def step5_surprise2():
data = surprise.Dataset.load_builtin('ml-100k')
# user item rate
# print(data)
# print(data.raw_ratings)
# 모든 컬럼에 있는 데이터를 가져와야함.
# df = pd.DataFrame(data.raw_ratings, columns=['user', 'item', 'rate', 'id'])
option1 = {'name': 'msd'}
option2 = {'name': 'cosine'}
option3 = {'name': 'pearson'}
# 추천 목록을 만들기 위한 객체 생성
# 본질을 흐리지 말것. 애매하게 하는게 제일 나쁘다.
# 학습용 데이터를 생성한다.
trainset = data.build_full_trainset()
algo = surprise.KNNBasic(sim_options=option3)
print('학습시작')
algo.fit(trainset)
# 추천 목록을 가져 온다.
result = algo.get_neighbors(196, k=3) # 추천 영화 목록
print('result type:',type(result))
for r1 in result:
print(r1)
def step5_surprise2():
# 사용할 영화 데이터 셋
data = surprise.Dataset.load_builtin('ml-100k')
# print(data)
# print(data.raw_ratings)
# df = pd.DataFrame(data.raw_ratings, columns=['user', 'item', 'rate', 'id'])
# del df['id']
# print(df)
# 유사도 계산 방식을 설정
# option1 = {'name': 'msd'}
# option2 = {'name': 'cosine'}
option3 = {'name': 'pearson'}
# 추천 목록을 만들기 위한 객체 생성
algo = surprise.KNNBasic(sim_options=option3)
# 학습한다.
trainset = data.build_full_trainset()
algo.fit(trainset)
# 추천 목록을 가져온다.
# k == 추천받을 상품의 수
result = algo.get_neighbors('196', k=5)
for r1 in result:
print(r1)
def learn(id):
print(id)
dataset = getData()
# 데이터 셋을 만든다.
df = pd.DataFrame(dataset)
# 데이터를 읽어와 surprise에서 사용하는 데이터 형태로
# 만들어주는 객체, rating_sacle=(최소, 최대) <-평점기준
reader = sp.Reader(rating_scale=(0.0, 5))
# 딕셔너리에 담겨있는 데이터의 이름
# 데이터셋을 만들 때 첫번재 이름이 사용자 구분값, 두번째
# 이름이 상품 구분값, 세번째 이름이 평점으로 인식하여
# 데이터를 읽어들이고 데이터셋으로 만든다.
col_list = ['user_id', 'wine_id', 'points']
data = sp.Dataset.load_from_df(df[col_list], reader)
# 학습할 모델
model = sp.KNNBasic(sim_options={'name': 'pearson'})
# 학습한다.
trainset = data.build_full_trainset()
model.fit(trainset)
result = model.get_neighbors(id, k=5)
print(result)
rec_list = list()
for r in result:
rec_list.append(str(dataset['wine_id'][r]))
print(dataset['wine_id'][r])
winelist = ','.join(rec_list)
return winelist
def step5_surprise():
name_list, movie_list, rating_dic = data_to_dic()
# print(rating_dic)
# 데이터 셋을 만든다.
df = pd.DataFrame(rating_dic)
# rating_scale : 데이터에 담긴 평점의 범위
reader = surprise.Reader(rating_scale=(0.0, 5.0))
# 딕셔너리에 담겨져 있는 리스트의 이름
col_list = ['user_id', 'item_id', 'rating']
data = surprise.Dataset.load_from_df(df[col_list], reader)
trainset = data.build_full_trainset()
# 학습한다.
# 유사도 계산 방식을 설정
# option1 = {'name': 'msd'}
# option2 = {'name': 'cosine'}
option3 = {'name': 'pearson'}
# 추천 목록을 만들기 위한 객체 생성
algo = surprise.KNNBasic(sim_options=option3)
algo.fit(trainset)
# 소이현에 대해 영화를 추천받는다.
index = name_list.index('소이현')
result = algo.get_neighbors(index, k=3)
for r1 in result:
# r1이 1부터 시작해서 -1을 해준다.
print(movie_list[r1-1])
def surprise_basicKNN(trainset, finalset):
"Basic K Nearest Neighbours model"
algo = spr.KNNBasic()
algo.fit(trainset)
predictions_final = algo.test(finalset)
return spr_estimate_to_vect(predictions_final)
def train(where, k):
# df_to_dict = recur_dictify(pd.read_pickle('../../../data/over_10review_stores.pkl'))
# store_list = [] # 사용자 목록을 담을 리스트
# user_set = set() # 음식점 목록을 담을 set
#
# # store 수 만큼 반복
# for store_key in df_to_dict:
# store_list.append(store_key)
#
# for user_key in df_to_dict[store_key]:
# user_set.add(user_key)
#
# user_list = list(user_set)
df = pd.read_pickle("../../data/dic_to_train_stores.pkl")
reader = surprise.Reader(rating_scale=(1, 5))
col_list = ['store_id', 'user_id', 'score']
data = surprise.Dataset.load_from_df(df[col_list], reader)
# Train
trainset = data.build_full_trainset()
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
# 사용자의 음식점을 추천한다.
# where = input('store id : ')
print("\n")
user_list = pd.read_pickle(
"../../data/Item_based_user_list.pkl")[0].tolist()
store_list = pd.read_pickle(
"../../data/Item_based_store_list.pkl")[0].tolist()
# user_list = dff.user.unique().tolist()
# store_list = dff.store.unique().tolist()
index = store_list.index(int(where))
print('store_idx : ', index)
print("\n")
result = algo.get_neighbors(index, k=k) # k=10
print(where, "와 유사한 음식점은?")
print(result)
print("\n")
# 음식점에 대한 유저를 추천한다.
print(where, "를 평가한 당신에게 추천하는 친구:", "\n")
recommend_user_list = []
for r1 in result:
max_rating = data.df[data.df["store_id"] == r1]["score"].max()
user_id = data.df[(data.df["score"] == max_rating)
& (data.df["store_id"] == r1)]["user_id"].values
for user in user_id:
recommend_user_list.append(user_list[user])
# print(user_list[user])
return recommend_user_list
def train(dataframe, k):
# df_to_dict = recur_dictify(pd.read_pickle('../data/over_10review_peoples.pkl'))
# name_list = [] # 사용자 목록을 담을 리스트
# store_set = set() # 음식점 목록을 담을 set
#
# # 유저 수 만큼 반복
# for user_key in df_to_dict:
# name_list.append(user_key)
#
# for sto_key in df_to_dict[user_key]:
# store_set.add(sto_key)
#
# store_list = list(store_set)
df = dataframe
reader = surprise.Reader(rating_scale=(1, 5))
col_list = ['user_id', 'store_id', 'score']
data = surprise.Dataset.load_from_df(df[col_list], reader)
# Train
trainset = data.build_full_trainset()
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
user_id = input('유저 id:')
# 사용자의 음식점을 추천한다.
who = user_id
print("\n")
name_list = pd.read_pickle("../data/user_based_name_list.pkl")[0].tolist()
store_list = pd.read_pickle(
"../data/user_based_store_list.pkl")[0].tolist()
# name_list = dff.user.unique().tolist()
# store_list = dff.store.unique().tolist()
index = name_list.index(int(who))
print('user_idx : ', index)
print("\n")
result = algo.get_neighbors(index, k=k) # k=5
print(who, "에게 유사한 사용자는?")
print(result)
print("\n")
# user 에 대해 음식점을 추천한다.
print(who, "에게 추천하는 음식점:", "\n")
for r1 in result:
max_rating = data.df[data.df["user_id"] == r1]["score"].max()
sto_id = data.df[(data.df["score"] == max_rating)
& (data.df["user_id"] == r1)]["store_id"].values
for sto in sto_id:
print(store_list[sto])
def trainModels(self):
#when importing from a DF, need to specify the scale of the ratings in order to get best performance
reader = surprise.Reader(rating_scale=(self.scale_low,
self.scale_high))
data = surprise.Dataset.load_from_df(self.rawdata, reader)
trainset = data.build_full_trainset()
testset = trainset.build_anti_testset()
self.rmse = []
self.predictions = []
print("=== Training with Collaborative KNN ===")
sim_options = {
'name': 'cosine',
'user_based': False
} # compute similarities between items
self.cKNN = surprise.KNNBasic(k=40, sim_options=sim_options)
self.cKNN.fit(trainset)
self.predictions.append(self.cKNN.test(testset))
self.rmse.append(
surprise.accuracy.rmse(self.predictions[0], verbose=True))
minR = self.rmse[0]
self.algoIndex = 0
print("=== Matrix Factorization ===")
self.SVD = surprise.prediction_algorithms.matrix_factorization.SVD(
n_factors=30, n_epochs=10, biased=True)
self.SVD.fit(trainset)
self.predictions.append(self.SVD.test(testset))
self.rmse.append(
surprise.accuracy.rmse(self.predictions[1], verbose=True))
if (minR > self.rmse[1]):
self.algoIndex = 1
minR = self.rmse[1]
print("=== Co-clustering ===")
self.Co = surprise.prediction_algorithms.co_clustering.CoClustering(
n_cltr_u=4, n_cltr_i=4, n_epochs=25)
self.Co.fit(trainset)
self.predictions.append(self.Co.test(testset))
self.rmse.append(
surprise.accuracy.rmse(self.predictions[2], verbose=True))
if (minR > self.rmse[2]):
self.algoIndex = 2
minR = self.rmse[2]
print("=== Slope One Collaborative Filtering ===")
self.slope = surprise.prediction_algorithms.slope_one.SlopeOne()
self.slope.fit(trainset)
self.predictions.append(self.slope.test(testset))
self.rmse.append(
surprise.accuracy.rmse(self.predictions[3], verbose=True))
if (minR > self.rmse[3]):
self.algoIndex = 3
def train(who, k):
df = pd.read_pickle("../data/dic_to_train.pkl")
reader = surprise.Reader(rating_scale=(1, 5))
col_list = ['user_id', 'store_id', 'score']
data = surprise.Dataset.load_from_df(df[col_list], reader)
# benchmark = []
# from surprise import SVD, SVDpp, SlopeOne, NMF, NormalPredictor, KNNBasic, KNNBaseline, KNNWithMeans, KNNWithZScore, BaselineOnly, CoClustering
# # from sklearn.model_selection import cross_validate 사이킷런의 크로스벨리데이션이 아니다.
# from surprise.model_selection import cross_validate
# benchmark = []
# # 모든 알고리즘을 literate화 시켜서 반복문을 실행시킨다.
# for algorithm in [SVD(), SVDpp(), SlopeOne(), NMF(), NormalPredictor(), KNNBaseline(), KNNBasic(), KNNWithMeans(), KNNWithZScore(), BaselineOnly(), CoClustering()]:
# # 교차검증을 수행하는 단계.
# results = cross_validate(algorithm, data, measures=['RMSE'], cv=3, verbose=False)
# # 결과 저장과 알고리즘 이름 추가.
# tmp = pd.DataFrame.from_dict(results).mean(axis=0)
# tmp = tmp.append(pd.Series([str(algorithm).split(' ')[0].split('.')[-1]], index=['Algorithm']))
# benchmark.append(tmp)
# print(pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_rmse') )
# Train
trainset = data.build_full_trainset()
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
name_list = pd.read_pickle("../data/user_based_name_list.pkl")[0].tolist()
store_list = pd.read_pickle(
"../data/user_based_store_list.pkl")[0].tolist()
# name_list = dff.user.unique().tolist()
# store_list = dff.store.unique().tolist()
index = name_list.index(int(who))
neighbors = algo.get_neighbors(index, k=k) # k=5
# Recommend store to user
recommend_store_list = []
for i in neighbors:
max_rating = data.df[data.df["user_id"] == i]["score"].max()
store = data.df[(data.df["score"] == max_rating)
& (data.df["user_id"] == i)]["store_id"].values
for idx in store:
recommend_store_list.append(store_list[idx])
return recommend_store_list
def basicKNN(train, test):
"""
Run the basic KNN model from Surprise library.
@param train: the training set in the Surprise format.
@param test: the test set in the Surprise format.
@return: the predictions in a numpy array.
"""
algo = spr.KNNBasic()
algo.fit(train)
predictions = algo.test(test)
return get_predictions(predictions)
def main(args):
user_item_based = 'item_based' if args.item_based else 'user_based'
filename = '_'.join([
args.exp_name, args.algorithm, args.sim_name, user_item_based,
str(args.num_rows)
]) + '.pkl'
output_file = Path(filename)
if output_file.exists():
print(f'ERROR! Output file {output_file} already exists. Exiting!')
sys.exit(1)
print(f'Saving scores in {output_file}\n')
reader = surprise.Reader(rating_scale=(1, 5))
df = pq.read_table('all_ratings_with_indices.parquet',
columns=['user_idx', 'movie_idx',
'rating']).to_pandas()
df.user_idx = df.user_idx.astype(np.uint32)
df.movie_idx = df.movie_idx.astype(np.uint16)
df.rating = df.rating.astype(np.uint8)
print(df.dtypes)
data = surprise.Dataset.load_from_df(df[:args.num_rows], reader=reader)
del df
sim_options = {
'name': args.sim_name,
'user_based': False if args.item_based else True
}
if args.algorithm == 'knn':
algo = surprise.KNNBasic(sim_options=sim_options)
elif args.algorithm == 'baseline':
algo = surprise.BaselineOnly()
elif args.algorithm == 'normal':
algo = surprise.NormalPredictor()
elif args.algorithm == 'knn_zscore':
algo = surprise.KNNWithZScore(sim_options=sim_options)
elif args.algorithm == 'svd':
algo = surprise.SVD()
elif args.algorithm == 'nmf':
algo = surprise.NMF()
else:
print(f'Algorithm {args.algorithm} is not a valid choice.')
scores = surprise.model_selection.cross_validate(algo,
data,
cv=args.cv_folds,
verbose=True,
n_jobs=-1)
pickle.dump(scores, open(output_file, 'wb'))
def algo_tester(data_object):
'''
Produces a dataframe displaying all the different RMSE's, test & train times of the different surprise algorithms
---Parameters---
data_object(variable) created from the read_data_surprise function
---Returns---
returns a dataframe where you can compare the performance of different algorithms
'''
benchmark = []
algos = [
sp.SVDpp(),
sp.SVD(),
sp.SlopeOne(),
sp.NMF(),
sp.NormalPredictor(),
sp.KNNBaseline(),
sp.KNNBasic(),
sp.KNNWithMeans(),
sp.KNNWithZScore(),
sp.BaselineOnly(),
sp.CoClustering()
]
# Iterate over all algorithms
for algorithm in algos:
# Perform cross validation
results = cross_validate(algorithm,
data_object,
measures=['RMSE'],
cv=3,
verbose=False)
# Get results & append algorithm name
tmp = pd.DataFrame.from_dict(results).mean(axis=0)
tmp = tmp.append(
pd.Series([str(algorithm).split(' ')[0].split('.')[-1]],
index=['Algorithm']))
benchmark.append(tmp)
benchmark = pd.DataFrame(benchmark).set_index('Algorithm').sort_values(
'test_rmse')
return benchmark
def run(self):
df = pd.read_csv(self.input_csv, delimiter=";")
lower_rating = df['score'].min()
upper_rating = df['score'].max()
print('Score range: {0} to {1}'.format(lower_rating, upper_rating))
reader = surprise.Reader(rating_scale=(df['score'].min(),
df['score'].max()))
data = surprise.Dataset.load_from_df(df, reader)
trainset = data.build_full_trainset()
chosen_k = math.ceil(math.sqrt(len(df['userId'].unique())) + 1)
algo = surprise.KNNBasic(k=chosen_k,
sim_options={
'name': 'pearson_baseline',
'user_based': True
})
algo.fit(trainset)
testset = trainset.build_anti_testset()
predictions = algo.test(testset)
del df
del reader
top_n = self.get_top_n(predictions, n=10)
df_top_rated = pd.DataFrame(columns=['userId', 'itemId', 'est'])
for uid, user_ratings in top_n.items():
for iid, est in user_ratings:
df_top_rated.loc[len(df_top_rated)] = [uid, iid, est]
df_top_rated.to_csv(self.output_csv,
sep=';',
encoding='utf-8',
index=False)
return df_top_rated
#validate = surprise.model_selection.cross_validate(alg, data, verbose = True)
def __init__(self):
warnings.filterwarnings('ignore')
with open('./LogDic.pickle', 'rb') as f:
df_to_dict = pickle.load(f)
print("----------Let's dictionary----------", df_to_dict)
cos_set = set()
self.name_list = []
for user_key in df_to_dict:
self.name_list.append(user_key)
for cos_key in df_to_dict[user_key]:
cos_set.add(cos_key)
self.cos_list = list(cos_set)
rating_dic = {'Nickname': [], 'ProductIdx': [], 'rating': []}
for name_key in df_to_dict:
for cos_key in df_to_dict[name_key]:
a1 = self.name_list.index(name_key)
a2 = self.cos_list.index(cos_key)
a3 = df_to_dict[name_key][cos_key]
rating_dic['Nickname'].append(a1)
rating_dic['ProductIdx'].append(a2)
rating_dic['rating'].append(a3)
df = pd.DataFrame(rating_dic)
reader = surprise.Reader(rating_scale=(1, 5))
col_list = ['Nickname', 'ProductIdx', 'rating']
self.data = surprise.Dataset.load_from_df(df[col_list], reader)
# print("----------Let's training----------")
trainset = self.data.build_full_trainset()
option = {'name': 'pearson'}
self.algo = surprise.KNNBasic(sim_options=option)
self.algo.fit(trainset)
def model_fit(self):
'''
Train model using surprise.SVD algorithm.
'''
self.build_trainset()
algo = self._algo_choise
if algo == 'SVD':
self.algorithm = surprise.SVD()
elif algo == 'Baseline':
self.algorithm = surprise.BaselineOnly()
elif algo == 'SlopeOne':
self.algorithm = surprise.SlopeOne()
elif algo == 'CoClustering':
self.algorithm = surprise.CoClustering()
else:
self.algorithm = surprise.KNNBasic()
print('Training Recommender System using %s...' % algo)
self.algorithm.fit(self.trainset)
self.ratings_changed = False
print('Done')
def step5_surprise():
name_list, movie_list, rating_dic = data_to_dic()
print(rating_dic)
# 데이터 셋을 만든다.
df = pd.DataFrame(rating_dic)
# rating_scale : 데이터에 담긴 평점이 범위
reader = surprise.Reader(rating_scale=(0.0, 5.0)) # 0~5점의 범위를 가지고 있다.
# print(rating_dic.keys())
col_list = [key for key in rating_dic.keys()]
data = surprise.Dataset.load_from_df(df[col_list],reader)
print(data)
trainset = data.build_full_trainset()
option1 = {'name':'pearson'}
algo = surprise.KNNBasic(sim_options=option1)
algo.fit(trainset)
# 소이현에 대해 영화를 추천 받는다.
index = name_list.index('소이현')
result = algo.get_neighbors(index, k=3) #iid 자리 -> 대상 인간
for r1 in result:
print(movie_list[r1-1]) # r1이 1번부터 시작하나보네
def step5_surprise() :
# 데이터를 가져온다.
name_list, movie_list, rating_dic = data_to_dic()
# print(rating_dic)
# 데이터 셋을 만든다.
df = pd.DataFrame(rating_dic)
# rating_scale : 데이터에 담긴 평점의 범위
reader = surprise.Reader(rating_scale=(0.0, 5.0))
# 딕셔너리에 담겨져 있는 리스트의 이름
col_list = ['user_id', 'item_id', 'rating']
data = surprise.Dataset.load_from_df(df[col_list], reader)
# 학습 한다.
trainset = data.build_full_trainset()
option1 = {'name' : 'pearson'}
algo = surprise.KNNBasic(sim_options=option1)
algo.fit(trainset)
# 소이현에 대해 영화를 추천받는다.
index = name_list.index('소이현')
result = algo.get_neighbors(index, k=3)
for r1 in result :
print(movie_list[r1 - 1])
'user_based': False # compute similarities between items
}
mean_ap = []
precision = []
recall = []
fscore = []
normalized_DCG = []
mean_ap_train = []
precision_train = []
recall_train = []
fscore_train = []
normalized_DCG_train = []
for k_val in ks:
print(k_val)
algo = surprise.KNNBasic(k=k_val, sim_options=sim_options)
pr = 0
re = 0
fs = 0
ap = 0
nd = 0
pr_train = 0
re_train = 0
fs_train = 0
ap_train = 0
nd_train = 0
for trainset, testset in data.folds():
algo.train(trainset)
predictions_on_test = algo.test(testset)
precisions_test, recalls_test = precision_recall_at_k(
import surprise as sp
from surprise import Dataset
from surprise.model_selection import cross_validate
import NetflixDataLoad
#for 100000 rows for fast processing
data = Dataset.load_from_df(
NetflixDataLoad.df_filterd[['Cust_Id', 'Movie_Id', 'Rating']][:100000])
n_folds = 5
for algo in [sp.SVD(), sp.SVDpp(), sp.KNNBasic(), sp.KNNWithMeans()]:
print(
cross_validate(algo,
data,
measures=['RMSE', 'MAE'],
cv=n_folds,
verbose=True))
# Output Example
# Evaluating RMSE, MAE of algorithm SVD on 5 split(s).
#
# Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std
# RMSE 0.9311 0.9370 0.9320 0.9317 0.9391 0.9342 0.0032
# MAE 0.7350 0.7375 0.7341 0.7342 0.7375 0.7357 0.0015
# Fit time 6.53 7.11 7.23 7.15 3.99 6.40 1.23
# Test time 0.26 0.26 0.25 0.15 0.13 0.21 0.06
return results
np.random.seed(0)
file_path = 'data/user_artists_log.dat'
reader = Reader(line_format='user item rating', sep='\t')
data = Dataset.load_from_file(file_path, reader=reader)
trainset = data.build_full_trainset()
testset = trainset.build_anti_testset()
# 2 - User-based Recommendation
uid_list = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
# TODO - 2-1-1. KNNBasic, cosine
sim_options = {'name': 'cosine'}
algo = surprise.KNNBasic(sim_options=sim_options)
algo.fit(trainset)
results = get_top_n(algo, testset, uid_list, n=10, user_based=True)
with open('2-1-1_results.txt', 'w') as f:
for uid, ratings in sorted(results.items(), key=lambda x: int(x[0])):
f.write('User ID %s top-10 results\n' % uid)
for iid, score in ratings:
f.write('Item ID %s\tscore %s\n' % (iid, str(score)))
f.write('\n')
# TODO - 2-1-2. KNNWithMeans, pearson
sim_options2 = {'name': 'pearson'}
algo = surprise.KNNWithMeans(sim_options=sim_options2)
algo.fit(trainset)
results = get_top_n(algo, testset, uid_list, n=10, user_based=True)
with open('2-1-2_results.txt', 'w') as f:
def KNN_train(BASE_DIR):
print('KNN 학습 시작 : ', str(datetime.now())[10:19])
conn = pymysql.connect(host=config('HOST'),
port=3306,
user=config('USER'),
password=config('PASSWORD'),
db=config('DB'))
sql = 'SELECT * FROM wouldyouci.accounts_rating'
data = pd.read_sql_query(sql, conn)
conn.close()
df = data[['user_id', 'movie_id', 'score']]
# 리뷰 n개 이상 달린 영화
# 5개 이상 달린 영화
n1 = 5
filter_movies = df['movie_id'].value_counts() >= n1
filter_movies = filter_movies[filter_movies].index.tolist()
# n개 이상 평가한 유저
n2 = 5
filter_users = df['user_id'].value_counts() >= n2
filter_users = filter_users[filter_users].index.tolist()
df_new = df[df['movie_id'].isin(filter_movies)
& df['user_id'].isin(filter_users)]
df_to_dict = recur_dictify(df_new)
user_list = []
movie_set = set()
# 유저 수 만큼 반복한다
for user in df_to_dict:
user_list.append(user)
# 현재 사용자가 본 영화 목록을 set에 담는다.
for movie in df_to_dict[user]:
movie_set.add(movie)
movie_list = list(movie_set)
# 학습할 데이터를 준비한다.
rating_dic = {'user_id': [], 'movie_id': [], 'score': []}
# 유저 수 만큼 반복
for user in df_to_dict:
# 해당 유저가 본 영화 수 만큼 반복
for movie in df_to_dict[user]:
# 유저 인덱스 번호를 추출
u_index = user_list.index(user)
# 영화 인덱스 번호를 추출
m_index = movie_list.index(movie)
# 평점을 가져온다
score = df_to_dict[user][movie]
# 딕셔너리에 담는다
rating_dic['user_id'].append(u_index)
rating_dic['movie_id'].append(m_index)
rating_dic['score'].append(score)
# 데이터셋 만들기
df = pd.DataFrame(rating_dic)
# 학습
reader = surprise.Reader(rating_scale=(0.5, 5.0))
# surprise에서 사용할 데이터셋을 구성할 때 필요한 이름
# 데이터가 저장되어 있는 딕셔너리의 컬럼 이름
col_list = ['user_id', 'movie_id', 'score']
data = surprise.Dataset.load_from_df(df_new[col_list], reader)
# 학습한다
trainset = data.build_full_trainset()
# Pearson similarity 사용
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
recommand_dic = {
'user_id': [],
'movie_id': [],
}
for user_key in df_new['user_id'].unique():
index = user_list.index(user_key)
result = algo.get_neighbors(index, k=5)
recom_set = set()
for i in result:
max_rating = data.df[data.df['user_id'] ==
user_list[i]]['score'].max()
recom_movies = data.df[(data.df['score'] == max_rating) & (
data.df['user_id'] == user_list[i])]['movie_id'].values
for item in recom_movies:
recom_set.add(item)
for item in recom_set:
recommand_dic['user_id'].append(user_key)
recommand_dic['movie_id'].append(item)
pickle = pd.DataFrame(recommand_dic)
path = os.path.join(BASE_DIR, 'KNN.p')
pd.to_pickle(pickle, path)
print('종료 : ', str(datetime.now())[10:19])
y = []
y_plot = []
counter = 0
x_plot = []
time_plot = []
mem_plot = []
for i in range(1, 10):
reader = surprise.Reader(name=None, line_format='user item rating', sep=',', skip_lines=1)
data = surprise.Dataset.load_from_file('/Users/keyadesai/Desktop/Recommendation Engine/ratings_woheader.csv',
reader=reader)
# data.split(5)
algo = surprise.KNNBasic()
param_grid = {'n_epochs': [i], 'lr_all': [0.005],
'reg_all': [0.02]}
gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)
gs.fit(data)
results_df = pd.DataFrame.from_dict(gs.cv_results)
y.append(results_df.mean_test_rmse)
y_plot.append(y[counter][0])
x_plot.append(i)
print(y_plot)
plt.plot(x_plot, y_plot, 'ro')
epochs=2,
validation_split=0.1,
shuffle=True)
y_pred = model.predict([df_hybrid_test['User'], df_hybrid_test['Movie'], test_tfidf])
y_true = df_hybrid_test['Rating'].values
rmse = np.sqrt(mean_squared_error(y_pred=y_pred, y_true=y_true))
print('\n\nTesting Result With Keras Hybrid Deep Learning: {:.4f} RMSE'.format(rmse))
# Load dataset into surprise specific data-structure
data = sp.Dataset.load_from_df(df_filterd[['User', 'Movie', 'Rating']].sample(20000), sp.Reader())
benchmark = []
# Iterate over all algorithms
for algorithm in [sp.SVD(), sp.SVDpp(), sp.SlopeOne(), sp.NMF(), sp.NormalPredictor(), sp.KNNBaseline(), sp.KNNBasic(), sp.KNNWithMeans(), sp.KNNWithZScore(), sp.BaselineOnly(), sp.CoClustering()]:
# Perform cross validation
results = cross_validate(algorithm, data, measures=['RMSE', 'MAE'], cv=3, verbose=False)
# Get results & append algorithm name
tmp = pd.DataFrame.from_dict(results).mean(axis=0)
tmp = tmp.append(pd.Series([str(algorithm).split(' ')[0].split('.')[-1]], index=['Algorithm']))
# Store data
benchmark.append(tmp)
# Store results
surprise_results = pd.DataFrame(benchmark).set_index('Algorithm').sort_values('test_rmse', ascending=False)
# Get data
data = surprise_results[['test_rmse', 'test_mae']]
def get_queryset(self):
# user_reviews = pd.DataFrame(list(models.Review.objects.all().values()))
# df = user_reviews[['userid','storeid','bornyear']].head(100)
# userid = self.request.query_params.get("user", "")
# if userid is not None:
user_reviews = pd.DataFrame(list(models.Review.objects.all().values()))
df = user_reviews[['userid', 'storeid', 'bornyear']].head(1000)
df_to_dict = recur_dictify(df)
print(df_to_dict)
name_list = [] # 사용자 목록을 담을 리스트 # 중복불가
cos_set = set() # 맛집 목록을 담을 set # 중복 가능
# user_key 는 음식점 id 가 나온다
for user_key in df_to_dict:
name_list.append(user_key)
# name_list [1070][1070, 6757][1070, 6757, 8272]
for cos_key in df_to_dict[user_key]:
# cost_key = 음식점 id 가 나온다 (216, 58, 149)
cos_set.add(cos_key)
# cost_set = {216}{216, 58}
# for user_score in a[score]:
# user_gender.append[user_score]
# 학습할 데이터를 준비
rating_dic = {
'user': [],
'store': [],
'born_year': []
# 'gender': [],
# 'bornyear': []
}
cos_list = list(cos_set)
# 사용자의 수 만큼 반복
for name_key in df_to_dict:
# 해당 사용자가 본 맛집 수만큼 반복
for cos_key in df_to_dict[name_key]:
# 사용자 인덱스 번호를 추출한다
a1 = name_list.index(name_key)
# 맛집 인덱스 번호를 추출한다.
a2 = cos_list.index(cos_key)
# 나이를 가져온다.
a3 = df_to_dict[name_key][cos_key]
rating_dic['user'].append(a1)
rating_dic['store'].append(a2)
rating_dic['born_year'].append(a3)
# rating_dic['gender'].append(a4)
# rating_dic['bornyear'].append(a5)
# print((rating_dic['user']))
# print((rating_dic['store_name']))
# print(len(rating_dic['score']))
df = pd.DataFrame(rating_dic)
reader = surprise.Reader(rating_scale=(1, 5))
cos_list2 = ['user', 'store', 'born_year']
data = surprise.Dataset.load_from_df(df[cos_list2], reader)
trainset = data.build_full_trainset()
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
index = name_list.index(1070)
print('user_index: ', index)
print("\n")
result = algo.get_neighbors(index, k=5)
print("당신과 유사한 사용자? :", result)
print("\n")
print("당신에게 추천할만한 맛집 :", "\n")
a = []
for r1 in result:
max_rating = data.df[data.df["user"] == r1]["born_year"].max()
cos_id = data.df[(data.df["born_year"] == max_rating)
& (data.df["user"] == r1)]["store"].values
for cos_item in cos_id:
a.append(cos_list[cos_item])
# item_list = cos_list[a]
print(a)
queryset = models.Store.objects.all().filter(id=a[0])
print(a[0])
for i in a:
select = models.Store.objects.all().filter(id=i)
print(i)
queryset = queryset | select
print(queryset)
return queryset
rating_matrix = df.pivot_table(index="userId", columns="movieId", values="rating")
sparse_matrix = sparse.csr_matrix(rating_matrix)
'''
movies = pd.read_csv("exportedData/moviesExp.csv", usecols=["title","movieId"])
ratings = pd.read_csv("exportedData/ratingsExp.csv")
evalData = pd.merge(movies, ratings, on="movieId", how="inner")
df = pd.read_table("exportedData/ratingsExp.csv", sep= ',').drop("timestamp", axis=1)
df.head()
recoms = pd.DataFrame()
reader = surprise.Reader(rating_scale=(0.5,5.0))
data = surprise.Dataset.load_from_df(df, reader)
trainset, testset = train_test_split(data, test_size=0.25)
#alg = surprise.SVD()
alg = surprise.KNNBasic()
out = alg.fit(data.build_full_trainset())
def recom(uid, recomms_count):
movieIds = df["movieId"].unique()
rated_movies = df.loc[df["userId"] == uid, "movieId"]
iid_to_pred = np.setdiff1d(movieIds, rated_movies)
test_data = [[uid, iid, 5.0] for iid in iid_to_pred]
predictions = alg.test(test_data)
pred_ratings = np.array([pred.est for pred in predictions])
indice_max = np.argpartition(pred_ratings, -recomms_count)[-recomms_count:]
iid = iid_to_pred[indice_max]
iid_to_title = [i for i in range(0, recomms_count)]
measures=['RMSE', 'MAE'],
cv=5,
verbose=True)
print('SVD--------------')
print(svd_temp)
normalPredictor = surprise.NormalPredictor()
normalPredictor_temp = surprise.model_selection.cross_validate(
normalPredictor, rating_data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print('normalPredictor--------------')
print(normalPredictor_temp)
baselineOnly = surprise.BaselineOnly()
baselineOnly_temp = surprise.model_selection.cross_validate(
baselineOnly, rating_data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print('baselineOnly-----------------')
print(baselineOnly_temp)
knnBasic = surprise.KNNBasic()
knnBasic_temp = surprise.model_selection.cross_validate(
knnBasic, rating_data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print('knnBasic-----------------')
print(knnBasic_temp)
knnWithMeans = surprise.KNNWithMeans()
knnWithMeans_temp = surprise.model_selection.cross_validate(
knnWithMeans, rating_data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print('knnWithMeans-----------------')
print(knnWithMeans_temp)
knnBaseline = surprise.KNNBaseline()
knnBaseline_temp = surprise.model_selection.cross_validate(
knnBaseline, rating_data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print('knnBaseline-----------------')
print(knnBaseline_temp)
svdpp = surprise.SVDpp()
idcgs[uid] = sum(rel_true/discount_true)
dcg = sum(dcgu for (_,dcgu) in dcgs.items())
idcg = sum(idcgu for (_,idcgu) in idcgs.items())
return dcg/idcg
data = pd.read_csv('sampled.csv')
print "Users: "+str(len(np.unique(data['User-ID'])))+ " items: "+str(len(np.unique(data['ISBN'])))
print "No. of ratings: "+str(len(data))
sim_options = {'name': 'pearson',
'user_based': False
}
algo_knn = surprise.KNNBasic(k=5, sim_options=sim_options)
algo_svd = surprise.SVD(n_factors = 10, lr_all= 0.001, reg_all =1)
#Around 80% train data for each of these splits
sample_sizes = [0.4, 0.2, 0.1,0.05, 0.01]
time_knn = []
time_svd = []
for s in sample_sizes:
a = data.sample(frac = s, random_state = 111)
print "s= "+str(len(a))
print("Removing users with less than 20 ratings....")
b = a.groupby('User-ID').filter(lambda x: len(x) >= 20)
densityu = (float(len(b))/(len(np.unique(b['User-ID']))*len(np.unique(b['ISBN']))))*100
sparsityNew
img2 = plt.spy(imgpivotNew, markersize=0.1)
####################################################################################################
data = surprise.Dataset.load_from_df(df_ratings, reader)
user_based = {"name": "cosine", "user_based": True}
item_based = {"name": "cosine", "user_based": False}
#############
# User Based
#############
trainset = data.build_full_trainset()
# Using KNNBasic algorithm
alg = surprise.KNNBasic(sim_options=user_based)
# Training model
alg.fit(trainset)
def recom(uid, recomms_count):
movieIds = df["movieId"].unique()
rated_movies = df.loc[df["userId"] == uid, "movieId"]
iid_to_pred = np.setdiff1d(movieIds, rated_movies)
test_data = [[uid, iid, 5.0] for iid in iid_to_pred]
predictions = alg.test(test_data)
pred_ratings = np.array([pred.est for pred in predictions])
indice_max = np.argpartition(pred_ratings, -recomms_count)[-recomms_count:]
iid = iid_to_pred[indice_max]
def main(train_df, target_df, cache_name="test", force_recompute=[]):
"""Train multiple models on train_df and predicts target_df
Predictions are cached. If the indices don't match the indices of
target_df, the cache is discarded.
By default, if a method was already computed it is not recomputed again
(except if the method name is listed in force_recompute). cache_name
is the name to use to read and write the cache.
Arguments:
train_df {dataframe} -- Training dataframe
target_df {dataframe} -- Testing dataframe
Keyword Arguments:
cache_name {str} -- Name to use for caching (default: {"test"})
force_recompute {list} -- Name(s) of methods to recompute, whether or
not it was already computed. Useful to only recompute single methods
without discarding the rest. (default: {[]})
Returns:
Dataframe -- Dataframe with predictions for each methods as columns,
IDs as indices
"""
global algo_in_use
CACHED_DF_FILENAME = os.path.dirname(
os.path.abspath(__file__)) +\
"/cache/cached_predictions_{}.pkl".format(cache_name)
train_df = preprocess_df(train_df)
trainset = pandas_to_data(train_df)
ids_to_predict = target_df["Id"].to_list()
# try to retrieve backup dataframe
try:
print("Retrieving cached predictions")
all_algos_preds_df = pd.read_pickle(CACHED_DF_FILENAME)
print("Ensuring cached IDs match given IDs")
assert sorted(ids_to_predict) == sorted(
all_algos_preds_df.index.values)
print("Indices match, continuing")
except (FileNotFoundError, AssertionError):
print("No valid cached predictions found")
all_algos_preds_df = pd.DataFrame(ids_to_predict, columns=["Id"])
all_algos_preds_df.set_index("Id", inplace=True)
all_algos = {
"SVD": spr.SVD(n_factors=200, n_epochs=100),
"Baseline": spr.BaselineOnly(),
"NMF": spr.NMF(n_factors=30, n_epochs=100),
"Slope One": spr.SlopeOne(),
"KNN Basic": spr.KNNBasic(k=60),
"KNN Means": spr.KNNWithMeans(k=60),
"KNN Baseline": spr.KNNBaseline(),
"KNN Zscore": spr.KNNWithZScore(k=60),
"SVD ++": spr.SVDpp(n_factors=40, n_epochs=100),
"Co Clustering": spr.CoClustering()
}
for name in all_algos:
print("##### {} ####".format(name))
if name in force_recompute and name in all_algos_preds_df.columns:
all_algos_preds_df.drop(name, axis=1, inplace=True)
if name in all_algos_preds_df.columns:
print("Already computed {}, skipping".format(name))
continue
algo = all_algos[name]
time.sleep(1)
algo.fit(trainset)
time.sleep(1)
algo_in_use = algo
print("Generating predictions...")
predictions = parallelize_predictions(ids_to_predict, 80)
print("Done. Merging with previous results")
this_algo_preds_df = pd.DataFrame(predictions, columns=["Id", name])
this_algo_preds_df.set_index("Id", inplace=True)
all_algos_preds_df = pd.merge(all_algos_preds_df,
this_algo_preds_df,
left_index=True,
right_index=True)
all_algos_preds_df.to_pickle(CACHED_DF_FILENAME)
print("DONE computing surprize")
return all_algos_preds_df
def recommend_new(request):
test = Test()
test.name = request.POST.get('name')
test.local = request.POST.get('local')
test.rating = request.POST.get('rating')
test.save()
if request.method == 'POST':
if (request.POST.get('q1') == '1'):
if (request.POST.get('q2') == '1'):
if (request.POST.get('q3') == '1'):
f = open('뚜벅혼자관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅혼자관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('뚜벅혼자휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅혼자휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q2') == '2'):
if (request.POST.get('q3') == '1'):
f = open('뚜벅2인관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅2인관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('뚜벅2인휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅2인휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q2') == '3'):
if (request.POST.get('q3') == '1'):
f = open('뚜벅3인관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅3인관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('뚜벅3인휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('뚜벅3인휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q1') == '2'):
if (request.POST.get('q2') == '1'):
if (request.POST.get('q3') == '1'):
f = open('자차혼자관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차혼자관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('자차혼자휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차혼자휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q2') == '2'):
if (request.POST.get('q3') == '1'):
f = open('자차2인관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차2인관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('자차2인휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차2인휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q2') == '3'):
if (request.POST.get('q3') == '1'):
f = open('자차3인관광.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차3인관광.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
elif (request.POST.get('q3') == '2'):
f = open('자차3인휴양.csv', 'a', newline='', encoding='utf-8')
wr = csv.writer(f)
wr.writerow([test.name, test.local, test.rating])
f.close()
warnings.filterwarnings('ignore')
data = pd.read_csv('자차3인휴양.csv',
encoding="utf-8",
sep=",",
error_bad_lines=False)
df = data[['id', '여행지', 'rating']]
df = df.drop_duplicates(['id', '여행지'], keep="last")
def recur_dictify(frame):
if len(frame.columns) == 1:
if frame.values.size == 1: return frame.values[0][0]
return frame.values.squeeze()
grouped = frame.groupby(frame.columns[0])
d = {k: recur_dictify(g.iloc[:, 1:]) for k, g in grouped}
return d
df_to_dict = recur_dictify(df)
name_list = []
local_set = set()
for user_key in df_to_dict:
name_list.append(user_key)
for local_key in df_to_dict[user_key]:
local_set.add(local_key)
local_list = list(local_set)
rating_dic = {'id': [], '여행지': [], 'rating': []}
for name_key in df_to_dict:
for cos_key in df_to_dict[name_key]:
a1 = name_list.index(name_key)
a2 = local_list.index(cos_key)
a3 = df_to_dict[name_key][cos_key]
rating_dic['id'].append(a1)
rating_dic['여행지'].append(a2)
rating_dic['rating'].append(a3)
df = pd.DataFrame(rating_dic)
reader = surprise.Reader(rating_scale=(1, 5))
data = surprise.Dataset.load_from_df(df[['id', '여행지', 'rating']], reader)
trainset = data.build_full_trainset()
option = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=option)
algo.fit(trainset)
index = name_list.index(test.name)
result = algo.get_neighbors(index, k=3)
def localtest():
for r1 in result:
max_rating = data.df[data.df["id"] == r1]["rating"].max()
local_id = data.df[(data.df["rating"] == max_rating)
& (data.df["id"] == r1)]["여행지"].values
for local_item in local_id:
return (local_list[local_item])
return render(request, 'recommend_result.html', {'localtest': localtest})
