hw08_boosting_clustering_borodin (1).ipynb

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      "name": "hw08_boosting_clustering_borodin.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHgmxWG_7lnE"
      },
      "source": [
        "# Введение в анализ данных\n",
        "## НИУ ВШЭ, 2020-2021 учебный год\n",
        "\n",
        "### Домашнее задание №8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOqjUI6igeLc"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import load_boston\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score, mean_squared_error"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tKaz0okgeLh"
      },
      "source": [
        "## Градиентный бустинг своими руками (2 + 2* баллов)\n",
        "\n",
        "Реализуйте алгоритм градиентного бустинга для регрессии. Напомним основные формулы.\n",
        "\n",
        "Обозначим текущую композицию на $N-1$ шаге за $a_{N - 1}(x_i)$. Следующий базовый алгоритм $b_N(x_i)$ обучается на ответах $-\\frac{\\partial L(y_i, z)}{\\partial z}\\Bigl|_{z = a_{N - 1}(x_i)}$, где $L(y_i, z)$ — значение функции потерь на объекте при правильном ответе $y_i$ и предсказании $z$. Композиция на следующем шаге получается следующим образом:\n",
        "\n",
        "$$\n",
        "a_N(x_i) = a_{N-1}(x_i) + \\nu\\gamma_Nb_N(x_i)\n",
        "$$\n",
        "\n",
        "Здесь $\\nu$ — гиперпараметр learning rate, $\\gamma_N$ — оптимальный вес, настраиваемый на каждом шаге алгоритма, который можно найти по следующей формуле (обратите внимание на отсутствие $\\nu$):\n",
        "\n",
        "$$\n",
        "\\gamma_N = \\mathrm{arg}\\min_\\gamma \\frac{1}{\\ell}\\sum\\limits_{i=1}^{\\ell}L\\left(y_i, a_{N - 1}(x_i) + \\gamma b_N(x_i)\\right)\n",
        "$$\n",
        "\n",
        "Можете принять $\\gamma_N = 1$ для каждого $N$. Реализуйте нахождение оптимального $\\gamma_N$ на каждом шаге, чтобы получить ещё 1 балл.\n",
        "\n",
        "В качестве функции потерь возьмите MSE.\n",
        "\n",
        "*Примечание. Вы можете использовать `DecisionTree` из `sklearn` и методы оптимизации из различных библиотек.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eu_ZIF9YgeLi"
      },
      "source": [
        "# это может помочь вам для поиска оптимальных gamma\n",
        "from scipy.optimize import minimize\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZB5Yt-LKgeLi"
      },
      "source": [
        "class GradientBoosting:\n",
        "    def __init__(self, n_estimators, max_depth, learning_rate=0.1):\n",
        "        \"\"\"\n",
        "        PARAMETERS:\n",
        "        n_estimators - number of trees in the ensemble\n",
        "        max_depth - maximum depth of a tree\n",
        "        learning_rate - coefficient by which new algorithm result is multiplied\n",
        "        \"\"\"\n",
        "        self.n_estimators = n_estimators\n",
        "        self.max_depth = max_depth\n",
        "        self.learning_rate = learning_rate\n",
        "        self.gamma = 1\n",
        "        self.gammas = []\n",
        "        \n",
        "        self.trees = []\n",
        "        \n",
        "    def fit(self, x, y):\n",
        "        \"\"\"\n",
        "        INPUT:\n",
        "        x - np.array of shape (k, d)\n",
        "        y - np.array of shape (k,)\n",
        "        \"\"\"\n",
        "        # Здесь нам нужно проитерироваться по n_estimators и обучить\n",
        "        # соответствующее количество деревьев с помощью _fit_predict_tree(),\n",
        "        # правильно обновляя y_new\n",
        "        # Деревья нужно где-то сохранить, чтобы затем использовать в predict()\n",
        "        \n",
        "        y_new = y\n",
        "        comp_n = 0\n",
        "        for i in range(self.n_estimators):\n",
        "            pred = self._fit_predict_tree(x, y_new)\n",
        "            comp_n += pred\n",
        "            y_new = -2*(comp_n - y) # градиент\n",
        "\n",
        "            \n",
        "\n",
        "    def _fit_predict_tree(self, x, y):\n",
        "        # Обучаем дерево и возвращаем его предикшн\n",
        "        tree = DecisionTreeRegressor(max_depth=self.max_depth)\n",
        "        tree.fit(x, y)\n",
        "        self.trees.append(tree)\n",
        "        result = tree.predict(x)\n",
        "\n",
        "        return self.gamma * self.learning_rate * result\n",
        "        \n",
        "    def predict(self, x):\n",
        "        \"\"\"\n",
        "        INPUT:\n",
        "        x - np.array of shape (m, d)\n",
        "        OUTPUT:\n",
        "        y_pred - np.array of shape (m,)\n",
        "        \"\"\"\n",
        "        y_pred = 0\n",
        "        for tree in self.trees:\n",
        "            pred = tree.predict(x)\n",
        "            y_pred += self.gamma * self.learning_rate * pred\n",
        "        return y_pred"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z-8PafFgeLj"
      },
      "source": [
        "Проверьте вашу реализацию на Boston dataset. Подберите оптимальные гиперпараметры, чтобы победить RandomForestRegressor как в обычном случае, так и при нахождении оптимального шага **(не меняйте параметры сида)**. При необходимости воспользуйтесь GridSearch. За это вы получите ещё 1 балл."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rxKS85vgeLj"
      },
      "source": [
        "boston = load_boston()\n",
        "X = boston.data\n",
        "y = boston.target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=13)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAOaQK2a2N4p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c48f8d8-4d31-4066-cc70-63d8e13a2e97"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf = RandomForestRegressor(max_features=4, n_estimators=640, random_state=19052019)\n",
        "\n",
        "rf.fit(X_train, y_train)\n",
        "mean_squared_error(y_test, rf.predict(X_test))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.635844078917064"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gt3vn0IgeLk"
      },
      "source": [
        "np.random.seed(19052019)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuq9J1A5560f"
      },
      "source": [
        "gb = GradientBoosting(100, 5)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiFdEsDs560f"
      },
      "source": [
        "gb.fit(X_train, y_train)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOh6CgO_560f"
      },
      "source": [
        "y_pred = gb.predict(X_test)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLasKyLXgeLk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5e1fd45-39ae-4a27-da14-2497d4806c28"
      },
      "source": [
        "mean_squared_error(y_test, y_pred)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13.138266262781455"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCWou8xPgeLl"
      },
      "source": [
        "Для удобства проверяющего укажите, пожалуйста, реализовали ли вы нахождение оптимального шага?\n",
        "\n",
        "- Нет"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgCTG0lbO1mK"
      },
      "source": [
        "## Прогнозируем задержки самолётов (2 балла)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVZibhKiM3j4"
      },
      "source": [
        "Поработаем с задачей про задержки самолётов. На основании доступных данных о рейсе вам нужно определить, будет ли он задержан на 15 минут.\n",
        "Воспользуйтесь любыми методами градиентного бустинга {XGboost, catboost, LightGBM} и GridSearch для достижения результата. Получите 1 балл за преодоление порога roc_auc_score 0.72 и ещё 1 балл за преодоление порога 0.74."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgLNfXKF560h"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuwqUkrfM3UI"
      },
      "source": [
        "train = pd.read_csv('https://raw.githubusercontent.com/PersDep/data-mining-intro-2021/main/hw08-boosting-clustering/flight_delays_train.csv')\n",
        "test = pd.read_csv('https://raw.githubusercontent.com/PersDep/data-mining-intro-2021/main/hw08-boosting-clustering/flight_delays_test.csv')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "af-whZAeNz5f",
        "outputId": "6e1d202c-8d88-4349-cbe3-44016be43f04"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 13,
      "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>Month</th>\n",
              "      <th>DayofMonth</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>DepTime</th>\n",
              "      <th>UniqueCarrier</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Dest</th>\n",
              "      <th>Distance</th>\n",
              "      <th>dep_delayed_15min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c-8</td>\n",
              "      <td>c-21</td>\n",
              "      <td>c-7</td>\n",
              "      <td>1934</td>\n",
              "      <td>AA</td>\n",
              "      <td>ATL</td>\n",
              "      <td>DFW</td>\n",
              "      <td>732</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>c-4</td>\n",
              "      <td>c-20</td>\n",
              "      <td>c-3</td>\n",
              "      <td>1548</td>\n",
              "      <td>US</td>\n",
              "      <td>PIT</td>\n",
              "      <td>MCO</td>\n",
              "      <td>834</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c-9</td>\n",
              "      <td>c-2</td>\n",
              "      <td>c-5</td>\n",
              "      <td>1422</td>\n",
              "      <td>XE</td>\n",
              "      <td>RDU</td>\n",
              "      <td>CLE</td>\n",
              "      <td>416</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c-11</td>\n",
              "      <td>c-25</td>\n",
              "      <td>c-6</td>\n",
              "      <td>1015</td>\n",
              "      <td>OO</td>\n",
              "      <td>DEN</td>\n",
              "      <td>MEM</td>\n",
              "      <td>872</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>c-10</td>\n",
              "      <td>c-7</td>\n",
              "      <td>c-6</td>\n",
              "      <td>1828</td>\n",
              "      <td>WN</td>\n",
              "      <td>MDW</td>\n",
              "      <td>OMA</td>\n",
              "      <td>423</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Month DayofMonth DayOfWeek  DepTime  ... Origin Dest Distance  dep_delayed_15min\n",
              "0   c-8       c-21       c-7     1934  ...    ATL  DFW      732                  N\n",
              "1   c-4       c-20       c-3     1548  ...    PIT  MCO      834                  N\n",
              "2   c-9        c-2       c-5     1422  ...    RDU  CLE      416                  N\n",
              "3  c-11       c-25       c-6     1015  ...    DEN  MEM      872                  N\n",
              "4  c-10        c-7       c-6     1828  ...    MDW  OMA      423                  Y\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5a3DGydN7eu",
        "outputId": "42439073-479d-4741-9b56-0f76b56812d7"
      },
      "source": [
        "# Референс\n",
        "\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "\n",
        "X_train = train[['Distance', 'DepTime']].values\n",
        "y_train = train['dep_delayed_15min'].map({'Y': 1, 'N': 0}).values\n",
        "X_test = test[['Distance', 'DepTime']].values\n",
        "X_train_part, X_valid, y_train_part, y_valid = train_test_split(X_train, y_train, test_size=0.3)\n",
        "\n",
        "xgb = XGBClassifier()\n",
        "xgb.fit(X_train_part, y_train_part)\n",
        "roc_auc_score(y_valid, xgb.predict_proba(X_valid)[:, 1])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6961759892424921"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wJK363f560i"
      },
      "source": [
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyeUSLTUETkc"
      },
      "source": [
        "cols = ['Month', 'DayofMonth', 'DayOfWeek', \n",
        "        'UniqueCarrier', 'Origin', 'Dest', \n",
        "        'dep_delayed_15min']"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4MiEla0FAOs"
      },
      "source": [
        "enc = LabelEncoder()\n",
        "for col in cols:\n",
        "    train[col] = enc.fit_transform(train[col])\n",
        "X_train, y_train = train.drop('dep_delayed_15min', 1), train['dep_delayed_15min']"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5pjZptA_7na",
        "outputId": "00192d5f-76a7-481d-dc6c-7e65159d1fa1"
      },
      "source": [
        "X_train_part, X_valid, y_train_part, y_valid = train_test_split(X_train, y_train, test_size=0.3)\n",
        "\n",
        "lgbm = LGBMClassifier(n_estimators=400, max_depth=12)\n",
        "lgbm.fit(X_train_part, y_train_part)\n",
        "roc_auc_score(y_valid, lgbm.predict_proba(X_valid)[:, 1])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7421156524553512"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDmcF1cP2bDe"
      },
      "source": [
        "## DBSCAN своими руками (2 + 2* баллов)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poDad0E52ii1"
      },
      "source": [
        "DBSCAN: Density-based spatial clustering of applications with noise\n",
        "\n",
        "Реализуйте класс dbscan.\n",
        "\n",
        "Алгоритм имеет два основных гиперпараметра:\n",
        "1. `eps` &mdash; радиус рассматриваемой окрестности\n",
        "2. `min_samples` &mdash; число соседей в окрестности\n",
        "\n",
        "Для выполнения кластеризации DBSCAN точки делятся на основные точки, достижимые по плотности точки и выпадающие следующим образом:\n",
        "\n",
        "- Точка $p$ является основной точкой, если по меньшей мере `min_samples` точек находятся на расстоянии, не превосходящем \n",
        "`eps` от неё. Говорят, что эти точки достижимы прямо из $p$.\n",
        "\n",
        "-  Точка $q$ прямо достижима из $p$, если точка $q$ находится на расстоянии, не большем `eps`, от точки $p$, и $p$ — основная точка.\n",
        "Точка $q$ достижима из $p$, если имеется путь \n",
        "$p_1,…,p_n$ где $p_1=p$ и $p_n=q$ , а каждая точка $p_{i+1}$ достижима прямо из $p_i$ (все точки на пути должны быть основными, за исключением $q$).\n",
        "\n",
        "Все точки, не достижимые из основных точек, считаются выбросами.\n",
        "\n",
        "Теперь, если $p$ является основной точкой, то она формирует кластер вместе со всеми точками (основными или неосновными), достижимыми из этой точки. Каждый кластер содержит по меньшей мере одну основную точку. Неосновные точки могут быть частью кластера, но они формируют его «край», поскольку не могут быть использованы для достижения других точек.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdC__UEtabvU"
      },
      "source": [
        "# Это может помочь вам для поиска дистанции между точками\n",
        "from scipy.spatial import distance"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czgUIv2E3GBm"
      },
      "source": [
        "# Рекомендации -- это только рекомендации\n",
        "class dbscan:\n",
        "    def __init__(self, eps, min_samples):\n",
        "        self.eps = eps\n",
        "        self.min_samples = min_samples\n",
        "        self.current_cluster = 0\n",
        "        self.noise_cluster = 0\n",
        "        self.visited = set()\n",
        "        self.clustered = set()\n",
        "        self.clusters = {self.noise_cluster: []}\n",
        "        \n",
        "    def fit_predict(self, X):\n",
        "        X = list(zip(X[:,0], X[:, 1]))\n",
        "        self.X = X\n",
        "        for p in X:\n",
        "            if p not in self.visited:\n",
        "                self.visited.add(p)\n",
        "                neighbours = self.range_query(p)\n",
        "                if len(neighbours) < self.min_samples:\n",
        "                    self.clusters[self.noise_cluster].append(p)\n",
        "                else:\n",
        "                    self.current_cluster += 1\n",
        "                    self.grow_cluster(p, neighbours)\n",
        "                    \n",
        "        clusters = []\n",
        "        for p in X:\n",
        "            for c in self.clusters.keys():\n",
        "                if p in self.clusters[c]:\n",
        "                    clusters.append(c)\n",
        "                    continue\n",
        "        return clusters\n",
        "\n",
        "    def grow_cluster(self, root, neighbours):\n",
        "        # В этой функции мы полностью строим кластер относительно переданной точки\n",
        "        # Т.е. реализуем цикл по структуре данных, изначально состоящей из уже\n",
        "        # известных соседей (neighbours) и в процессе пополняющейся\n",
        "        # \"соседями соседей\" с помощью range_query()\n",
        "        if self.current_cluster not in self.clusters:\n",
        "            self.clusters[self.current_cluster] = []\n",
        "        self.clusters[self.current_cluster].append(root)\n",
        "        self.clustered.add(root)\n",
        "        \n",
        "        while len(neighbours) != 0:\n",
        "            q = neighbours.pop()\n",
        "            if q not in self.visited:\n",
        "                self.visited.add(q)\n",
        "                q_neighbours = self.range_query(q)\n",
        "                if len(q_neighbours) > self.min_samples:\n",
        "                    neighbours.extend(q_neighbours)\n",
        "            if q not in self.clustered:\n",
        "                self.clustered.add(q)\n",
        "                self.clusters[self.current_cluster].append(q)\n",
        "                if q in self.clusters[self.noise_cluster]:\n",
        "                    self.clusters[self.noise_cluster].remove(q)\n",
        "\n",
        "    def range_query(self, x):\n",
        "        \"\"\"Возвращает точки на расстоянии <= eps от x\"\"\"\n",
        "        # С помощью функции dist()\n",
        "        res = []\n",
        "        for obj in self.X:\n",
        "            if self.dist(x, obj) < self.eps:\n",
        "                res.append(obj)\n",
        "        return res\n",
        "    \n",
        "    @staticmethod\n",
        "    def dist(a, b):\n",
        "        \"\"\"Метрика расстояния\"\"\"\n",
        "        # Попробуйте что-нибудь из scipy.spatial.distance\n",
        "        # Базовый вариант -- Евклидова метрика\n",
        "        return distance.euclidean(a, b)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmI34EpRCLD9"
      },
      "source": [
        "Проверим себя на простом примере:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "EFABkOA73JC6",
        "outputId": "6409d6b7-cf9a-4c10-e4e3-bd42f039d824"
      },
      "source": [
        "from sklearn.datasets import make_moons\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "X, _ = make_moons(n_samples=500, noise=0.1)\n",
        "plt.scatter(X[:,0], X[:,1])\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "XZOF9fa3ytob"
      },
      "source": [
        "Референс"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-xF0Vfltytoc",
        "outputId": "569c02d0-62cf-48d7-a15c-f2fbcef5a9d4"
      },
      "source": [
        "from sklearn.cluster import DBSCAN\n",
        "\n",
        "clusters = DBSCAN(eps=0.2, min_samples=10).fit_predict(X)\n",
        "\n",
        "plt.scatter(X[:,0], X[:,1], c=clusters)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCgWZPMwCycn"
      },
      "source": [
        "Кластеризуйте это с помощью своей версии DBSCAN:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMtgyZMR4h0a"
      },
      "source": [
        "dbscan_model = dbscan(0.2, 10)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "egOlGNXyytoc",
        "outputId": "c6d95022-c5c4-4ab9-d7bf-0924832cfd03"
      },
      "source": [
        "X, _ = make_moons(n_samples=500, noise=0.1)\n",
        "custom_clusters = dbscan_model.fit_predict(X)\n",
        "plt.scatter(X[:,0], X[:,1], c=custom_clusters)\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6S5stsxDBps"
      },
      "source": [
        "Получите 1 балл за ещё один интересный пример кластеризации (в качестве референса используйте предоставленное изображение. Нужно несколько интересных соседствующих форм):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uyZzFy1ytod"
      },
      "source": [
        "import cv2\n",
        "\n",
        "def img2points(path):\n",
        "    img = cv2.imread(path) \n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)[..., 0]\n",
        "    points = (img < 200).astype(int).nonzero()\n",
        "    features = np.array([points[1][::10], points[0][::10]]).T \n",
        "    return features"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ff6Ao8rvytod"
      },
      "source": [
        "meme = img2points('/content/mm.jpg')"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "oIb2Uw-Fytod",
        "outputId": "6c292dcf-e857-45a2-ba3b-240bfc579f86"
      },
      "source": [
        "plt.scatter(meme[:, 0], meme[:, 1])"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fedcfff5d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsNmJzwPytod"
      },
      "source": [
        "dbscan_model = dbscan(eps=15, min_samples=5)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVAyms3Mytoe"
      },
      "source": [
        "clusters = dbscan_model.fit_predict(meme)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "EH41wtKHytoe",
        "outputId": "2b3ea015-07a2-4c6d-83e3-92ab2afee092"
      },
      "source": [
        "plt.scatter(meme[:,0], meme[:,1], c=clusters)\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZRxN-y1DKoA"
      },
      "source": [
        "Получите ещё 1 балл, если время работы вашего алгоритма больше референсного не более чем на 20%:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjqYw7RSDkqd",
        "outputId": "26c81b40-5bef-4d8e-d6d8-739f707211fd"
      },
      "source": [
        "%%time\n",
        "%%capture\n",
        "clusters = DBSCAN(eps=0.2, min_samples=10).fit_predict(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 8.45 ms, sys: 2 µs, total: 8.46 ms\n",
            "Wall time: 6.98 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAeHvTniDtSB",
        "pycharm": {
          "name": "#%%\n"
        },
        "outputId": "aefefcf6-5f7f-4898-9e29-2e00a1438e99"
      },
      "source": [
        "%%time\n",
        "%%capture\n",
        "clusters = dbscan(eps=0.2, min_samples=10).fit_predict(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3.81 s, sys: 0 ns, total: 3.81 s\n",
            "Wall time: 3.81 s\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}