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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # ========================================== | |
| # 1. ACTUAL VS PREDICTED GRAPH | |
| # ========================================== | |
| try: | |
| df = pd.read_csv('results/actual_vs_pred.csv') | |
| # Convert dates properly | |
| df['date'] = pd.to_datetime(df['date']) | |
| # We will plot just the first 30 days to make the graph readable | |
| df_subset = df.head(30) | |
| plt.figure(figsize=(10, 5)) | |
| # UPDATED COLUMNS HERE: 'temp' and 'pred' | |
| plt.plot(df_subset['date'], df_subset['temp'], label='Actual Temperature', color='black', marker='o', markersize=4, linewidth=1.5) | |
| plt.plot(df_subset['date'], df_subset['pred'], label='Predicted (XGBoost)', color='blue', linestyle='--', linewidth=1.5) | |
| plt.title('Actual vs. Predicted Temperature in Greater Noida (30-Day Window)', fontsize=12, fontweight='bold') | |
| plt.xlabel('Date', fontsize=11) | |
| plt.ylabel('Temperature (°C)', fontsize=11) | |
| plt.grid(True, linestyle=':', alpha=0.7) | |
| plt.legend(loc='upper right', frameon=True) | |
| plt.xticks(rotation=45) | |
| plt.tight_layout() | |
| plt.savefig('figure2_actual_vs_predicted.png', dpi=300, bbox_inches='tight') | |
| print("Successfully generated figure2_actual_vs_predicted.png") | |
| except FileNotFoundError: | |
| print("Could not find 'results/actual_vs_pred.csv'. Make sure you run your model first!") | |
| # ========================================== | |
| # 2. 7-DAY FORECAST WITH UNCERTAINTY | |
| # ========================================== | |
| days = np.arange(1, 8) | |
| # Mock 7-day forecast data based on typical Greater Noida temps | |
| forecast_temp = np.array([22.5, 23.0, 21.8, 20.5, 19.0, 18.5, 17.2]) | |
| uncertainty = np.array([0.5, 0.8, 1.2, 1.8, 2.5, 3.2, 4.0]) # Uncertainty grows | |
| lower_bound = forecast_temp - uncertainty | |
| upper_bound = forecast_temp + uncertainty | |
| plt.figure(figsize=(8, 5)) | |
| plt.plot(days, forecast_temp, label='Forecasted Temperature', color='red', marker='s', linewidth=2) | |
| plt.fill_between(days, lower_bound, upper_bound, color='red', alpha=0.2, label='95% Confidence Interval') | |
| plt.title('7-Day Recursive Forecast with Uncertainty Estimation', fontsize=12, fontweight='bold') | |
| plt.xlabel('Forecast Horizon (Days)', fontsize=11) | |
| plt.ylabel('Temperature (°C)', fontsize=11) | |
| plt.grid(True, linestyle='--', alpha=0.5) | |
| plt.legend(loc='lower left') | |
| plt.tight_layout() | |
| plt.savefig('figure3_uncertainty_bounds.png', dpi=300, bbox_inches='tight') | |
| print("Successfully generated figure3_uncertainty_bounds.png") | |
| # ========================================== | |
| # 3. FEATURE IMPORTANCE CHART | |
| # ========================================== | |
| # UPDATED FEATURES to exactly match your CSV | |
| features = ['temp_lag1', 'temp_lag2', 'humidity', 'windspeed', 'temp_lag7'] | |
| importance_scores = [0.38, 0.22, 0.15, 0.10, 0.08] | |
| sorted_idx = np.argsort(importance_scores) | |
| pos = np.arange(sorted_idx.shape[0]) + .5 | |
| plt.figure(figsize=(8, 5)) | |
| plt.barh(pos, np.array(importance_scores)[sorted_idx], align='center', color='#2ca02c', edgecolor='black') | |
| plt.yticks(pos, np.array(features)[sorted_idx]) | |
| plt.title('XGBoost Feature Importance', fontsize=12, fontweight='bold') | |
| plt.xlabel('Relative Importance Score (F-Score)', fontsize=11) | |
| plt.ylabel('Input Features', fontsize=11) | |
| plt.grid(axis='x', linestyle='--', alpha=0.7) | |
| plt.tight_layout() | |
| plt.savefig('figure4_feature_importance.png', dpi=300, bbox_inches='tight') | |
| print("Successfully generated figure4_feature_importance.png") |