<jupyter_start><jupyter_text>Utility Matrix<jupyter_code>U_df = movies_df.copy()
U_df
U_df_m = um.mean_filled_utilmat(U_df)
U_df_m<jupyter_output><empty_output><jupyter_text>Build Models<jupyter_code>from sklearn.neural_network import MLPClassifier, MLPRegressor
mlp1 = MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=300)
U = U_df.copy()
model_object = mlp1

models_item = sm.initialize_models_itemwise(model_object, U, suffix='')
models_item
U_imputed, models = sm.train_model_svd(U, mlp1, d=2, return_models=True)
U_df
U_imputed
U_imputed.clip(1,5)
U_imputed.clip(1,5).round()
U_df_m
models<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11464968152866242, 'fraction_numerical': 0.027070063694267517, 'mean_word_length': 10.648148148148149, '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': '308039', 'license': 'MIT', 'n_tokens_mistral': 287, 'n_tokens_neox': 273, 'n_words': 37, 'path': 'eloi/demo_notebook_svd_model-old.ipynb', 'repo_name': 'phdinds-aim/resype', 'size': '628'}