Labs/lab_5_catboost_6131_Gershevskiy.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Лабораторная работа 5\n",
        "Градиентный бустинг с CatBoost\n",
        "\n",
        "Выполнил: Гершевский Егор Владимирович, гр.6131-010402D"
      ],
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      "cell_type": "markdown",
      "source": [
        "Подобрать набор данных для классификации (количество классов не имеет значения) и регрессии. Важно наличие числовых и категориальных признаков в данных. Опишите данные, что обозначает каждый столбец, какого типа (категориальный, вещественный, целочисленный, бинарный) данный столбец, при необходимости для категориальных в том числе бинарных признаков привести расшифровку их значений. Можно работать на тех же данных, что и в предыдущей Л.Р.: в этом случае сравнить с результатами лучшего классификатора из предыдущей работы."
      ],
      "metadata": {
        "id": "_gUzuCwp_7K6"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost"
      ],
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        },
        "id": "wXPh7Y446bpK",
        "outputId": "d458d5b1-5239-4dd7-9c2b-08061c403023"
      },
      "execution_count": 2,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-1.0.5-cp37-none-manylinux1_x86_64.whl (76.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 76.6 MB 48 kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.4.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (4.2.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (8.0.1)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-1.0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7ncChQZ46P3D"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from catboost import *\n",
        "from catboost import CatBoostClassifier\n",
        "from sklearn.metrics import accuracy_score, PrecisionRecallDisplay, mean_absolute_error, mean_squared_error\n",
        "from sklearn.metrics import plot_roc_curve, roc_auc_score, accuracy_score, recall_score, precision_score\n",
        "from warnings import filterwarnings\n",
        "filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/diabetes.csv\")"
      ],
      "metadata": {
        "id": "9C4d4len6eqY"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
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          "height": 204
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        "id": "NS3VLU4F8TQd",
        "outputId": "241ee84e-8844-4acd-9fb6-07e2c03af765"
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      "execution_count": 37,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "1 Применить классификатор CatBoostClassifier к вашему набору данных. Обратите внимание, что catboost умеет работать с категориальными признаками (параметр cat_features - список индексов категориальных признаков), поэтому использование `pd.factorize` / `pd.get_dummies` здесь лишнее.\n",
        "\n",
        "1) Вывести итерацию с наилучшим значением AUC (для бинарной классификации) или наилучшим значением Logloss.\n",
        "\n",
        "2) Вывести значения метрик качества классификации на тестовых данных (accuracy и precision / recall / AUC, если применимо).\n",
        "\n",
        "3) В случае бинарной классификации на основе CatBoostClassifier построить кривые PRC и ROC. Вычислить значения PRC-AUC и ROC-AUC."
      ],
      "metadata": {
        "id": "Tj0-T443ACze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['BMI'] = df['BMI'].astype(int)\n",
        "df = df.drop(['DiabetesPedigreeFunction'], axis=1)"
      ],
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        "id": "n1VANOqa-WYb"
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      "execution_count": 39,
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91feffac-9da3-446c-8c92-1da0f0c0c0a9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91feffac-9da3-446c-8c92-1da0f0c0c0a9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = df.drop(\"Outcome\", axis=1), df[\"Outcome\"]"
      ],
      "metadata": {
        "id": "smg_T99x8YHR"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = list(range(0, X.shape[1]))\n",
        "print(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCe3KCP28g8C",
        "outputId": "3e2d7525-0f19-489a-95ed-fef39a94360d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.35, random_state=42)"
      ],
      "metadata": {
        "id": "tF09A2hh-Gy6"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = CatBoostClassifier(\n",
        "    iterations=200,\n",
        "    random_seed=63,\n",
        "    learning_rate=0.15,\n",
        "    custom_loss=['AUC', 'Accuracy']\n",
        ")\n",
        "\n",
        "model.fit(\n",
        "    X_train, y_train,\n",
        "    cat_features=features,\n",
        "    eval_set=(X_test, y_test),\n",
        "    verbose=False,\n",
        "    plot=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "36a7513d2a64482bbee2cf587159fb9a"
          ]
        },
        "id": "L2MQuNw3-I49",
        "outputId": "ecc4790b-6705-4802-d283-2bee1d380687"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36a7513d2a64482bbee2cf587159fb9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7fcd4ce942d0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрим на лучшие параметры модели, лучшую итерацию и выведем лучшие score по метрикам"
      ],
      "metadata": {
        "id": "0seECj-BA8YT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Model is fitted: {model.is_fitted()}\")\n",
        "print(f\"Model params: {model.get_params()}\")\n",
        "print(f\"Best iteration: {model.get_best_iteration()}\")\n",
        "print(f\"Best score: {model.best_score_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTxicmyT-Kou",
        "outputId": "2a21bcc8-6764-4039-f0e3-33435b06102d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model is fitted: True\n",
            "Model params: {'iterations': 200, 'learning_rate': 0.15, 'random_seed': 63, 'custom_loss': ['AUC', 'Accuracy']}\n",
            "Best iteration: 30\n",
            "Best score: {'learn': {'Accuracy': 1.0, 'Logloss': 0.04413030811979137}, 'validation': {'Accuracy': 0.694, 'Logloss': 0.5778438440572304, 'AUC': 0.7282695931328076}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посчитаем значения метрик"
      ],
      "metadata": {
        "id": "y-JzGf0pA_Mp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy: {accuracy_score(y_test, model.predict(X_test))}\")\n",
        "print(f\"AUC: {roc_auc_score(y_test, model.predict(X_test))}\")\n",
        "print(f\"Precision: {precision_score(y_test, model.predict(X_test))}\")\n",
        "print(f\"Recall: {recall_score(y_test, model.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K46nspgw-5BO",
        "outputId": "787de2c1-3e0d-4fd0-9f9e-57b45d97d595"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.684\n",
            "AUC: 0.6014027496685044\n",
            "Precision: 0.6086956521739131\n",
            "Recall: 0.3146067415730337\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC-AUC кривая"
      ],
      "metadata": {
        "id": "cOGWMFucBBc_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "nJrrOEdC-74D",
        "outputId": "89d2e8cf-eeca-4dbe-b937-c1ea75fe5447"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fcd4ce9a950>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PRC-кривая и PRC-AUC"
      ],
      "metadata": {
        "id": "_Fw3n-frBDdp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PrecisionRecallDisplay.from_estimator(model, X_test, y_test, name=\"CatBoostClassifier\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "RDn3AKxK--RF",
        "outputId": "25e60af5-78f9-4e38-d9b6-1690d6565f97"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x7fcd4b6099d0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2 Применить регрессию CatBoostRegressor к набору данных для регрессии. Вывести номер итерации и значения метрик (MAE и MSE) для наилучшей итерации. Обратите внимание, что в задаче восстановления регрессии целевая переменная имеет непрерывные значения."
      ],
      "metadata": {
        "id": "Y8OqEQy3BGBw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_regressor = CatBoostRegressor(\n",
        "    custom_metric=[\"MAE\", \"RMSE\"], \n",
        "    cat_features=features\n",
        ")\n",
        "\n",
        "model_regressor.fit(\n",
        "    X_train, y_train,\n",
        "    eval_set=(X_test, y_test),\n",
        "    verbose=False,\n",
        "    plot=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "b38cf4fcc6a54fe1a312d3592feb6037"
          ]
        },
        "id": "7N9GEX2F_AmI",
        "outputId": "27b30a7c-5448-47c3-becc-275479897f87"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b38cf4fcc6a54fe1a312d3592feb6037"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostRegressor at 0x7fcd4b60c690>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Best iteration: {model_regressor.get_best_iteration()}\")\n",
        "print(f\"Best score: {model_regressor.get_best_score()}\")\n",
        "print(f\"Model params: {model_regressor.get_params()}\")\n",
        "print(f\"MAE: {mean_absolute_error(y_test, model_regressor.predict(X_test))}\")\n",
        "print(f\"MSE: {mean_squared_error(y_test, model_regressor.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jofZqa7d_Cyx",
        "outputId": "a01e5a5c-1fc7-4a52-c98c-6b5274b006b2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best iteration: 157\n",
            "Best score: {'learn': {'MAE': 0.06041099829651041, 'RMSE': 0.07813057604366098}, 'validation': {'MAE': 0.3755003849488953, 'RMSE': 0.4386788716572319}}\n",
            "Model params: {'loss_function': 'RMSE', 'custom_metric': ['MAE', 'RMSE'], 'cat_features': [0, 1, 2, 3, 4, 5, 6]}\n",
            "MAE: 0.37952844171565076\n",
            "MSE: 0.19243915243846207\n"
          ]
        }
      ]
    }
  ]
}