# Comparison of two algorithms¶

We will see in this notebook how we can compare the prediction accuracy of two algorithms.

In [1]:
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pickle
import os

import pandas as pd

from surprise import SVD
from surprise import KNNBasic
from surprise import Dataset
from surprise import dump
from surprise.accuracy import rmse

In [2]:
# We will train and test on the u1.base and u1.test files of the movielens-100k dataset.
# You can do it manually, or by running:

# Now, let's load the dataset
train_file = os.path.expanduser('~') + '/.surprise_data/ml-100k/ml-100k/u1.base'
test_file = os.path.expanduser('~') + '/.surprise_data/ml-100k/ml-100k/u1.test'

# We'll use the well-known SVD algorithm and a basic nearest neighbors approach.
algo_svd = SVD()
algo_knn = KNNBasic()

for trainset, testset in data.folds():
algo_svd.train(trainset)
predictions_svd = algo_svd.test(testset)

algo_knn.train(trainset)
predictions_knn = algo_knn.test(testset)

rmse(predictions_svd)
rmse(predictions_knn)

dump.dump('./dump_SVD', predictions_svd, algo_svd)
dump.dump('./dump_KNN', predictions_knn, algo_knn)

Computing the msd similarity matrix...
Done computing similarity matrix.
RMSE: 0.9500
RMSE: 0.9889

In [4]:
# The dumps have been saved and we can now use them whenever we want.

df_svd = pd.DataFrame(predictions_svd, columns=['uid', 'iid', 'rui', 'est', 'details'])
df_knn = pd.DataFrame(predictions_knn, columns=['uid', 'iid', 'rui', 'est', 'details'])

df_svd['err'] = abs(df_svd.est - df_svd.rui)
df_knn['err'] = abs(df_knn.est - df_knn.rui)


We now have two dataframes with the all the predictions for each algorithm. The cool thing is that, as both algorithm have been tested on the same testset, the indexes of the two dataframes are the same!

In [5]:
df_svd.head()

Out[5]:
uid iid rui est details err
0 1 6 5.0 3.300098 {'was_impossible': False} 1.699902
1 1 10 3.0 3.673812 {'was_impossible': False} 0.673812
2 1 12 5.0 4.746471 {'was_impossible': False} 0.253529
3 1 14 5.0 4.031972 {'was_impossible': False} 0.968028
4 1 17 3.0 2.744526 {'was_impossible': False} 0.255474
In [6]:
df_knn.head()

Out[6]:
uid iid rui est details err
0 1 6 5.0 3.468613 {'was_impossible': False, 'actual_k': 20} 1.531387
1 1 10 3.0 3.866290 {'was_impossible': False, 'actual_k': 40} 0.866290
2 1 12 5.0 4.538194 {'was_impossible': False, 'actual_k': 40} 0.461806
3 1 14 5.0 4.235741 {'was_impossible': False, 'actual_k': 40} 0.764259
4 1 17 3.0 3.228002 {'was_impossible': False, 'actual_k': 40} 0.228002
In [7]:
# Let's check how good are the KNN predictions when the SVD has a huge error:
df_knn[df_svd.err >= 3.5]

Out[7]:
uid iid rui est details err
4198 89 221 1.0 3.766186 {'was_impossible': False, 'actual_k': 40} 2.766186
7385 167 48 1.0 4.259556 {'was_impossible': False, 'actual_k': 40} 3.259556
7390 167 169 1.0 4.664991 {'was_impossible': False, 'actual_k': 40} 3.664991
13972 295 183 1.0 4.202611 {'was_impossible': False, 'actual_k': 40} 3.202611
15306 312 265 1.0 4.131875 {'was_impossible': False, 'actual_k': 40} 3.131875
19140 405 575 5.0 2.410506 {'was_impossible': False, 'actual_k': 36} 2.589494
In [8]:
# Well... Not much better.
# Now, let's look at the predictions of SVD on the 10 worst predictions for KNN
df_svd.iloc[df_knn.sort_values(by='err')[-10:].index]

Out[8]:
uid iid rui est details err
9406 208 302 1.0 4.326863 {'was_impossible': False} 3.326863
19089 405 169 1.0 3.039247 {'was_impossible': False} 2.039247
19785 436 132 1.0 4.243595 {'was_impossible': False} 3.243595
157 2 315 1.0 4.260016 {'was_impossible': False} 3.260016
8503 193 56 1.0 4.129742 {'was_impossible': False} 3.129742
5531 113 976 5.0 3.320028 {'was_impossible': False} 1.679972
7917 181 408 1.0 2.962557 {'was_impossible': False} 1.962557
7390 167 169 1.0 4.720954 {'was_impossible': False} 3.720954
7412 167 1306 5.0 3.350088 {'was_impossible': False} 1.649912
5553 114 1104 5.0 3.070772 {'was_impossible': False} 1.929228
In [9]:
# How different are the predictions from both algorithms ?
# Let's count the number of predictions for each rating value

import matplotlib.pyplot as plt
import matplotlib
%matplotlib notebook
matplotlib.style.use('ggplot')

figure, (ax1, ax2) = plt.subplots(1, 2)

df_svd.est.plot(kind='hist', title='SVD', ax=ax1)
df_knn.est.plot(kind='hist', title='KNN', ax=ax2)

# As expected, one of the drawbacks of the NN algorithms is that their predictions are often
# quite concentrated around the mean. The SVD algorithm seems more confortable predicting extreme rating values.

Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff756a46ef0>
In [11]:
# Question: when a user has rated only a small number of items (less than 10), which algorithm
# gives the best predictions on average?

def get_Iu(uid):
"""Return the number of items rated by given user

Args:
uid: The raw id of the user.
Returns:
The number of items rated by the user.
"""

try:
return len(trainset.ur[trainset.to_inner_uid(uid)])
except ValueError:  # user was not part of the trainset
return 0

df_knn['Iu'] = df_knn.uid.apply(get_Iu)
df_svd['Iu'] = df_svd.uid.apply(get_Iu)

df_knn[df_knn.Iu < 10].err.mean(), df_svd[df_svd.Iu < 10].err.mean()

Out[11]:
(1.0382962702232326, 1.0130235152149263)