[서프라이즈] 권장 기계 학습 시스템 - 영화 추천

협업 필터링, 필요한 단계를 달성하기 위해?

1. 수집 사용자 환경 설정이
유사한 사용자 또는 항목 찾기 2.
3. 계산이 권장

사용자 및 협력 기반 협업 필터링은 문서의 비교를 기반으로 필터링

예 :

from surprise import KNNBasic,SVD
from surprise import Dataset
from surprise import evaluate, print_perf
# http://surprise.readthedocs.io/en/stable/index.html
# http://files.grouplens.org/datasets/movielens/ml-100k-README.txt

# Load the movielens-100k dataset (download it if needed),
# and split it into 3 folds for cross-validation.
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

# We'll use the famous KNNBasic algorithm.
algo = KNNBasic()

# Evaluate performances of our algorithm on the dataset.
perf = evaluate(algo, data, measures=['RMSE', 'MAE'])

print_perf(perf)

Evaluating RMSE, MAE of algorithm KNNBasic.

------------
Fold 1
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9876
MAE:  0.7807
------------
Fold 2
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9871
MAE:  0.7796
------------
Fold 3
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9902
MAE:  0.7818
------------
------------
Mean RMSE: 0.9883
Mean MAE : 0.7807
------------
------------
        Fold 1  Fold 2  Fold 3  Mean    
MAE     0.7807  0.7796  0.7818  0.7807  
RMSE    0.9876  0.9871  0.9902  0.9883  

매트릭스 분해 모델

from surprise import GridSearch

param_grid = {'n_epochs': [5, 10], 'lr_all': [0.002, 0.005],
              'reg_all': [0.4, 0.6]}
grid_search = GridSearch(SVD, param_grid, measures=['RMSE', 'FCP'])
data = Dataset.load_builtin('ml-100k')
data.split(n_folds=3)

grid_search.evaluate(data)

------------
Parameters combination 1 of 8
params:  {'lr_all': 0.002, 'n_epochs': 5, 'reg_all': 0.4}
------------
Mean RMSE: 0.9972
Mean FCP : 0.6843
------------
------------
Parameters combination 2 of 8
params:  {'lr_all': 0.005, 'n_epochs': 5, 'reg_all': 0.4}
------------
Mean RMSE: 0.9734
Mean FCP : 0.6946
------------
------------
Parameters combination 3 of 8
params:  {'lr_all': 0.002, 'n_epochs': 10, 'reg_all': 0.4}
------------
Mean RMSE: 0.9777
Mean FCP : 0.6926
------------
------------
Parameters combination 4 of 8
params:  {'lr_all': 0.005, 'n_epochs': 10, 'reg_all': 0.4}
------------
Mean RMSE: 0.9635
Mean FCP : 0.6987
------------
------------
Parameters combination 5 of 8
params:  {'lr_all': 0.002, 'n_epochs': 5, 'reg_all': 0.6}
------------
Mean RMSE: 1.0029
Mean FCP : 0.6875
------------
------------
Parameters combination 6 of 8
params:  {'lr_all': 0.005, 'n_epochs': 5, 'reg_all': 0.6}
------------
Mean RMSE: 0.9820
Mean FCP : 0.6953
------------
------------
Parameters combination 7 of 8
params:  {'lr_all': 0.002, 'n_epochs': 10, 'reg_all': 0.6}
------------
Mean RMSE: 0.9860
Mean FCP : 0.6943
------------
------------
Parameters combination 8 of 8
params:  {'lr_all': 0.005, 'n_epochs': 10, 'reg_all': 0.6}
------------
Mean RMSE: 0.9733
Mean FCP : 0.6991
------------

영향 평가

import pandas as pd  

results_df = pd.DataFrame.from_dict(grid_search.cv_results)
results_df

협업 필터링 항목을 바탕으로

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
import os
import io

from surprise import KNNBaseline
from surprise import Dataset


def read_item_names():


    file_name = ('./ml-100k/u.item')
    rid_to_name = {}
    name_to_rid = {}
    with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
        for line in f:
            line = line.split('|')
            rid_to_name[line[0]] = line[1]
            name_to_rid[line[1]] = line[0]

    return rid_to_name, name_to_rid



data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
sim_options = {'name': 'pearson_baseline', 'user_based': False}
algo = KNNBaseline(sim_options=sim_options)
algo.train(trainset)

권장 사용자는 10 영화를 좋아한다

toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=10)
toy_story_neighbors

toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)
                       for inner_id in toy_story_neighbors)
toy_story_neighbors = (rid_to_name[rid]
                       for rid in toy_story_neighbors)

print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
    print(movie)

The 10 nearest neighbors of Toy Story are:
While You Were Sleeping (1995)
Batman (1989)
Dave (1993)
Mrs. Doubtfire (1993)
Groundhog Day (1993)
Raiders of the Lost Ark (1981)
Maverick (1994)
French Kiss (1995)
Stand by Me (1986)
Net, The (1995)