<jupyter_start><jupyter_text>'sWeights': [(0.1, 0.01, 0.001, 1.0),  (0.7894, 0.6326, 0.5993, 0.8334),  (0.0017262676501632068,   0.008305419917138446,   0.01920234360696631,   0.0009486832980505138)],<jupyter_code>train_sizes_nn = 8250000*np.array((0.1, 0.01, 0.001, 1.0))
with open("Higgs_scores.pkl", 'rb') as higgs_scores_file:
    train_sizes_cb, results_cb = pickle.load(higgs_scores_file)
with open("Higgs_scores_cwola.pkl", "rb") as higgs_scores_file:
    _, results_cwola_cb = pickle.load(higgs_scores_file)
results_cb.update(results_cwola_cb)
for train_sizes, results, filename in (
    (train_sizes_nn, results_nn, "Higgs_NN.pdf"),
    (train_sizes_cb, results_cb, "Higgs_cb.pdf")):
    fig, ax = plt.subplots(figsize=(3.5, 3.5))
    for method_name, method_result in results.items():
        order = np.argsort(train_sizes)
        ax.errorbar(train_sizes[order],
                    np.array(method_result[0])[order],
                    yerr=np.array(method_result[1])[order], label=method_name)
    ax.legend()
    ax.set_xlabel("Train size")
    ax.set_ylabel("ROC AUC")
    ax.set_xscale("log")
    plt.tight_layout()
    fig.savefig(filename, bbox="tight");<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '482616', 'license': 'Apache-2.0', 'n_tokens_mistral': 589, 'n_tokens_neox': 495, 'n_words': 68, 'path': 'Plot Higgs.ipynb', 'repo_name': 'yandexdataschool/ML-sWeights-experiments', 'size': '1204'}