update 01
This commit is contained in:
Victor Phan
@@ -0,0 +1,786 @@
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"""
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Auto Report Generator for Land Classification
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Tự động tạo báo cáo HTML chi tiết sau training/prediction
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"""
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import json
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from datetime import datetime
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from pathlib import Path
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import base64
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import io
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# Optional: for generating charts
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try:
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import matplotlib
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matplotlib.use('Agg') # Non-interactive backend
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import matplotlib.pyplot as plt
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import numpy as np
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MATPLOTLIB_AVAILABLE = True
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except ImportError:
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MATPLOTLIB_AVAILABLE = False
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def generate_confusion_matrix_image(conf_matrix, class_names):
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"""Tạo hình ảnh confusion matrix dạng base64"""
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if not MATPLOTLIB_AVAILABLE:
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return None
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try:
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fig, ax = plt.subplots(figsize=(10, 8))
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conf_matrix = np.array(conf_matrix)
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im = ax.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)
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ax.figure.colorbar(im, ax=ax)
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ax.set(xticks=np.arange(len(class_names)),
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yticks=np.arange(len(class_names)),
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xticklabels=class_names, yticklabels=class_names,
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title='Confusion Matrix',
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ylabel='Thực tế (True)',
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xlabel='Dự đoán (Predicted)')
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
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# Add text annotations
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thresh = conf_matrix.max() / 2.
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for i in range(len(class_names)):
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for j in range(len(class_names)):
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ax.text(j, i, format(conf_matrix[i, j], 'd'),
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ha="center", va="center",
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color="white" if conf_matrix[i, j] > thresh else "black")
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fig.tight_layout()
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# Convert to base64
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
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buf.seek(0)
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img_base64 = base64.b64encode(buf.read()).decode('utf-8')
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plt.close(fig)
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return img_base64
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except Exception as e:
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print(f"Error generating confusion matrix image: {e}")
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return None
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def generate_class_distribution_chart(class_names, classification_report):
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"""Tạo biểu đồ phân bố các class dạng base64"""
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if not MATPLOTLIB_AVAILABLE:
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return None
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try:
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# Extract support (number of samples) for each class
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supports = []
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for cls in class_names:
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if cls in classification_report:
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supports.append(classification_report[cls].get('support', 0))
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else:
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supports.append(0)
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fig, ax = plt.subplots(figsize=(10, 6))
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colors = plt.cm.Set3(np.linspace(0, 1, len(class_names)))
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bars = ax.bar(class_names, supports, color=colors)
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ax.set_xlabel('Loại đất')
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ax.set_ylabel('Số mẫu')
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ax.set_title('Phân bố số mẫu theo loại đất')
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plt.xticks(rotation=45, ha='right')
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# Add value labels on bars
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for bar, val in zip(bars, supports):
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ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
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str(int(val)), ha='center', va='bottom', fontsize=9)
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fig.tight_layout()
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# Convert to base64
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
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buf.seek(0)
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img_base64 = base64.b64encode(buf.read()).decode('utf-8')
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plt.close(fig)
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return img_base64
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except Exception as e:
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print(f"Error generating class distribution chart: {e}")
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return None
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def generate_metrics_chart(class_names, classification_report):
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"""Tạo biểu đồ precision/recall/f1 cho từng class"""
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if not MATPLOTLIB_AVAILABLE:
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return None
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try:
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precisions = []
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recalls = []
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f1_scores = []
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for cls in class_names:
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if cls in classification_report:
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precisions.append(classification_report[cls].get('precision', 0))
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recalls.append(classification_report[cls].get('recall', 0))
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f1_scores.append(classification_report[cls].get('f1-score', 0))
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else:
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precisions.append(0)
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recalls.append(0)
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f1_scores.append(0)
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x = np.arange(len(class_names))
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width = 0.25
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fig, ax = plt.subplots(figsize=(12, 6))
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bars1 = ax.bar(x - width, precisions, width, label='Precision', color='#3498db')
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bars2 = ax.bar(x, recalls, width, label='Recall', color='#2ecc71')
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bars3 = ax.bar(x + width, f1_scores, width, label='F1-Score', color='#e74c3c')
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ax.set_xlabel('Loại đất')
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ax.set_ylabel('Score')
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ax.set_title('Precision / Recall / F1-Score theo loại đất')
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ax.set_xticks(x)
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ax.set_xticklabels(class_names, rotation=45, ha='right')
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ax.legend()
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ax.set_ylim(0, 1.1)
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# Add grid
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ax.yaxis.grid(True, linestyle='--', alpha=0.7)
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fig.tight_layout()
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# Convert to base64
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
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buf.seek(0)
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img_base64 = base64.b64encode(buf.read()).decode('utf-8')
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plt.close(fig)
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return img_base64
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except Exception as e:
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print(f"Error generating metrics chart: {e}")
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return None
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def generate_training_report(training_result, config=None):
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"""
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Tạo báo cáo HTML cho kết quả training
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Args:
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training_result: Dict chứa kết quả từ train_model()
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config: Dict chứa cấu hình training (optional)
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Returns:
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Tuple (report_path, report_html)
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"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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# Extract data from result
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train_acc = training_result.get('train_accuracy', 0) * 100
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test_acc = training_result.get('test_accuracy', 0) * 100
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train_samples = training_result.get('training_samples', 0)
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test_samples = training_result.get('testing_samples', 0)
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test_size = training_result.get('test_size', 0.2)
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classes = training_result.get('classes', [])
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cls_report = training_result.get('classification_report', {})
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conf_matrix = training_result.get('confusion_matrix', [])
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model_type = training_result.get('model_type', 'unknown')
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model_path = training_result.get('model_path', '')
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bbox = training_result.get('bbox', [])
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time_range = training_result.get('time_range', '')
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resolution = training_result.get('resolution', 20)
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# Generate charts
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conf_matrix_img = generate_confusion_matrix_image(conf_matrix, classes) if conf_matrix else None
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class_dist_img = generate_class_distribution_chart(classes, cls_report) if cls_report else None
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metrics_img = generate_metrics_chart(classes, cls_report) if cls_report else None
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# Build classification report table
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cls_report_rows = ""
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for cls in classes:
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if cls in cls_report:
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metrics = cls_report[cls]
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cls_report_rows += f"""
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<tr>
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<td><strong>{cls}</strong></td>
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<td>{metrics.get('precision', 0):.3f}</td>
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<td>{metrics.get('recall', 0):.3f}</td>
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<td>{metrics.get('f1-score', 0):.3f}</td>
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<td>{int(metrics.get('support', 0))}</td>
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</tr>
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"""
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# Add averages
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for avg_type in ['macro avg', 'weighted avg']:
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if avg_type in cls_report:
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metrics = cls_report[avg_type]
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cls_report_rows += f"""
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<tr style="background-color: #f0f0f0; font-weight: bold;">
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<td>{avg_type}</td>
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<td>{metrics.get('precision', 0):.3f}</td>
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<td>{metrics.get('recall', 0):.3f}</td>
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<td>{metrics.get('f1-score', 0):.3f}</td>
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<td>{int(metrics.get('support', 0))}</td>
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</tr>
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"""
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# Build confusion matrix table (fallback if no image)
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conf_matrix_table = ""
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if conf_matrix:
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conf_matrix_table = "<table class='conf-matrix'><tr><th></th>"
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for cls in classes:
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conf_matrix_table += f"<th>{cls}</th>"
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conf_matrix_table += "</tr>"
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for i, row in enumerate(conf_matrix):
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conf_matrix_table += f"<tr><th>{classes[i]}</th>"
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for val in row:
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conf_matrix_table += f"<td>{val}</td>"
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conf_matrix_table += "</tr>"
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conf_matrix_table += "</table>"
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# HTML Template
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html = f"""
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<!DOCTYPE html>
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<html lang="vi">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Training Report - {timestamp}</title>
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<style>
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* {{
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}}
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body {{
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background: #f5f5f5;
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padding: 20px;
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line-height: 1.6;
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}}
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.container {{
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max-width: 1200px;
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margin: 0 auto;
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background: white;
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border-radius: 15px;
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box-shadow: 0 10px 40px rgba(0,0,0,0.1);
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overflow: hidden;
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}}
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.header {{
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 40px;
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text-align: center;
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}}
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.header h1 {{
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font-size: 2.5em;
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margin-bottom: 10px;
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}}
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.header .subtitle {{
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opacity: 0.9;
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font-size: 1.1em;
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}}
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.content {{
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padding: 40px;
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}}
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.section {{
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margin-bottom: 40px;
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}}
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.section h2 {{
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color: #667eea;
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border-bottom: 3px solid #667eea;
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padding-bottom: 10px;
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margin-bottom: 20px;
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font-size: 1.5em;
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}}
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.stats-grid {{
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 20px;
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margin-bottom: 30px;
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}}
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.stat-card {{
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background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
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padding: 25px;
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border-radius: 10px;
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text-align: center;
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border: 1px solid #667eea30;
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}}
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.stat-card .value {{
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font-size: 2.5em;
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font-weight: bold;
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color: #667eea;
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}}
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.stat-card .label {{
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color: #666;
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margin-top: 5px;
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}}
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.stat-card.success .value {{
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color: #28a745;
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}}
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.stat-card.warning .value {{
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color: #ffc107;
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}}
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table {{
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width: 100%;
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border-collapse: collapse;
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margin: 20px 0;
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}}
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th, td {{
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padding: 12px 15px;
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text-align: left;
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border-bottom: 1px solid #ddd;
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}}
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th {{
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background: #667eea;
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color: white;
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}}
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tr:hover {{
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background-color: #f5f5f5;
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}}
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.conf-matrix {{
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font-size: 14px;
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}}
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.conf-matrix th, .conf-matrix td {{
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text-align: center;
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padding: 8px;
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}}
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.chart-container {{
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text-align: center;
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margin: 20px 0;
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}}
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.chart-container img {{
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max-width: 100%;
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border-radius: 10px;
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box-shadow: 0 4px 15px rgba(0,0,0,0.1);
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}}
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.info-box {{
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background: #e3f2fd;
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padding: 20px;
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border-radius: 10px;
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border-left: 5px solid #2196f3;
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margin: 20px 0;
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}}
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.info-row {{
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display: flex;
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margin: 10px 0;
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}}
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.info-label {{
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font-weight: bold;
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width: 200px;
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color: #555;
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}}
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.info-value {{
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color: #333;
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}}
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.footer {{
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background: #f8f9fa;
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padding: 20px;
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text-align: center;
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color: #666;
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font-size: 14px;
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}}
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@media print {{
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body {{
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background: white;
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padding: 0;
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}}
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.container {{
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box-shadow: none;
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}}
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}}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>📊 Báo Cáo Training Model</h1>
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<p class="subtitle">Land Classification - {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
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</div>
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<div class="content">
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<!-- Summary Stats -->
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<div class="section">
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<h2>📈 Tóm Tắt Kết Quả</h2>
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<div class="stats-grid">
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<div class="stat-card success">
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<div class="value">{train_acc:.1f}%</div>
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<div class="label">Train Accuracy</div>
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</div>
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<div class="stat-card {'success' if test_acc >= 80 else 'warning'}">
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||||
<div class="value">{test_acc:.1f}%</div>
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<div class="label">Test Accuracy</div>
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</div>
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||||
<div class="stat-card">
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||||
<div class="value">{train_samples}</div>
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||||
<div class="label">Training Samples</div>
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</div>
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||||
<div class="stat-card">
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||||
<div class="value">{test_samples}</div>
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<div class="label">Testing Samples</div>
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</div>
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<div class="stat-card">
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<div class="value">{len(classes)}</div>
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<div class="label">Số Classes</div>
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</div>
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<div class="stat-card">
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<div class="value">{test_size*100:.0f}%</div>
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<div class="label">Test Size</div>
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</div>
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</div>
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</div>
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||||
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<!-- Configuration Info -->
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<div class="section">
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<h2>⚙️ Cấu Hình Training</h2>
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<div class="info-box">
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<div class="info-row">
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<span class="info-label">🤖 Model Type:</span>
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<span class="info-value">{model_type.upper()}</span>
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</div>
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<div class="info-row">
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<span class="info-label">📍 Khu vực (bbox):</span>
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||||
<span class="info-value">{bbox}</span>
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</div>
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||||
<div class="info-row">
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||||
<span class="info-label">📅 Thời gian:</span>
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<span class="info-value">{time_range}</span>
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</div>
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||||
<div class="info-row">
|
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<span class="info-label">📐 Độ phân giải:</span>
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<span class="info-value">{resolution}m</span>
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||||
</div>
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||||
<div class="info-row">
|
||||
<span class="info-label">💾 Model Path:</span>
|
||||
<span class="info-value">{model_path}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Classification Report -->
|
||||
<div class="section">
|
||||
<h2>📋 Classification Report</h2>
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Loại đất</th>
|
||||
<th>Precision</th>
|
||||
<th>Recall</th>
|
||||
<th>F1-Score</th>
|
||||
<th>Support</th>
|
||||
</tr>
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||||
</thead>
|
||||
<tbody>
|
||||
{cls_report_rows}
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<!-- Metrics Chart -->
|
||||
{'<div class="section"><h2>📊 Biểu Đồ Metrics</h2><div class="chart-container"><img src="data:image/png;base64,' + metrics_img + '" alt="Metrics Chart"></div></div>' if metrics_img else ''}
|
||||
|
||||
<!-- Class Distribution -->
|
||||
{'<div class="section"><h2>📊 Phân Bố Số Mẫu</h2><div class="chart-container"><img src="data:image/png;base64,' + class_dist_img + '" alt="Class Distribution"></div></div>' if class_dist_img else ''}
|
||||
|
||||
<!-- Confusion Matrix -->
|
||||
<div class="section">
|
||||
<h2>🔢 Confusion Matrix</h2>
|
||||
{'<div class="chart-container"><img src="data:image/png;base64,' + conf_matrix_img + '" alt="Confusion Matrix"></div>' if conf_matrix_img else conf_matrix_table}
|
||||
</div>
|
||||
|
||||
<!-- Classes List -->
|
||||
<div class="section">
|
||||
<h2>🏷️ Danh Sách Các Loại Đất</h2>
|
||||
<div class="info-box">
|
||||
<ul style="list-style: none; display: flex; flex-wrap: wrap; gap: 10px;">
|
||||
{''.join([f'<li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">{cls}</li>' for cls in classes])}
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="footer">
|
||||
<p>🌍 Land Classification Training System | Generated: {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
|
||||
<p>Data Source: Microsoft Planetary Computer (Sentinel-2 L2A, Sentinel-1 RTC)</p>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
# Save report
|
||||
reports_dir = Path("reports")
|
||||
reports_dir.mkdir(exist_ok=True)
|
||||
|
||||
report_filename = f"training_report_{timestamp}.html"
|
||||
report_path = reports_dir / report_filename
|
||||
|
||||
with open(report_path, 'w', encoding='utf-8') as f:
|
||||
f.write(html)
|
||||
|
||||
return str(report_path), html
|
||||
|
||||
|
||||
def generate_prediction_report(prediction_result, config=None):
|
||||
"""
|
||||
Tạo báo cáo HTML cho kết quả prediction
|
||||
|
||||
Args:
|
||||
prediction_result: Dict chứa kết quả prediction
|
||||
config: Dict chứa cấu hình prediction (optional)
|
||||
|
||||
Returns:
|
||||
Tuple (report_path, report_html)
|
||||
"""
|
||||
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
# Extract data
|
||||
output_file = prediction_result.get('output_file', '')
|
||||
shape = prediction_result.get('shape', [0, 0])
|
||||
unique_classes = prediction_result.get('unique_classes', [])
|
||||
bbox = prediction_result.get('bbox', [])
|
||||
time_range = prediction_result.get('time_range', '')
|
||||
n_features = prediction_result.get('n_features', 0)
|
||||
used_radar = prediction_result.get('used_radar', False)
|
||||
model_used = prediction_result.get('model_used', '')
|
||||
|
||||
# Calculate area (approximate)
|
||||
if len(bbox) == 4:
|
||||
# Approximate calculation (1 degree ≈ 111km at equator)
|
||||
width_km = (bbox[2] - bbox[0]) * 111 * 0.85 # cos adjustment for Vietnam
|
||||
height_km = (bbox[3] - bbox[1]) * 111
|
||||
area_km2 = width_km * height_km
|
||||
else:
|
||||
area_km2 = 0
|
||||
|
||||
total_pixels = shape[0] * shape[1] if len(shape) == 2 else 0
|
||||
|
||||
# HTML Template
|
||||
html = f"""
|
||||
<!DOCTYPE html>
|
||||
<html lang="vi">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Prediction Report - {timestamp}</title>
|
||||
<style>
|
||||
* {{
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
box-sizing: border-box;
|
||||
}}
|
||||
body {{
|
||||
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
|
||||
background: #f5f5f5;
|
||||
padding: 20px;
|
||||
line-height: 1.6;
|
||||
}}
|
||||
.container {{
|
||||
max-width: 1200px;
|
||||
margin: 0 auto;
|
||||
background: white;
|
||||
border-radius: 15px;
|
||||
box-shadow: 0 10px 40px rgba(0,0,0,0.1);
|
||||
overflow: hidden;
|
||||
}}
|
||||
.header {{
|
||||
background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
|
||||
color: white;
|
||||
padding: 40px;
|
||||
text-align: center;
|
||||
}}
|
||||
.header h1 {{
|
||||
font-size: 2.5em;
|
||||
margin-bottom: 10px;
|
||||
}}
|
||||
.content {{
|
||||
padding: 40px;
|
||||
}}
|
||||
.section {{
|
||||
margin-bottom: 40px;
|
||||
}}
|
||||
.section h2 {{
|
||||
color: #ff6b6b;
|
||||
border-bottom: 3px solid #ff6b6b;
|
||||
padding-bottom: 10px;
|
||||
margin-bottom: 20px;
|
||||
}}
|
||||
.stats-grid {{
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
||||
gap: 20px;
|
||||
}}
|
||||
.stat-card {{
|
||||
background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
|
||||
padding: 25px;
|
||||
border-radius: 10px;
|
||||
text-align: center;
|
||||
border: 1px solid #ff6b6b30;
|
||||
}}
|
||||
.stat-card .value {{
|
||||
font-size: 2em;
|
||||
font-weight: bold;
|
||||
color: #ff6b6b;
|
||||
}}
|
||||
.stat-card .label {{
|
||||
color: #666;
|
||||
margin-top: 5px;
|
||||
}}
|
||||
.info-box {{
|
||||
background: #fff3cd;
|
||||
padding: 20px;
|
||||
border-radius: 10px;
|
||||
border-left: 5px solid #ff6b6b;
|
||||
margin: 20px 0;
|
||||
}}
|
||||
.info-row {{
|
||||
display: flex;
|
||||
margin: 10px 0;
|
||||
}}
|
||||
.info-label {{
|
||||
font-weight: bold;
|
||||
width: 200px;
|
||||
color: #555;
|
||||
}}
|
||||
.class-badge {{
|
||||
display: inline-block;
|
||||
background: #ff6b6b;
|
||||
color: white;
|
||||
padding: 8px 15px;
|
||||
border-radius: 20px;
|
||||
margin: 5px;
|
||||
}}
|
||||
.footer {{
|
||||
background: #f8f9fa;
|
||||
padding: 20px;
|
||||
text-align: center;
|
||||
color: #666;
|
||||
}}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<div class="header">
|
||||
<h1>🗺️ Báo Cáo Dự Đoán</h1>
|
||||
<p>Land Classification Prediction - {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
|
||||
</div>
|
||||
|
||||
<div class="content">
|
||||
<div class="section">
|
||||
<h2>📈 Tóm Tắt Kết Quả</h2>
|
||||
<div class="stats-grid">
|
||||
<div class="stat-card">
|
||||
<div class="value">{total_pixels:,}</div>
|
||||
<div class="label">Tổng số Pixels</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="value">{shape[0]}x{shape[1]}</div>
|
||||
<div class="label">Kích thước (px)</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="value">{area_km2:.1f}</div>
|
||||
<div class="label">Diện tích (km²)</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="value">{len(unique_classes)}</div>
|
||||
<div class="label">Số Classes</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="value">{n_features}</div>
|
||||
<div class="label">Số Features</div>
|
||||
</div>
|
||||
<div class="stat-card">
|
||||
<div class="value">{'✅' if used_radar else '❌'}</div>
|
||||
<div class="label">Sử dụng Radar</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="section">
|
||||
<h2>⚙️ Thông Tin Chi Tiết</h2>
|
||||
<div class="info-box">
|
||||
<div class="info-row">
|
||||
<span class="info-label">🤖 Model sử dụng:</span>
|
||||
<span>{model_used}</span>
|
||||
</div>
|
||||
<div class="info-row">
|
||||
<span class="info-label">📍 Khu vực (bbox):</span>
|
||||
<span>{bbox}</span>
|
||||
</div>
|
||||
<div class="info-row">
|
||||
<span class="info-label">📅 Thời gian:</span>
|
||||
<span>{time_range}</span>
|
||||
</div>
|
||||
<div class="info-row">
|
||||
<span class="info-label">💾 Output file:</span>
|
||||
<span>{output_file}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="section">
|
||||
<h2>🏷️ Các Classes Phát Hiện</h2>
|
||||
<div>
|
||||
{''.join([f'<span class="class-badge">{cls}</span>' for cls in unique_classes])}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="footer">
|
||||
<p>🌍 Land Classification System | Generated: {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
|
||||
# Save report
|
||||
reports_dir = Path("reports")
|
||||
reports_dir.mkdir(exist_ok=True)
|
||||
|
||||
report_filename = f"prediction_report_{timestamp}.html"
|
||||
report_path = reports_dir / report_filename
|
||||
|
||||
with open(report_path, 'w', encoding='utf-8') as f:
|
||||
f.write(html)
|
||||
|
||||
return str(report_path), html
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Test report generation
|
||||
test_result = {
|
||||
"success": True,
|
||||
"train_accuracy": 0.95,
|
||||
"test_accuracy": 0.87,
|
||||
"training_samples": 800,
|
||||
"testing_samples": 200,
|
||||
"test_size": 0.2,
|
||||
"classes": ["Lua", "Rung", "Nuoc", "Dan_cu", "Cay_lau_nam"],
|
||||
"model_type": "xgboost",
|
||||
"model_path": "model_train/model_xgboost_20251221.joblib",
|
||||
"bbox": [105.6, 9.3, 106.2, 9.8],
|
||||
"time_range": "2023-03-01/2023-05-31",
|
||||
"resolution": 20,
|
||||
"classification_report": {
|
||||
"Lua": {"precision": 0.92, "recall": 0.89, "f1-score": 0.90, "support": 50},
|
||||
"Rung": {"precision": 0.88, "recall": 0.91, "f1-score": 0.89, "support": 45},
|
||||
"Nuoc": {"precision": 0.95, "recall": 0.93, "f1-score": 0.94, "support": 40},
|
||||
"Dan_cu": {"precision": 0.85, "recall": 0.82, "f1-score": 0.83, "support": 35},
|
||||
"Cay_lau_nam": {"precision": 0.80, "recall": 0.85, "f1-score": 0.82, "support": 30},
|
||||
"macro avg": {"precision": 0.88, "recall": 0.88, "f1-score": 0.88, "support": 200},
|
||||
"weighted avg": {"precision": 0.88, "recall": 0.87, "f1-score": 0.87, "support": 200}
|
||||
},
|
||||
"confusion_matrix": [
|
||||
[45, 2, 1, 1, 1],
|
||||
[3, 41, 0, 1, 0],
|
||||
[1, 0, 37, 1, 1],
|
||||
[2, 1, 1, 29, 2],
|
||||
[1, 1, 1, 2, 26]
|
||||
]
|
||||
}
|
||||
|
||||
path, html = generate_training_report(test_result)
|
||||
print(f"Report generated: {path}")
|
||||
Reference in New Issue
Block a user