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+158810 +161230 +157660 +155740 +155690 +151220 +144490 +142460 +137780 +128920 +119160 +110950 +105300 +101650 +99310 +99960 +104820 +112000 +121790 +130420 +138280 +147900 +155270 +160740 +165840 +168690 +170150 +170680 +168970 +164370 +155900 +150860 +144480 +133350 diff --git a/lightgbm_model/scripts/__init__.py b/lightgbm_model/scripts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..651585a8e21988e5f2bcc574a14316bd9575dc6a --- /dev/null +++ b/lightgbm_model/scripts/__init__.py @@ -0,0 +1,2 @@ +# __init__.py + diff --git a/lightgbm_model/scripts/__pycache__/__init__.cpython-311.pyc b/lightgbm_model/scripts/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ad14ffdbdafbf4a7fc66bc700eb2c6d9fe2bf01b Binary files /dev/null and b/lightgbm_model/scripts/__pycache__/__init__.cpython-311.pyc differ diff --git a/lightgbm_model/scripts/__pycache__/config_lightgbm.cpython-311.pyc b/lightgbm_model/scripts/__pycache__/config_lightgbm.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0e56df4fddcea9facd2f1d2644ed2719550f0e00 Binary files /dev/null and b/lightgbm_model/scripts/__pycache__/config_lightgbm.cpython-311.pyc differ diff --git a/lightgbm_model/scripts/config_lightgbm.py b/lightgbm_model/scripts/config_lightgbm.py new file mode 100644 index 0000000000000000000000000000000000000000..aab690852c5833bb02816da82048ed22cc614d97 --- /dev/null +++ b/lightgbm_model/scripts/config_lightgbm.py @@ -0,0 +1,36 @@ +# config.py +import os + +# === Paths === +BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +DATA_PATH = os.path.join(BASE_DIR, "..", "data", "processed", "energy_consumption_aggregated_cleaned.csv") +RESULTS_DIR = os.path.join(BASE_DIR, "results") +MODEL_DIR = os.path.join(BASE_DIR, "model") + +# === Feature-Definition === +FEATURES = [ + "hour_sin", "hour_cos", + "weekday_sin", "weekday_cos", + "rolling_mean_6h", + "month_sin", "month_cos", + "temperature_c", + "consumption_last_week", + "consumption_yesterday", + "consumption_last_hour" +] +TARGET = "consumption_MW" + +# === Hyperparameters fpr LightGBM === +LIGHTGBM_PARAMS = { + 'learning_rate': 0.05, + 'num_leaves': 15, + 'max_depth': 5, + 'lambda_l1': 1.0, + 'lambda_l2': 0.0, + 'min_split_gain': 0.0, + 'n_estimators': 1000, + 'objective': 'regression'} + +# === Early Stopping === +EARLY_STOPPING_ROUNDS = 50 + diff --git a/lightgbm_model/scripts/eval/__pycache__/eval_lightgbm.cpython-311.pyc b/lightgbm_model/scripts/eval/__pycache__/eval_lightgbm.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..713870c54f3384441ed3dc0ead912f105f1378b5 Binary files /dev/null and b/lightgbm_model/scripts/eval/__pycache__/eval_lightgbm.cpython-311.pyc differ diff --git a/lightgbm_model/scripts/eval/eval_lightgbm.py b/lightgbm_model/scripts/eval/eval_lightgbm.py new file mode 100644 index 0000000000000000000000000000000000000000..c506cc2fc072f6eeda29582dd9278c312c438fdc --- /dev/null +++ b/lightgbm_model/scripts/eval/eval_lightgbm.py @@ -0,0 +1,107 @@ +# eval_model.py + +import os +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import pickle +from sklearn.metrics import mean_absolute_error, mean_squared_error +from lightgbm_model.scripts.config_lightgbm import RESULTS_DIR, MODEL_DIR, DATA_PATH +from joblib import load + +# === Ergebnisse-Ordner vorbereiten === +os.makedirs(RESULTS_DIR, exist_ok=True) + +# === Modell und eval_result laden === +# Modell laden +with open(os.path.join(MODEL_DIR, "lightgbm_final_model.pkl"), "rb") as f: + model = pickle.load(f) + +# Eval laden +with open(os.path.join(RESULTS_DIR, "lightgbm_eval_result.pkl"), "rb") as f: + eval_result = pickle.load(f) +X_train = pd.read_csv(os.path.join(RESULTS_DIR, "X_train.csv")) +X_test = pd.read_csv(os.path.join(RESULTS_DIR, "X_test.csv")) +y_test = pd.read_csv(os.path.join(RESULTS_DIR, "y_test.csv")) + +# === Lernkurve === +train_rmse = eval_result['training']['rmse'] +valid_rmse = eval_result['valid_1']['rmse'] + +plt.figure(figsize=(10, 5)) +plt.plot(train_rmse, label='Train RMSE') +plt.plot(valid_rmse, label='Valid RMSE') +plt.axvline(model.best_iteration_, color='gray', linestyle='--', label='Best Iteration') +plt.xlabel("Boosting Round") +plt.ylabel("RMSE") +plt.title("LightGBM Learning Curve") +plt.legend() +plt.tight_layout() +plt.savefig(os.path.join(RESULTS_DIR, "lightgbm_learning_curve.png")) +#plt.show() + +# === Metriken berechnen === +y_pred = model.predict(X_test) +mae = mean_absolute_error(y_test, y_pred) +rmse = np.sqrt(mean_squared_error(y_test, y_pred)) +mape = np.mean(np.abs((y_test.values.flatten() - y_pred) / np.where(y_test.values.flatten() == 0, 1e-10, y_test.values.flatten()))) * 100 + +print(f"Test MAPE: {mape:.5f} %") +print(f"Test MAE: {mae:.5f}") +print(f"Test RMSE: {rmse:.5f}") + +# === Feature Importance === +feature_importance = pd.DataFrame({ + "Feature": X_train.columns, + "Importance": model.feature_importances_ +}).sort_values(by="Importance", ascending=False) + +plt.figure(figsize=(10, 6)) +plt.barh(feature_importance["Feature"], feature_importance["Importance"]) +plt.xlabel("Feature Importance") +plt.title("LightGBM Feature Importance") +plt.gca().invert_yaxis() +plt.tight_layout() +plt.savefig(os.path.join(RESULTS_DIR, "lightgbm_feature_importance.png")) +#plt.show() + +# === Vergleichsplots === +results_df = pd.DataFrame({ + "True Consumption (MW)": y_test.values.flatten(), + "Predicted Consumption (MW)": y_pred +}) + +# Timestamps anhängen +full_df = pd.read_csv(DATA_PATH) +test_dates = full_df.iloc[int(len(full_df)*0.8):]["date"].reset_index(drop=True) +results_df["Timestamp"] = pd.to_datetime(test_dates) + +# Voller Plot +plt.figure(figsize=(15, 6)) +plt.plot(results_df["Timestamp"], results_df["True Consumption (MW)"], label="True", color="darkblue") +plt.plot(results_df["Timestamp"], results_df["Predicted Consumption (MW)"], label="Predicted", color="red", linestyle="--") +plt.title("Predicted vs True Consumption") +plt.xlabel("Timestamp") +plt.ylabel("Consumption (MW)") +plt.legend() +plt.tight_layout() +plt.savefig(os.path.join(RESULTS_DIR, "lightgbm_comparison_plot.png")) +#plt.show() + +# Subset Plot +subset = results_df.iloc[:len(results_df) // 10] +plt.figure(figsize=(15, 6)) +plt.plot(subset["Timestamp"], subset["True Consumption (MW)"], label="True", color="darkblue") +plt.plot(subset["Timestamp"], subset["Predicted Consumption (MW)"], label="Predicted", color="red", linestyle="--") +plt.title("Predicted vs True (First decile)") +plt.xlabel("Timestamp") +plt.ylabel("Consumption (MW)") +plt.legend() +plt.tight_layout() +plt.savefig(os.path.join(RESULTS_DIR, "lightgbm_prediction_with_timestamp.png")) +#plt.show() + + +# === Ens message === +print("\nEvaluation completed.") +print(f"All Plots stored in:\n→ {RESULTS_DIR}") diff --git a/lightgbm_model/scripts/train/__pycache__/train_lightgbm.cpython-311.pyc b/lightgbm_model/scripts/train/__pycache__/train_lightgbm.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69ec516570bb4c34b29a4e2f6f768a8fad802f7b Binary files /dev/null and b/lightgbm_model/scripts/train/__pycache__/train_lightgbm.cpython-311.pyc differ diff --git a/lightgbm_model/scripts/train/train_lightgbm.py b/lightgbm_model/scripts/train/train_lightgbm.py new file mode 100644 index 0000000000000000000000000000000000000000..48573c884f05b380650fde4d6d3269939bec7153 --- /dev/null +++ b/lightgbm_model/scripts/train/train_lightgbm.py @@ -0,0 +1,78 @@ +# train_lightgbm.py + +import os +import pickle +import pandas as pd +import numpy as np +import lightgbm as lgb +from lightgbm import LGBMRegressor, early_stopping, record_evaluation + +from lightgbm_model.scripts.config_lightgbm import ( + DATA_PATH, + FEATURES, + TARGET, + LIGHTGBM_PARAMS, + EARLY_STOPPING_ROUNDS, + RESULTS_DIR, + MODEL_DIR +) + +# === Load Data === +df = pd.read_csv(DATA_PATH) + +# Drop date (used later for plots only) +df = df.drop(columns=["date"], errors="ignore") + +# === Time-based Split (70% train, 10% valid, 20% test) === +train_size = int(len(df) * 0.7) +valid_size = int(len(df) * 0.1) +df_train = df.iloc[:train_size] +df_valid = df.iloc[train_size:train_size + valid_size] +df_test = df.iloc[train_size + valid_size:] + +X_train, y_train = df_train[FEATURES], df_train[TARGET] +X_valid, y_valid = df_valid[FEATURES], df_valid[TARGET] +X_test, y_test = df_test[FEATURES], df_test[TARGET] + + +# === Init LightGBM model === +eval_result = {} + +model = LGBMRegressor( + **LIGHTGBM_PARAMS, + verbosity=-1 +) + +model.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_valid, y_valid)], + eval_metric="rmse", + callbacks=[ + early_stopping(EARLY_STOPPING_ROUNDS), + record_evaluation(eval_result) + ] +) + +# === Save model === +os.makedirs(MODEL_DIR, exist_ok=True) +model_path = os.path.join(MODEL_DIR, "lightgbm_final_model.pkl") + +with open(model_path, "wb") as f: + pickle.dump(model, f) + +# === Save evaluation results === +os.makedirs(RESULTS_DIR, exist_ok=True) +eval_result_path = os.path.join(RESULTS_DIR, "lightgbm_eval_result.pkl") + +with open(eval_result_path, "wb") as f: + pickle.dump(eval_result, f) + +print(f"Model saved to: {model_path}") +print(f"Eval results saved to: {eval_result_path}") + +# === Save data for evaluation === +X_train.to_csv(os.path.join(RESULTS_DIR, "X_train.csv"), index=False) +X_test.to_csv(os.path.join(RESULTS_DIR, "X_test.csv"), index=False) +y_test.to_csv(os.path.join(RESULTS_DIR, "y_test.csv"), index=False) + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a904e345aa85664cc25d96fc0524b5fb61d4375 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,31 @@ +# ============================= +# Requirements for Energy Prediction Project +# ============================= + +# Python 3.11 environment recommended since moments dont work with later versions + +# Moment Foundation Model (forecasting backbone) +momentfm @ git+https://github.com/moment-timeseries-foundation-model/moment.git@37a8bde4eb3dd340bebc9b54a3b893bcba62cd4f + +# === Core Python stack === +numpy==1.25.2 # Numerical operations +pandas==2.2.2 # Data manipulation and analysis +matplotlib==3.10.0 # Plotting and visualizations + +# === Machine Learning === +scikit-learn==1.6.1 # Evaluation metrics and preprocessing utilities +torch==2.6.0 # PyTorch with CUDA 12.4 (GPU support) +torchvision==0.21.0+cu124 # Optional (can support visual tasks, not critical here) +torchaudio==2.6.0+cu124 # Optional (comes with torch install, can stay) + +# === Utilities === +tqdm==4.67.1 # Progress bars +ipywidgets>=8.0 # Enables tqdm progress bars in Jupyter/Colab +pprintpp==0.4.0 # Prettier print formatting for nested dicts (used for model output check) + +# === lightgbm === +lightgbm==4.3.0 # Boosted Trees for tabular modeling (used for baseline and feature selection) + +# === Streamlit App === +streamlit>=1.30.0 +plotly>=5.0.0 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..2820c12fba6887f6907bcf11270aae701bd66c3c --- /dev/null +++ b/setup.py @@ -0,0 +1,7 @@ +from setuptools import setup, find_packages + +setup( + name="energy_prediction", + version="0.1", + packages=find_packages(), +) diff --git a/streamlit_simulation/__init__.py b/streamlit_simulation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..651585a8e21988e5f2bcc574a14316bd9575dc6a --- /dev/null +++ b/streamlit_simulation/__init__.py @@ -0,0 +1,2 @@ +# __init__.py + diff --git a/streamlit_simulation/__pycache__/config_streamlit.cpython-311.pyc b/streamlit_simulation/__pycache__/config_streamlit.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fa9603d33bb7e626893a8e993661e62d3b918a75 Binary files /dev/null and b/streamlit_simulation/__pycache__/config_streamlit.cpython-311.pyc differ diff --git a/streamlit_simulation/__pycache__/config_streamlit.cpython-312.pyc b/streamlit_simulation/__pycache__/config_streamlit.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..18b4585b6916c2545249229f04ef2571ea7fccf2 Binary files /dev/null and b/streamlit_simulation/__pycache__/config_streamlit.cpython-312.pyc differ diff --git a/streamlit_simulation/app.py b/streamlit_simulation/app.py new file mode 100644 index 0000000000000000000000000000000000000000..91efe2648675368f6c106b0ba8d7bb323a76d179 --- /dev/null +++ b/streamlit_simulation/app.py @@ -0,0 +1,535 @@ +import sys +import os +import streamlit as st +import pickle +import pandas as pd +import time +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +import warnings +import torch + +from config_streamlit import (MODEL_PATH_LIGHTGBM, DATA_PATH, TRAIN_RATIO, + TEXT_COLOR, HEADER_COLOR, ACCENT_COLOR, + BUTTON_BG, BUTTON_HOVER_BG, BG_COLOR, + INPUT_BG, PROGRESS_COLOR, PLOT_COLOR + ) +from lightgbm_model.scripts.config_lightgbm import FEATURES +from transformer_model.scripts.utils.informer_dataset_class import InformerDataset +from transformer_model.scripts.training.load_basis_model import load_moment_model +from transformer_model.scripts.config_transformer import CHECKPOINT_DIR, FORECAST_HORIZON, SEQ_LEN +from sklearn.preprocessing import StandardScaler + + +# ============================== Layout ============================== + +# Streamlit & warnings config +warnings.filterwarnings("ignore", category=FutureWarning) +st.set_page_config(page_title="Electricity Consumption Forecast", layout="wide") + +#CSS part +st.markdown(f""" + +""", unsafe_allow_html=True) + +st.title("Electricity Consumption Forecast: Hourly Simulation") +st.write("Welcome to the simulation interface!") + +# ============================== Session State Init ============================== +def init_session_state(): + defaults = { + "is_running": False, + "start_index": 0, + "true_vals": [], + "pred_vals": [], + "true_timestamps": [], + "pred_timestamps": [], + "last_fig": None, + "valid_pos": 0 + } + for key, value in defaults.items(): + if key not in st.session_state: + st.session_state[key] = value + +init_session_state() + +# ============================== Loaders ============================== + +@st.cache_data +def load_lightgbm_model(): + with open(MODEL_PATH_LIGHTGBM, "rb") as f: + return pickle.load(f) + +@st.cache_resource +def load_transformer_model_and_dataset(): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Load model + model = load_moment_model() + checkpoint_path = os.path.join(CHECKPOINT_DIR, "model_final.pth") + model.load_state_dict(torch.load(checkpoint_path, map_location=device)) + model.to(device) + model.eval() + + # Datasets + train_dataset = InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON, random_seed=13) + test_dataset = InformerDataset(data_split="test", forecast_horizon=FORECAST_HORIZON, random_seed=13) + test_dataset.scaler = train_dataset.scaler + + return model, test_dataset, device + +@st.cache_data +def load_data(): + df = pd.read_csv(DATA_PATH, parse_dates=["date"]) + return df + + +# ============================== Utility Functions ============================== + +def predict_transformer_step(model, dataset, idx, device): + """Performs a single prediction step with the transformer model.""" + timeseries, _, input_mask = dataset[idx] + timeseries = torch.tensor(timeseries, dtype=torch.float32).unsqueeze(0).to(device) + input_mask = torch.tensor(input_mask, dtype=torch.bool).unsqueeze(0).to(device) + + with torch.no_grad(): + output = model(x_enc=timeseries, input_mask=input_mask) + + pred = output.forecast[:, 0, :].cpu().numpy().flatten() + + # Rückskalieren + dummy = np.zeros((len(pred), dataset.n_channels)) + dummy[:, 0] = pred + pred_original = dataset.scaler.inverse_transform(dummy)[:, 0] + + return float(pred_original[0]) + + +def init_simulation_layout(): + """Creates layout containers for plot and info sections.""" + col1, spacer, col2 = st.columns([3, 0.2, 1]) + plot_title = col1.empty() + plot_container = col1.empty() + x_axis_label = col1.empty() + info_container = col2.empty() + return plot_title, plot_container, x_axis_label, info_container + + +def create_prediction_plot(pred_timestamps, pred_vals, true_timestamps, true_vals, window_hours, y_min=None, y_max=None): + """Generates the matplotlib figure for plotting prediction vs. actual.""" + fig, ax = plt.subplots(figsize=(8, 5), constrained_layout=True, facecolor=PLOT_COLOR) + ax.set_facecolor(PLOT_COLOR) + + ax.plot(pred_timestamps[-window_hours:], pred_vals[-window_hours:], label="Prediction", color="#EF233C", linestyle="--") + if true_vals: + ax.plot(true_timestamps[-window_hours:], true_vals[-window_hours:], label="Actual", color="#0077B6") + + ax.set_ylabel("Consumption (MW)", fontsize=8, color=TEXT_COLOR) + ax.legend( + fontsize=8, + loc="upper left", + bbox_to_anchor=(0, 0.95), + facecolor= INPUT_BG, # INPUT_BG + edgecolor= ACCENT_COLOR, # ACCENT_COLOR + labelcolor= TEXT_COLOR # TEXT_COLOR + ) + ax.yaxis.grid(True, linestyle=':', linewidth=0.5, alpha=0.7) + ax.set_ylim(y_min, y_max) + ax.xaxis.set_major_locator(mdates.DayLocator(interval=1)) + ax.xaxis.set_major_formatter(mdates.DateFormatter("%m-%d")) + ax.tick_params(axis="x", labelrotation=0, labelsize=5, colors=TEXT_COLOR) + ax.tick_params(axis="y", labelsize=5, colors=TEXT_COLOR) + #fig.patch.set_facecolor('#e6ecf0') # outer area + + for spine in ax.spines.values(): + spine.set_visible(False) + + st.session_state.last_fig = fig + return fig + + +def render_simulation_view(timestamp, prediction, actual, progress, fig, paused=False): + """Displays the simulation plot and metrics in the UI.""" + title = "Actual vs. Prediction (Paused)" if paused else "Actual vs. Prediction" + plot_title.markdown( + f"
" + f"{title}
", + unsafe_allow_html=True + ) + plot_container.pyplot(fig) + + st.markdown("
", unsafe_allow_html=True) + x_axis_label.markdown( + f"
" + f"Time
", + unsafe_allow_html=True + ) + + with info_container.container(): + st.markdown("
", unsafe_allow_html=True) + st.markdown( + f"Time: {timestamp}", + unsafe_allow_html=True + ) + + st.metric("Prediction", f"{prediction:,.0f} MW" if prediction is not None else "–") + st.metric("Actual", f"{actual:,.0f} MW" if actual is not None else "–") + st.caption("Simulation Progress") + st.progress(progress) + + if len(st.session_state.true_vals) > 1: + true_arr = np.array(st.session_state.true_vals) + pred_arr = np.array(st.session_state.pred_vals[:-1]) + + min_len = min(len(true_arr), len(pred_arr)) #just start if there are 2 actual values + if min_len >= 1: + errors = np.abs(true_arr[:min_len] - pred_arr[:min_len]) + mape = np.mean(errors / np.where(true_arr[:min_len] == 0, 1e-10, true_arr[:min_len])) * 100 + mae = np.mean(errors) + max_error = np.max(errors) + + st.divider() + st.markdown( + f"Interim Metrics", + unsafe_allow_html=True + ) + st.metric("MAPE (so far)", f"{mape:.2f} %") + st.metric("MAE (so far)", f"{mae:,.0f} MW") + st.metric("Max Error", f"{max_error:,.0f} MW") + + + +# ============================== Data Preparation ============================== + +df_full = load_data() + +# Split Train/Test +train_size = int(len(df_full) * TRAIN_RATIO) +test_df_raw = df_full.iloc[train_size:].reset_index(drop=True) + +# Start at first full hour (00:00) +first_full_day_index = test_df_raw[test_df_raw["date"].dt.time == pd.Timestamp("00:00:00").time()].index[0] +test_df_full = test_df_raw.iloc[first_full_day_index:].reset_index(drop=True) + +# Select simulation window via date picker +min_date = test_df_full["date"].min().date() +max_date = test_df_full["date"].max().date() + +# ============================== UI Controls ============================== + +st.markdown("### Simulation Settings") +col1, col2 = st.columns([1, 1]) + +with col1: + st.markdown("**General Settings**") + model_choice = st.selectbox("Choose prediction model", ["LightGBM", "Transformer Model (moments)"]) + if model_choice == "Transformer Model(moments)": + st.caption("⚠️ Note: Transformer model runs slower without GPU. (Use Speed = 10)") + window_days = st.selectbox("Display window (days)", options=[3, 5, 7], index=0) + window_hours = window_days * 24 + speed = st.slider("Speed", 1, 10, 5) + +with col2: + st.markdown(f"**Date Range** (from {min_date} to {max_date})") + start_date = st.date_input("Start Date", value=min_date, min_value=min_date, max_value=max_date) + end_date = st.date_input("End Date", value=max_date, min_value=min_date, max_value=max_date) + + +# ============================== Data Preparation (filtered) ============================== + +# final filtered date window +test_df_filtered = test_df_full[ + (test_df_full["date"].dt.date >= start_date) & + (test_df_full["date"].dt.date <= end_date) +].reset_index(drop=True) + +# For progression bar +total_steps_ui = len(test_df_filtered) + +# ============================== Buttons ============================== + +st.markdown("### Start Simulation") +col1, col2, col3 = st.columns([1, 1, 14]) +with col1: + play_pause_text = "▶️ Start" if not st.session_state.is_running else "⏸️ Pause" + if st.button(play_pause_text): + st.session_state.is_running = not st.session_state.is_running + st.rerun() +with col2: + reset_button = st.button("🔄 Reset") + +# Reset logic +if reset_button: + st.session_state.start_index = 0 + st.session_state.pred_vals = [] + st.session_state.true_vals = [] + st.session_state.pred_timestamps = [] + st.session_state.true_timestamps = [] + st.session_state.last_fig = None + st.session_state.is_running = False + st.session_state.valid_pos = 0 + st.rerun() + +# Auto-reset on critical parameter change while running +if st.session_state.is_running and ( + start_date != st.session_state.get("last_start_date") or + end_date != st.session_state.get("last_end_date") or + model_choice != st.session_state.get("last_model_choice") +): + st.session_state.start_index = 0 + st.session_state.pred_vals = [] + st.session_state.true_vals = [] + st.session_state.pred_timestamps = [] + st.session_state.true_timestamps = [] + st.session_state.last_fig = None + st.session_state.valid_pos = 0 + st.rerun() + +# Track current selections for change detection +st.session_state.last_start_date = start_date +st.session_state.last_end_date = end_date +st.session_state.last_model_choice = model_choice + + +# ============================== Paused Mode ============================== + +if not st.session_state.is_running and st.session_state.last_fig is not None: + st.write("Simulation paused...") + plot_title, plot_container, x_axis_label, info_container = init_simulation_layout() + + timestamp = st.session_state.pred_timestamps[-1] if st.session_state.pred_timestamps else "–" + prediction = st.session_state.pred_vals[-1] if st.session_state.pred_vals else None + actual = st.session_state.true_vals[-1] if st.session_state.true_vals else None + progress = st.session_state.start_index / total_steps_ui + + render_simulation_view(timestamp, prediction, actual, progress, st.session_state.last_fig, paused=True) + + +# ============================== initialize values ============================== + +#if lightGbm use testdata from above +if model_choice == "LightGBM": + test_df = test_df_filtered.copy() + +#Shared state references for storing predictions and ground truths + +true_vals = st.session_state.true_vals +pred_vals = st.session_state.pred_vals +true_timestamps = st.session_state.true_timestamps +pred_timestamps = st.session_state.pred_timestamps + +# ============================== LightGBM Simulation ============================== + +if model_choice == "LightGBM" and st.session_state.is_running: + model = load_lightgbm_model() + st.write("Simulation started...") + st.markdown('
', unsafe_allow_html=True) + + plot_title, plot_container, x_axis_label, info_container = init_simulation_layout() + + for i in range(st.session_state.start_index, len(test_df)): + if not st.session_state.is_running: + break + + current = test_df.iloc[i] + timestamp = current["date"] + features = current[FEATURES].values.reshape(1, -1) + prediction = model.predict(features)[0] + + pred_vals.append(prediction) + pred_timestamps.append(timestamp) + + if i >= 1: + prev_actual = test_df.iloc[i - 1]["consumption_MW"] + prev_time = test_df.iloc[i - 1]["date"] + true_vals.append(prev_actual) + true_timestamps.append(prev_time) + + fig = create_prediction_plot( + pred_timestamps, pred_vals, + true_timestamps, true_vals, + window_hours, + y_min= test_df_filtered["consumption_MW"].min() - 2000, + y_max= test_df_filtered["consumption_MW"].max() + 2000 + ) + + render_simulation_view(timestamp, prediction, prev_actual if i >= 1 else None, i / len(test_df), fig) + + plt.close(fig) # Speicher freigeben + + st.session_state.start_index = i + 1 + time.sleep(1 / (speed + 1e-9)) + + st.success("Simulation completed!") + + + +# ============================== Transformer Simulation ============================== + +if model_choice == "Transformer Model(moments)": + if st.session_state.is_running: + st.write("Simulation started (Transformer)...") + st.markdown('
', unsafe_allow_html=True) + + plot_title, plot_container, x_axis_label, info_container = init_simulation_layout() + + # Zugriff auf Modell, Dataset, Device + model, test_dataset, device = load_transformer_model_and_dataset() + data = test_dataset.data # bereits skaliert + scaler = test_dataset.scaler + n_channels = test_dataset.n_channels + + test_start_idx = len(InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON)) + SEQ_LEN + base_timestamp = pd.read_csv(DATA_PATH, parse_dates=["date"])["date"].iloc[test_start_idx] #get original timestamp for later, cause not in dataset anymore + + # Schritt 1: Finde Index, ab dem Stunde = 00:00 ist + offset = 0 + while (base_timestamp + pd.Timedelta(hours=offset)).time() != pd.Timestamp("00:00:00").time(): + offset += 1 + + # Neuer Startindex in der Simulation + start_index = offset + + # Session-State bei Bedarf initial setzen + if "start_index" not in st.session_state or st.session_state.start_index == 0: + st.session_state.start_index = start_index + + + # Vorbereiten: Liste der gültigen i-Werte im gewünschten Zeitraum + valid_indices = [] + for i in range(start_index, len(test_dataset)): + timestamp = base_timestamp + pd.Timedelta(hours=i) + if start_date <= timestamp.date() <= end_date: + valid_indices.append(i) + + # Fortschrittsanzeige + total_steps = len(valid_indices) + + # Aktueller Fortschritt in der Liste (nicht: globaler Dataset-Index!) + if "valid_pos" not in st.session_state: + st.session_state.valid_pos = 0 + + # Hauptschleife: Nur noch über gültige Indizes iterieren + for relative_idx, i in enumerate(valid_indices[st.session_state.valid_pos:]): + + #for i in range(st.session_state.start_index, len(test_dataset)): + if not st.session_state.is_running: + break + + current_pred = predict_transformer_step(model, test_dataset, i, device) + current_time = base_timestamp + pd.Timedelta(hours=i) + + pred_vals.append(current_pred) + pred_timestamps.append(current_time) + + if i >= 1: + prev_actual = test_dataset[i - 1][1][0, 0] # erster Forecast-Wert der letzten Zeile + # Rückskalieren + dummy_actual = np.zeros((1, n_channels)) + dummy_actual[:, 0] = prev_actual + actual_val = scaler.inverse_transform(dummy_actual)[0, 0] + + true_time = current_time - pd.Timedelta(hours=1) + + if true_time >= pd.to_datetime(start_date): + true_vals.append(actual_val) + true_timestamps.append(true_time) + + # Plot erzeugen + fig = create_prediction_plot( + pred_timestamps, pred_vals, + true_timestamps, true_vals, + window_hours, + y_min= test_df_filtered["consumption_MW"].min() - 2000, + y_max= test_df_filtered["consumption_MW"].max() + 2000 + ) + if len(pred_vals) >= 2 and len(true_vals) >= 1: + render_simulation_view(current_time, current_pred, actual_val if i >= 1 else None, st.session_state.valid_pos / total_steps, fig) + + plt.close(fig) # Speicher freigeben + + st.session_state.valid_pos += 1 + time.sleep(1 / (speed + 1e-9)) + + st.success("Simulation completed!") + + +# ============================== Scroll Sync ============================== + +st.markdown(""" + +""", unsafe_allow_html=True) + diff --git a/streamlit_simulation/config_streamlit.py b/streamlit_simulation/config_streamlit.py new file mode 100644 index 0000000000000000000000000000000000000000..68ce1e5b376a9b3be5a8c69dc7865070588d49ac --- /dev/null +++ b/streamlit_simulation/config_streamlit.py @@ -0,0 +1,26 @@ +# config_streamlit +import os + +# Base directory → points to the project root +BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) + +# Model paths +MODEL_PATH_LIGHTGBM = os.path.join(BASE_DIR, "lightgbm_model", "model", "lightgbm_final_model.pkl") +MODEL_PATH_TRANSFORMER = os.path.join(BASE_DIR, "transformer_model", "model", "checkpoints", "model_final.pth") + +# Data path +DATA_PATH = os.path.join(BASE_DIR, "data", "processed", "energy_consumption_aggregated_cleaned.csv") + +# Color palette for Streamlit layout +TEXT_COLOR = "#004080" # Primary text color (clean dark blue) +HEADER_COLOR = "#002855" # Accent color for headings +ACCENT_COLOR = "#9bb2cc" # For borders, highlights, etc. +BUTTON_BG = "#dee7f0" # Background color for buttons +BUTTON_HOVER_BG = "#cbd9e6" # Hover color for buttons +BG_COLOR = "#ffffff" # Page background +INPUT_BG = "#f2f6fa" # Background for select boxes, inputs +PROGRESS_COLOR = "#0077B6" # Progress bar color +PLOT_COLOR = "white" # Plot background color + +# Constants +TRAIN_RATIO = 0.7 # Train/test split ratio used by both models diff --git a/transformer_model/results/evaluation_metrics.json b/transformer_model/results/evaluation_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..09694e19b8d883575150ad8abeb03693b4babd6c --- /dev/null +++ b/transformer_model/results/evaluation_metrics.json @@ -0,0 +1 @@ +{"RMSE": 3933.5735661100834, "MAPE": 2.3222167044878006, "R2": 0.97211754322052} \ No newline at end of file diff --git a/transformer_model/results/test_results.csv b/transformer_model/results/test_results.csv new file mode 100644 index 0000000000000000000000000000000000000000..c4d100fd33e7c8fe08c80d7206ba6ce47c578143 --- /dev/null +++ b/transformer_model/results/test_results.csv @@ -0,0 +1,19892 @@ +Timestamp,True Consumption (MW),Predicted Consumption (MW) +2016-04-26 04:00:00,82610.0,86666.31 +2016-04-26 05:00:00,80450.0,86124.805 +2016-04-26 06:00:00,81670.0,87888.16 +2016-04-26 07:00:00,87470.0,93600.016 +2016-04-26 08:00:00,95380.0,101803.805 +2016-04-26 09:00:00,102170.0,106167.86 +2016-04-26 10:00:00,105840.0,108323.836 +2016-04-26 11:00:00,107060.0,108119.36 +2016-04-26 12:00:00,108050.0,108036.69 +2016-04-26 13:00:00,108210.0,107848.71 +2016-04-26 14:00:00,107840.0,107302.54 +2016-04-26 15:00:00,108290.0,108071.12 +2016-04-26 16:00:00,107370.0,108973.64 +2016-04-26 17:00:00,106180.0,108400.81 +2016-04-26 18:00:00,105520.0,107264.83 +2016-04-26 19:00:00,105660.0,105657.33 +2016-04-26 20:00:00,105030.0,104183.54 +2016-04-26 21:00:00,107430.0,104621.58 +2016-04-26 22:00:00,109320.0,104856.3 +2016-04-26 23:00:00,105240.0,102236.53 +2016-04-27 00:00:00,97350.0,96692.53 +2016-04-27 01:00:00,90440.0,91314.47 +2016-04-27 02:00:00,86040.0,86196.85 +2016-04-27 03:00:00,83450.0,83059.52 +2016-04-27 04:00:00,81820.0,81167.055 +2016-04-27 05:00:00,81600.0,80921.445 +2016-04-27 06:00:00,83920.0,83592.62 +2016-04-27 07:00:00,90990.0,90266.48 +2016-04-27 08:00:00,99890.0,99277.94 +2016-04-27 09:00:00,106810.0,104280.88 +2016-04-27 10:00:00,109590.0,108007.836 +2016-04-27 11:00:00,110870.0,109465.125 +2016-04-27 12:00:00,111960.0,111166.055 +2016-04-27 13:00:00,111460.0,112257.52 +2016-04-27 14:00:00,110990.0,112072.586 +2016-04-27 15:00:00,110930.0,111889.46 +2016-04-27 16:00:00,110530.0,110793.87 +2016-04-27 17:00:00,110710.0,110283.69 +2016-04-27 18:00:00,110460.0,110573.805 +2016-04-27 19:00:00,110920.0,109946.56 +2016-04-27 20:00:00,110500.0,110397.11 +2016-04-27 21:00:00,111240.0,111462.125 +2016-04-27 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a/transformer_model/results/training_metrics.json b/transformer_model/results/training_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..bed1a6063e611a63a140d860af0860211adff2d9 --- /dev/null +++ b/transformer_model/results/training_metrics.json @@ -0,0 +1 @@ +{"train_losses": [0.17907951894225974, 0.11743136870736444, 0.10286305829986463, 0.095653260748457, 0.09064765630698786, 0.08855325479177233, 0.08623282216515275, 0.08489166740133372, 0.08422152720884994], "test_mses": [0.07641124725341797, 0.050424233078956604, 0.03807574138045311, 0.032122015953063965, 0.026808083057403564, 0.02273257076740265, 0.02027367614209652, 0.018922727555036545, 0.017820490524172783], "test_maes": [0.1691250056028366, 0.1388522833585739, 0.12234506011009216, 0.11616843193769455, 0.10695459693670273, 0.09815964102745056, 0.09287288039922714, 0.0910905972123146, 0.0890081524848938]} \ No newline at end of file diff --git a/transformer_model/scripts/__init__.py 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0000000000000000000000000000000000000000..6b36581c0b1925ac86b84c90ede4beacc6c65e8c --- /dev/null +++ b/transformer_model/scripts/config_transformer.py @@ -0,0 +1,31 @@ +# config.py +import os + +# Base Directory +BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) + +# Data paths +DATA_PATH = os.path.join(BASE_DIR, "..", "data", "processed", "energy_consumption_aggregated_cleaned.csv") + +# Other paths +CHECKPOINT_DIR = os.path.join(BASE_DIR, "model", "checkpoints") +RESULTS_DIR = os.path.join(BASE_DIR, "results") + + +# ========== Model Settings ========== +SEQ_LEN = 512 # Input sequence length (number of time steps the model sees) +FORECAST_HORIZON = 1 # Number of future steps the model should predict +HEAD_DROPOUT = 0.1 # Dropout in the head to prevent overfitting +WEIGHT_DECAY = 0.0 # L2 regularization (0 means off) + +# ========== Training Settings ========== +MAX_EPOCHS = 9 # Optimal number of epochs based on performance curve +BATCH_SIZE = 32 # Batch size for training and evaluation +LEARNING_RATE = 1e-4 # Base learning rate +MAX_LR = 1e-4 # Max LR for OneCycleLR scheduler +GRAD_CLIP = 5.0 # Gradient clipping threshold + +# ========== Freezing Strategy ========== +FREEZE_ENCODER = True +FREEZE_EMBEDDER = True +FREEZE_HEAD = False #just unfreeze the last forecasting head for finetuning diff --git a/transformer_model/scripts/evaluation/__init__.py b/transformer_model/scripts/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5267186f7dd23ee7cb08e910fc01dd55bf41f586 --- /dev/null +++ b/transformer_model/scripts/evaluation/__init__.py @@ -0,0 +1 @@ +# __init__ \ No newline at end of file diff --git a/transformer_model/scripts/evaluation/__pycache__/__init__.cpython-311.pyc b/transformer_model/scripts/evaluation/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..223d03a5bf9293e3c3437a6aaf5d0a9f72aa0076 Binary files /dev/null and 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0000000000000000000000000000000000000000..d409c0a6e07fea28b8b800024b094eb215bada5c --- /dev/null +++ b/transformer_model/scripts/evaluation/evaluate.py @@ -0,0 +1,124 @@ +# evaluate.py + +import os +import json +import torch +import logging +import numpy as np +import pandas as pd +from tqdm import tqdm + +from sklearn.metrics import mean_squared_error, r2_score + +from transformer_model.scripts.config_transformer import BASE_DIR, RESULTS_DIR, CHECKPOINT_DIR, DATA_PATH, FORECAST_HORIZON, SEQ_LEN +from transformer_model.scripts.training.load_basis_model import load_moment_model +from transformer_model.scripts.utils.informer_dataset_class import InformerDataset +from momentfm.utils.utils import control_randomness +from transformer_model.scripts.utils.check_device import check_device + + +# Setup logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + +def evaluate(): + control_randomness(seed=13) + # Set device + device, backend, scaler = check_device() + logging.info(f"Evaluation is running on: {backend} ({device})") + + # Load final model + model = load_moment_model() + checkpoint_path = os.path.join(CHECKPOINT_DIR, "model_final.pth") + model.load_state_dict(torch.load(checkpoint_path, map_location=device)) + model.to(device) + model.eval() + logging.info(f"Loaded final model from: {checkpoint_path}") + + # Recreate training dataset to get the fitted scaler + train_dataset = InformerDataset( + data_split="train", + random_seed=13, + forecast_horizon=FORECAST_HORIZON + ) + + # Use its scaler in the test dataset + test_dataset = InformerDataset( + data_split="test", + random_seed=13, + forecast_horizon=FORECAST_HORIZON + ) + + test_dataset.scaler = train_dataset.scaler + + test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False) + + trues, preds = [], [] + + with torch.no_grad(): + for timeseries, forecast, input_mask in tqdm(test_loader, desc="Evaluating on test set"): + timeseries = timeseries.float().to(device) + forecast = forecast.float().to(device) + input_mask = input_mask.to(device) # <- wichtig! + + output = model(x_enc=timeseries, input_mask=input_mask) + + trues.append(forecast.cpu().numpy()) + preds.append(output.forecast.cpu().numpy()) + + + trues = np.concatenate(trues, axis=0) + preds = np.concatenate(preds, axis=0) + + # Extract only first feature (consumption) + true_values = trues[:, 0, :] + pred_values = preds[:, 0, :] + + # Inverse normalization + n_features = test_dataset.n_channels + true_reshaped = np.column_stack([true_values.flatten()] + [np.zeros_like(true_values.flatten())] * (n_features - 1)) + pred_reshaped = np.column_stack([pred_values.flatten()] + [np.zeros_like(pred_values.flatten())] * (n_features - 1)) + + true_original = test_dataset.scaler.inverse_transform(true_reshaped)[:, 0] + pred_original = test_dataset.scaler.inverse_transform(pred_reshaped)[:, 0] + + + # Build timestamp index, since date got cutted out in informerdataset we need original dataset and use the index of the beginning of testdata to get the date + csv_path = os.path.join(DATA_PATH) + df = pd.read_csv(csv_path, parse_dates=["date"]) + + train_len = len(train_dataset) + test_start_idx = train_len + SEQ_LEN + start_timestamp = df["date"].iloc[test_start_idx] + logging.info(f"[DEBUG] timestamp: {start_timestamp}") + + timestamps = [start_timestamp + pd.Timedelta(hours=i) for i in range(len(true_original))] + + df = pd.DataFrame({ + "Timestamp": timestamps, + "True Consumption (MW)": true_original, + "Predicted Consumption (MW)": pred_original + }) + + # Save results to CSV + os.makedirs(RESULTS_DIR, exist_ok=True) + results_path = os.path.join(RESULTS_DIR, "test_results.csv") + df.to_csv(results_path, index=False) + logging.info(f"Saved prediction results to: {results_path}") + + # Evaluation metrics + mse = mean_squared_error(df["True Consumption (MW)"], df["Predicted Consumption (MW)"]) + rmse = np.sqrt(mse) + mape = np.mean(np.abs((df["True Consumption (MW)"] - df["Predicted Consumption (MW)"]) / df["True Consumption (MW)"])) * 100 + r2 = r2_score(df["True Consumption (MW)"], df["Predicted Consumption (MW)"]) + + # Save metrics to JSON + metrics = {"RMSE": float(rmse), "MAPE": float(mape), "R2": float(r2)} + metrics_path = os.path.join(RESULTS_DIR, "evaluation_metrics.json") + with open(metrics_path, "w") as f: + json.dump(metrics, f) + + logging.info(f"Saved evaluation metrics to: {metrics_path}") + logging.info(f"RMSE: {rmse:.3f} | MAPE: {mape:.2f}% | R²: {r2:.3f}") + +if __name__ == "__main__": + evaluate() diff --git a/transformer_model/scripts/evaluation/plot_metrics.py b/transformer_model/scripts/evaluation/plot_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..43f0bb3a714f309446f0819f34c3a82ce269e5b7 --- /dev/null +++ b/transformer_model/scripts/evaluation/plot_metrics.py @@ -0,0 +1,77 @@ +# plot_metrics.py + +import os +import json +import pandas as pd +import matplotlib.pyplot as plt +from transformer_model.scripts.config_transformer import RESULTS_DIR + +# === Plot 1: Training Metrics === + +# Load training metrics +training_metrics_path = os.path.join(RESULTS_DIR, "training_metrics.json") +with open(training_metrics_path, "r") as f: + metrics = json.load(f) + +train_losses = metrics["train_losses"] +test_mses = metrics["test_mses"] +test_maes = metrics["test_maes"] + +plt.figure(figsize=(10, 6)) +plt.plot(range(1, len(train_losses) + 1), train_losses, label="Train Loss", color="blue") +plt.plot(range(1, len(test_mses) + 1), test_mses, label="Test MSE", color="red") +plt.plot(range(1, len(test_maes) + 1), test_maes, label="Test MAE", color="green") +plt.xlabel("Epoch") +plt.ylabel("Loss / Metric") +plt.title("Training Loss vs Test Metrics") +plt.legend() +plt.grid(True) + +plot_path = os.path.join(RESULTS_DIR, "training_plot.png") +plt.savefig(plot_path) +print(f"[Saved] Training metrics plot: {plot_path}") +plt.show() + + +# === Plot 2: Predictions vs Ground Truth (Full Range) === + +# Load comparison results +comparison_path = os.path.join(RESULTS_DIR, "test_results.csv") +df_comparison = pd.read_csv(comparison_path, parse_dates=["Timestamp"]) + +plt.figure(figsize=(15, 6)) +plt.plot(df_comparison["Timestamp"], df_comparison["True Consumption (MW)"], label="True", color="darkblue") +plt.plot(df_comparison["Timestamp"], df_comparison["Predicted Consumption (MW)"], label="Predicted", color="red", linestyle="--") +plt.title("Energy Consumption: Predictions vs Ground Truth") +plt.xlabel("Time") +plt.ylabel("Consumption (MW)") +plt.legend() +plt.grid(True) +plt.tight_layout() + +plot_path = os.path.join(RESULTS_DIR, "comparison_plot_full.png") +plt.savefig(plot_path) +print(f"[Saved] Full range comparison plot: {plot_path}") +plt.show() + + +# === Plot 3: Predictions vs Ground Truth (First Month) === + +first_month_start = df_comparison["Timestamp"].min() +first_month_end = first_month_start + pd.Timedelta(days=25) +df_first_month = df_comparison[(df_comparison["Timestamp"] >= first_month_start) & (df_comparison["Timestamp"] <= first_month_end)] + +plt.figure(figsize=(15, 6)) +plt.plot(df_first_month["Timestamp"], df_first_month["True Consumption (MW)"], label="True", color="darkblue") +plt.plot(df_first_month["Timestamp"], df_first_month["Predicted Consumption (MW)"], label="Predicted", color="red", linestyle="--") +plt.title("Energy Consumption (First Month): Predictions vs Ground Truth") +plt.xlabel("Time") +plt.ylabel("Consumption (MW)") +plt.legend() +plt.grid(True) +plt.tight_layout() + +plot_path = os.path.join(RESULTS_DIR, "comparison_plot_1month.png") +plt.savefig(plot_path) +print(f"[Saved] 1-Month comparison plot: {plot_path}") +plt.show() diff --git a/transformer_model/scripts/training/__init__.py b/transformer_model/scripts/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5267186f7dd23ee7cb08e910fc01dd55bf41f586 --- /dev/null +++ b/transformer_model/scripts/training/__init__.py @@ -0,0 +1 @@ +# __init__ \ No newline at end of file diff --git a/transformer_model/scripts/training/__pycache__/__init__.cpython-311.pyc b/transformer_model/scripts/training/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9bac5e3e1918225c6289b299181399484b8ccbf8 Binary files /dev/null and b/transformer_model/scripts/training/__pycache__/__init__.cpython-311.pyc differ diff --git a/transformer_model/scripts/training/__pycache__/load_basis_model.cpython-311.pyc b/transformer_model/scripts/training/__pycache__/load_basis_model.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0c7f67406343e8b50b59c9f3e758c646b478ac35 Binary files /dev/null and b/transformer_model/scripts/training/__pycache__/load_basis_model.cpython-311.pyc differ diff --git a/transformer_model/scripts/training/__pycache__/train.cpython-311.pyc b/transformer_model/scripts/training/__pycache__/train.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc4c0737540caf289a92a6682589cb2010077454 Binary files /dev/null and b/transformer_model/scripts/training/__pycache__/train.cpython-311.pyc differ diff --git a/transformer_model/scripts/training/load_basis_model.py b/transformer_model/scripts/training/load_basis_model.py new file mode 100644 index 0000000000000000000000000000000000000000..12eb57343589af99eab10419e4917e2ca8b77e45 --- /dev/null +++ b/transformer_model/scripts/training/load_basis_model.py @@ -0,0 +1,67 @@ +# load_basis_model.py +# Load and initialize the base MOMENT model before finetuning + +import torch +import logging +from momentfm import MOMENTPipeline +from transformer_model.scripts.config_transformer import ( + FORECAST_HORIZON, + FREEZE_ENCODER, + FREEZE_EMBEDDER, + FREEZE_HEAD, + WEIGHT_DECAY, + HEAD_DROPOUT, + SEQ_LEN +) + +# Setup logging +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") + + +def load_moment_model(): + """ + Loads and configures the MOMENT model for forecasting. + """ + logging.info("Loading MOMENT model...") + model = MOMENTPipeline.from_pretrained( + "AutonLab/MOMENT-1-large", + model_kwargs={ + 'task_name': 'forecasting', + 'forecast_horizon': FORECAST_HORIZON, # default = 1 + 'head_dropout': HEAD_DROPOUT, # default = 0.1 + 'weight_decay': WEIGHT_DECAY, # default = 0.0 + 'freeze_encoder': FREEZE_ENCODER, # default = True + 'freeze_embedder': FREEZE_EMBEDDER, # default = True + 'freeze_head': FREEZE_HEAD # default = False + } + ) + + model.init() + logging.info("Model initialized successfully.") + return model + + +def print_trainable_params(model): + """ + Logs all trainable (unfrozen) parameters of the model. + """ + logging.info("Unfrozen parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + logging.info(f" {name}") + + +def test_dummy_forward(model): + """ + Performs a dummy forward pass to verify the model runs without error. + """ + logging.info("Running dummy forward pass with random tensors to see if model is running.") + dummy_x = torch.randn(16, 1, SEQ_LEN) + output = model(x_enc=dummy_x) + logging.info("Dummy forward pass successful.") + + +if __name__ == "__main__": + model = load_moment_model() + print_trainable_params(model) + test_dummy_forward(model) diff --git a/transformer_model/scripts/training/train.py b/transformer_model/scripts/training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..a843402bbb3747dbc2893e245fceb10876dfbf15 --- /dev/null +++ b/transformer_model/scripts/training/train.py @@ -0,0 +1,202 @@ +# train.py + +import os +import json +import time +import logging +import numpy as np +import torch +from tqdm import tqdm +from sklearn.metrics import mean_squared_error, mean_absolute_error + +from transformer_model.scripts.config_transformer import ( + BASE_DIR, + MAX_EPOCHS, + BATCH_SIZE, + LEARNING_RATE, + MAX_LR, + GRAD_CLIP, + FORECAST_HORIZON, + CHECKPOINT_DIR, + RESULTS_DIR +) + +from transformer_model.scripts.training.load_basis_model import load_moment_model +from transformer_model.scripts.utils.create_dataloaders import create_dataloaders +from transformer_model.scripts.utils.check_device import check_device +from momentfm.utils.utils import control_randomness + + +# === Setup logging === +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + +def train(): + # Start timing + start_time = time.time() + + # Setup device (CUDA / DirectML / CPU) and AMP scaler + device, backend, scaler = check_device() + + # Load base model + model = load_moment_model().to(device) + + # Set random seeds for reproducibility + control_randomness(seed=13) + + # Setup loss function and optimizer + criterion = torch.nn.MSELoss().to(device) + optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) + + # Load data + train_loader, test_loader = create_dataloaders() + + # Setup learning rate scheduler (OneCycle policy) + total_steps = len(train_loader) * MAX_EPOCHS + scheduler = torch.optim.lr_scheduler.OneCycleLR( + optimizer, + max_lr=MAX_LR, + total_steps=total_steps, + pct_start=0.3 + ) + + # Ensure output folders exist + os.makedirs(CHECKPOINT_DIR, exist_ok=True) + os.makedirs(RESULTS_DIR, exist_ok=True) + + # Store metrics + train_losses, test_mses, test_maes = [], [], [] + + best_mae = float("inf") + best_epoch = None + no_improve_epochs = 0 + patience = 5 + + for epoch in range(MAX_EPOCHS): + model.train() + epoch_losses = [] + + for timeseries, forecast, input_mask in tqdm(train_loader, desc=f"Epoch {epoch}"): + timeseries = timeseries.float().to(device) + input_mask = input_mask.to(device) + forecast = forecast.float().to(device) + + # Zero gradients + optimizer.zero_grad(set_to_none=True) + + # Forward pass (with AMP if enabled) + if scaler: + with torch.amp.autocast(device_type="cuda"): + output = model(x_enc=timeseries, input_mask=input_mask) + loss = criterion(output.forecast, forecast) + else: + output = model(x_enc=timeseries, input_mask=input_mask) + loss = criterion(output.forecast, forecast) + + # Backward pass + optimization + if scaler: + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP) + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP) + optimizer.step() + + epoch_losses.append(loss.item()) + + average_train_loss = np.mean(epoch_losses) + train_losses.append(average_train_loss) + logging.info(f"Epoch {epoch}: Train Loss = {average_train_loss:.4f}") + + # === Evaluation === + model.eval() + trues, preds = [], [] + + with torch.no_grad(): + for timeseries, forecast, input_mask in test_loader: + timeseries = timeseries.float().to(device) + input_mask = input_mask.to(device) + forecast = forecast.float().to(device) + + if scaler: + with torch.amp.autocast(device_type="cuda"): + output = model(x_enc=timeseries, input_mask=input_mask) + else: + output = model(x_enc=timeseries, input_mask=input_mask) + + trues.append(forecast.detach().cpu().numpy()) + preds.append(output.forecast.detach().cpu().numpy()) + + trues = np.concatenate(trues, axis=0) + preds = np.concatenate(preds, axis=0) + + + # Reshape for sklearn metrics + trues_2d = trues.reshape(trues.shape[0], -1) + preds_2d = preds.reshape(preds.shape[0], -1) + + mse = mean_squared_error(trues_2d, preds_2d) + mae = mean_absolute_error(trues_2d, preds_2d) + + test_mses.append(mse) + test_maes.append(mae) + logging.info(f"Epoch {epoch}: Test MSE = {mse:.4f}, MAE = {mae:.4f}") + + # === Early Stopping Check === + if mae < best_mae: + best_mae = mae + best_epoch = epoch + no_improve_epochs = 0 + + # Save best model + best_model_path = os.path.join(CHECKPOINT_DIR, "best_model.pth") + torch.save(model.state_dict(), best_model_path) + logging.info(f"New best model saved to: {best_model_path} (MAE: {best_mae:.4f})") + else: + no_improve_epochs += 1 + logging.info(f"No improvement in MAE for {no_improve_epochs} epoch(s).") + + if no_improve_epochs >= patience: + logging.info("Early stopping triggered.") + break + + # Save checkpoint + checkpoint_path = os.path.join(CHECKPOINT_DIR, f"model_epoch_{epoch}.pth") + torch.save(model.state_dict(), checkpoint_path) + + scheduler.step() + + logging.info(f"Best model was at epoch {best_epoch} with MAE: {best_mae:.4f}") + + # Save final model + final_model_path = os.path.join(CHECKPOINT_DIR, "model_final.pth") + torch.save(model.state_dict(), final_model_path) + logging.info(f"Final model saved to: {final_model_path}") + logging.info(f"Final Test MSE: {test_mses[-1]:.4f}, MAE: {test_maes[-1]:.4f}") + + # Save training metrics + metrics = { + "train_losses": [float(x) for x in train_losses], + "test_mses": [float(x) for x in test_mses], + "test_maes": [float(x) for x in test_maes] + } + + metrics_path = os.path.join(RESULTS_DIR, "training_metrics.json") + with open(metrics_path, "w") as f: + json.dump(metrics, f) + logging.info(f"Training metrics saved to: {metrics_path}") + + # Done + elapsed = time.time() - start_time + logging.info(f"Training complete in {elapsed / 60:.2f} minutes.") + + +# === Entry Point === +if __name__ == "__main__": + try: + train() + except Exception as e: + logging.error(f"Training failed: {e}") diff --git a/transformer_model/scripts/utils/__init__.py b/transformer_model/scripts/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5267186f7dd23ee7cb08e910fc01dd55bf41f586 --- /dev/null +++ b/transformer_model/scripts/utils/__init__.py @@ -0,0 +1 @@ +# __init__ \ No newline at end of file diff --git a/transformer_model/scripts/utils/__pycache__/__init__.cpython-311.pyc b/transformer_model/scripts/utils/__pycache__/__init__.cpython-311.pyc new file 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a/transformer_model/scripts/utils/__pycache__/informer_dataset_class.cpython-311.pyc b/transformer_model/scripts/utils/__pycache__/informer_dataset_class.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2472ab7f9b30c52ac9f70f04bf5da4a5103220b1 Binary files /dev/null and b/transformer_model/scripts/utils/__pycache__/informer_dataset_class.cpython-311.pyc differ diff --git a/transformer_model/scripts/utils/check_device.py b/transformer_model/scripts/utils/check_device.py new file mode 100644 index 0000000000000000000000000000000000000000..d27735d059064f25608e5d4203e804d210ec6fec --- /dev/null +++ b/transformer_model/scripts/utils/check_device.py @@ -0,0 +1,51 @@ +import torch +import importlib +import subprocess +import sys + +def install_package(package_name): + subprocess.check_call([sys.executable, "-m", "pip", "install", package_name]) + +def check_device(): + # **Check for NVIDIA GPU (CUDA)** + if torch.cuda.is_available(): + device = torch.device("cuda") # Use NVIDIA GPU + backend = "CUDA (NVIDIA)" + mixed_precision = True # Use Automatic Mixed Precision (AMP) + + # **If no NVIDIA GPU, check for AMD GPU (DirectML) only in Windows** + else: + try: + # Only try DirectML if the environment is Windows and DirectML is installed + if "win32" in sys.platform: + torch_directml = importlib.import_module("torch_directml") + if torch_directml.device_count() > 0: + device = torch_directml.device() # Use AMD GPU with DirectML + backend = "DirectML (AMD)" + mixed_precision = False # No AMP for AMD GPU + else: + raise ImportError # AMD GPU not found + else: + device = torch.device("cpu") + backend = "CPU" + mixed_precision = False # No AMP for CPU + + except ImportError: + # If DirectML is not installed or AMD GPU not found + device = torch.device("cpu") + backend = "CPU" + mixed_precision = False # No AMP for CPU + + # Print the chosen device info + print(f"Training is running on: {backend} ({device})") + + # **Initialize scaler (only for NVIDIA)** + if mixed_precision: + scaler = torch.amp.GradScaler() + else: + scaler = None # No scaler needed for AMD/CPU + + return device, backend, scaler + +if __name__ == "__main__": + device, backend, scaler = check_device() diff --git a/transformer_model/scripts/utils/create_dataloaders.py b/transformer_model/scripts/utils/create_dataloaders.py new file mode 100644 index 0000000000000000000000000000000000000000..d773ed53e25d3f9fe3381fbf868fb9a80c31e4bb --- /dev/null +++ b/transformer_model/scripts/utils/create_dataloaders.py @@ -0,0 +1,28 @@ +# create_dataloaders.py + +import logging +from torch.utils.data import DataLoader +from transformer_model.scripts.utils.informer_dataset_class import InformerDataset +from transformer_model.scripts.config_transformer import BATCH_SIZE, FORECAST_HORIZON +from momentfm.utils.utils import control_randomness + +def create_dataloaders(): + logging.info("Setting random seeds...") + control_randomness(seed=13) + + logging.info("Loading training dataset...") + train_dataset = InformerDataset(data_split="train", random_seed=13, forecast_horizon=FORECAST_HORIZON) + logging.info("Train set loaded — Samples: %d | Features: %d", len(train_dataset), train_dataset.n_channels) + + logging.info("Loading test dataset...") + test_dataset = InformerDataset(data_split="test", random_seed=13, forecast_horizon=FORECAST_HORIZON) + logging.info("Test set loaded — Samples: %d | Features: %d", len(test_dataset), test_dataset.n_channels) + + train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) + test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True) + + logging.info("Dataloaders created successfully.") + return train_loader, test_loader + +if __name__ == "__main__": + create_dataloaders() diff --git a/transformer_model/scripts/utils/informer_dataset_class.py b/transformer_model/scripts/utils/informer_dataset_class.py new file mode 100644 index 0000000000000000000000000000000000000000..20500404d3d97fd4c504d419a1bafe09d0cd9403 --- /dev/null +++ b/transformer_model/scripts/utils/informer_dataset_class.py @@ -0,0 +1,121 @@ +# informer_dataset.py + +import logging +import pandas as pd +import numpy as np +from sklearn.preprocessing import StandardScaler +from typing import Optional +from transformer_model.scripts.config_transformer import SEQ_LEN, DATA_PATH + +logging.basicConfig(level=logging.INFO) + + +class InformerDataset: + def __init__( + self, + forecast_horizon: Optional[int], + data_split: str = "train", + data_stride_len: int = 1, + task_name: str = "forecasting", + random_seed: int = 42, + ): + """ + Parameters + ---------- + forecast_horizon : int + Length of the prediction sequence. + data_split : str + 'train' or 'test'. + data_stride_len : int + Stride length between time windows. + task_name : str + 'forecasting' or 'imputation'. + random_seed : int + For reproducibility. + """ + + self.seq_len = SEQ_LEN + self.forecast_horizon = forecast_horizon + self.full_file_path_and_name = DATA_PATH + self.data_split = data_split + self.data_stride_len = data_stride_len + self.task_name = task_name + self.random_seed = random_seed + + self._read_data() + + def _get_borders(self): + train_ratio = 0.7 + n_train = int(self.length_timeseries_original * train_ratio) + n_test = self.length_timeseries_original - n_train + + train_end = n_train + test_start = train_end - self.seq_len + test_end = test_start + n_test + self.seq_len + + #logging.info(f"Train range: 0 to {train_end}") + #logging.info(f"Test range: {test_start} to {test_end}") + + return slice(0, train_end), slice(test_start, test_end) + + def _read_data(self): + self.scaler = StandardScaler() + + df = pd.read_csv(self.full_file_path_and_name) + self.length_timeseries_original = df.shape[0] + self.n_channels = df.shape[1] - 1 # exclude timestamp column + + df.drop(columns=["date"], inplace=True) + df = df.infer_objects(copy=False).interpolate(method="cubic") + + data_splits = self._get_borders() + train_data = df[data_splits[0]] + + self.scaler.fit(train_data.values) + df = self.scaler.transform(df.values) + + if self.data_split == "train": + self.data = df[data_splits[0], :] + elif self.data_split == "test": + self.data = df[data_splits[1], :] + + self.length_timeseries = self.data.shape[0] + + #logging.info(f"{self.data_split.capitalize()} set loaded.") + #logging.info(f"Time series length: {self.length_timeseries}") + #logging.info(f"Number of features: {self.n_channels}") + + def __getitem__(self, index): + seq_start = self.data_stride_len * index + seq_end = seq_start + self.seq_len + input_mask = np.ones(self.seq_len) + + if self.task_name == "forecasting": + pred_end = seq_end + self.forecast_horizon + + if pred_end > self.length_timeseries: + pred_end = self.length_timeseries + seq_end = seq_end - self.forecast_horizon + seq_start = seq_end - self.seq_len + + timeseries = self.data[seq_start:seq_end, :].T + forecast = self.data[seq_end:pred_end, :].T + + return timeseries, forecast, input_mask + + elif self.task_name == "imputation": + if seq_end > self.length_timeseries: + seq_end = self.length_timeseries + seq_end = seq_end - self.seq_len + + timeseries = self.data[seq_start:seq_end, :].T + + return timeseries, input_mask + + def __len__(self): + if self.task_name == "imputation": + return (self.length_timeseries - self.seq_len) // self.data_stride_len + 1 + elif self.task_name == "forecasting": + return ( + self.length_timeseries - self.seq_len - self.forecast_horizon + ) // self.data_stride_len + 1