k_NN_Example.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/k_NN_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
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        "id": "3p0johk5ve6q"
      },
      "source": [
        "## Метод $k$-ближайших соседей"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0FTyBQtve6t"
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      "source": [
        "Чтение данных"
      ]
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GxA17-ZPve6u"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "# train_data = pd.read_csv(\"Task_data.csv\", delimiter=',', index_col='id')\n",
        "train_data = pd.DataFrame(np.array([\n",
        "    [22, 86, 0],\n",
        "    [78, 32, 1],\n",
        "    [54, 50, 1],\n",
        "    [68, 80, 0],\n",
        "    [18, 12, 0],\n",
        "    [85, 48, 0],\n",
        "    [78, 10, 0],\n",
        "    [50, 74, 0],\n",
        "    [30, 95, 0],\n",
        "    [91, 12, 0]\n",
        "]), columns=['X', 'Y', 'Class'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWexM7xAve6v"
      },
      "source": [
        "Вывод всех строк данных"
      ]
    },
    {
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              "    X   Y  Class\n",
              "0  22  86      0\n",
              "1  78  32      1\n",
              "2  54  50      1\n",
              "3  68  80      0\n",
              "4  18  12      0\n",
              "5  85  48      0\n",
              "6  78  10      0\n",
              "7  50  74      0\n",
              "8  30  95      0\n",
              "9  91  12      0"
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        "train_data"
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      "source": [
        "Отбор данных для предикторов, удаление столбца Class"
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              "    X   Y\n",
              "0  22  86\n",
              "1  78  32\n",
              "2  54  50\n",
              "3  68  80\n",
              "4  18  12"
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          },
          "metadata": {},
          "execution_count": 35
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      ],
      "source": [
        "X = pd.DataFrame(train_data.drop(['Class'], axis=1))\n",
        "X.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xDOea2eEve6x"
      },
      "source": [
        "Отбор столбца Class для отклика"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ofiWdBO_ve6y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a45acfce-5a05-45c5-974d-9596b8d2f025"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "y = pd.DataFrame(train_data['Class']).values.ravel()\n",
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dz2xL8Mwve6y"
      },
      "source": [
        "Подключение библиотеки и задание параметров модели:\n",
        "n_neighbors — число соседей;\n",
        "p — используемое расстояние. Манхэттенское расстояние — p=1, евклидово расстояние — p=2.\n",
        "Дополнительные параметры — https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ICptQOyYve6y",
        "outputId": "e24467f2-bc26-4593-ee92-906b74415246",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3, p=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3, p=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3, p=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "neigh = KNeighborsClassifier(n_neighbors=3, p=1)\n",
        "neigh.fit(X, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ND6kvDqNve6z"
      },
      "source": [
        "Новый объект, который необходимо классифицировать"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hjarslptve6z"
      },
      "outputs": [],
      "source": [
        "NewObject = [30, 30]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JtHZhknve6z"
      },
      "source": [
        "Назначенный класс:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TSwW61zove6z",
        "outputId": "8eb54c94-590f-4fee-88c2-facff4f77ce7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "neigh.predict([NewObject])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xqxz9mDSve6z"
      },
      "source": [
        "Вероятности отнесения к классам 0 и 1 соответственно:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nu2G5IMlve6z",
        "outputId": "136e1027-7cf9-4182-fdf8-ccbbcd09e445",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.33333333, 0.66666667]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "neigh.predict_proba([NewObject])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcNY_qT_ve6z"
      },
      "source": [
        "Вывод расстояний до $k$ соседей в порядке удаления (первый массив — расстояния, второй — идентификаторы объектов). <b>Обращаем внимание</b>, что метод возвращает индексы ближайших соседей с нуля, что не соответствует индексации в таблице."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OCjKb2FTve60",
        "outputId": "37a9e27a-8cc8-4683-d024-81ed758f26fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[30., 44., 50.]]), array([[4, 2, 1]]))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "neigh.kneighbors([NewObject])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvZSmPBXve60"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIVPRRRUve60"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eT-faQzZ0E2F"
      },
      "execution_count": null,
      "outputs": []
    }
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