51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
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import pickle
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from pathlib import Path
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import matplotlib.pyplot as plt
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from sklearn.metrics import auc
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results_path = Path(
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"/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/log/DeepSAD/subter_selective"
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)
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# Paths to your result files
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deepsad_results_file = results_path / "results.pkl"
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isoforest_results_file = results_path / "results_isoforest.pkl"
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# Load DeepSAD precision-recall data
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with deepsad_results_file.open("rb") as f:
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data = pickle.load(f)
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deep_precision, deep_recall, _ = data["test_prc"]
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# Compute AP for DeepSAD
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deep_ap = auc(deep_recall, deep_precision)
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# Load IsoForest precision-recall data
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with isoforest_results_file.open("rb") as f:
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data = pickle.load(f)
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iso_precision, iso_recall, _ = data["test_prc"]
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# Compute AP for IsoForest
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iso_ap = auc(iso_recall, iso_precision)
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# Create plot
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plt.figure(figsize=(8, 6))
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# Plot DeepSAD PR curve
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plt.plot(deep_recall, deep_precision, color="b", label=f"DeepSAD (AP = {deep_ap:.2f})")
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# Plot IsoForest PR curve
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plt.plot(iso_recall, iso_precision, color="r", label=f"IsoForest (AP = {iso_ap:.2f})")
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# Labels
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plt.xlabel("Recall")
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plt.ylabel("Precision")
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plt.title("Precision-Recall Curve (Single Run)")
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# Add legend
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plt.legend(loc="upper right")
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# Save and/or show plot
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plt.savefig("pr_curve_single_run.png")
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plt.show()
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