def test_model_nrms(mind_resource_path): train_news_file = os.path.join(mind_resource_path, "train", r"news.tsv") train_behaviors_file = os.path.join(mind_resource_path, "train", r"behaviors.tsv") valid_news_file = os.path.join(mind_resource_path, "valid", r"news.tsv") valid_behaviors_file = os.path.join(mind_resource_path, "valid", r"behaviors.tsv") wordEmb_file = os.path.join(mind_resource_path, "utils", "embedding.npy") userDict_file = os.path.join(mind_resource_path, "utils", "uid2index.pkl") wordDict_file = os.path.join(mind_resource_path, "utils", "word_dict.pkl") yaml_file = os.path.join(mind_resource_path, "utils", r"nrms.yaml") if not os.path.exists(train_news_file): download_deeprec_resources( r"https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(mind_resource_path, "train"), "MINDdemo_train.zip", ) if not os.path.exists(valid_news_file): download_deeprec_resources( r"https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(mind_resource_path, "valid"), "MINDdemo_dev.zip", ) if not os.path.exists(yaml_file): download_deeprec_resources( r"https://recodatasets.z20.web.core.windows.net/newsrec/", os.path.join(mind_resource_path, "utils"), "MINDdemo_utils.zip", ) hparams = prepare_hparams( yaml_file, wordEmb_file=wordEmb_file, wordDict_file=wordDict_file, userDict_file=userDict_file, epochs=1, ) assert hparams is not None iterator = MINDIterator model = NRMSModel(hparams, iterator) assert model.run_eval(valid_news_file, valid_behaviors_file) is not None assert isinstance( model.fit(train_news_file, train_behaviors_file, valid_news_file, valid_behaviors_file), BaseModel, )
def test_model_nrms(tmp): yaml_file = os.path.join(tmp, "nrms.yaml") train_file = os.path.join(tmp, "train.txt") valid_file = os.path.join(tmp, "test.txt") wordEmb_file = os.path.join(tmp, "embedding.npy") if not os.path.exists(yaml_file): download_deeprec_resources( "https://recodatasets.blob.core.windows.net/newsrec/", tmp, "nrms.zip") hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, epochs=1) assert hparams is not None iterator = NewsIterator model = NRMSModel(hparams, iterator) assert model.run_eval(valid_file) is not None assert isinstance(model.fit(train_file, valid_file), BaseModel)
download_deeprec_resources(mind_url, os.path.join(data_path, 'train'), mind_train_dataset) if not os.path.exists(valid_news_file): download_deeprec_resources(mind_url, \ os.path.join(data_path, 'valid'), mind_dev_dataset) if not os.path.exists(yaml_file): download_deeprec_resources(r'https://recodatasets.blob.core.windows.net/newsrec/', \ os.path.join(data_path, 'utils'), mind_utils) hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, \ wordDict_file=wordDict_file, userDict_file=userDict_file, \ epochs=epochs, show_step=10) print("[NRMS] Config,", hparams) iterator = MINDIterator model = NRMSModel(hparams, iterator, seed=seed) print("[NRMS] First run:", model.run_eval(valid_news_file, fast_valid_behaviors_file)) model.fit(train_news_file, train_behaviors_file, valid_news_file, fast_valid_behaviors_file, model_save_path=model_dir) # res_syn = model.run_eval(valid_news_file, valid_behaviors_file) # print(res_syn)
if model_type == 'nrms': iterator = MINDIterator model = NRMSModel(hparams, iterator, seed=seed) elif model_type == 'naml': iterator = MINDAllIterator model = NAMLModel(hparams, iterator, seed=seed) elif model_type == 'npa': iterator = MINDIterator model = NPAModel(hparams, iterator, seed=seed) elif model_type == 'nrmma': iterator = MINDAllIterator model = NRMMAModel(hparams, iterator, seed=seed) else: raise NotImplementedError(f"{exp_name} is not implemented") # In[8]: model_path = os.path.join(exp_path, model_type) model_name = model_type + '_ckpt' model.fit(train_news_file, train_behaviors_file, valid_news_file, valid_behaviors_file, model_path=model_path, model_name=model_name) res_syn = model.run_eval(valid_news_file, valid_behaviors_file) logging.info(res_syn) # ## Reference # \[1\] Wu et al. "Neural News Recommendation with Multi-Head Self-Attention." in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)<br> # \[2\] Wu, Fangzhao, et al. "MIND: A Large-scale Dataset for News Recommendation" Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://msnews.github.io/competition.html <br> # \[3\] GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/
os.path.join(data_root, 'utils'), mind_utils) hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, wordDict_file=wordDict_file, userDict_file=userDict_file, batch_size=batch_size, epochs=epochs, show_step=10) logger.debug(f"hparams: {hparams}") iterator = MINDIterator model = NRMSModel(hparams, iterator, seed=seed) logger.info(model.run_eval(valid_news_file, valid_behaviors_file)) model.fit(train_news_file, train_behaviors_file, valid_news_file, valid_behaviors_file) res_syn = model.run_eval(valid_news_file, valid_behaviors_file) logger.debug(f"res_syn: {res_syn}") sb.glue("res_syn", res_syn) model_path = os.path.join(BASE_DIR, "ckpt") os.makedirs(model_path, exist_ok=True) model.model.save_weights(os.path.join(model_path, "nrms_ckpt")) group_impr_indexes, group_labels, group_preds = model.run_fast_eval(