def test_matrix_factorization():
print('mf test.............')
since = time.time()
# 潜在因子数
K = 2
# m x n のレーティング行列
R = np.array([[5, 3, 0, 1], [4, 0, 0, 1], [1, 1, 0, 5], [1, 0, 0, 4],
[0, 1, 5, 4]])
m, n = R.shape
# m x K のユーザ行列P
P = np.random.rand(m, K)
# n x K のアイテム行列Q
Q = np.random.rand(n, K)
P_new, Q_new = mf.run(R,
P,
Q,
K,
steps=5000,
alpha=0.0002,
beta=0.2,
threshold=0.001)
evaluate(P, Q, R, P_new, Q_new)
assert False
def test_movielens(load_dataset):
print('mf test.............')
# Load dataset
df_rating = load_dataset
# print(df_rating.head())
m = max(np.unique(df_rating.userId))
n = max(np.unique(df_rating.movieId))
# m = len(np.unique(df_rating.userId))
# n = len(np.unique(df_rating.movieId))
print('m x n: {} x {}'.format(m, n))
R = np.zeros([m, n])
print(type(df_rating.userId[0]))
print(type(df_rating.movieId[0]))
print(R[df_rating.userId[0]][df_rating.movieId[0]])
print(np.unique(df_rating.rating))
print(sum(df_rating.rating.isnull()))
for i, s in df_rating.iterrows():
if s.rating == 0: continue
u_id = int(s.userId)
m_id = int(s.movieId)
R[u_id - 1][m_id - 1] = s.rating
print(R)
# 潜在因子数
K = 2
# m x n のレーティング行列
m, n = df_rating[['userId', 'movieId']].shape
print(m, n)
R = np.array([[5, 3, 0, 1], [4, 0, 0, 1], [1, 1, 0, 5], [1, 0, 0, 4],
[0, 1, 5, 4]])
m, n = R.shape
# m x K のユーザ行列P
P = np.random.rand(m, K)
# n x K のアイテム行列Q
Q = np.random.rand(n, K)
P_new, Q_new = mf.run(R,
P,
Q,
K,
steps=5000,
alpha=0.0002,
beta=0.2,
threshold=0.001)
evaluate(P, Q, R, P_new, Q_new)
assert False