<jupyter_start><jupyter_text>Performs feature engineering on both the train and test set. **All transforms are fit on the training set, exactly like with Standard Scaler — the test features never "see" their corresponding targets.**<jupyter_code>auctions_v2, auctions_test_v2 = mcnultymod.feature_eng([auctions, auctions_test])
auctions_v2.to_pickle("../data/auctionsclosed_v2.pkl")
auctions_test_v2.to_pickle("../data/auctionsclosed_test_v2.pkl")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10062893081761007, 'fraction_numerical': 0.012578616352201259, 'mean_word_length': 8.018867924528301, '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': '506150', 'license': 'MIT', 'n_tokens_mistral': 164, 'n_tokens_neox': 162, 'n_words': 37, 'path': '3-detroit-auctions/code/10-mcnulty-feature-engineering-final.ipynb', 'repo_name': 'davidluther/Metis-Projects', 'size': '477'}