def evaluate(model, test_dataset, N):
"""
评估模型
:param N: 推荐的商品个数
:param K: 搜索邻近的用户个数
:return: 精确率(precision), 召回率(recall)
"""
recommens = model.recommend_users(test_dataset.keys(), N=N)
recall = metric.recall(recommends=recommens, tests=test_dataset)
precision = metric.precision(recommends=recommens, tests=test_dataset)
return precision, recall
def evaluate(model, train_dataset, test_dataset, N):
"""
评估模型
:param N: 推荐的商品个数
:param K: 搜索邻近的用户个数
:return: 精确率(precision), 召回率(recall)
"""
recommens = model.recommend_users(test_dataset.keys(), N=N)
all_items = lastfm_reader.get_all_items(train_dataset, test_dataset)
recall = metric.recall(recommends=recommens, tests=test_dataset)
precision = metric.precision(recommends=recommens, tests=test_dataset)
coverage = metric.coverage(recommends=recommens, all_items=all_items)
return precision, recall, coverage
def evaluate(model, test_dataset, N, K=None):
"""
评估模型
:param N: 推荐的商品个数
:param K: 搜索邻近的用户个数
:return: 精确率(precision), 召回率(recall)
"""
test_dataset = delicious_reader.get_all_items(test_dataset)
recommens = model.recommend_users(test_dataset.keys(), N=N, K=K)
recall = metric.recall(recommends=recommens, tests=test_dataset)
precision = metric.precision(recommends=recommens, tests=test_dataset)
return precision, recall
def evaluate(model, train_dataset, test_dataset, N, K):
"""
评估模型
:param N: 推荐的商品个数
:param K: 搜索邻近的用户个数
:return: 精确率(precision), 召回率(recall), 覆盖率(coverage)
"""
recommens = model.recommend_users(test_dataset.keys(), N=N, K=K)
all_items = movielen_reader.get_all_items(train_dataset, test_dataset)
item_popularity = train_popularity(train_dataset)
recall = metric.recall(recommends=recommens, tests=test_dataset)
precision = metric.precision(recommends=recommens, tests=test_dataset)
coverage = metric.coverage(recommends=recommens, all_items=all_items)
popularity = metric.popularity(item_popular=item_popularity,
recommends=recommens)
return precision, recall, coverage, popularity
