wehub-resource-sync
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import collections
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import json
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import matplotlib.pyplot as plt
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import numpy as np
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import argparse
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def plot_bar_chart(data, file_path):
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indices = list(range(len(data)))
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# Create the bar chart
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plt.figure(figsize=(4, 4))
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bars = plt.bar(indices, data)
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# Customize the chart
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plt.title('Bar Chart of Array Values')
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plt.xlabel('Frequency', fontsize=16)
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plt.ylabel('Amount Difference between Correct and Incorrect Prediction', fontsize=12)
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# Add value labels on top of each bar
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# for i, bar in enumerate(bars):
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# height = bar.get_height()
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# plt.text(bar.get_x() + bar.get_width() / 2., height,
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# f'{data[i]}',
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# ha='center', va='bottom',
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# fontsize=12)
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# Color positive and negative bars differently
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for i, value in enumerate(data):
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if value >= 0:
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bars[i].set_color('blue')
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else:
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bars[i].set_color('red')
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plt.tick_params(axis='x', labelsize=15) # Change the font size of the x-axis scale
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plt.tick_params(axis='y', labelsize=15) # Change the font size of the y-axis scale
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plt.savefig(file_path, bbox_inches='tight', dpi=800, format='png')
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def draw_line_plot(data, file_path):
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# Compute histogram data
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x = np.arange(0.1, 1.1, 0.1)
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# Create the line plot
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plt.figure(figsize=(4, 4))
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plt.plot(x, data, color='#658873', marker='o', linestyle='-')
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# Change the size of the font in the legent
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# plt.legend(prop={"size": 15})
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plt.tick_params(axis='x', labelsize=15) # Change the font size of the x-axis scale
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plt.tick_params(axis='y', labelsize=15) # Change the font size of the y-axis scale
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plt.grid(True)
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# Add labels and title
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# plt.xlabel('Top-1 Averaged Frequency', fontsize=16)
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plt.ylabel('Ratio of Correct Predictions', fontsize=16)
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# plt.legend(loc='upper left')
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plt.savefig(file_path, bbox_inches='tight', dpi=800, format='png')
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_file", type=str)
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parser.add_argument("--output_file", type=str)
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parser.add_argument("--n", type=int, default=128)
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args = parser.parse_args()
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data = json.load(open(args.input_file))
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freq_pos = collections.Counter()
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freq_neg = collections.Counter()
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freq = collections.Counter()
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for item in data:
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if item["sc_res"]:
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freq_pos[item["sc_freq"]] += 1
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else:
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freq_neg[item["sc_freq"]] += 1
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freq[item["sc_freq"]] += 1
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diffs = []
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for i in range(args.n + 1):
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tmp = 0
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if i in freq_pos:
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tmp += freq_pos[i]
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if i in freq_neg:
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tmp -= freq_neg[i]
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diffs.append(tmp)
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plot_bar_chart(diffs, args.output_file)
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ps = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
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reversed_acc_pos = collections.Counter()
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reversed_acc = collections.Counter()
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for p in ps:
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for key, value in freq_pos.items():
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if key / args.n >= p:
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reversed_acc_pos[p] += value
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for key, value in freq.items():
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if key / args.n >= p:
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reversed_acc[p] += value
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p_ratio = {p: reversed_acc_pos[p] / reversed_acc[p] for p in ps}
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draw_line_plot(list(p_ratio.values()), args.output_file.replace(".png", "_line.png"))
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if __name__ == "__main__":
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main()
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