[サプライズ]推奨機械学習システム - 映画の推薦

協調フィルタリングを達成するために、必要な手順？

1.収集、ユーザの好みが
似たユーザーやアイテムの検索2.
3.計算が推奨しました

ユーザーと記事の比較に基づいて協調フィルタリングに基づく協調フィルタリング

例：

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  

行列分解モデル

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
------------

影響評価

import pandas as pd  

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

協調フィルタリングの項目に基づいて、

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)

推奨ユーザーが10本の映画が好き

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)