRUVGDJkiVSh0EkQqtuCJEBtVqNO3fuSB0GkQhNxhLShkVGRkKhUKC+vh4lJSXw\n8PCAvb29eJpRYmKi1CESDijRE9KGlZWVPfB9d3d3TpEQKdHQDSFtmLu7O9zd3fHNN9+Ivze9RuSB\nEj0hMlBQUGD12mKxIDc3V6JoCG+06oaQNmz//v3Yv38/jEYjZs2aBQAQBAF2dnYYO3asxNERXmiM\nnhAZ2LVrF6ZPny51GEQilOgJkYmamhqUlJTAaDSK1wICAiSMiPBCQzeEyMCxY8eQkpKCiooK9O7d\nGxqNBmq1GtHR0VKHRjigyVhCZCAlJQWxsbHo2rUroqOjER8fjw4dOkgdFuGEEj0hMtCuXTu0a9cO\nQEOBsx49eqCoqEjiqAgvNHRDiAx07twZtbW1ePLJJ7Fy5Up06NCBNkvJCE3GEiIz169fR11dHQID\nA2FnR309OaBET4hMZGZmori4GKNGjUJVVRUMBgOdMCUTNEZPiAzs3bsXBw4cwIEDBwAAZrMZ69at\nkzgqwgslekJk4Pz584iKioKDgwOAhjF7vV4vcVSEF0r0hMiAnZ0dFAoFFAoFgIazY4l80EwMITIQ\nEhKCzZs3o7a2Fr/88gtOnDiBMWPGSB0W4YQmYwmRiatXr+LKlSsQBAGBgYEYNGiQ1CERTijRE9KG\n/fDDD/Dz80OfPn2gUqmkDodIhIZuCGnDysvL8c9//hOFhYXw8vKCn5+f+OPs7Cx1eIQT6tETIgNm\nsxk3b95EVlYWNBoNsrOz4eTkhLVr10odGuGAevSEyIDRaIRer0ddXR30ej3c3Nzg5eUldViEE+rR\nE9KGbdq0CQUFBXB0dETfvn3FHxq2kRdaR09IG6bT6WAymdCpUyd07twZXbp0ofLEMkQ9ekLaOEEQ\nkJ+fD41Gg6ysLOTn58PZ2RlqtRpTpkyROjzCAY3RE9LGKRQKeHl5oUOHDnBycoKTkxPS09ORk5ND\niV4mqEdPSBuWkpIi9uTt7OygVqvh5+cHtVoNLy8vKJU0eisH1KMnpA0rKyvDU089hVmzZsHNzU3q\ncIhEqEdPCCFtHD23EUJIG0eJnhBC2jhK9IQQ0sZRoieEkDbufwBfsnAdIo5iPQAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvPn4oPElQsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f43c388c-4f3f-45e2-b407-a53662994302"
      },
      "source": [
        "# Select features with chi2\n",
        "bestfeatures = SelectKBest(score_func = chi2, k = 10)\n",
        "fit = bestfeatures.fit(features.drop(columns = features.columns[(features < 0).any()], axis = 1), targets)\n",
        "\n",
        "chi2_scores = pd.DataFrame(fit.scores_)\n",
        "\n",
        "feature_columns = pd.DataFrame(features.drop(columns = features.columns[(features < 0).any()], axis = 1).columns)\n",
        "\n",
        "features_scores = pd.concat([feature_columns, chi2_scores], axis = 1)\n",
        "\n",
        "features_scores.columns = ['name', 'score']\n",
        "\n",
        "feat_chi = features_scores.nlargest(10, 'score').iloc[:, 0]\n",
        "\n",
        "feat_chi"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35           log_Electricity Use - Grid Purchase (kBtu)\n",
              "14               Electricity Use - Grid Purchase (kBtu)\n",
              "34                           log_Natural Gas Use (kBtu)\n",
              "36        log_Weather Normalized Site Electricity (kWh)\n",
              "9     Weather Normalized Site Electricity Intensity ...\n",
              "15            Weather Normalized Site Electricity (kWh)\n",
              "12                               Natural Gas Use (kBtu)\n",
              "10    Weather Normalized Site Natural Gas Intensity ...\n",
              "13     Weather Normalized Site Natural Gas Use (therms)\n",
              "28                             log_DOF Gross Floor Area\n",
              "Name: name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyrKr9aylgMg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f2a6f228-7488-4049-8e28-b56254277101"
      },
      "source": [
        "# Combine top features\n",
        "et_clf = pd.DataFrame(et_clf)\n",
        "\n",
        "et_clf = et_clf.values\n",
        "\n",
        "feat_chi = pd.DataFrame(feat_chi)\n",
        "\n",
        "feat_chi = feat_chi.values\n",
        "\n",
        "merged_features = np.concatenate((feat_chi, et_clf))\n",
        "\n",
        "merged_features = np.unique(merged_features)\n",
        "\n",
        "merged_features"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Electricity Use - Grid Purchase (kBtu)', 'Natural Gas Use (kBtu)',\n",
              "       'Site EUI (kBtu/ft²)', 'Source EUI (kBtu/ft²)',\n",
              "       'Total GHG Emissions (Metric Tons CO2e)',\n",
              "       'Weather Normalized Site Electricity (kWh)',\n",
              "       'Weather Normalized Site Electricity Intensity (kWh/ft²)',\n",
              "       'Weather Normalized Site Natural Gas Intensity (therms/ft²)',\n",
              "       'Weather Normalized Site Natural Gas Use (therms)',\n",
              "       'log_DOF Gross Floor Area',\n",
              "       'log_Direct GHG Emissions (Metric Tons CO2e)',\n",
              "       'log_ENERGY STAR Score',\n",
              "       'log_Electricity Use - Grid Purchase (kBtu)',\n",
              "       'log_Natural Gas Use (kBtu)', 'log_Property Id',\n",
              "       'log_Site EUI (kBtu/ft²)', 'log_Source EUI (kBtu/ft²)',\n",
              "       'log_Weather Normalized Site Electricity (kWh)',\n",
              "       'log_Weather Normalized Source EUI (kBtu/ft²)'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnWk_yUslzVq",
        "colab_type": "text"
      },
      "source": [
        "## Standartization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpHP_vmnl1Ln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X, X_test, y, y_test = train_test_split(features, targets, test_size = 0.2, random_state = 42)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_scl = scaler.fit_transform(X)\n",
        "X_test_scl = scaler.fit_transform(X_test)\n",
        "\n",
        "X_scl = pd.DataFrame(X_scl, columns = X.columns)\n",
        "X_test_scl = pd.DataFrame(X_test_scl, columns = X_test.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kk-CJ3CPmRB0",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nINlmHZkmg15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lg_model = LinearRegression(copy_X = True, fit_intercept = True, n_jobs = None, normalize = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdeUAdZRm6Az",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "32dc8bed-e5f5-4b1c-d35d-de5bc16e750e"
      },
      "source": [
        "lg_model.fit(X_scl[merged_features], y)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jObk7TZ3m7vI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e5076e65-55a0-4d88-c97e-4e95b12ef2de"
      },
      "source": [
        "lg_model.score(X_scl[merged_features], y)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7690551612743349"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCihXPhTpaAC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = lg_model.predict(X_test_scl[merged_features])\n",
        "mae = mean_absolute_error(y_test, preds.argmax(axis = 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvBvZ6F6pyU8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "56c6e104-146a-4b34-e42d-ab1659115ba9"
      },
      "source": [
        "mae"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.15966822187662"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L93XCLRnoxyp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad3b318d-ae40-4f26-a863-02abca8194c5"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7713, 115)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-an0ygOoUZM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "3a8be48e-78fd-4ab7-ac12-d1ce1085ec4f"
      },
      "source": [
        "pca = decomposition.PCA().fit(X)\n",
        "\n",
        "plt.figure(figsize = (10, 7))\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_), color = 'k', lw = 2)\n",
        "plt.xlabel('Number of components')\n",
        "plt.ylabel('Total explained variance')\n",
        "plt.xlim(0, 6)\n",
        "plt.yticks(np.arange(0, 1.1, 0.1))\n",
        "plt.axvline(5, c = 'g', ls = '-')\n",
        "plt.axhline(0.95, c = 'r')\n",
        "plt.show();"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Fi7V//35JksPh0ODBgzVo0CA5HA4fJwSAmo1yBtQQmZmZSk5O1sqVK1VYWChJuvjiixUf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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaRmSqpXpKBc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "01ce2234-fd04-44ba-90de-663b88150b38"
      },
      "source": [
        "np.cumsum(pca.explained_variance_ratio_)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.38013716, 0.61504001, 0.80660652, 0.91809342, 0.94711897,\n",
              "       0.97517627, 0.98375126, 0.9890155 , 0.99238051, 0.99548289,\n",
              "       0.99685527, 0.99793067, 0.99838682, 0.99873078, 0.99904753,\n",
              "       0.99934233, 0.99951546, 0.99966823, 0.99979756, 0.99990458,\n",
              "       0.99998074, 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
              "       1.        , 1.        , 1.        , 1.        , 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hic9ACcJqOuV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "86435d26-7675-4045-a9ac-3c1d97fcece1"
      },
      "source": [
        "tsne_best = TSNE(random_state = 17)\n",
        "\n",
        "X_tsne = tsne_best.fit_transform(X)\n",
        "\n",
        "plt.figure(figsize = (12, 10))\n",
        "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c = y.values.flatten(), \n",
        "            edgecolor = 'none', alpha = 0.7, s = 40,\n",
        "            cmap = plt.cm.get_cmap('nipy_spectral', 10))\n",
        "plt.colorbar()\n",
        "plt.title('EnergySTAR. t-SNE projection')"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'EnergySTAR. t-SNE projection')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nkb894uC0TC8XOWLw9SulxGfDe8iOw1pAsd2kw6+uHDr43UxItOHXKqboSbeV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SM9hOLiM7O0dITxDfRw3mwTIzSUwNuEYryfWJ7kgbZmFs3fyeyaj7J3s6SnJ0l80jnccuYp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\nXrYfpO2bAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCn0C_RQuguN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# With dispersion of 0.95 features can be compressed to 5 from 115\n",
        "pca = decomposition.PCA(n_components = 5)\n",
        "features_reduced = pca.fit_transform(features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFsiyLZfwOER",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X, X_test, y, y_test = train_test_split(pd.DataFrame(features_reduced), targets, test_size = 0.2, random_state = 42)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_scl = scaler.fit_transform(X)\n",
        "X_test_scl = scaler.fit_transform(X_test)\n",
        "\n",
        "X_scl = pd.DataFrame(X_scl, columns = X.columns)\n",
        "X_test_scl = pd.DataFrame(X_test_scl, columns = X_test.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Robnb_f6vDU_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "33677bfe-8e9b-41a5-9bf6-37d669519e73"
      },
      "source": [
        "lg_model.fit(X_scl, y)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcaafTDwvJv4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "23eeee83-1f75-47bf-aea0-653e918e54ac"
      },
      "source": [
        "lg_model.score(X_scl, y)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02079024522574746"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Eo9yi0_vQJ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = lg_model.predict(X_test_scl)\n",
        "mae = mean_absolute_error(y_test, preds.argmax(axis = 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wnUHt5nxIkK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "84609ee5-dc94-42f4-befd-167f3aa823f9"
      },
      "source": [
        "mae"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.15966822187662"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHmmFXaaXucg",
        "colab_type": "text"
      },
      "source": [
        "# Useful links"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhtnERqUXw8V",
        "colab_type": "text"
      },
      "source": [
        "* https://habr.com/ru/company/ods/blog/325654/\n",
        "* https://fooobar.com/questions/16311024/analysis-of-wine-quality-dataset\n",
        "* https://habr.com/ru/post/304214/\n",
        "* https://habr.com/ru/company/ods/blog/322626/\n",
        "* https://habr.com/ru/company/ods/blog/323210/\n",
        "* https://distill.pub/2016/misread-tsne/\n",
        "* https://www.kaggle.com/sonalisingh1411/pca-on-wine-dataset\n",
        "* https://goksinan.github.io/machine/pca-on-wine-data/\n",
        "* http://www.cs.nthu.edu.tw/~shwu/courses/ml/labs/02_EDA_PCA/02_EDA_PCA.html\n",
        "\n",
        "* https://feature-engine.readthedocs.io/en/latest/index.html"
      ]
    }
  ]
}