<jupyter_start><jupyter_text>Rating models<jupyter_code>import matplotlib.pyplot as plt

from movie_ratings import config
from taskchain import MultiChain<jupyter_output><empty_output><jupyter_text>All features<jupyter_code>features_dir = 'all_features'
chains = MultiChain.from_dir(config.TASKS_DIR, config.CONFIGS_DIR / 'rating_model' / features_dir, global_vars=config)
chains
for name, chain in chains.items():
    print(f'{name:>20}: {chain.test_metrics.value["RMSE"]:.3f} {chain.test_metrics.value["MAE"]:.3f}')<jupyter_output>baseline: 1.136 0.873
   linear_regression: 0.931 0.693
                  nn: 0.953 0.725
tf_linear_regression: 0.914 0.689<jupyter_text>Basic features<jupyter_code>features_dir = 'basic_features'
chains = MultiChain.from_dir(config.TASKS_DIR, config.CONFIGS_DIR / 'rating_model' / features_dir, global_vars=config)
print(chains)
for name, chain in chains.items():
    print(f'{name:>20}: {chain.test_metrics.value["RMSE"]:.3f} {chain.test_metrics.value["MAE"]:.3f}')<jupyter_output>baseline: 1.136 0.873
   linear_regression: 0.972 0.722
{'fraction_non_alphanumeric': 0.13352007469654528, 'fraction_numerical': 0.05228758169934641, 'mean_word_length': 6.602836879432624, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '63633', 'license': 'MIT', 'n_tokens_mistral': 447, 'n_tokens_neox': 404, 'n_words': 63, 'path': 'example/scripts/rating_model.ipynb', 'repo_name': 'flowerchecker/taskchain', 'size': '1071'}