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
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)
val_tr_matrix, _, user_id_map = dataframe_to_csr_matrix(
val_tr_df, item_id_map=item_id_map, **common_params)
val_te_matrix, _, _ = dataframe_to_csr_matrix(val_te_df,
item_id_map=item_id_map,
user_id_map=user_id_map,
**common_params)
val_tr_dataset = RecommendationDataset(val_tr_matrix, val_te_matrix)
metrics = [Recall(k=20), Recall(k=50), NDCG(k=100)]
evaluator = RecommenderEvaluator(recommender, metrics)
metrics_accumulated = evaluator.evaluate(val_tr_dataset, batch_size=500)
for metric in metrics_accumulated:
log.info('{}: {}'.format(metric, np.mean(metrics_accumulated[metric])))