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"metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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", + "\n", + "def task1_preprocessing() -> None:\n", + " try:\n", + " data = pd.read_csv(\"dataset/opsd_raw.csv\", parse_dates=[\"utc_timestamp\"])\n", + " except FileNotFoundError:\n", + " return\n", + "\n", + " cols = [\n", + " \"utc_timestamp\",\n", + " \"DK_load_actual_entsoe_transparency\",\n", + " \"DK_wind_generation_actual\",\n", + " \"DK_solar_generation_actual\"\n", + " ]\n", + "\n", + " missing_cols = [col for col in cols if col not in data.columns]\n", + " if missing_cols:\n", + " return\n", + "\n", + " data = data[cols].copy()\n", + " data.columns = [\"timestamp\", \"load\", \"wind\", \"solar\"]\n", + "\n", + " data.dropna(inplace=True)\n", + "\n", + " data.set_index(\"timestamp\", inplace=True)\n", + "\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", + " daily_data.index = pd.to_datetime(daily_data.index)\n", + "\n", + " df = pd.DataFrame({\n", + " \"timestamp\": daily_data.index,\n", + " \"data\": daily_data.values,\n", + " \"season\": daily_data.index.map(lambda x: get_season(x.month))\n", + " })\n", + "\n", + " X = np.stack(df[\"data\"].values)\n", + " y = df[\"season\"].values\n", + "\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_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", + " )\n", + "\n", + " scaler = StandardScaler()\n", + " \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", + "\n", + " np.savez(\"processed_data.npz\",\n", + " X_train=X_train_scaled,\n", + " X_val=X_val_scaled,\n", + " X_test=X_test_scaled,\n", + " y_train=y_train,\n", + " y_val=y_val,\n", + " y_test=y_test,\n", + " classes=label_encoder.classes_,\n", + " allow_pickle=True)\n", + "\n", + " plot_sample_profiles(df)\n", + "\n", + "def get_season(month: int) -> str:\n", + " if month in [12, 1, 2]: return \"winter\"\n", + " if month in [3, 4, 5]: return \"spring\"\n", + " if month in [6, 7, 8]: return \"summer\"\n", + " return \"autumn\"\n", + "\n", + "def plot_sample_profiles(df: pd.DataFrame) -> None:\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " for season in [\"winter\", \"spring\", \"summer\", \"autumn\"]:\n", + " try:\n", + " season_data = df[df[\"season\"] == season].iloc[0][\"data\"]\n", + " plt.plot(season_data[0], label=f\"{season} load\")\n", + " except IndexError:\n", + " pass\n", + " plt.title(\"Daily energy consumption profiles by season\")\n", + " plt.xlabel(\"Hour of day (0-23)\")\n", + " plt.ylabel(\"Power (MW)\")\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.savefig(\"load_profiles.png\")\n", + " plt.show()\n", + "\n", + "task1_preprocessing()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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", + "/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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " autumn 0.76 0.70 0.73 73\n", + " spring 0.84 0.59 0.70 83\n", + " summer 0.72 0.92 0.81 83\n", + " winter 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.neural_network import MLPClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import classification_report\n", + "\n", + "def task2_mlp():\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", + " return\n", + "\n", + " mlp = MLPClassifier(max_iter=500, random_state=42)\n", + "\n", + " param_grid = {\n", + " \"hidden_layer_sizes\": [(50,), (100,), (50, 50), (100, 100)],\n", + " \"activation\": [\"relu\", \"tanh\"],\n", + " \"solver\": [\"adam\", \"sgd\"],\n", + " \"alpha\": [0.0001, 0.001],\n", + " }\n", + " \n", + " grid_search = GridSearchCV(mlp, param_grid, cv=5, scoring=\"accuracy\", n_jobs=-1)\n", + " grid_search.fit(X_train.reshape(X_train.shape[0], -1), y_train)\n", + "\n", + " best_model = grid_search.best_estimator_\n", + " y_pred = best_model.predict(X_test.reshape(X_test.shape[0], -1))\n", + "\n", + " print(classification_report(y_test, y_pred, target_names=classes))\n", + "\n", + "task2_mlp()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Assignment2/processed_data.npz b/Assignment2/processed_data.npz index 0873d9f578ec9f33cefeda731ff297e87e5ed46d..acf6db4546511e16e1976b10f65e1b7f6a6ec52d 100644 GIT binary patch delta 217 zcmeDE?A7lj=T0vLec(%tD%Llg{g(Pg{6hHg{_6Xg`SPpQWn%yWb|CCzWMFu=T?ohn0D&nL4gdfE diff --git a/Assignment2/requirements.txt b/Assignment2/requirements.txt new file mode 100644 index 0000000..09efb99 --- /dev/null +++ b/Assignment2/requirements.txt @@ -0,0 +1,5 @@ +pandas +numpy +scikit-learn +matplotlib +tensorflow