Add data visualization for predicted burn

predictions_data_vis.py

@@ -0,0 +1,96 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from matplotlib.colors import Normalize
+
+def get_color_for_value(value, cmap, min_value, max_value):
+    norm = Normalize(vmin=min_value, vmax=max_value)
+    return cmap(norm(value))
+
+def create_color_map(color_matrix, cmap=cm.coolwarm):
+    norm = Normalize(vmin=np.min(color_matrix), vmax=np.max(color_matrix))
+    cmap = cm.coolwarm  # You can choose a different colormap if desired
+    norm = Normalize(vmin=min(map(min, color_matrix)), vmax=max(map(max, color_matrix)))
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
+    return cmap, sm
+
+
+def draw_grid(matrix, error_matrix, color_matrix, filename='output_plot.pdf'):  # Set the default filename
+    # # Create a color map
+    # norm = Normalize(vmin=np.min(color_matrix), vmax=np.max(color_matrix))
+    # cmap = cm.coolwarm  # You can choose a different colormap if desired
+    # norm = Normalize(vmin=min(map(min, color_matrix)), vmax=max(map(max, color_matrix)))
+    # sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    # sm.set_array([])
+    cmap, sm = create_color_map(color_matrix)
+
+    rows, cols = len(matrix), len(matrix[0])
+
+    fig, ax = plt.subplots()
+
+    # Draw horizontal lines
+    for i in range(rows + 1):
+        ax.axhline(i, color='black')
+
+    # Draw vertical lines
+    for i in range(cols + 1):
+        ax.axvline(i, color='black')
+
+    # Display numbers in the grid with at most 3 digits and fill cells based on color_matrix
+    for i in reversed(range(rows)):
+        for j in reversed(range(cols)):
+            number = matrix[i][j]
+            formatted_number = '{:.3f}'.format(number)[:6]  # Ensure at most 3 digits before the decimal point
+
+            error_value = error_matrix[i][j]
+            color = get_color_for_value(error_value, cmap, np.min(color_matrix), np.max(color_matrix))
+
+            ax.add_patch(plt.Rectangle((j, i), 1, 1, fill=True, color=color))
+            ax.text(j + 0.5, i + 0.5, formatted_number, ha='center', va='center', fontsize=12, color='white')
+
+    ax.set_xlim(0, cols)
+    ax.set_ylim(0, rows)
+    ax.set_xticks([])  # Remove x-axis ticks
+    ax.set_yticks([])  # Remove y-axis ticks
+    ax.invert_yaxis()
+
+    # Add color bar
+    cbar = plt.colorbar(sm, ax=ax, orientation='vertical', pad=0.02)
+    cbar.set_label('Absolute Error', rotation=270, labelpad=15)
+    cbar.set_ticks([np.min(color_matrix), np.max(color_matrix)])
+    cbar.set_ticklabels(['{:.3f}'.format(np.min(color_matrix)), '{:.3f}'.format(np.max(color_matrix))])
+
+    # Add label above the matrix (centered with the matrix)
+    plt.text(cols / 2, rows + 0.5, 'Predicted Values', ha='center', va='center', fontsize=14)
+
+    plt.grid(False)
+
+    # Save the plot as a PDF file
+    plt.savefig(filename, format='pdf')
+
+    # Show the plot (if needed)
+    # plt.show()
+
+def main():
+    color_matrix = None
+    base_path = "./train-results/predicted-data-vis/"
+
+    data_files = ["burn_6.csv", "burn_11.csv", "burn_16.csv", "burn_21.csv"]
+    for file in data_files:
+        df = pd.read_csv(base_path + file)
+        df['absolute_error'] = abs(df['Actual'] - df['Predicted'])
+
+        actual = np.array(df['Actual']).reshape(6, 6)
+        predicted = np.array(df['Predicted']).reshape(6, 6)
+        abs_error = np.array(df['absolute_error']).reshape(6, 6)
+
+        # set the color scale to have the same range for all plots
+        if file == "burn_6.csv":
+            color_matrix = abs_error
+
+        draw_grid(predicted, abs_error, color_matrix, filename=f'{base_path}{file.replace(".csv", "")}.pdf')
+
+if __name__ == '__main__':
+    main()

train-results/predicted-data-vis/burn_11.csv

@@ -0,0 +1,37 @@
+Actual,Predicted
+33.00999999999999,2.441566
+0.13,1.287153
+30.04,9.662184
+0.04,1.4504021
+0.0,0.57912457
+0.0,1.5314125
+0.02,0.33348063
+4.25,4.697654
+0.18,1.2343149
+0.04,1.4911749
+0.0,0.50423425
+0.01,0.71346486
+0.0,2.164581
+0.0,1.8728352
+1.0,0.56869173
+0.19,1.6881206
+0.0,1.2043515
+0.0,0.57622427
+0.46,0.62170124
+0.08,0.12295965
+0.67,0.73713386
+0.81,1.0150232
+0.28,0.786072
+0.03,3.3052664
+0.9599999999999999,1.2515504
+0.1,0.37647727
+0.12,0.77600485
+0.04,0.7376241
+0.39,1.1295924
+0.0,1.2982188
+0.04,1.1077411
+0.67,2.7119973
+0.03,0.8327027
+0.09,1.1176233
+12.039999999999997,7.7226386
+0.02,1.3766172

train-results/predicted-data-vis/burn_16.csv

@@ -0,0 +1,37 @@
+Actual,Predicted
+0.03,0.09476566
+0.0,0.14770453
+0.0,0.028910527
+0.0,0.16033459
+0.0,0.3812819
+0.0,0.029445633
+0.0,0.12857425
+0.0,0.34443325
+0.0,0.5202863
+0.0,0.30236974
+0.0,0.15392835
+0.0,0.011927954
+0.0,0.21076563
+0.0,0.12555586
+0.0,0.22733401
+0.0,0.31207517
+0.0,0.086362086
+0.0,0.04986767
+0.0,0.39596963
+1.2,0.2759399
+0.0,0.09705978
+0.03,0.21685342
+0.0,0.09518449
+0.0,0.08952272
+0.0,0.079145834
+0.0,0.041401234
+0.0,0.02609378
+0.0,0.33272585
+0.0,0.17639832
+0.0,0.09980161
+0.0,0.2981202
+0.0,0.035559975
+0.0,0.01701772
+0.0,0.21755119
+0.0,0.07301146
+0.0,0.08434749

train-results/predicted-data-vis/burn_21.csv

@@ -0,0 +1,37 @@
+Actual,Predicted
+0.0,0.25413516
+0.0,0.089969926
+0.0,0.10162594
+0.0,0.16334783
+0.0,0.100038044
+0.0,0.058781154
+0.0,0.15500419
+0.0,0.21634062
+0.0,0.40450987
+0.0,0.074236505
+0.0,0.06423112
+0.0,0.009109741
+0.0,0.11614813
+0.0,0.16305806
+0.0,0.32475373
+0.0,0.119550474
+0.0,0.048834827
+0.0,0.019283405
+0.0,0.4140485
+0.0,0.21384089
+0.0,0.13307574
+0.0,0.34860623
+0.0,0.15205687
+0.0,0.029667513
+0.0,0.1426917
+0.0,0.12611139
+0.0,0.2047455
+0.0,0.23884013
+0.0,0.06072749
+0.0,0.0063803126
+0.02,0.2802802
+0.0,0.6642211
+0.0,0.07595109
+0.0,0.20884931
+0.0,0.08108409
+0.0,0.021940004

train-results/predicted-data-vis/burn_6.csv

@@ -0,0 +1,37 @@
+Actual,Predicted
+0.20000000000000004,0.12333784
+6.01,1.7319317
+0.22,0.69740134
+0.01,0.14848521
+0.0,0.036329575
+0.0,0.07671892
+0.0,0.02537092
+0.8400000000000001,0.20883751
+7.279999999999999,0.46068564
+0.0,0.02859186
+0.0,0.057903297
+0.0,0.042104594
+0.02,0.06016002
+0.0,0.47121012
+0.02,0.14980581
+0.03,0.08425264
+0.0,0.052084606
+0.0,0.040004183
+0.22,0.17706552
+0.0,0.13211879
+0.0,0.16416736
+0.63,0.2605115
+0.02,0.013563856
+0.01,0.038166553
+3.12,0.8644007
+0.02,0.3797151
+0.04,0.15504032
+4.4,1.4288893
+0.01,0.0021220404
+0.0,0.034538448
+0.0,0.32518405
+298.0400000000001,27.212484
+0.02,0.5952123
+0.01,0.44371238
+0.0,0.22072323
+0.0,0.05316649