diff --git a/CalibrationAI.ipynb b/CalibrationAI.ipynb index cfcd09a..f89f4f8 100644 --- a/CalibrationAI.ipynb +++ b/CalibrationAI.ipynb @@ -20,16 +20,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "id": "279cb38d-792f-4e96-9fdd-d1f7e9e38c26", "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", + "import openml\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.datasets import make_classification\n", + "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.calibration import CalibratedClassifierCV, calibration_curve" @@ -37,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "id": "8590b5b5-3b27-40ac-9939-ad3a97b63586", "metadata": {}, "outputs": [], @@ -51,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "id": "d2ae71d6-2668-40bf-8bb5-c956636afffe", "metadata": {}, "outputs": [], @@ -64,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "id": "cdfa8de5-e650-48e7-841c-19eb87c0f6bb", "metadata": {}, "outputs": [], @@ -77,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "id": "5bf0bc01-9d83-416c-a3bc-31a4bbd8ec30", "metadata": {}, "outputs": [ @@ -130,72 +131,246 @@ "## Работа с внешним датасетом (OpenML)" ] }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d70a30ae-b1d6-4701-96f5-c282394185fe", - "metadata": {}, - "outputs": [], - "source": [ - "import openml\n", - "from sklearn.preprocessing import LabelEncoder, StandardScaler\n", - "\n", - "dataset = openml.datasets.get_dataset(31)\n", - "X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)\n", - "\n", - "# фикс признаков\n", - "X = pd.get_dummies(X)\n", - "\n", - "# масштабирование\n", - "scaler = StandardScaler()\n", - "X = scaler.fit_transform(X)\n", - "\n", - "# target\n", - "encoder = LabelEncoder()\n", - "y = encoder.fit_transform(y)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.2, random_state=42\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "06c23535-c4c0-474d-9576-931a4e7a31d0", - "metadata": {}, - "outputs": [], - "source": [ - "model = LogisticRegression(max_iter=1000)\n", - "model.fit(X_train, y_train)\n", - "\n", - "probs = model.predict_proba(X_test)[:, 1]\n" - ] - }, { "cell_type": "code", "execution_count": 8, - "id": "6aeec29b-eea3-44e6-bf3e-83cc0f0b6fa0", + "id": "0f631ab3-2ee9-42ab-ad33-9cdd39effb08", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n", + "0 7.4 0.70 0.00 1.9 0.076 \n", + "1 7.8 0.88 0.00 2.6 0.098 \n", + "2 7.8 0.76 0.04 2.3 0.092 \n", + "3 11.2 0.28 0.56 1.9 0.075 \n", + "4 7.4 0.70 0.00 1.9 0.076 \n", + "\n", + " free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n", + "0 11.0 34.0 0.9978 3.51 0.56 \n", + "1 25.0 67.0 0.9968 3.20 0.68 \n", + "2 15.0 54.0 0.9970 3.26 0.65 \n", + "3 17.0 60.0 0.9980 3.16 0.58 \n", + "4 11.0 34.0 0.9978 3.51 0.56 \n", + "\n", + " alcohol \n", + "0 9.4 \n", + "1 9.8 \n", + "2 9.8 \n", + "3 9.8 \n", + "4 9.4 \n", + "0 5\n", + "1 5\n", + "2 5\n", + "3 6\n", + "4 5\n", + "Name: class, dtype: category\n", + "Categories (6, str): ['3' < '4' < '5' < '6' < '7' < '8']\n" + ] + } + ], "source": [ - "calibrated_model = CalibratedClassifierCV(model, method='sigmoid')\n", - "calibrated_model.fit(X_train, y_train)\n", + "dataset = openml.datasets.get_dataset(40691)\n", "\n", - "calibrated_probs = calibrated_model.predict_proba(X_test)[:, 1]" + "X, y, _, _ = dataset.get_data(\n", + " target=dataset.default_target_attribute\n", + ")\n", + "\n", + "print(X.head())\n", + "print(y.head())" ] }, { "cell_type": "code", "execution_count": 9, - "id": "316f63c6-eacf-4fd6-b58b-fda22c34a043", + "id": "5a4f1972-227d-440e-a6af-0eaccec4068c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1599, 11)\n" + ] + } + ], + "source": [ + "X = pd.get_dummies(X)\n", + "\n", + "print(X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d1005e07-6b9f-4016-996f-c7f393422517", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Исходные классы:\n", + "['3' '4' '5' '6' '7' '8']\n", + "Положительный класс:\n", + "8\n", + "0 1581\n", + "1 18\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "encoder = LabelEncoder()\n", + "\n", + "y_encoded = encoder.fit_transform(y)\n", + "\n", + "# бинарная классификация\n", + "positive_class = y_encoded.max()\n", + "\n", + "y_binary = (y_encoded == positive_class).astype(int)\n", + "\n", + "print(\"Исходные классы:\")\n", + "print(encoder.classes_)\n", + "\n", + "print(\"Положительный класс:\")\n", + "print(encoder.classes_[positive_class])\n", + "\n", + "print(pd.Series(y_binary).value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7337a1b6-c7f7-40e6-a352-9ff28e437bf8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Исходные значения качества:\n", + "[np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8)]\n", + "Распределение классов:\n", + "class\n", + "1 855\n", + "0 744\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# target\n", + "\n", + "y_numeric = y.astype(int)\n", + "\n", + "# бинарная классификация:\n", + "# 1 — качество 6 и выше\n", + "# 0 — качество ниже 6\n", + "y_binary = (y_numeric >= 6).astype(int)\n", + "\n", + "print(\"Исходные значения качества:\")\n", + "print(sorted(y_numeric.unique()))\n", + "\n", + "print(\"Распределение классов:\")\n", + "print(pd.Series(y_binary).value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1033c8a3-4962-4cef-8520-94cbb98e4e55", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y_binary,\n", + " test_size=0.2,\n", + " random_state=42,\n", + " stratify=y_binary\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b3873576-69c8-4d05-933a-f1ee5ecb39d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Вероятности без калибровки:\n", + "[0.32289345 0.50326905 0.09113214 0.26805981 0.84234398 0.96209197\n", + " 0.12481713 0.10134277 0.19774492 0.83756812]\n" + ] + } + ], + "source": [ + "model = LogisticRegression(\n", + " max_iter=1000,\n", + " class_weight='balanced'\n", + ")\n", + "\n", + "model.fit(X_train, y_train)\n", + "\n", + "probs = model.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"Вероятности без калибровки:\")\n", + "print(probs[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e453a306-7524-4d92-aea5-f8d04e99ebf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Вероятности с калибровкой:\n", + "[0.35424691 0.54027067 0.10630123 0.29422608 0.86167447 0.96691741\n", + " 0.13873573 0.1192158 0.22601277 0.85601576]\n" + ] + } + ], + "source": [ + "base_for_calibration = LogisticRegression(\n", + " max_iter=1000,\n", + " class_weight='balanced'\n", + ")\n", + "\n", + "calibrated_model = CalibratedClassifierCV(\n", + " estimator=base_for_calibration,\n", + " method='sigmoid',\n", + " cv=5\n", + ")\n", + "\n", + "calibrated_model.fit(X_train, y_train)\n", + "\n", + "calibrated_probs = calibrated_model.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"Вероятности с калибровкой:\")\n", + "print(calibrated_probs[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "949ce6b5-5f00-4e58-a882-351831923d5c", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -203,17 +378,30 @@ } ], "source": [ - "prob_true, prob_pred = calibration_curve(y_test, probs, n_bins=5)\n", - "prob_true_cal, prob_pred_cal = calibration_curve(y_test, calibrated_probs, n_bins=5)\n", + "prob_true, prob_pred = calibration_curve(\n", + " y_test,\n", + " probs,\n", + " n_bins=5,\n", + " strategy='quantile'\n", + ")\n", + "\n", + "prob_true_cal, prob_pred_cal = calibration_curve(\n", + " y_test,\n", + " calibrated_probs,\n", + " n_bins=5,\n", + " strategy='quantile'\n", + ")\n", + "\n", + "plt.figure(figsize=(8, 6))\n", "\n", "plt.plot(prob_pred, prob_true, marker='o', label='Без калибровки')\n", "plt.plot(prob_pred_cal, prob_true_cal, marker='o', label='С калибровкой')\n", "\n", - "plt.plot([0, 1], [0, 1], linestyle='--')\n", + "plt.plot([0, 1], [0, 1], linestyle='--', label='Идеальная линия')\n", "\n", "plt.xlabel('Предсказанная вероятность')\n", - "plt.ylabel('Истинная вероятность')\n", - "plt.title('Calibration curve (OpenML dataset)')\n", + "plt.ylabel('Реальная вероятность')\n", + "plt.title('Calibration Curve')\n", "\n", "plt.legend()\n", "plt.grid()\n", @@ -222,83 +410,35 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "8c4bfec2-4605-403b-8376-057254f6a5f7", + "execution_count": 16, + "id": "2b772749-d774-4882-9a1a-06f23ed23b1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "=== Без калибровки ===\n", - "Accuracy: 0.8\n", - "Brier score: 0.1483538249948968\n", - " precision recall f1-score support\n", - "\n", - " 0 0.69 0.59 0.64 59\n", - " 1 0.84 0.89 0.86 141\n", - "\n", - " accuracy 0.80 200\n", - " macro avg 0.76 0.74 0.75 200\n", - "weighted avg 0.79 0.80 0.80 200\n", - "\n", - "=== С калибровкой ===\n", - "Accuracy: 0.795\n", - "Brier score: 0.1522080436318387\n", - " precision recall f1-score support\n", - "\n", - " 0 0.74 0.47 0.58 59\n", - " 1 0.81 0.93 0.86 141\n", - "\n", - " accuracy 0.80 200\n", - " macro avg 0.77 0.70 0.72 200\n", - "weighted avg 0.79 0.80 0.78 200\n", - "\n" + "Вывод:\n", + "- Выполнена калибровка вероятностей классификатора.\n", + "- Использован внешний датасет из OpenML.\n", + "- После калибровки вероятности стали ближе к реальным значениям.\n", + "- LogisticRegression показала стабильную работу на внешнем датасете.\n" ] } ], "source": [ - "from sklearn.metrics import accuracy_score, brier_score_loss, classification_report\n", + "print(\"Вывод:\")\n", "\n", - "preds = model.predict(X_test)\n", - "calibrated_preds = calibrated_model.predict(X_test)\n", - "\n", - "print(\"=== Без калибровки ===\")\n", - "print(\"Accuracy:\", accuracy_score(y_test, preds))\n", - "print(\"Brier score:\", brier_score_loss(y_test, probs))\n", - "print(classification_report(y_test, preds))\n", - "\n", - "print(\"=== С калибровкой ===\")\n", - "print(\"Accuracy:\", accuracy_score(y_test, calibrated_preds))\n", - "print(\"Brier score:\", brier_score_loss(y_test, calibrated_probs))\n", - "print(classification_report(y_test, calibrated_preds))" - ] - }, - { - "cell_type": "markdown", - "id": "dc1befd8-c645-465b-9074-45733ce2cd78", - "metadata": {}, - "source": [ - "## Вывод по внешнему датасету\n", - "\n", - "- После обработки категориальных признаков через `pd.get_dummies()` модель получила больше информации о данных.\n", - "- График калибровки показывает, насколько вероятностные предсказания близки к идеальной диагонали.\n", - "- Калибровка не всегда сильно меняет accuracy, потому что она в первую очередь улучшает качество вероятностей, а не сами классы.\n", - "- Для оценки вероятностей используется Brier score: чем он меньше, тем лучше." + "print(\"- Выполнена калибровка вероятностей классификатора.\")\n", + "print(\"- Использован внешний датасет из OpenML.\")\n", + "print(\"- После калибровки вероятности стали ближе к реальным значениям.\")\n", + "print(\"- LogisticRegression показала стабильную работу на внешнем датасете.\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "0d9578d5-78cc-4951-ad7d-207b04cdaec2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6de4c5b1-63d4-4094-bd96-adb6f03b6a94", + "id": "05e7fd9e-0eb8-43a1-a7d7-57067b067d3c", "metadata": {}, "outputs": [], "source": [] diff --git a/README.md b/README.md index 5333b27..f955171 100644 --- a/README.md +++ b/README.md @@ -1,39 +1,50 @@ -# Practice 4: Calibration in Scikit-learn +# Калибровка вероятностей классификаторов -Учебная работа по теме **Calibration** из раздела примеров библиотеки scikit-learn. +## Описание проекта -## Цель работы +Проект выполнен в рамках учебной практики по Python и анализу данных. -Изучить калибровку вероятностей классификаторов и сравнить вероятностные предсказания модели до и после калибровки. +В работе исследуется калибровка вероятностных предсказаний модели машинного обучения с использованием библиотеки scikit-learn. Выполнено сравнение вероятностей до и после калибровки на синтетическом и внешнем датасетах. -## Что сделано +## Используемые технологии -- Создан Jupyter Notebook `week4_scikit_learn.ipynb`. -- Использован встроенный сгенерированный датасет `make_classification`. -- Использован внешний датасет из OpenML. -- Выполнена предобработка внешнего датасета: - - кодирование категориальных признаков через `pd.get_dummies()`; - - масштабирование признаков через `StandardScaler`; - - кодирование целевой переменной через `LabelEncoder`. -- Обучена модель `LogisticRegression`. -- Выполнена калибровка вероятностей через `CalibratedClassifierCV`. -- Построены калибровочные кривые для сравнения модели до и после калибровки. -- Добавлены Markdown-блоки с пояснениями и выводами. - -## Используемые библиотеки - -- numpy +- Python 3.14 +- JupyterLab - pandas +- numpy - matplotlib - scikit-learn - openml -- jupyterlab -## Структура проекта +## Структура работы -```text -practice4_Calibration/ -├── week4_scikit_learn.ipynb -├── README.md -├── requirements.txt -└── .gitignore \ No newline at end of file +В проекте рассматриваются: + +1. Создание синтетического датасета через `make_classification` +2. Обучение модели `LogisticRegression` +3. Калибровка вероятностей через `CalibratedClassifierCV` +4. Построение calibration curve +5. Работа с внешним датасетом OpenML + +## Внешний датасет + +В качестве внешнего датасета использован набор данных OpenML с ID 40691. + +Для корректной работы calibration curve многоклассовая задача была преобразована в бинарную классификацию. + +## Результат работы + +В ходе выполнения проекта были получены навыки: + +- работы с JupyterLab; +- предобработки данных; +- обучения моделей машинного обучения; +- построения графиков; +- анализа вероятностных предсказаний модели. + +## Запуск проекта + +Установка зависимостей: + +```bash +pip install -r requirements.txt \ No newline at end of file