혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

처음하는 파이썬 머신러닝 부트캠프

쉽게! 캐글 문제와 함께 분류,회귀,군집화 익히기

7. 파이썬 surprise 라이브러리의 SVD 모델¶

The prediction $\hat{r}_{ui}$ is set as:

$\hat{r}_{ui} = \mu + b_u + b_i + q_i^Tp_u$

If user $u$ is unknown, then the bias $b_u$ and the factors $p_u$ are assumed to be zero. The same applies for item $i$ with $b_i$ and $q_i$.

최소화는 SGD 사용 $$\begin{split}b_u \leftarrow b_u + \gamma (e_{ui} - \lambda b_u)\\ b_i \leftarrow b_i + \gamma (e_{ui} - \lambda b_i)\\ p_u \leftarrow p_u + \gamma (e_{ui} \cdot q_i - \lambda p_u)\\ q_i \leftarrow q_i + \gamma (e_{ui} \cdot p_u - \lambda q_i)\end{split}$$

주요 파라미터
- n_factors – The number of factors. Default is 100.
- n_epochs – The number of iteration of the SGD procedure. Default is 20.
- lr_all – The learning rate for all parameters. Default is 0.005

7.1. MovieLens 데이터를 기반으로 하는 실제 예제¶

MovieLens 데이터 로드¶

In [2]:

import os
import pandas as pd
from surprise import SVD
from surprise import Dataset
from surprise import dump


data = Dataset.load_builtin('ml-100k')

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

처음하는 파이썬 머신러닝 부트캠프

쉽게! 캐글 문제와 함께 분류,회귀,군집화 익히기

자세히 알아보기 모든 강좌 보기

In [3]:

df = pd.DataFrame(data.raw_ratings, columns=["user", "item", "rate", "id"])

In [4]:

df.head()

Out[4]:

	user	item	rate	id
0	196	242	3.0	881250949
1	186	302	3.0	891717742
2	22	377	1.0	878887116
3	244	51	2.0	880606923
4	166	346	1.0	886397596

트레이닝 + 모델 저장¶

In [5]:

import os

from surprise import SVD
from surprise import Dataset
from surprise import dump


data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()

algo = SVD()
algo.train(trainset)

# Compute predictions of the 'original' algorithm.
predictions = algo.test(trainset.build_testset())

# Dump algorithm and reload it.
file_name = os.path.expanduser('dump_file')
dump.dump(file_name, algo=algo)
_, loaded_algo = dump.load(file_name)

# We now ensure that the algo is still the same by checking the predictions.
predictions_loaded_algo = loaded_algo.test(trainset.build_testset())
assert predictions == predictions_loaded_algo
print('Predictions are the same')

Predictions are the same

정확도 계산¶

In [6]:

from surprise import Dataset
from surprise import SVD
from surprise import accuracy


data = Dataset.load_builtin('ml-100k')

algo = SVD()

trainset = data.build_full_trainset()
algo.train(trainset)

testset = trainset.build_testset()
predictions = algo.test(testset)

accuracy.rmse(predictions)

RMSE: 0.6763

Out[6]:

0.6763421071136434

모델 최적화 (파라미터 튜닝)¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

처음하는 파이썬 머신러닝 부트캠프

쉽게! 캐글 문제와 함께 분류,회귀,군집화 익히기

자세히 알아보기 모든 강좌 보기

surprise의 GridSearch class
- 주요 파라미터
  - algo_class (AlgoBase) – A class object of of the algorithm to evaluate.
  - param_grid (dict) – The dictionary has algo_class parameters as keys (string) and list of parameters as the desired values to try. All combinations will be evaluated with desired algorithm.
  - measures (list of string) – The performance measures to compute. Allowed names are function names as defined in the accuracy module. Default is ['rmse', 'mae'].
  - verbose (int) – Level of verbosity. If 0, nothing is printed. If 1, accuracy measures for each parameters combination are printed, with combination values. If 2, folds accuracy values are also printed. Default is 1

In [7]:

import random

from surprise import SVD
from surprise import Dataset
from surprise import accuracy
from surprise import GridSearch


# Load the full dataset.
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

# Select your best algo with grid search.
print('Grid Search...')
param_grid = {'n_epochs': [5, 10], 'lr_all': [0.002, 0.005]}
grid_search = GridSearch(SVD, param_grid, measures=['RMSE'], verbose=1)
print(grid_search.best_params)
grid_search.evaluate(data)

Grid Search...
defaultdict(<class 'list'>, {})
Running grid search for the following parameter combinations:
{'n_epochs': 5, 'lr_all': 0.002}
{'n_epochs': 5, 'lr_all': 0.005}
{'n_epochs': 10, 'lr_all': 0.002}
{'n_epochs': 10, 'lr_all': 0.005}
Resulsts:
{'n_epochs': 5, 'lr_all': 0.002}
{'RMSE': 0.98969008899623423}
----------
{'n_epochs': 5, 'lr_all': 0.005}
{'RMSE': 0.96388170867799128}
----------
{'n_epochs': 10, 'lr_all': 0.002}
{'RMSE': 0.96890656349975923}
----------
{'n_epochs': 10, 'lr_all': 0.005}
{'RMSE': 0.95219313886797485}
----------

사용자별 영화 추천 예 - offline 방식으로 별도 테이블을 만들어서 저장하고, 해당 사용자 로그인시 웹에서 추천 시나리오¶

In [16]:

from collections import defaultdict

from surprise import SVD
from surprise import Dataset


def get_top_n(predictions, n=10):
    '''Return the top-N recommendation for each user from a set of predictions.

    Args:
        predictions(list of Prediction objects): The list of predictions, as
            returned by the test method of an algorithm.
        n(int): The number of recommendation to output for each user. Default
            is 10.

    Returns:
    A dict where keys are user (raw) ids and values are lists of tuples:
        [(raw item id, rating estimation), ...] of size n.
    '''

    # First map the predictions to each user.
    top_n = defaultdict(list)
    for uid, iid, true_r, est, _ in predictions:
        top_n[uid].append((iid, est))

    # Then sort the predictions for each user and retrieve the k highest ones.
    for uid, user_ratings in top_n.items():
        user_ratings.sort(key=lambda x: x[1], reverse=True)
        top_n[uid] = user_ratings[:n]

    return top_n


# First train an SVD algorithm on the movielens dataset.
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
algo = SVD()
algo.train(trainset)

# Than predict ratings for all pairs (u, i) that are NOT in the training set.
testset = trainset.build_anti_testset()
predictions = algo.test(testset)

top_n = get_top_n(predictions, n=10)

# Print the recommended items for each user
for uid, user_ratings in top_n.items():
    print(uid, [iid for (iid, _) in user_ratings])

196 ['197', '64', '408', '474', '318', '963', '484', '127', '480', '193']
186 ['114', '611', '166', '656', '318', '83', '432', '59', '408', '169']
22 ['100', '183', '180', '135', '408', '12', '603', '178', '483', '357']
244 ['408', '515', '285', '480', '12', '127', '474', '1007', '483', '23']
166 ['172', '318', '169', '181', '408', '511', '357', '64', '515', '166']
298 ['173', '64', '408', '515', '513', '12', '89', '661', '258', '169']
115 ['179', '276', '285', '169', '408', '134', '180', '603', '1449', '483']
253 ['515', '174', '114', '493', '963', '603', '408', '251', '169', '357']
305 ['515', '657', '603', '513', '213', '496', '114', '694', '604', '659']
6 ['603', '657', '641', '190', '1449', '57', '654', '48', '958', '114']
62 ['169', '408', '427', '313', '603', '178', '223', '194', '963', '175']
286 ['480', '318', '427', '515', '474', '8', '641', '12', '306', '923']
200 ['302', '144', '114', '603', '59', '488', '493', '64', '178', '12']
210 ['318', '169', '64', '408', '513', '285', '272', '178', '191', '474']
224 ['64', '923', '513', '59', '496', '161', '742', '95', '357', '427']
303 ['169', '509', '313', '199', '48', '180', '498', '178', '193', '242']
122 ['285', '408', '483', '512', '515', '603', '59', '272', '528', '654']
194 ['169', '408', '1142', '313', '251', '525', '190', '963', '484', '603']
291 ['474', '408', '318', '169', '657', '190', '170', '114', '963', '320']
234 ['57', '302', '59', '408', '251', '169', '522', '856', '114', '1007']
119 ['408', '169', '513', '657', '474', '60', '603', '483', '48', '285']
167 ['483', '183', '515', '484', '488', '174', '127', '647', '1449', '71']
299 ['180', '64', '9', '530', '427', '199', '178', '223', '528', '750']
308 ['137', '1039', '1142', '251', '923', '474', '86', '114', '246', '963']
95 ['258', '408', '313', '169', '318', '12', '166', '272', '963', '19']
38 ['64', '333', '480', '462', '223', '316', '479', '197', '805', '553']
102 ['318', '64', '408', '169', '657', '474', '190', '178', '357', '480']
63 ['169', '357', '45', '318', '127', '223', '12', '474', '64', '178']
160 ['357', '12', '318', '98', '180', '134', '427', '64', '178', '654']
50 ['408', '923', '127', '169', '114', '483', '187', '318', '170', '64']
301 ['408', '169', '657', '114', '963', '923', '480', '223', '306', '485']
225 ['169', '59', '114', '611', '189', '313', '513', '515', '483', '168']
290 ['169', '178', '173', '963', '166', '251', '12', '427', '408', '513']
97 ['56', '12', '170', '64', '483', '480', '427', '48', '178', '318']
157 ['611', '474', '408', '511', '318', '98', '223', '56', '483', '512']
181 ['82', '692', '174', '64', '98', '963', '316', '204', '79', '210']
278 ['483', '12', '57', '318', '114', '59', '484', '657', '408', '474']
276 ['134', '191', '178', '114', '480', '190', '657', '511', '520', '663']
7 ['313', '1142', '302', '856', '1039', '293', '272', '408', '493', '242']
10 ['318', '427', '187', '272', '408', '923', '114', '79', '313', '1449']
284 ['178', '408', '483', '657', '427', '357', '480', '64', '318', '513']
201 ['493', '488', '657', '661', '641', '494', '169', '487', '498', '963']
287 ['169', '408', '285', '515', '134', '59', '603', '483', '189', '493']
246 ['89', '603', '302', '116', '474', '134', '114', '1142', '191', '408']
242 ['474', '515', '480', '174', '96', '272', '216', '483', '408', '178']
249 ['474', '641', '190', '1142', '180', '657', '530', '316', '127', '178']
99 ['318', '357', '136', '525', '408', '169', '114', '195', '272', '187']
178 ['496', '272', '705', '169', '513', '408', '923', '485', '251', '114']
251 ['318', '174', '357', '657', '195', '191', '98', '483', '169', '208']
81 ['64', '302', '286', '483', '272', '187', '480', '180', '265', '174']
260 ['408', '169', '190', '511', '223', '963', '12', '603', '205', '923']
25 ['483', '64', '59', '515', '318', '603', '513', '22', '172', '178']
59 ['114', '408', '124', '1073', '1142', '223', '302', '157', '178', '246']
72 ['408', '286', '169', '316', '143', '523', '657', '923', '251', '302']
87 ['313', '272', '483', '603', '169', '28', '178', '114', '59', '12']
42 ['313', '520', '178', '483', '170', '191', '22', '169', '657', '963']
292 ['318', '357', '12', '191', '474', '114', '427', '192', '498', '651']
20 ['528', '316', '408', '487', '173', '923', '275', '484', '315', '313']
13 ['136', '408', '1021', '644', '479', '169', '496', '945', '246', '1137']
138 ['408', '114', '50', '520', '205', '169', '189', '127', '923', '174']
60 ['408', '114', '169', '190', '318', '1194', '657', '198', '127', '963']
57 ['408', '169', '174', '172', '963', '114', '313', '483', '923', '165']
223 ['513', '174', '265', '59', '64', '172', '50', '187', '483', '482']
189 ['302', '427', '408', '12', '223', '169', '98', '357', '251', '515']
243 ['408', '474', '272', '603', '513', '427', '114', '479', '528', '313']
92 ['127', '187', '357', '178', '603', '114', '427', '272', '483', '657']
241 ['64', '480', '318', '479', '483', '408', '178', '474', '603', '657']
254 ['318', '483', '427', '223', '170', '963', '651', '520', '96', '528']
293 ['408', '197', '191', '178', '641', '269', '919', '318', '268', '515']
127 ['603', '480', '174', '96', '79', '172', '64', '169', '333', '429']
222 ['187', '169', '177', '663', '963', '511', '1142', '42', '129', '114']
267 ['134', '178', '357', '513', '316', '199', '641', '205', '302', '603']
11 ['272', '408', '512', '474', '285', '114', '64', '483', '59', '124']
8 ['64', '408', '169', '168', '178', '191', '100', '483', '98', '134']
162 ['127', '603', '963', '100', '64', '59', '169', '483', '178', '657']
279 ['199', '98', '613', '178', '496', '657', '936', '527', '701', '520']
145 ['127', '511', '318', '205', '179', '498', '272', '434', '208', '168']
28 ['169', '272', '172', '183', '408', '127', '963', '114', '318', '520']
135 ['169', '318', '408', '50', '483', '474', '64', '657', '172', '22']
32 ['483', '12', '603', '173', '318', '64', '515', '513', '480', '490']
90 ['484', '1449', '408', '487', '653', '114', '963', '655', '745', '510']
216 ['89', '176', '187', '484', '203', '180', '963', '641', '511', '478']
250 ['483', '513', '488', '657', '603', '178', '522', '512', '427', '205']
271 ['483', '251', '611', '1449', '484', '166', '641', '93', '223', '646']
265 ['923', '318', '134', '169', '488', '1039', '190', '8', '357', '197']
198 ['513', '408', '603', '124', '657', '178', '169', '483', '134', '12']
168 ['59', '513', '611', '114', '483', '480', '530', '408', '316', '923']
110 ['318', '513', '316', '59', '483', '511', '1194', '169', '427', '480']
58 ['285', '14', '114', '179', '154', '60', '23', '316', '86', '513']
237 ['318', '657', '170', '272', '515', '313', '251', '285', '57', '641']
94 ['285', '137', '919', '124', '511', '114', '1142', '116', '408', '198']
128 ['963', '272', '408', '313', '511', '661', '316', '12', '513', '285']
44 ['169', '178', '127', '12', '408', '199', '484', '1007', '661', '302']
264 ['427', '480', '496', '132', '174', '172', '483', '178', '127', '318']
41 ['114', '169', '89', '187', '923', '172', '480', '408', '483', '513']
82 ['165', '408', '478', '196', '59', '98', '136', '187', '114', '285']
262 ['408', '127', '12', '170', '603', '272', '357', '479', '513', '483']
174 ['22', '116', '64', '180', '516', '316', '921', '524', '48', '307']
43 ['483', '963', '515', '96', '1194', '481', '178', '520', '136', '357']
84 ['316', '923', '483', '515', '513', '603', '272', '169', '480', '251']
269 ['86', '958', '408', '652', '511', '60', '709', '275', '1137', '753']
259 ['127', '408', '603', '272', '483', '511', '50', '318', '191', '205']
85 ['603', '408', '178', '169', '285', '484', '302', '114', '1449', '653']
213 ['427', '480', '408', '178', '191', '498', '169', '316', '114', '302']
121 ['483', '64', '603', '511', '191', '302', '251', '657', '474', '170']
49 ['654', '474', '127', '603', '340', '23', '180', '357', '582', '169']
155 ['318', '480', '357', '127', '408', '114', '427', '64', '12', '603']
68 ['223', '190', '169', '64', '191', '408', '318', '483', '100', '427']
172 ['318', '64', '272', '22', '12', '963', '169', '408', '479', '83']
19 ['169', '64', '603', '483', '357', '316', '127', '408', '114', '511']
268 ['199', '657', '187', '318', '513', '430', '1194', '488', '64', '603']
5 ['484', '511', '134', '156', '187', '12', '246', '190', '7', '203']
80 ['169', '174', '408', '603', '657', '357', '98', '251', '173', '56']
66 ['318', '172', '174', '513', '923', '657', '483', '511', '114', '408']
18 ['272', '315', '114', '481', '124', '653', '657', '320', '251', '525']
26 ['318', '511', '603', '474', '357', '480', '513', '498', '483', '12']
130 ['408', '318', '520', '169', '923', '513', '59', '114', '320', '481']
256 ['272', '524', '661', '1194', '641', '923', '525', '603', '408', '318']
1 ['483', '1137', '408', '302', '320', '285', '318', '515', '1449', '512']
56 ['12', '318', '313', '923', '963', '302', '427', '657', '496', '223']
15 ['513', '657', '923', '172', '603', '528', '87', '511', '199', '963']
207 ['474', '963', '603', '496', '165', '519', '480', '408', '178', '498']
232 ['169', '427', '114', '408', '480', '190', '479', '134', '12', '89']
52 ['603', '408', '12', '272', '483', '64', '169', '50', '199', '114']
161 ['169', '357', '603', '114', '12', '64', '480', '190', '251', '512']
148 ['513', '483', '515', '657', '963', '480', '272', '170', '512', '64']
125 ['313', '265', '651', '480', '178', '169', '237', '603', '121', '12']
83 ['496', '313', '143', '923', '272', '528', '318', '427', '173', '487']
272 ['408', '114', '318', '64', '513', '316', '511', '488', '180', '480']
151 ['165', '48', '513', '269', '313', '285', '923', '936', '511', '316']
54 ['408', '89', '178', '515', '172', '64', '474', '479', '427', '134']
16 ['408', '169', '187', '313', '251', '186', '1039', '165', '132', '82']
91 ['496', '272', '173', '923', '178', '100', '196', '251', '316', '302']
294 ['318', '169', '172', '174', '64', '480', '173', '513', '98', '114']
229 ['357', '50', '318', '603', '100', '98', '483', '475', '496', '285']
36 ['408', '64', '169', '12', '513', '114', '923', '50', '178', '357']
70 ['318', '64', '12', '251', '515', '98', '178', '513', '603', '199']
14 ['169', '178', '114', '480', '136', '484', '246', '651', '185', '89']
295 ['474', '165', '514', '408', '189', '169', '923', '963', '114', '223']
233 ['513', '657', '479', '59', '316', '170', '474', '496', '408', '963']
214 ['923', '663', '183', '589', '272', '48', '654', '611', '963', '199']
192 ['169', '134', '12', '318', '408', '98', '64', '511', '136', '603']
100 ['318', '513', '515', '169', '480', '64', '59', '1137', '530', '190']
307 ['657', '223', '134', '603', '512', '190', '513', '199', '480', '285']
297 ['408', '276', '169', '483', '134', '189', '488', '60', '318', '190']
193 ['923', '169', '408', '318', '480', '483', '165', '603', '657', '427']
113 ['313', '272', '169', '513', '198', '285', '178', '12', '515', '474']
275 ['313', '483', '194', '59', '488', '178', '316', '641', '272', '190']
219 ['474', '408', '603', '483', '50', '173', '169', '1449', '190', '178']
218 ['318', '483', '357', '511', '272', '59', '316', '515', '479', '64']
123 ['169', '474', '211', '180', '519', '408', '611', '318', '603', '272']
158 ['603', '64', '357', '134', '169', '654', '192', '191', '515', '178']
302 ['12', '483', '318', '114', '657', '480', '408', '98', '185', '156']
23 ['12', '114', '474', '168', '169', '1142', '135', '198', '513', '963']
296 ['318', '12', '197', '302', '408', '1449', '603', '173', '64', '511']
33 ['12', '603', '169', '191', '64', '272', '127', '408', '318', '483']
154 ['56', '127', '408', '114', '64', '179', '318', '12', '657', '192']
77 ['408', '12', '169', '136', '525', '61', '469', '178', '603', '116']
270 ['302', '474', '1137', '515', '427', '512', '480', '132', '648', '479']
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611 ['169', '408', '59', '923', '513', '192', '483', '64', '318', '520']
617 ['432', '114', '59', '1449', '408', '512', '603', '487', '315', '182']
618 ['357', '520', '511', '272', '488', '194', '208', '611', '199', '480']
614 ['474', '318', '313', '496', '657', '513', '64', '483', '132', '178']
609 ['191', '427', '923', '302', '511', '59', '896', '318', '513', '494']
615 ['50', '511', '513', '169', '408', '174', '64', '12', '313', '134']
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620 ['515', '272', '318', '474', '408', '316', '64', '12', '513', '427']
571 ['963', '513', '272', '408', '483', '641', '169', '515', '479', '603']
619 ['178', '318', '191', '204', '496', '199', '196', '498', '210', '185']
613 ['483', '515', '178', '313', '496', '657', '169', '511', '474', '512']
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621 ['12', '190', '114', '199', '408', '42', '169', '513', '318', '272']
604 ['483', '172', '12', '515', '170', '50', '64', '174', '318', '114']
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612 ['169', '483', '408', '515', '178', '59', '302', '603', '313', '657']
627 ['127', '408', '483', '114', '192', '603', '50', '480', '657', '519']
623 ['89', '174', '480', '515', '318', '178', '172', '272', '169', '427']
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625 ['98', '318', '12', '114', '513', '511', '178', '272', '89', '316']
629 ['318', '169', '313', '408', '483', '480', '657', '211', '114', '963']
633 ['169', '272', '408', '185', '603', '483', '480', '178', '316', '512']
632 ['178', '313', '496', '427', '187', '408', '603', '659', '511', '272']
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634 ['98', '318', '357', '511', '603', '520', '133', '169', '64', '1039']
639 ['480', '408', '657', '169', '963', '316', '89', '603', '50', '530']
630 ['313', '57', '488', '513', '169', '481', '189', '512', '921', '633']
642 ['408', '316', '963', '513', '520', '515', '265', '923', '509', '9']
637 ['483', '313', '178', '641', '216', '114', '272', '513', '657', '487']
640 ['98', '603', '480', '272', '483', '408', '127', '923', '285', '511']
626 ['100', '474', '318', '50', '511', '127', '134', '185', '513', '89']
643 ['178', '191', '313', '134', '64', '135', '272', '661', '480', '475']
598 ['50', '169', '64', '174', '408', '318', '191', '513', '89', '285']
638 ['169', '318', '64', '178', '641', '513', '963', '28', '408', '313']
635 ['223', '50', '173', '603', '172', '1007', '483', '515', '474', '480']
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648 ['313', '258', '1039', '493', '270', '651', '333', '237', '408', '655']
647 ['272', '169', '318', '603', '408', '64', '483', '114', '515', '316']
650 ['178', '12', '318', '513', '169', '114', '408', '64', '963', '1449']
651 ['50', '496', '178', '357', '318', '174', '64', '513', '527', '483']
654 ['966', '64', '923', '190', '707', '487', '528', '427', '493', '485']
653 ['483', '114', '169', '408', '513', '963', '241', '484', '651', '59']
655 ['169', '408', '488', '487', '641', '168', '151', '177', '430', '189']
649 ['408', '318', '357', '12', '114', '64', '169', '272', '98', '483']
658 ['114', '474', '483', '513', '657', '64', '134', '12', '251', '272']
656 ['64', '127', '187', '480', '656', '313', '923', '427', '180', '174']
660 ['169', '408', '513', '199', '480', '12', '246', '127', '528', '114']
659 ['169', '923', '59', '12', '408', '427', '641', '318', '114', '1063']
646 ['603', '657', '483', '515', '169', '408', '12', '963', '641', '513']
663 ['172', '511', '195', '166', '169', '657', '408', '963', '114', '482']
664 ['178', '515', '474', '488', '114', '199', '923', '511', '170', '661']
657 ['316', '127', '318', '12', '59', '64', '313', '963', '98', '196']
665 ['408', '169', '178', '64', '114', '963', '513', '318', '199', '657']
666 ['408', '512', '165', '641', '922', '921', '923', '170', '285', '528']
661 ['12', '22', '483', '98', '641', '187', '963', '136', '661', '251']
662 ['64', '169', '408', '190', '56', '318', '170', '132', '474', '114']
667 ['483', '127', '320', '512', '169', '923', '276', '408', '100', '134']
641 ['272', '408', '169', '56', '923', '12', '313', '474', '114', '512']
668 ['12', '169', '603', '127', '513', '285', '515', '89', '64', '611']
673 ['923', '178', '169', '408', '483', '64', '427', '480', '223', '963']
671 ['313', '133', '496', '64', '318', '272', '513', '178', '317', '205']
669 ['513', '185', '134', '488', '317', '316', '1449', '606', '178', '285']
676 ['513', '408', '478', '178', '479', '199', '89', '484', '694', '963']
674 ['22', '963', '513', '174', '923', '59', '187', '318', '408', '657']
652 ['169', '483', '12', '493', '64', '515', '661', '114', '603', '22']
677 ['480', '12', '318', '313', '64', '11', '169', '474', '89', '203']
682 ['515', '603', '408', '134', '199', '178', '114', '606', '641', '513']
679 ['114', '408', '513', '657', '12', '251', '198', '59', '923', '48']
684 ['511', '357', '272', '313', '316', '114', '512', '603', '427', '480']
685 ['963', '483', '169', '657', '408', '100', '59', '513', '318', '1194']
683 ['50', '496', '603', '483', '12', '134', '174', '98', '173', '408']
691 ['515', '127', '12', '272', '313', '169', '483', '520', '199', '89']
672 ['169', '408', '114', '474', '483', '178', '427', '603', '963', '64']
692 ['169', '190', '318', '64', '98', '178', '408', '483', '50', '12']
690 ['96', '318', '527', '493', '515', '22', '408', '169', '603', '963']
689 ['408', '169', '657', '511', '603', '963', '114', '83', '528', '513']
686 ['479', '408', '923', '511', '513', '657', '272', '1194', '488', '169']
693 ['408', '169', '114', '657', '190', '603', '316', '189', '513', '481']
688 ['242', '51', '346', '474', '265', '86', '257', '387', '392', '486']
697 ['408', '169', '285', '493', '98', '64', '483', '1449', '519', '116']
698 ['318', '169', '223', '813', '64', '320', '98', '272', '475', '79']
670 ['313', '408', '357', '64', '114', '513', '427', '169', '963', '28']
694 ['313', '64', '272', '488', '512', '12', '408', '963', '1449', '170']
680 ['56', '483', '12', '174', '114', '484', '178', '963', '606', '48']
705 ['963', '709', '966', '923', '166', '515', '519', '57', '490', '125']
701 ['483', '603', '480', '169', '427', '223', '408', '923', '515', '98']
699 ['603', '480', '427', '357', '12', '408', '474', '483', '178', '519']
704 ['515', '483', '169', '408', '64', '513', '199', '190', '114', '474']
707 ['357', '169', '114', '408', '709', '98', '520', '272', '213', '512']
700 ['483', '603', '172', '513', '496', '89', '64', '479', '12', '22']
687 ['657', '318', '480', '408', '169', '474', '513', '187', '127', '98']
695 ['127', '98', '318', '12', '408', '483', '187', '175', '657', '64']
675 ['170', '64', '12', '513', '408', '357', '187', '114', '50', '180']
708 ['174', '963', '173', '64', '22', '318', '204', '169', '483', '172']
709 ['313', '408', '272', '114', '963', '12', '357', '133', '169', '134']
711 ['515', '507', '474', '14', '513', '357', '1449', '603', '285', '659']
710 ['474', '408', '513', '657', '647', '427', '488', '923', '480', '661']
712 ['272', '22', '318', '313', '923', '114', '357', '659', '480', '427']
715 ['169', '408', '357', '483', '127', '191', '199', '178', '478', '114']
713 ['64', '408', '474', '657', '12', '318', '114', '513', '357', '169']
716 ['302', '313', '170', '408', '657', '513', '272', '1449', '963', '251']
681 ['318', '513', '64', '190', '483', '408', '169', '923', '45', '251']
678 ['408', '12', '174', '318', '169', '114', '64', '272', '963', '511']
719 ['316', '483', '143', '480', '59', '521', '496', '22', '174', '190']
702 ['173', '169', '50', '408', '603', '64', '178', '651', '199', '172']
721 ['483', '178', '169', '205', '210', '515', '257', '183', '408', '1194']
714 ['480', '174', '22', '515', '172', '318', '79', '513', '316', '408']
717 ['318', '59', '511', '657', '169', '651', '408', '197', '603', '191']
718 ['98', '408', '318', '64', '427', '22', '169', '528', '178', '603']
696 ['408', '318', '169', '603', '512', '192', '474', '64', '528', '480']
722 ['169', '603', '408', '657', '513', '59', '48', '357', '483', '64']
724 ['963', '174', '12', '114', '318', '64', '408', '479', '474', '520']
727 ['480', '513', '313', '64', '963', '166', '430', '478', '641', '59']
725 ['178', '169', '483', '48', '511', '923', '408', '170', '480', '1194']
706 ['408', '169', '515', '313', '172', '12', '480', '64', '483', '22']
720 ['408', '98', '12', '488', '169', '175', '64', '56', '127', '114']
729 ['603', '64', '22', '89', '169', '174', '178', '479', '50', '136']
726 ['483', '408', '173', '603', '114', '169', '174', '479', '480', '50']
728 ['318', '483', '59', '12', '64', '114', '178', '199', '316', '169']
703 ['174', '513', '408', '603', '178', '511', '272', '172', '64', '96']
738 ['12', '114', '483', '520', '513', '302', '657', '963', '251', '272']
736 ['98', '114', '318', '480', '272', '408', '474', '169', '315', '657']
734 ['178', '64', '427', '251', '513', '480', '79', '963', '520', '12']
730 ['318', '64', '483', '963', '127', '657', '199', '603', '510', '661']
743 ['127', '357', '318', '511', '64', '56', '169', '251', '50', '427']
742 ['318', '64', '192', '474', '483', '12', '515', '316', '484', '197']
737 ['408', '318', '190', '316', '603', '134', '60', '9', '178', '286']
733 ['178', '483', '50', '64', '169', '134', '603', '1194', '272', '357']
745 ['22', '318', '408', '474', '313', '484', '525', '496', '657', '199']
740 ['318', '98', '483', '603', '197', '480', '169', '515', '513', '114']
735 ['357', '64', '96', '313', '513', '187', '191', '178', '705', '89']
747 ['520', '191', '193', '512', '177', '114', '251', '657', '1039', '340']
723 ['408', '603', '313', '127', '483', '12', '199', '657', '498', '513']
739 ['174', '169', '181', '408', '520', '511', '114', '659', '189', '313']
749 ['318', '272', '513', '493', '265', '520', '525', '427', '479', '657']
748 ['12', '127', '190', '513', '87', '1142', '11', '178', '136', '246']
746 ['313', '318', '169', '520', '272', '357', '143', '178', '191', '12']
731 ['178', '513', '169', '479', '657', '519', '318', '408', '251', '199']
750 ['50', '12', '318', '272', '483', '408', '64', '520', '174', '963']
741 ['318', '483', '127', '64', '511', '12', '474', '191', '408', '169']
751 ['357', '169', '318', '657', '199', '520', '64', '604', '474', '251']
756 ['302', '483', '408', '169', '285', '114', '963', '64', '199', '657']
757 ['12', '178', '408', '318', '357', '480', '641', '187', '427', '114']
752 ['318', '657', '12', '178', '515', '143', '169', '1007', '513', '921']
758 ['408', '318', '89', '357', '169', '114', '511', '178', '515', '661']
732 ['657', '408', '318', '483', '511', '98', '480', '223', '114', '50']
762 ['127', '483', '357', '50', '205', '178', '64', '474', '603', '114']
744 ['408', '64', '474', '318', '169', '98', '513', '1194', '480', '114']
754 ['169', '408', '50', '64', '190', '963', '48', '318', '114', '172']
753 ['318', '59', '493', '603', '57', '496', '498', '143', '169', '519']
763 ['408', '64', '169', '114', '178', '657', '45', '479', '641', '513']
764 ['169', '657', '515', '59', '603', '408', '357', '480', '165', '190']
767 ['408', '64', '169', '923', '318', '114', '272', '357', '603', '48']
769 ['127', '89', '50', '192', '603', '488', '185', '114', '408', '513']
755 ['22', '408', '169', '313', '173', '114', '64', '318', '272', '923']
771 ['285', '512', '963', '513', '659', '64', '856', '45', '657', '519']
768 ['408', '64', '318', '114', '169', '963', '22', '174', '12', '186']
773 ['483', '48', '199', '647', '474', '513', '114', '514', '657', '178']
765 ['474', '603', '169', '64', '408', '515', '479', '484', '483', '191']
772 ['603', '427', '480', '483', '408', '127', '318', '64', '357', '169']
766 ['56', '513', '408', '488', '190', '603', '169', '511', '100', '1007']
774 ['169', '408', '513', '483', '657', '302', '480', '603', '285', '487']
760 ['513', '178', '603', '187', '519', '408', '483', '515', '127', '514']
761 ['172', '174', '210', '22', '89', '114', '333', '684', '64', '480']
777 ['963', '64', '272', '408', '169', '735', '22', '83', '82', '520']
759 ['178', '318', '64', '513', '480', '199', '196', '22', '174', '483']
776 ['12', '172', '316', '64', '197', '42', '180', '482', '313', '963']
780 ['923', '482', '651', '64', '483', '963', '484', '114', '178', '408']
779 ['408', '313', '22', '64', '483', '114', '178', '12', '318', '317']
778 ['169', '114', '182', '408', '127', '489', '192', '156', '89', '12']
782 ['64', '22', '169', '318', '174', '385', '357', '191', '172', '511']
786 ['64', '272', '483', '427', '963', '408', '12', '613', '251', '474']
784 ['318', '64', '50', '22', '174', '172', '483', '169', '12', '181']
770 ['474', '98', '603', '515', '512', '480', '272', '48', '483', '408']
788 ['515', '136', '57', '169', '190', '493', '242', '408', '484', '923']
789 ['511', '172', '64', '313', '357', '318', '483', '408', '187', '661']
790 ['12', '169', '923', '963', '483', '114', '180', '272', '198', '478']
787 ['169', '168', '318', '50', '513', '173', '474', '511', '174', '172']
783 ['318', '483', '657', '480', '513', '511', '64', '12', '272', '357']
785 ['657', '480', '483', '408', '1194', '357', '172', '169', '179', '98']
794 ['98', '169', '12', '483', '223', '408', '64', '56', '190', '178']
781 ['285', '427', '12', '611', '493', '511', '408', '166', '114', '641']
796 ['166', '651', '963', '657', '923', '656', '207', '1642', '513', '242']
795 ['114', '285', '23', '408', '127', '483', '190', '192', '530', '223']
793 ['408', '169', '318', '64', '474', '483', '302', '190', '223', '114']
798 ['513', '408', '519', '127', '64', '963', '22', '479', '178', '657']
791 ['483', '114', '169', '316', '408', '511', '199', '223', '172', '174']
802 ['313', '50', '22', '172', '515', '272', '174', '181', '64', '318']
800 ['318', '64', '98', '169', '191', '190', '493', '483', '603', '134']
804 ['169', '178', '313', '963', '223', '923', '408', '190', '613', '272']
803 ['100', '483', '357', '318', '98', '12', '302', '285', '127', '190']
775 ['64', '357', '127', '318', '408', '483', '657', '169', '98', '427']
792 ['272', '515', '98', '64', '513', '318', '408', '603', '87', '480']
799 ['12', '100', '169', '98', '511', '408', '603', '114', '64', '357']
805 ['483', '302', '1449', '657', '114', '512', '647', '1194', '242', '474']
806 ['603', '285', '479', '134', '657', '173', '64', '513', '251', '165']
807 ['923', '963', '513', '64', '57', '611', '166', '306', '482', '603']
797 ['174', '313', '408', '496', '22', '427', '483', '172', '12', '318']
801 ['603', '480', '174', '272', '172', '408', '318', '64', '169', '12']
809 ['515', '408', '169', '12', '513', '923', '114', '134', '480', '178']
815 ['64', '170', '223', '8', '480', '497', '963', '272', '12', '408']
817 ['12', '483', '169', '408', '427', '521', '606', '657', '646', '489']
821 ['408', '199', '12', '285', '657', '50', '316', '515', '83', '923']
818 ['12', '483', '479', '169', '408', '484', '272', '641', '173', '474']
814 ['64', '50', '313', '174', '96', '22', '318', '178', '408', '963']
812 ['313', '64', '483', '170', '168', '318', '183', '479', '172', '50']
823 ['357', '603', '483', '179', '169', '9', '132', '272', '496', '185']
825 ['313', '318', '69', '22', '169', '963', '328', '300', '408', '64']
827 ['318', '64', '169', '408', '483', '1194', '98', '657', '603', '251']
829 ['127', '483', '12', '64', '654', '357', '56', '187', '191', '180']
811 ['603', '480', '174', '79', '172', '483', '408', '178', '127', '318']
830 ['318', '169', '496', '483', '603', '604', '199', '659', '64', '178']
826 ['64', '169', '963', '408', '483', '513', '641', '166', '480', '28']
831 ['127', '169', '408', '172', '357', '318', '513', '483', '180', '515']
819 ['480', '483', '408', '127', '318', '56', '64', '169', '114', '474']
828 ['603', '48', '98', '169', '474', '64', '189', '318', '511', '285']
808 ['174', '172', '408', '127', '64', '169', '12', '50', '963', '316']
835 ['657', '603', '479', '483', '493', '64', '170', '59', '169', '408']
833 ['86', '603', '482', '169', '318', '1449', '171', '131', '693', '661']
836 ['127', '64', '408', '483', '484', '50', '480', '199', '98', '169']
816 ['515', '480', '174', '479', '483', '318', '89', '199', '187', '513']
838 ['64', '483', '603', '178', '488', '493', '963', '269', '251', '923']
839 ['64', '659', '408', '318', '603', '14', '169', '496', '474', '519']
840 ['318', '408', '178', '124', '641', '251', '192', '258', '488', '1021']
832 ['603', '12', '657', '272', '64', '318', '963', '169', '408', '357']
810 ['474', '144', '98', '194', '603', '1137', '100', '515', '427', '512']
844 ['169', '64', '408', '114', '57', '357', '528', '479', '483', '657']
843 ['11', '272', '156', '315', '513', '707', '483', '12', '475', '178']
834 ['357', '64', '114', '318', '483', '172', '12', '174', '170', '169']
846 ['169', '408', '313', '750', '251', '963', '223', '114', '189', '646']
837 ['408', '64', '474', '315', '272', '134', '480', '641', '83', '513']
813 ['169', '318', '408', '64', '483', '657', '114', '178', '474', '190']
842 ['318', '127', '357', '603', '408', '56', '498', '657', '197', '1449']
847 ['515', '408', '316', '169', '483', '469', '59', '83', '64', '189']
848 ['272', '408', '64', '169', '513', '114', '313', '60', '515', '22']
822 ['483', '318', '657', '50', '114', '474', '199', '498', '178', '98']
852 ['318', '12', '173', '513', '64', '134', '272', '114', '923', '603']
851 ['96', '89', '195', '520', '488', '178', '205', '513', '197', '69']
849 ['242', '302', '474', '144', '98', '194', '603', '181', '515', '165']
854 ['408', '114', '474', '169', '251', '1142', '172', '320', '480', '654']
850 ['272', '64', '923', '313', '498', '178', '651', '258', '169', '483']
858 ['178', '64', '408', '272', '56', '169', '316', '488', '513', '480']
853 ['313', '513', '57', '134', '408', '923', '114', '170', '96', '169']
855 ['318', '22', '357', '180', '64', '316', '511', '408', '12', '173']
824 ['483', '427', '603', '114', '318', '169', '178', '12', '513', '64']
845 ['357', '64', '169', '14', '98', '114', '318', '603', '483', '408']
841 ['318', '64', '474', '408', '169', '963', '657', '483', '427', '12']
859 ['174', '64', '169', '50', '513', '173', '172', '318', '408', '12']
862 ['408', '318', '611', '190', '513', '316', '320', '30', '607', '114']
856 ['100', '178', '174', '474', '483', '408', '172', '114', '64', '12']
820 ['64', '12', '285', '127', '515', '603', '511', '98', '272', '483']
863 ['60', '513', '408', '223', '48', '11', '190', '12', '921', '189']
860 ['187', '483', '127', '318', '50', '12', '480', '357', '64', '511']
857 ['313', '272', '169', '483', '496', '64', '408', '603', '134', '223']
864 ['285', '187', '313', '320', '272', '170', '887', '480', '512', '302']
865 ['483', '12', '64', '514', '114', '652', '173', '48', '172', '480']
868 ['483', '134', '137', '171', '175', '48', '190', '514', '428', '60']
867 ['408', '169', '513', '114', '302', '127', '285', '1007', '357', '190']
861 ['923', '480', '483', '169', '223', '318', '427', '963', '528', '515']
870 ['408', '114', '173', '251', '515', '187', '205', '320', '285', '512']
871 ['169', '408', '963', '64', '318', '657', '496', '1007', '320', '144']
875 ['408', '199', '114', '316', '483', '178', '127', '285', '318', '513']
876 ['408', '169', '357', '474', '963', '190', '64', '12', '56', '98']
872 ['318', '190', '169', '174', '50', '963', '64', '513', '98', '408']
866 ['483', '515', '64', '357', '657', '408', '50', '603', '174', '318']
877 ['187', '127', '174', '64', '318', '172', '511', '963', '654', '100']
873 ['174', '318', '496', '50', '513', '195', '484', '12', '657', '64']
880 ['408', '169', '9', '313', '474', '963', '134', '480', '178', '340']
878 ['313', '408', '480', '520', '272', '169', '963', '134', '133', '513']
869 ['169', '474', '408', '285', '114', '483', '190', '172', '170', '603']
881 ['318', '923', '516', '656', '12', '272', '258', '603', '190', '607']
879 ['98', '64', '357', '169', '408', '318', '272', '12', '515', '285']
883 ['179', '654', '1007', '23', '474', '32', '114', '483', '200', '268']
882 ['318', '64', '178', '480', '408', '603', '483', '657', '320', '513']
884 ['483', '318', '168', '474', '496', '64', '178', '132', '657', '513']
886 ['169', '408', '199', '511', '478', '1062', '210', '705', '114', '251']
885 ['64', '22', '190', '313', '114', '923', '198', '408', '963', '480']
889 ['316', '176', '490', '136', '963', '272', '251', '478', '661', '205']
874 ['318', '56', '64', '657', '12', '474', '408', '192', '483', '169']
892 ['199', '474', '408', '512', '513', '223', '520', '923', '185', '603']
890 ['56', '178', '475', '100', '318', '408', '180', '191', '12', '169']
893 ['357', '408', '114', '513', '318', '169', '480', '127', '657', '427']
887 ['165', '275', '174', '483', '507', '189', '169', '60', '306', '707']
891 ['603', '515', '272', '64', '22', '923', '408', '316', '318', '79']
894 ['192', '187', '474', '483', '408', '180', '178', '169', '185', '654']
896 ['194', '169', '357', '242', '521', '498', '178', '408', '523', '165']
897 ['318', '192', '242', '357', '64', '251', '480', '100', '519', '427']
901 ['318', '313', '496', '143', '657', '483', '64', '178', '79', '603']
899 ['22', '191', '313', '12', '316', '251', '963', '513', '143', '165']
903 ['923', '169', '603', '408', '178', '174', '498', '661', '474', '480']
904 ['483', '199', '647', '515', '98', '318', '1449', '64', '641', '484']
907 ['242', '302', '265', '451', '603', '1137', '515', '480', '498', '208']
905 ['408', '483', '50', '513', '127', '603', '657', '515', '474', '114']
902 ['408', '173', '132', '64', '169', '357', '190', '496', '114', '98']
898 ['50', '174', '173', '483', '169', '64', '318', '172', '12', '178']
895 ['318', '127', '474', '483', '64', '357', '302', '98', '285', '408']
906 ['603', '483', '318', '357', '427', '169', '89', '178', '64', '657']
900 ['114', '408', '1007', '513', '134', '172', '515', '514', '12', '1194']
908 ['272', '64', '169', '178', '285', '480', '313', '520', '659', '705']
916 ['127', '285', '124', '513', '272', '408', '963', '114', '187', '116']
911 ['127', '169', '64', '318', '408', '515', '286', '527', '251', '493']
912 ['408', '603', '22', '480', '165', '923', '657', '8', '313', '651']
914 ['64', '100', '357', '483', '173', '475', '251', '127', '50', '318']
918 ['513', '114', '408', '357', '515', '483', '474', '528', '657', '169']
919 ['474', '603', '134', '493', '512', '190', '357', '496', '185', '427']
921 ['22', '64', '318', '208', '1063', '12', '451', '216', '513', '258']
910 ['408', '64', '318', '169', '114', '132', '191', '480', '208', '8']
913 ['496', '251', '124', '242', '313', '513', '199', '165', '194', '1039']
915 ['483', '480', '132', '134', '657', '318', '654', '169', '408', '427']
922 ['483', '169', '479', '603', '923', '134', '480', '511', '12', '165']
923 ['98', '194', '603', '427', '512', '480', '132', '479', '272', '483']
928 ['474', '603', '515', '427', '512', '480', '275', '498', '132', '174']
927 ['333', '57', '479', '1194', '136', '656', '603', '89', '357', '272']
924 ['136', '199', '22', '483', '513', '657', '479', '48', '651', '603']
929 ['408', '64', '657', '603', '513', '173', '272', '114', '285', '357']
931 ['408', '480', '427', '185', '603', '169', '483', '474', '170', '488']
917 ['12', '64', '275', '408', '589', '23', '59', '603', '357', '185']
932 ['318', '50', '408', '132', '127', '604', '187', '12', '268', '272']
909 ['480', '132', '483', '408', '318', '64', '169', '50', '114', '657']
934 ['318', '923', '166', '480', '8', '408', '98', '306', '357', '114']
933 ['191', '192', '520', '496', '663', '134', '185', '272', '525', '657']
935 ['657', '513', '114', '480', '64', '408', '641', '272', '515', '59']
938 ['357', '483', '963', '12', '318', '511', '657', '174', '615', '172']
940 ['408', '603', '479', '528', '512', '114', '169', '923', '483', '22']
888 ['408', '318', '169', '483', '189', '64', '127', '187', '512', '272']
925 ['64', '12', '318', '515', '22', '603', '520', '169', '408', '178']
942 ['408', '64', '169', '114', '483', '178', '190', '98', '251', '223']
937 ['169', '174', '12', '483', '181', '302', '963', '127', '923', '172']
926 ['12', '408', '483', '515', '50', '45', '134', '169', '603', '64']
943 ['272', '223', '169', '114', '180', '175', '1142', '209', '480', '357']
939 ['242', '144', '246', '98', '603', '100', '196', '526', '427', '165']
936 ['483', '302', '603', '169', '513', '191', '474', '98', '185', '208']
930 ['318', '272', '513', '603', '528', '496', '313', '98', '83', '660']
920 ['316', '275', '64', '923', '483', '427', '169', '408', '516', '83']
941 ['64', '285', '483', '12', '127', '169', '190', '657', '515', '663']

알고리즘 평가¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

처음하는 파이썬 머신러닝 부트캠프

쉽게! 캐글 문제와 함께 분류,회귀,군집화 익히기

자세히 알아보기 모든 강좌 보기

In [17]:

import surprise
from surprise import Dataset
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

In [23]:

sim_options = {'name': 'msd'}
algo = surprise.KNNBasic(sim_options=sim_options)
surprise.evaluate(algo, data)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9911
MAE:  0.7833
------------
Fold 2
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9833
MAE:  0.7771
------------
Fold 3
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9894
MAE:  0.7819
------------
------------
Mean RMSE: 0.9879
Mean MAE : 0.7808
------------
------------

Out[23]:

CaseInsensitiveDefaultDict(list,
                           {'mae': [0.78332085007914287,
                             0.7771041490260826,
                             0.78185859406389302],
                            'rmse': [0.99109548719657858,
                             0.98332811659672703,
                             0.9893776110540401]})

In [24]:

sim_options = {'name': 'cosine'}
algo = surprise.KNNBasic(sim_options=sim_options)
surprise.evaluate(algo, data)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Computing the cosine similarity matrix...
Done computing similarity matrix.
RMSE: 1.0244
MAE:  0.8108
------------
Fold 2
Computing the cosine similarity matrix...
Done computing similarity matrix.
RMSE: 1.0187
MAE:  0.8061
------------
Fold 3
Computing the cosine similarity matrix...
Done computing similarity matrix.
RMSE: 1.0226
MAE:  0.8094
------------
------------
Mean RMSE: 1.0219
Mean MAE : 0.8088
------------
------------

Out[24]:

CaseInsensitiveDefaultDict(list,
                           {'mae': [0.81079510431791924,
                             0.80605631128339117,
                             0.80941768920884594],
                            'rmse': [1.0243634073960175,
                             1.0187482414191331,
                             1.0225720777877443]})

In [25]:

sim_options = {'name': 'pearson'}
algo = surprise.KNNBasic(sim_options=sim_options)
surprise.evaluate(algo, data)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Computing the pearson similarity matrix...
Done computing similarity matrix.
RMSE: 1.0239
MAE:  0.8120
------------
Fold 2
Computing the pearson similarity matrix...
Done computing similarity matrix.
RMSE: 1.0167
MAE:  0.8083
------------
Fold 3
Computing the pearson similarity matrix...
Done computing similarity matrix.
RMSE: 1.0214
MAE:  0.8099
------------
------------
Mean RMSE: 1.0207
Mean MAE : 0.8101
------------
------------

Out[25]:

CaseInsensitiveDefaultDict(list,
                           {'mae': [0.81200632767328063,
                             0.80826931547507119,
                             0.80989597847789219],
                            'rmse': [1.0239246403571562,
                             1.0167128227144424,
                             1.0213664721488831]})

In [58]:

sim_options = {'name': 'pearson_baseline'}
algo = surprise.KNNBasic(sim_options=sim_options)
surprise.evaluate(algo, data)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Estimating biases using als...
Computing the pearson_baseline similarity matrix...
Done computing similarity matrix.
RMSE: 1.0049
MAE:  0.7968
------------
Fold 2
Estimating biases using als...
Computing the pearson_baseline similarity matrix...
Done computing similarity matrix.
RMSE: 1.0103
MAE:  0.7985
------------
Fold 3
Estimating biases using als...
Computing the pearson_baseline similarity matrix...
Done computing similarity matrix.
RMSE: 1.0110
MAE:  0.8002
------------
------------
Mean RMSE: 1.0087
Mean MAE : 0.7985
------------
------------

Out[58]:

CaseInsensitiveDefaultDict(list,
                           {'mae': [0.79675695245350209,
                             0.7984790061210334,
                             0.8002162404915526],
                            'rmse': [1.0048624637086425,
                             1.0102925000197331,
                             1.0110090350939811]})

In [26]:

algo = surprise.SVD()
surprise.evaluate(algo, data)

Evaluating RMSE, MAE of algorithm SVD.

------------
Fold 1
RMSE: 0.9498
MAE:  0.7498
------------
Fold 2
RMSE: 0.9458
MAE:  0.7463
------------
Fold 3
RMSE: 0.9446
MAE:  0.7453
------------
------------
Mean RMSE: 0.9467
Mean MAE : 0.7471
------------
------------

Out[26]:

CaseInsensitiveDefaultDict(list,
                           {'mae': [0.749794336106132,
                             0.7462662006765739,
                             0.74533450362095799],
                            'rmse': [0.94976543487583887,
                             0.94580670524240762,
                             0.94459061787140985]})

잔재미코딩

파이썬 surprise 라이브러리의 SVD 모델

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

7. 파이썬 surprise 라이브러리의 SVD 모델¶

7.1. MovieLens 데이터를 기반으로 하는 실제 예제¶

MovieLens 데이터 로드¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

트레이닝 + 모델 저장¶

정확도 계산¶

모델 최적화 (파라미터 튜닝)¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

사용자별 영화 추천 예 - offline 방식으로 별도 테이블을 만들어서 저장하고, 해당 사용자 로그인시 웹에서 추천 시나리오¶

추천성능 평가¶

알고리즘 평가¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

정확도(Precision), 재현율(Recall)과 참고사항

잔재미코딩

파이썬 기초 (문제풀이)

파이썬과 객체지향 프로그래밍

파이썬과 컴퓨터 사이언스(자료구조)

파이썬과 컴퓨터 사이언스(기본 알고리즘)

파이썬 특수 문법(데코레이터, 이터레이터등)

파이썬과 컴퓨터 사이언스(고급 알고리즘)

웹크롤링 기본

웹크롤링 심화

다양한 포멧의 데이터 수집

데이터 분석(추천시스템)

운영체제(OS) 부트캠프

리눅스 (linux) 익히기

클라우드 컴퓨팅 (AWS)

웹서비스 기술 익히기

데이터베이스 기본 (MySQL)

데이터베이스 심화 (MySQL+AWS)

데이터베이스 예제코드 (MySQL+AWS)

데이터베이스 NoSQL 기본 (mongoDB)

데이터베이스 NoSQL 심화 (mongoDB)

파이썬 surprise 라이브러리의 SVD 모델

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

7. 파이썬 surprise 라이브러리의 SVD 모델¶

7.1. MovieLens 데이터를 기반으로 하는 실제 예제¶

MovieLens 데이터 로드¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

트레이닝 + 모델 저장¶

정확도 계산¶

모델 최적화 (파라미터 튜닝)¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

사용자별 영화 추천 예 - offline 방식으로 별도 테이블을 만들어서 저장하고, 해당 사용자 로그인시 웹에서 추천 시나리오¶

추천성능 평가¶

알고리즘 평가¶

혹시 본 자료가 도움이 되셨다면, 이런 식으로 잘 정리된 코드/자료로 만든 제 강의 한번 봐주실래요?

SVD (SVD와 Latent Factor 모형) 정확도(Precision), 재현율(Recall)과 참고사항

정확도(Precision), 재현율(Recall)과 참고사항