diff --git a/Assignment2/Assignment2.ipynb b/Assignment2/Assignment2.ipynb index 3f64a0c..e4c7f04 100644 --- a/Assignment2/Assignment2.ipynb +++ b/Assignment2/Assignment2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -14,7 +14,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -27,10 +27,13 @@ "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "import matplotlib.pyplot as plt\n", "\n", "def task1_preprocessing() -> None:\n", + " \"\"\"\n", + " Выполняет EDA и предобработку данных для прогнозирования сезонности на основе данных о потреблении энергии в Дании.\n", + " \"\"\"\n", " # 1. Загрузка данных\n", " try:\n", " data = pd.read_csv(\"dataset/opsd_raw.csv\", parse_dates=[\"utc_timestamp\"])\n", @@ -45,29 +48,29 @@ " \"DK_wind_generation_actual\", \n", " \"DK_solar_generation_actual\"\n", " ]\n", + "\n", " # Проверка наличия столбцов в данных\n", " missing_cols = [col for col in cols if col not in data.columns]\n", " if missing_cols:\n", " print(f\"Ошибка: Отсутствуют столбцы {missing_cols} в данных.\")\n", " return\n", + " \n", " data = data[cols].copy()\n", " data.columns = [\"timestamp\", \"load\", \"wind\", \"solar\"]\n", " \n", - " # 3. Обработка пропущенных значений\n", - " for col in [\"load\", \"wind\", \"solar\"]:\n", - " data[col].fillna(method='ffill', inplace=True) # Заполняем пропуски предыдущими значениями\n", - " data[col].fillna(method='bfill', inplace=True) # Если остались пропуски, заполняем следующими значениями\n", + " # 3. Обработка пропущенных значений:\n", + " # Удаляем строки, где есть хоть одно пропущенное значение\n", + " data.dropna(inplace=True)\n", "\n", " # 4. Формирование массивов 24x3 (часы × признаки)\n", " data.set_index(\"timestamp\", inplace=True)\n", " \n", " # Группируем по дням и оставляем только дни с ровно 24 записями\n", - " daily_data = (\n", - " data.groupby(data.index.date) # Группировка по дате\n", - " .filter(lambda x: len(x) == 24) # Фильтруем группы с ровно 24 строками\n", - " .groupby(data.index.date) # Повторная группировка по дате\n", - " .apply(lambda x: x[[\"load\", \"wind\", \"solar\"]].values.T) # Преобразуем в массивы\n", + " daily_data = data.groupby(data.index.date).apply(\n", + " lambda x: x[[\"load\", \"wind\", \"solar\"]].values.T\n", + " if len(x) == 24 else None\n", " )\n", + " daily_data = daily_data.dropna()\n", " \n", " # Преобразуем индексы в формат datetime для дальнейшей работы\n", " daily_data.index = pd.to_datetime(daily_data.index)\n", @@ -83,8 +86,17 @@ " X = np.stack(df[\"data\"].values)\n", " y = df[\"season\"].values\n", " \n", + " # Check for NaNs *before* train-test split\n", + " if np.isnan(X).any() or not np.isfinite(X).all():\n", + " print(\"Ошибка: X содержит NaN или бесконечные значения перед train-test split.\")\n", + " return\n", + " \n", + " # Encode labels\n", + " label_encoder = LabelEncoder()\n", + " y_encoded = label_encoder.fit_transform(y)\n", + " \n", " X_train, X_temp, y_train, y_temp = train_test_split(\n", - " X, y, test_size=0.3, stratify=y, random_state=42\n", + " X, y_encoded, test_size=0.3, stratify=y_encoded, random_state=42\n", " )\n", " X_val, X_test, y_val, y_test = train_test_split(\n", " X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42\n", @@ -92,9 +104,24 @@ " \n", " # 7. Масштабирование\n", " scaler = StandardScaler()\n", - " X_train_scaled = scaler.fit_transform(X_train.reshape(-1, 3)).reshape(X_train.shape)\n", - " X_val_scaled = scaler.transform(X_val.reshape(-1, 3)).reshape(X_val.shape)\n", - " X_test_scaled = scaler.transform(X_test.reshape(-1, 3)).reshape(X_test.shape)\n", + " # Проверяем, что массивы не содержат NaN перед масштабированием\n", + " if np.isnan(X_train).any() or not np.isfinite(X_train).all():\n", + " print(\"Ошибка: X_train содержит NaN или бесконечные значения перед масштабированием.\")\n", + " return\n", + " X_train_reshaped = X_train.reshape(-1, 3)\n", + " X_train_scaled = scaler.fit_transform(X_train_reshaped).reshape(X_train.shape)\n", + " \n", + " if np.isnan(X_val).any() or not np.isfinite(X_val).all():\n", + " print(\"Ошибка: X_val содержит NaN или бесконечные значения перед масштабированием.\")\n", + " return\n", + " X_val_reshaped = X_val.reshape(-1, 3)\n", + " X_val_scaled = scaler.transform(X_val_reshaped).reshape(X_val.shape)\n", + " \n", + " if np.isnan(X_test).any() or not np.isfinite(X_test).all():\n", + " print(\"Ошибка: X_test содержит NaN или бесконечные значения перед масштабированием.\")\n", + " return\n", + " X_test_reshaped = X_test.reshape(-1, 3)\n", + " X_test_scaled = scaler.transform(X_test_reshaped).reshape(X_test.shape)\n", " \n", " # 8. Сохранение данных\n", " try:\n", @@ -104,7 +131,9 @@ " X_test=X_test_scaled,\n", " y_train=y_train,\n", " y_val=y_val,\n", - " y_test=y_test)\n", + " y_test=y_test,\n", + " classes=label_encoder.classes_,\n", + " allow_pickle=True)\n", " print(\"Данные успешно сохранены в processed_data.npz\")\n", " except Exception as e:\n", " print(f\"Ошибка при сохранении данных: {e}\")\n", @@ -122,8 +151,12 @@ "def plot_sample_profiles(df: pd.DataFrame) -> None:\n", " plt.figure(figsize=(12, 6))\n", " for season in [\"winter\", \"spring\", \"summer\", \"autumn\"]:\n", - " sample = df[df[\"season\"] == season].iloc[0][\"data\"]\n", - " plt.plot(sample[0], label=f\"{season} load\")\n", + " try:\n", + " # Get the first day's data for the given season\n", + " season_encoded = df[df[\"season\"] == season].iloc[0][\"data\"]\n", + " plt.plot(season_encoded[0], label=f\"{season} load\") # Plot the 'load' data\n", + " except IndexError:\n", + " print(f\"Предупреждение: Нет данных для сезона {season} для построения графика.\")\n", " plt.title(\"Суточные профили потребления энергии по сезонам\")\n", " plt.xlabel(\"Час дня (0-23)\")\n", " plt.ylabel(\"Мощность (МВт)\")\n", @@ -138,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -313,8 +346,6 @@ "/var/data/python/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/var/data/python/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/var/data/python/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, @@ -322,26 +353,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Лучшие параметры: {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100, 100), 'solver': 'adam'}\n", - "Точность (Accuracy): 0.7810\n", - "Точность (Precision): 0.7951\n", - "Полнота (Recall): 0.7810\n", - "F1-Score: 0.7708\n" + "Лучшие параметры: {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50,), 'solver': 'adam'}\n", + "Точность (Accuracy): 0.7587\n", + " precision recall f1-score support\n", + "\n", + " 0 0.76 0.70 0.73 73\n", + " 1 0.84 0.59 0.70 83\n", + " 2 0.72 0.92 0.81 83\n", + " 3 0.74 0.83 0.78 76\n", + "\n", + " accuracy 0.76 315\n", + " macro avg 0.77 0.76 0.75 315\n", + "weighted avg 0.77 0.76 0.75 315\n", + "\n" ] } ], "source": [ "import numpy as np\n", - "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.neural_network import MLPClassifier\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.metrics import accuracy_score, classification_report\n", "import warnings\n", "\n", - "def task2_model_training() -> None:\n", - " # Игнорируем предупреждения для более чистого вывода\n", + "def task2_mlp():\n", + " \"\"\"\n", + " Строит и обучает многослойный перцептрон (MLP) для классификации сезонов.\n", + " \"\"\"\n", " warnings.filterwarnings(\"ignore\")\n", - "\n", - " # 1. Загрузка предварительно обработанных данных\n", + " # 1. Загрузка данных\n", " try:\n", " data = np.load(\"processed_data.npz\", allow_pickle=True)\n", " X_train, X_val, X_test = data[\"X_train\"], data[\"X_val\"], data[\"X_test\"]\n", @@ -349,10 +389,10 @@ " except FileNotFoundError:\n", " print(\"Ошибка: Файл processed_data.npz не найден. Запустите task1_preprocessing() перед выполнением этой задачи.\")\n", " return\n", - " \n", - " # 2. Определение модели MLPClassifier\n", + "\n", + " # 2. Определение модели\n", " mlp = MLPClassifier(max_iter=500, random_state=42)\n", - " \n", + "\n", " # 3. Настройка гиперпараметров с использованием GridSearchCV\n", " param_grid = {\n", " \"hidden_layer_sizes\": [(50,), (100,), (50, 50), (100, 100)],\n", @@ -361,39 +401,124 @@ " \"alpha\": [0.0001, 0.001],\n", " }\n", " grid_search = GridSearchCV(mlp, param_grid, cv=5, scoring=\"accuracy\", n_jobs=-1)\n", - " \n", + "\n", " print(\"Запуск GridSearchCV...\")\n", - " # Убедитесь, что X_train не содержит NaN или бесконечные значения\n", - " if np.isnan(X_train).any() or not np.isfinite(X_train).all():\n", - " print(\"Ошибка: X_train содержит NaN или бесконечные значения. Проверьте данные.\")\n", - " return\n", - " \n", " grid_search.fit(X_train.reshape(X_train.shape[0], -1), y_train)\n", - " \n", + "\n", " # 4. Лучшая модель и её параметры\n", " best_model = grid_search.best_estimator_\n", " print(\"Лучшие параметры:\", grid_search.best_params_)\n", - " \n", + "\n", " # 5. Оценка модели на тестовом наборе\n", - " # Убедитесь, что X_test не содержит NaN или бесконечные значения\n", - " if np.isnan(X_test).any() or not np.isfinite(X_test).all():\n", - " print(\"Ошибка: X_test содержит NaN или бесконечные значения. Проверьте данные.\")\n", + " y_pred = best_model.predict(X_test.reshape(X_test.shape[0], -1))\n", + "\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " print(f\"Точность (Accuracy): {accuracy:.4f}\")\n", + " print(classification_report(y_test, y_pred))\n", + "\n", + "# Запуск обучения MLP\n", + "task2_mlp()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Computed output size would be negative. Received `inputs shape=(None, 0, 64)`, `kernel shape=(3, 64, 32)`, `dilation_rate=[1]`.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[59]\u001b[39m\u001b[32m, line 74\u001b[39m\n\u001b[32m 71\u001b[39m model.save(\u001b[33m\"\u001b[39m\u001b[33m1d_cnn_model.h5\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 73\u001b[39m \u001b[38;5;66;03m# Запуск обучения 1D CNN\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m74\u001b[39m \u001b[43mtask3_1d_cnn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[59]\u001b[39m\u001b[32m, line 33\u001b[39m, in \u001b[36mtask3_1d_cnn\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 30\u001b[39m y_test = to_categorical(y_test, num_classes)\n\u001b[32m 32\u001b[39m \u001b[38;5;66;03m# Построение модели 1D CNN\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m model = \u001b[43mSequential\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 34\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mConv1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilters\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkernel_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactivation\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrelu\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_shape\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m24\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Corrected input shape\u001b[39;49;00m\n\u001b[32m 35\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMaxPooling1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mConv1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilters\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkernel_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactivation\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrelu\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mMaxPooling1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 38\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFlatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDense\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactivation\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mrelu\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 40\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDropout\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[43m \u001b[49m\u001b[43mtf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactivation\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43msoftmax\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 42\u001b[39m \u001b[43m\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 44\u001b[39m \u001b[38;5;66;03m# Компиляция модели\u001b[39;00m\n\u001b[32m 45\u001b[39m model.compile(optimizer=Adam(learning_rate=\u001b[32m0.001\u001b[39m),\n\u001b[32m 46\u001b[39m loss=\u001b[33m'\u001b[39m\u001b[33mcategorical_crossentropy\u001b[39m\u001b[33m'\u001b[39m,\n\u001b[32m 47\u001b[39m metrics=[\u001b[33m'\u001b[39m\u001b[33maccuracy\u001b[39m\u001b[33m'\u001b[39m])\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/models/sequential.py:76\u001b[39m, in \u001b[36mSequential.__init__\u001b[39m\u001b[34m(self, layers, trainable, name)\u001b[39m\n\u001b[32m 74\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m layers:\n\u001b[32m 75\u001b[39m \u001b[38;5;28mself\u001b[39m.add(layer, rebuild=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m76\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_maybe_rebuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/models/sequential.py:149\u001b[39m, in \u001b[36mSequential._maybe_rebuild\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 147\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m._layers[\u001b[32m0\u001b[39m], InputLayer) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._layers) > \u001b[32m1\u001b[39m:\n\u001b[32m 148\u001b[39m input_shape = \u001b[38;5;28mself\u001b[39m._layers[\u001b[32m0\u001b[39m].batch_shape\n\u001b[32m--> \u001b[39m\u001b[32m149\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_shape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 150\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m._layers[\u001b[32m0\u001b[39m], \u001b[33m\"\u001b[39m\u001b[33minput_shape\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._layers) > \u001b[32m1\u001b[39m:\n\u001b[32m 151\u001b[39m \u001b[38;5;66;03m# We can build the Sequential model if the first layer has the\u001b[39;00m\n\u001b[32m 152\u001b[39m \u001b[38;5;66;03m# `input_shape` property. This is most commonly found in Functional\u001b[39;00m\n\u001b[32m 153\u001b[39m \u001b[38;5;66;03m# model.\u001b[39;00m\n\u001b[32m 154\u001b[39m input_shape = \u001b[38;5;28mself\u001b[39m._layers[\u001b[32m0\u001b[39m].input_shape\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/layers/layer.py:230\u001b[39m, in \u001b[36mLayer.__new__..build_wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 228\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m obj._open_name_scope():\n\u001b[32m 229\u001b[39m obj._path = current_path()\n\u001b[32m--> \u001b[39m\u001b[32m230\u001b[39m \u001b[43moriginal_build_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 231\u001b[39m \u001b[38;5;66;03m# Record build config.\u001b[39;00m\n\u001b[32m 232\u001b[39m signature = inspect.signature(original_build_method)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/models/sequential.py:195\u001b[39m, in \u001b[36mSequential.build\u001b[39m\u001b[34m(self, input_shape)\u001b[39m\n\u001b[32m 193\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._layers[\u001b[32m1\u001b[39m:]:\n\u001b[32m 194\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m195\u001b[39m x = \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 196\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[32m 197\u001b[39m \u001b[38;5;66;03m# Can happen if shape inference is not implemented.\u001b[39;00m\n\u001b[32m 198\u001b[39m \u001b[38;5;66;03m# TODO: consider reverting inbound nodes on layers processed.\u001b[39;00m\n\u001b[32m 199\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:122\u001b[39m, in \u001b[36mfilter_traceback..error_handler\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 119\u001b[39m filtered_tb = _process_traceback_frames(e.__traceback__)\n\u001b[32m 120\u001b[39m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[32m 121\u001b[39m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e.with_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 124\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", + "\u001b[36mFile \u001b[39m\u001b[32m/var/data/python/lib/python3.12/site-packages/keras/src/ops/operation_utils.py:221\u001b[39m, in \u001b[36mcompute_conv_output_shape\u001b[39m\u001b[34m(input_shape, filters, kernel_size, strides, padding, data_format, dilation_rate)\u001b[39m\n\u001b[32m 219\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(output_spatial_shape)):\n\u001b[32m 220\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m none_dims \u001b[38;5;129;01mand\u001b[39;00m output_spatial_shape[i] < \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m221\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 222\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mComputed output size would be negative. Received \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 223\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`inputs shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minput_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m`, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 224\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`kernel shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkernel_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m`, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 225\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`dilation_rate=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdilation_rate\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 226\u001b[39m )\n\u001b[32m 227\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m padding == \u001b[33m\"\u001b[39m\u001b[33msame\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m padding == \u001b[33m\"\u001b[39m\u001b[33mcausal\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m 228\u001b[39m output_spatial_shape = np.floor((spatial_shape - \u001b[32m1\u001b[39m) / strides) + \u001b[32m1\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: Computed output size would be negative. Received `inputs shape=(None, 0, 64)`, `kernel shape=(3, 64, 32)`, `dilation_rate=[1]`." + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.utils import to_categorical\n", + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "def task3_1d_cnn():\n", + " \"\"\"\n", + " Строит и обучает одномерную сверточную нейронную сеть (1D CNN) для классификации сезонов.\n", + " \"\"\"\n", + " # Загрузка предобработанных данных\n", + " try:\n", + " data = np.load(\"processed_data.npz\", allow_pickle=True)\n", + " X_train, X_val, X_test = data[\"X_train\"], data[\"X_val\"], data[\"X_test\"]\n", + " y_train, y_val, y_test = data[\"y_train\"], data[\"y_val\"], data[\"y_test\"]\n", + " classes = data[\"classes\"]\n", + " except FileNotFoundError:\n", + " print(\"Ошибка: Файл processed_data.npz не найден. Запустите task1_preprocessing() перед выполнением этой задачи.\")\n", " return\n", " \n", - " y_pred = best_model.predict(X_test.reshape(X_test.shape[0], -1))\n", - " \n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " precision = precision_score(y_test, y_pred, average=\"weighted\")\n", - " recall = recall_score(y_test, y_pred, average=\"weighted\")\n", - " f1 = f1_score(y_test, y_pred, average=\"weighted\")\n", - " \n", - " print(f\"Точность (Accuracy): {accuracy:.4f}\")\n", - " print(f\"Точность (Precision): {precision:.4f}\")\n", - " print(f\"Полнота (Recall): {recall:.4f}\")\n", - " print(f\"F1-Score: {f1:.4f}\")\n", + " num_classes = len(classes)\n", + " y_train = to_categorical(y_train, num_classes)\n", + " y_val = to_categorical(y_val, num_classes)\n", + " y_test = to_categorical(y_test, num_classes)\n", "\n", - "# Запуск обучения модели\n", - "task2_model_training()\n" + " # Построение модели 1D CNN\n", + " model = Sequential([\n", + " tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(3, 24)), # Corrected input shape\n", + " tf.keras.layers.MaxPooling1D(pool_size=2),\n", + " tf.keras.layers.Conv1D(filters=32, kernel_size=3, activation='relu'),\n", + " tf.keras.layers.MaxPooling1D(pool_size=2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(64, activation='relu'),\n", + " tf.keras.layers.Dropout(0.5),\n", + " tf.keras.layers.Dense(num_classes, activation='softmax')\n", + " ])\n", + "\n", + " # Компиляция модели\n", + " model.compile(optimizer=Adam(learning_rate=0.001),\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + " X_train = np.transpose(X_train, (0, 2, 1))\n", + " X_val = np.transpose(X_val, (0, 2, 1))\n", + " X_test = np.transpose(X_test, (0, 2, 1))\n", + " \n", + " # Обучение модели\n", + " history = model.fit(X_train, y_train,\n", + " validation_data=(X_val, y_val),\n", + " epochs=50,\n", + " batch_size=32,\n", + " verbose=1)\n", + "\n", + " # Оценка модели на тестовом наборе\n", + " y_pred = model.predict(X_test)\n", + " y_pred_classes = np.argmax(y_pred, axis=1)\n", + " y_test_classes = np.argmax(y_test, axis=1)\n", + "\n", + " # Вычисление метрик\n", + " accuracy = accuracy_score(y_test_classes, y_pred_classes)\n", + " print(f\"Точность (Accuracy): {accuracy:.4f}\")\n", + " print(classification_report(y_test_classes, y_pred_classes, target_names=classes))\n", + "\n", + " # Сохранение модели\n", + " model.save(\"1d_cnn_model.h5\")\n", + "\n", + "# Запуск обучения 1D CNN\n", + "task3_1d_cnn()\n" ] } ], diff --git a/Assignment2/load_profiles.png b/Assignment2/load_profiles.png index f1b7f06..8a8b8a3 100644 Binary files a/Assignment2/load_profiles.png and b/Assignment2/load_profiles.png differ diff --git a/Assignment2/processed_data.npz b/Assignment2/processed_data.npz index 1e18adf..0873d9f 100644 Binary files a/Assignment2/processed_data.npz and b/Assignment2/processed_data.npz differ