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)打印结果：
Evaluating RMSE, MAE of algorithm KNNBasic.
------------
Fold 1
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9867
MAE: 0.7792
------------
Fold 2
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9884
MAE: 0.7811
------------
Fold 3
Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9896
MAE: 0.7826
------------
------------
Mean RMSE: 0.9883
Mean MAE : 0.7810
------------
------------
Fold 1 Fold 2 Fold 3 Mean
RMSE 0.9867 0.9884 0.9896 0.9883
MAE 0.7792 0.7811 0.7826 0.7810
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)打印结果：
Running grid search for the following parameter combinations:
{'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.4}
{'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.6}
{'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.4}
{'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.6}
{'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.4}
{'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.6}
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.6}
Resulsts:
{'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.4}
{'RMSE': 0.9969328477745982, 'FCP': 0.683368400695696}
----------
{'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.6}
{'RMSE': 1.0033151605930943, 'FCP': 0.6867249347580507}
----------
{'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.4}
{'RMSE': 0.9734942565850515, 'FCP': 0.6940454873982795}
----------
{'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.6}
{'RMSE': 0.9823131855683238, 'FCP': 0.6944827981040061}
----------
{'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.4}
{'RMSE': 0.977887292257368, 'FCP': 0.6923914815948694}
----------
{'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.6}
{'RMSE': 0.9862324957086702, 'FCP': 0.69290504024308}
----------
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}
{'RMSE': 0.9636592234524777, 'FCP': 0.6981147216456689}
----------
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.6}
{'RMSE': 0.9734215277751971, 'FCP': 0.6982590050003091}
----------
# best RMSE score
print(grid_search.best_score['RMSE'])
# combination of parameters that gave the best RMSE score
print(grid_search.best_params['RMSE'])
# best FCP score
print(grid_search.best_score['FCP'])
# combination of parameters that gave the best FCP score
print(grid_search.best_params['FCP'])打印结果：
0.9636592234524777
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}
0.6982590050003091
{'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.6}
import pandas as pd
results_df = pd.DataFrame.from_dict(grid_search.cv_results)
results_df打印结果：
| params | scores | |
|---|---|---|
| 0 | {'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.4} | {'RMSE': 0.9969328477745982, 'FCP': 0.68336840... |
| 1 | {'n_epochs': 5, 'lr_all': 0.002, 'reg_all': 0.6} | {'RMSE': 1.0033151605930943, 'FCP': 0.68672493... |
| 2 | {'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.4} | {'RMSE': 0.9734942565850515, 'FCP': 0.69404548... |
| 3 | {'n_epochs': 5, 'lr_all': 0.005, 'reg_all': 0.6} | {'RMSE': 0.9823131855683238, 'FCP': 0.69448279... |
| 4 | {'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.4} | {'RMSE': 0.977887292257368, 'FCP': 0.692391481... |
| 5 | {'n_epochs': 10, 'lr_all': 0.002, 'reg_all': 0.6} | {'RMSE': 0.9862324957086702, 'FCP': 0.69290504... |
| 6 | {'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4} | {'RMSE': 0.9636592234524777, 'FCP': 0.69811472... |
| 7 | {'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.6} | {'RMSE': 0.9734215277751971, 'FCP': 0.6982590 |
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 = ('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()#转换成最初标准的矩阵形式
sim_options = {'name': 'pearson_baseline', 'user_based': False}
algo = KNNBaseline(sim_options=sim_options)
algo.train(trainset)rid_to_name, name_to_rid = read_item_names()
toy_story_raw_id = name_to_rid['Now and Then (1995)']#先找电影的Id
toy_story_raw_id1053'
toy_story_inner_id = algo.trainset.to_inner_iid(toy_story_raw_id)
toy_story_inner_id#在矩阵中的ID打印结果：
961
toy_story_neighbors = algo.get_neighbors(toy_story_inner_id, k=5)#找离当前电影最近的5个电影
toy_story_neighbors打印结果：
[291, 82, 366, 528, 179]
toy_story_neighbors = (algo.trainset.to_raw_iid(inner_id)
for inner_id in toy_story_neighbors)#先转回电影的Id
toy_story_neighbors = (rid_to_name[rid]
for rid in toy_story_neighbors)#再转回电影的名字
print()
print('The 10 nearest neighbors of Toy Story are:')
for movie in toy_story_neighbors:
print(movie)打印结果：
The 10 nearest neighbors of Toy Story are: While You Were Sleeping (1995) Batman (1989) Dave (1993) Mrs. Doubtfire (1993) Groundhog Day (1993)