Table of Contents¶

1 Data Challenge : Mangaki - September 2017

1.1 Reading data

1.2 First prediction model

1.3 Better predicted models

1.3.1 Using watched.csv

1.3.1.1 Maping string-rating to probability of seeing the movie

1.3.2 Using titles.csv

1.4 Evaluation from the data challenge platform

Data Challenge : Mangaki - September 2017¶

See here for more information.

Author: Lilian Besson.

License: MIT License.

Reading data¶

We have a few CSV files, let start by reading them.

In [1]:

from tqdm import tqdm
import numpy as np
import pandas as pd

In [2]:

!ls -larth *.csv

-rw-r--r-- 1 lilian lilian  350K juin  27 15:10 titles.csv
-rw-r--r-- 1 lilian lilian  3,2M juin  27 15:25 watched.csv
-rw-r--r-- 1 lilian lilian 1010K juin  27 15:34 test.csv
-rw-r--r-- 1 lilian lilian  124K juin  28 17:55 train.csv
-rw-r--r-- 1 lilian lilian  2,4M juin  28 17:57 submission.csv

In [3]:

!cp -vf submission.csv submission.csv.old

'submission.csv' -> 'submission.csv.old'

In [57]:

train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
titles = pd.read_csv("titles.csv")
watched = pd.read_csv("watched.csv")

In [56]:

np.unique(titles.category)

Out[56]:

array(['album', 'anime', 'manga'], dtype=object)

Just to check they have correctly been read:

In [58]:

train[:5]
len(train)
min(train['user_id']), max(train['user_id'])
min(train['work_id']), max(train['work_id'])

Out[58]:

	user_id	work_id	rating
0	50	4041	0
1	508	1713	0
2	1780	7053	1
3	658	8853	0
4	1003	9401	0

Out[58]:

(1, 1982)

Out[58]:

(2, 9884)

In [6]:

test[:5]
len(test)
min(test['user_id']), max(test['user_id'])
min(test['work_id']), max(test['work_id'])

Out[6]:

	user_id	work_id
0	486	1086
1	1509	3296
2	617	1086
3	270	9648
4	459	3647

Out[6]:

(0, 1982)

Out[6]:

(2, 9884)

In [7]:

watched[:5]
len(watched)
min(watched['user_id']), max(watched['user_id'])
min(watched['work_id']), max(watched['work_id'])

Out[7]:

	user_id	work_id	rating
0	717	8025	dislike
1	1106	1027	neutral
2	1970	3949	neutral
3	1685	9815	like
4	1703	3482	like

Out[7]:

(0, 1982)

Out[7]:

(0, 9896)

First prediction model¶

For each movie, compute the empirical average rating of users who saw it, using data from the train data.
And simply use this to predict for the other users in test data.

In [32]:

submission = test.copy()

In [33]:

total_average_rating = train.rating.mean()

In [34]:

submission[:5]
len(submission)

Out[34]:

	user_id	work_id
0	486	1086
1	1509	3296
2	617	1086
3	270	9648
4	459	3647

Out[34]:

In [35]:

works_id = np.unique(np.append(test.work_id.unique(), train.work_id.unique()))

In [36]:

mean_ratings = pd.DataFrame(data={'mean_rating': 0}, index=works_id)
mean_ratings[:5]
len(mean_ratings)

Out[36]:

	mean_rating
2	0
3	0
4	0
5	0
9	0

Out[36]:

In [37]:

computed_means = pd.DataFrame(data={'mean_rating': train.groupby('work_id').mean()['rating']}, index=works_id)
computed_means[:5]
len(computed_means)

Out[37]:

	mean_rating
2	0.230769
3	NaN
4	0.500000
5	0.333333
9	0.200000

Out[37]:

In [38]:

mean_ratings.update(computed_means)

In [39]:

mean_ratings[:10]
len(mean_ratings)

Out[39]:

	mean_rating
2	0.230769
3	0.000000
4	0.500000
5	0.333333
9	0.200000
22	0.000000
23	0.000000
24	0.000000
27	0.333333
28	0.333333

Out[39]:

In [41]:

submission = submission.join(mean_ratings, on='work_id')
submission.rename_axis({'mean_rating': 'prob_willsee'}, axis="columns", inplace=True)

In [43]:

# in case of mean on empty values
submission.fillna(value=total_average_rating, inplace=True)

In [51]:

submission[:10]

Out[51]:

	user_id	work_id	prob_willsee
0	486	1086	0.440000
1	1509	3296	0.000000
2	617	1086	0.440000
3	270	9648	0.529412
4	459	3647	0.333333
5	41	3562	0.724138
6	1780	9156	0.333333
7	284	5502	0.500000
8	1521	7250	0.500000
9	130	8209	0.500000

Let save it to submission_naive1.csv:

In [52]:

submission.to_csv("submission_naive1.csv", index=False)

In [49]:

!ls -larth submission_naive1.csv

-rw-rw-r-- 1 lilian lilian 2,0M sept. 24 14:40 submission_naive1.csv

Better predicted models¶

Using `watched.csv`¶

The bonus data set watched can give a lot of information. There is 200000 entries in it and only 100000 in test.csv.

In [66]:

len(test), len(watched)

Out[66]:

(100015, 198970)

In [68]:

ratings = np.unique(watched.rating).tolist()
ratings

Out[68]:

['dislike', 'like', 'love', 'neutral']

In [67]:

watched[:5]

Out[67]:

	user_id	work_id	rating
0	717	8025	dislike
1	1106	1027	neutral
2	1970	3949	neutral
3	1685	9815	like
4	1703	3482	like

Maping string-rating to probability of seeing the movie¶

By using the train data (user, work) that are also in watched, we can learn to map string rating, i.e., 'dislike', 'neutral', 'like', 'love', to probability of having see the movie.

In [84]:

watched.rename_axis({'rating': 'strrating'}, axis="columns", inplace=True)

In [85]:

watched[:5]

Out[85]:

	user_id	work_id	strrating
0	717	8025	dislike
1	1106	1027	neutral
2	1970	3949	neutral
3	1685	9815	like
4	1703	3482	like

In [69]:

train[:5]

Out[69]:

	user_id	work_id	rating
0	50	4041	0
1	508	1713	0
2	1780	7053	1
3	658	8853	0
4	1003	9401	0

Is there pairs (user, work) for which both train data and watched data are available (i.e., both see/notsee and liked/disliked) ?

In [109]:

train.merge(watched, on=['user_id', 'work_id'])

Out[109]:

	user_id	work_id	rating	strrating

And what about test data?

In [108]:

test.merge(watched, on=['user_id', 'work_id'])

Out[108]:

	user_id	work_id	strrating

In [144]:

test.merge(watched, on=['work_id'])

Out[144]:

	user_id_x	work_id	user_id_y	strrating
0	486	1086	1120	neutral
1	486	1086	1934	dislike
2	486	1086	684	neutral
3	486	1086	45	neutral
4	486	1086	1245	neutral
5	486	1086	1705	dislike
6	486	1086	1671	like
7	486	1086	1082	neutral
8	486	1086	1672	dislike
9	486	1086	1606	dislike
10	486	1086	1392	dislike
11	486	1086	1463	dislike
12	486	1086	1668	neutral
13	486	1086	657	neutral
14	486	1086	466	dislike
15	486	1086	114	neutral
16	486	1086	1795	like
17	486	1086	843	neutral
18	486	1086	910	dislike
19	486	1086	831	neutral
20	486	1086	1416	dislike
21	486	1086	356	neutral
22	486	1086	1125	dislike
23	486	1086	1587	dislike
24	486	1086	1962	like
25	486	1086	208	dislike
26	486	1086	231	neutral
27	486	1086	610	dislike
28	486	1086	1333	like
29	486	1086	181	neutral
...	...	...	...	...
17983485	1238	4595	1635	like
17983486	1238	4595	1268	like
17983487	1238	4595	1559	like
17983488	1238	4595	561	dislike
17983489	1238	4595	872	neutral
17983490	1238	4595	1802	neutral
17983491	1238	4595	297	like
17983492	1238	4595	274	like
17983493	1238	4595	968	neutral
17983494	1238	4595	962	dislike
17983495	425	4595	1635	like
17983496	425	4595	1268	like
17983497	425	4595	1559	like
17983498	425	4595	561	dislike
17983499	425	4595	872	neutral
17983500	425	4595	1802	neutral
17983501	425	4595	297	like
17983502	425	4595	274	like
17983503	425	4595	968	neutral
17983504	425	4595	962	dislike
17983505	802	4595	1635	like
17983506	802	4595	1268	like
17983507	802	4595	1559	like
17983508	802	4595	561	dislike
17983509	802	4595	872	neutral
17983510	802	4595	1802	neutral
17983511	802	4595	297	like
17983512	802	4595	274	like
17983513	802	4595	968	neutral
17983514	802	4595	962	dislike

17983515 rows × 4 columns

No! So we can forget about the user_id, and we will learn how to map liked/disliked to see/notsee for each movie.

In [105]:

all_train = watched.merge(train, on='work_id')
all_train[:5]

Out[105]:

	user_id_x	work_id	strrating	user_id_y	rating
0	717	8025	dislike	863	0
1	717	8025	dislike	329	0
2	717	8025	dislike	1046	0
3	717	8025	dislike	794	0
4	717	8025	dislike	820	1

In [106]:

del all_train['user_id_x']
del all_train['user_id_y']

We can delete the user_id axes.

In [107]:

all_train[:5]

Out[107]:

	work_id	strrating	rating
0	8025	dislike	0
1	8025	dislike	0
2	8025	dislike	0
3	8025	dislike	0
4	8025	dislike	1

We can first get the average rating of each work:

In [130]:

all_train.groupby('work_id').rating.mean()[:10]

Out[130]:

work_id
2     0.230769
4     0.500000
5     0.333333
9     0.200000
23    0.000000
24    0.000000
27    0.333333
28    0.333333
33    0.250000
48    0.400000
Name: rating, dtype: float64

This table now contains, for each work, a list of mapping from strrating to rating. It can be combined into a concise mapping, like in this form:

In [80]:

mapping_strrating_probwillsee = {
    'dislike': 0,
    'neutral': 0.50,
    'like': 0.75,
    'love': 1,
}

Manually, for instance for one movie:

In [129]:

all_train[(all_train.work_id == 8025) & (all_train.strrating == 'dislike')]

Out[129]:

	work_id	strrating	rating
0	8025	dislike	0
1	8025	dislike	0
2	8025	dislike	0
3	8025	dislike	0
4	8025	dislike	1
5	8025	dislike	0
6	8025	dislike	0
7	8025	dislike	1
8	8025	dislike	0
9	8025	dislike	0
10	8025	dislike	0
11	8025	dislike	0
12	8025	dislike	0
13	8025	dislike	1
14	8025	dislike	0
15	8025	dislike	1
16	8025	dislike	0
17	8025	dislike	1
18	8025	dislike	1
19	8025	dislike	0
20	8025	dislike	0
21	8025	dislike	0
22	8025	dislike	0
23	8025	dislike	1
24	8025	dislike	0
25	8025	dislike	0
26	8025	dislike	1
27	8025	dislike	0
28	8025	dislike	0
29	8025	dislike	0
...	...	...	...
17754	8025	dislike	0
17755	8025	dislike	0
17756	8025	dislike	0
17757	8025	dislike	0
17758	8025	dislike	0
17759	8025	dislike	1
17760	8025	dislike	0
17761	8025	dislike	1
17762	8025	dislike	0
17763	8025	dislike	1
17764	8025	dislike	1
17784	8025	dislike	0
17785	8025	dislike	0
17786	8025	dislike	0
17787	8025	dislike	0
17788	8025	dislike	1
17789	8025	dislike	0
17790	8025	dislike	0
17791	8025	dislike	1
17792	8025	dislike	0
17793	8025	dislike	0
17794	8025	dislike	0
17795	8025	dislike	0
17796	8025	dislike	0
17797	8025	dislike	1
17798	8025	dislike	0
17799	8025	dislike	1
17800	8025	dislike	0
17801	8025	dislike	1
17802	8025	dislike	1

4294 rows × 3 columns

In [133]:

all_train[all_train.work_id == 8025].rating.mean()

Out[133]:

0.31578947368421051

In [134]:

len(all_train[(all_train.work_id == 8025) & (all_train.strrating == 'dislike')].rating)
all_train[(all_train.work_id == 8025) & (all_train.strrating == 'dislike')].rating.mean()

Out[134]:

0.31578947368421051

In [135]:

len(all_train[(all_train.work_id == 8025) & (all_train.strrating == 'neutral')].rating)
all_train[(all_train.work_id == 8025) & (all_train.strrating == 'neutral')].rating.mean()

Out[135]:

0.31578947368421051

In [141]:

len(all_train[(all_train.work_id == 8025) & (all_train.strrating == 'like')].rating)
all_train[(all_train.work_id == 8025) & (all_train.strrating == 'like')].rating.mean()

Out[141]:

0.31578947368421051

In [142]:

len(all_train[(all_train.work_id == 8025) & (all_train.strrating == 'love')].rating)
all_train[(all_train.work_id == 8025) & (all_train.strrating == 'love')].rating.mean()

Out[142]:

0.31578947368421051

That's weird!

Using `titles.csv`¶

I don't think I want to use the titles, but clustering the works by categories could help, maybe.

In [63]:

categories = np.unique(titles.category).tolist()
categories

Out[63]:

['album', 'anime', 'manga']

In [132]:

for cat in categories:
    print("There is {:>5} work(s) in category '{}'.".format(sum(titles.category == cat), cat))

There is  2808 work(s) in category 'manga'.
There is     1 work(s) in category 'album'.
There is  7088 work(s) in category 'anime'.

One category is alone, let rewrite it to 'anime'.

In [65]:

categories = {
    'anime': 0,
    'album': 0,
    'manga': 1,
}

TODO !

Evaluation from the data challenge platform¶

TODO !

Table of Contents¶

Data Challenge : Mangaki - September 2017¶

Reading data¶

First prediction model¶

Better predicted models¶

Using watched.csv¶

Maping string-rating to probability of seeing the movie¶

Using titles.csv¶

Evaluation from the data challenge platform¶

Using `watched.csv`¶

Using `titles.csv`¶