<jupyter_start><jupyter_text>check that hyperparams are correct<jupyter_code>results.drop('results', axis=1)<jupyter_output><empty_output><jupyter_text>visualize bases<jupyter_code>def viz_basis(D, R=5, C=6):
    i = 0
    vmin = np.min(D)
    vmax = np.max(D)
    plt.figure(figsize=(C * 3, R * 3), dpi=200)
    for r in range(R):
        for c in range(C):
            plt.subplot(R, C, i + 1)
            plt.plot(D[i]) #, vmin=vmin, vmax=vmax, cmap='viridis')
            plt.axis('off')
            i += 1
    plt.tight_layout()
    plt.show()
fname = 'lda_30_uniform.pkl'
D = pkl.load(open(fname, 'rb')).components_
viz_basis(D)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16265060240963855, 'fraction_numerical': 0.01957831325301205, 'mean_word_length': 3.3181818181818183, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '212970', 'license': 'MIT', 'n_tokens_mistral': 291, 'n_tokens_neox': 275, 'n_words': 48, 'path': 'ex_simulation/02_sim_simple_analyze.ipynb', 'repo_name': 'csinva/transformation-importance', 'size': '664'}