def NMF_Mat(df):
model_nmf = NMF(n_components=10, init='random', random_state=0)
m = model_nmf.fit_transform(df)
h = model_nmf.components_
nmf_mat = m @ h
return nmf_mat
size_user = max(user)
size_movie = max(movie)
with open(path + 'movies.csv') as csvfile:
reader = csv.DictReader(csvfile)
mvid_genr = dict([(int(row['movieId']), row['genres']) for row in reader])
#create ratings matrix
R = np.zeros((size_user, size_movie))
for rate in rate_list:
R[rate[0] - 1][rate[1] - 1] = rate[2]
#NMF
R_s = sparse.csr_matrix(R)
nmf = NMF(n_components=20, init='random', random_state=0)
U = nmf.fit_transform(R)
V = (nmf.components_).T
top_10_list = []
for i in range(20):
temp = np.argsort(V[:, i])
temp += 1
reverse = temp[::-1]
top_10_list.append(reverse[:10])
top_10_genr_list = []
for id_ls in top_10_list:
sub_list = []
for id in id_ls:
sub_list.append(mvid_genr[id])
top_10_genr_list.append(sub_list)
