kopia lustrzana https://github.com/thinkst/zippy
92 wiersze
2.9 KiB
Python
92 wiersze
2.9 KiB
Python
#!/usr/bin/env python3
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.metrics import roc_curve, auc
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import re
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from junitparser import JUnitXml
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MODEL = 'zippy-zlib'
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PRESETS = range(0, 10)
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SKIPCASES = ['gpt2', 'gpt3']
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MAX_PER_CASE = 500
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plt.figure()
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for preset in PRESETS:
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xml = JUnitXml.fromfile(f'{MODEL}-{preset}.xml')
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cases = []
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for suite in xml:
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for case in suite:
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cases.append(case)
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truths = []
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scores = []
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per_case = {}
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fails_per_case = {}
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for c in cases:
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if c.name is None:
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print("ERROR")
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continue
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cname = re.sub('\[.*$', '', c.name)
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if any(sub in cname for sub in SKIPCASES):
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continue
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if cname in per_case.keys():
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per_case[cname] += 1
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else:
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per_case[cname] = 1
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fails_per_case[cname] = 0
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if per_case[cname] > MAX_PER_CASE:
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continue
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try:
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score = float(c._elem.getchildren()[0].getchildren()[0].values()[1])
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except:
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continue
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if 'human' in c.name:
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truths.append(1)
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if c.is_passed:
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scores.append(score)
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else:
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fails_per_case[cname] += 1
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scores.append(score * -1.0)
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else: # AI
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truths.append(-1.0)
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if c.is_passed:
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scores.append(score * -1.0)
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else:
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fails_per_case[cname] += 1
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scores.append(score)
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y_true = np.array(truths)
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y_scores = np.array(scores)
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print("Failures per case for " + MODEL + ' ' + str(preset))
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#print(fails_per_case)
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tf = 0
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for k in fails_per_case.keys():
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tf += fails_per_case[k]
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print('Total fails: ' + str(tf))
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tp = len(cases) - tf
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# Compute the false positive rate (FPR), true positive rate (TPR), and threshold values
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fpr, tpr, thresholds = roc_curve(y_true, y_scores)
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gmeans = np.sqrt(tpr * (1-fpr))
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ix = np.argmax(gmeans)
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print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))
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#print(thresholds)
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# calculate the g-mean for each threshold
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# locate the index of the largest g-mean
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# Calculate the area under the ROC curve (AUC)
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roc_auc = auc(fpr, tpr)
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# Plot the ROC curve
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plt.plot(fpr, tpr, lw=2, label=f'{MODEL.split("-")[1].capitalize()}-{preset}: ROC curve (%Acc = {tp/len(cases):0.2f}; AUC = {roc_auc:0.2f})')
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plt.scatter(fpr[ix], tpr[ix], marker='o', color='black')#, label=model.capitalize() + ': Best @ threshold = %0.2f' % thresholds[ix])
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plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label="Random classifier")
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plt.xlim([0.0, 1.0])
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plt.ylim([0.0, 1.05])
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plt.xlabel('False Positive Rate')
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plt.ylabel('True Positive Rate')
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plt.title('Receiver Operating Characteristic for LLM detection')
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plt.legend(loc="lower right")
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plt.savefig('preset_ai_detect_roc.png') |