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48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import statistics
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import matplotlib.pyplot as plt
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def read_results():
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thresholds = []
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precision_m1_means = []
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recall_m1_means = []
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precision_m2_means = []
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recall_m2_means = []
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with open("./method1/result/result_all.txt") as result_m1, open("./method2/result/result_all.txt") as result_m2:
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lines_m1 = result_m1.readlines()
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for line in lines_m1:
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line_split = line.split(sep=" ")
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if line_split[0] == "#" or line_split[0] == "#\n":
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continue
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thresholds.append(int(line_split[0]))
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precision_m1_str = line_split[1::2]
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precision_m1_str.remove("\n")
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precision_m1 = [float(i) for i in precision_m1_str]
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precision_m1_means.append(statistics.mean(precision_m1))
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recall_m1 = [float(i) for i in line_split[2::2]]
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recall_m1_means.append(statistics.mean(recall_m1))
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lines_m2 = result_m2.readlines()
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for line in lines_m2:
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line_split = line.split(sep=" ")
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if line_split[0] == "#" or line_split[0] == "#\n":
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continue
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precision_m2_str = line_split[1::2]
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precision_m2_str.remove("\n")
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precision_m2 = [float(i) for i in precision_m2_str]
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precision_m2_means.append(statistics.mean(precision_m2))
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recall_m2 = [float(i) for i in line_split[2::2]]
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recall_m2_means.append(statistics.mean(recall_m2))
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return thresholds, precision_m1_means, precision_m2_means, recall_m1_means, recall_m2_means
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def compare_results():
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means = read_results()
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plt.plot(means[3], means[1])
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plt.plot(means[4], means[2])
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plt.show()
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if __name__ == "__main__":
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compare_results()
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