def _evaluate(self, eval_dataset, num_recommendations, metrics, batch_size=1, num_users=None):
if self.model is None:
raise Exception('Model not initialized')
self.model.eval()
recommender = InferenceRecommender(self, num_recommendations)
evaluator = RecommenderEvaluator(recommender, metrics)
results = evaluator.evaluate(eval_dataset, batch_size=batch_size, num_users=num_users)
return results
'user_col': 'uid',
'item_col': 'sid',
'inter_col': 'watched',
}
method = 'inference'
model_file = model_dir + 'bce_ns_d_0.0_n_0.5_200_epoch_100.model'
index_file = model_dir + 'bce_ns_d_0.0_n_0.5_200_epoch_100.model.index'
num_recommendations = 100
if method == 'inference':
model = DynamicAutoencoder()
recoder = Recoder(model)
recoder.init_from_model_file(model_file)
recommender = InferenceRecommender(recoder, num_recommendations)
elif method == 'similarity':
embeddings_index = AnnoyEmbeddingsIndex()
embeddings_index.load(index_file=index_file)
cache_embeddings_index = MemCacheEmbeddingsIndex(embeddings_index)
recommender = SimilarityRecommender(cache_embeddings_index,
num_recommendations,
scale=1,
n=50)
train_df = pd.read_csv(data_dir + 'train.csv')
val_te_df = pd.read_csv(data_dir + 'test_te.csv')
val_tr_df = pd.read_csv(data_dir + 'test_tr.csv')
train_matrix, item_id_map, _ = dataframe_to_csr_matrix(train_df,
**common_params)