<jupyter_start><jupyter_text>Check MAP@30<jupyter_code>scores = []
for js in tqdm((json.loads(s) for s in open(get_shard_path(15)))):
    recommended_items = top_products[:30]
    gt_items = js["target"][0]["product_ids"]
    ap = normalized_average_precision(gt_items, recommended_items)
    scores.append(ap)
np.mean(scores)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14325842696629212, 'fraction_numerical': 0.019662921348314606, 'mean_word_length': 7.3023255813953485, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '386897', 'license': 'MIT', 'n_tokens_mistral': 144, 'n_tokens_neox': 135, 'n_words': 19, 'path': 'sandbox/dummy_baseline.ipynb', 'repo_name': 'TimurDzhumakaev/retailhero-recommender-workspace', 'size': '356'}