推荐系统使用SciKit-Learn的cross_validate，缺少1个必需的位置参数：'y_true'

Question

我正在尝试为当地报纸（作为学校项目）创建推荐系统，但是当我尝试使用model_selection库中的cross_validate函数时，我遇到了麻烦。

我正在尝试使用SVD并获得f1分数。但我有点困惑。所以这是无监督学习，我没有测试集，所以我想使用KFolding进行交叉验证。我相信这个折叠的数量由cross_validate函数中的“cv”参数表示。它是否正确？

当我尝试运行代码时出现问题，因为我得到以下堆栈跟踪：https://hastebin.com/kidoqaquci.tex

我没有将任何内容传递给cross_validate函数的“y”参数，但这是错误的吗？这不是测试集应该去的地方吗？正如我所说，我没有任何测试集，因为这是无人监督的。我在这里看了3.1.1.1章中的例子：http://scikit-learn.org/stable/modules/cross_validation.html

看起来他们正在传递cross_validate函数中数据集的“目标”。但为什么他们既传递目标集又传递cv参数？ cv值是否大于1表示是否应该使用kfolding并且将遗漏的折叠用作目标（测试）集？

还是我完全误解了什么？为什么我在堆栈跟踪中得到“缺少参数”错误？

这是失败的代码：

from sklearn.model_selection import cross_val_score as cv
from sklearn.decomposition.truncated_svd import TruncatedSVD
import pandas as pd

# keywords_data_filename = 'keywords_data.txt'
active_data_filename = 'active_time_data.txt'

header = ['user_id', 'item_id', 'rating']
# keywords_data = pd.read_csv(keywords_data_filename, sep='*', names=header, engine='python')
active_time_data = pd.read_csv(active_data_filename, sep='*', names=header, engine='python')


# Number of users in current set
print('Number of unique users in current data-set', active_time_data.user_id.unique().shape[0])
print('Number of unique articles in current data-set', active_time_data.item_id.unique().shape[0])

# SVD allows us to look at our input matrix as a product of three smaller matrices; U, Z and V.
# In short this will help us discover concepts from the original input matrix,
# (subsets of users that like subsets of items)
# Note that use of SVD is not strictly restricted to user-item matrices
# https://www.youtube.com/watch?v=P5mlg91as1c

algorithm = TruncatedSVD()

# Finally we run our cross validation in n folds, where n is denoted by the cv parameter.
# Verbose can be adjusted by an integer to determine level of verbosity.
# We pass in our SVD algorithm as the estimator used to fit the data.
# X is our data set that we want to fit.
# Since our estimator (The SVD algorithm), We must either define our own estimator, or we can simply define how it
# score the fitting.
# Since we currently evaluate the enjoyment of our users per article highly binary, (Please see the rate_article fn in
# the filter script), we can easily decide our precision and recall based on whether or not our prediction exactly
# matches the binary rating field in the test set.
# This, the F1 scoring metric seems an intuitive choice for measuring our success, as it provides a balanced score
# based on the two.

cv(estimator=algorithm, X=active_time_data, scoring='f1', cv=5, verbose=True)