如何在scipy中创建评级csr_matrix？

Question

我有这种格式的csv文件:

userId  movieId rating  timestamp
1     31      2.5   1260759144
2     10      4     835355493
3     1197    5     1298932770
4     10      4     949810645

我想构造一个稀疏矩阵,其行为userId,列为movieID.我已将所有数据存储为名为"column"的字典,其中column ['user']包含用户ID,column ['movie']包含电影ID,列['rating']的评级如下:

f = open('ratings.csv','rb')
reader = csv.reader(f)
headers = ['user','movie','rating','timestamp']
column = {}
for h in headers:
    column[h] = []
for row in reader:
    for h, v in zip(headers, row):
        column[h].append(float(v))

当我将稀疏矩阵函数称为:

mat = scipy.sparse.csr_matrix((column['rating'],(column['user'],column['movie'])))

我得到"TypeError:无效的形状"

请帮忙

Answer 1

scipy.sparse.csr_matrix([column['rating'],column['user'],column['movie']])

您有一个由 1xn 维列表和 2xn 维列表组成的元组，这是行不通的。

PS：为了读取数据，您应该尝试 Pandas :-) ( http://pandas.pydata.org/pandas-docs/stable/ generated/pandas.read_csv.html )。最小的例子：

import pandas as pd

# Setup a dataframe from the CSV and make it sparse
df = pd.read_csv('ratings.csv')
df = df.to_sparse(fill_value=0)
print(df.head())