v2.14

[v2.14] - 4-17-2026

Added

• Add an interactivities plot and add evaluation metrics in model_evaluation.py: After running Model Assessment, three files are automatically saved locally: a PNG graphic, an HTML file, and a metrics.txt file. The HTML file contains an interactive Plotly-powered scatter plot with hover tooltips displaying the ID, Actual value, Predicted value, Agreement, and Difference for each data point. The plot also supports zooming, making it easy to explore high-density regions near the origin. New statistics:

  **Calculate MAE**
  MAE=np.sum(distance_arr)/len(X)
  MAE_percent = (MAE / int(grid_area)) * 100
  
  **agreement=intersection: Minimum of actual and predicted deforestation for each dot and add them all**
  agree_arr = [min(X[i],Y[i]) for i in range(len(X))]
  agree = np.sum(agree_arr)
  
  **union**
  union_arr = [max(X[i],Y[i]) for i in range(len(X))]
  union = np.sum(union_arr)
  
  **IoU**
  iou = agree/union * 100
  
  **Calculate the Difference**
  Absolute value of predicted deforestation minus actual deforestation of each point and add them all
  diff_arr=[abs(X[i] - Y[i]) for i in range(len(X))]
  difference=np.sum(diff_arr)
  

1. Added IoU on the plot graphic file
2. Added IoU, MAE, Agreement and Difference to txt file
3. html file with interactive hover mode with ID, Actual, Predicted, Agreement and Difference for each point. (#67)