recommender system of activities for users in MHC 3.0 coaching studies
Recommender diagram: https://app.lucidchart.com/invitations/accept/eedbfd98-0913-4939-9364-426563929ba2
config.py contains configuration details for:
a) connection to MySQLDB
b) paths to the PhysicalActivity.tsv, EnduranceSessions.tsv, ExerciseVideoLinks.tsv exercise metadata
c) Path to list of features users can modify preferences for (i.e. metadata/ModifiableFeatures.tsv)
d) Fitness thresholds (min=1, max=5, default =3)
execute:
python create_mysql_db.py
This sets up the MySQL database (mhc_rec), creates (overwrites!) the tables, and populates the "activity_features" and "activity_metadata" tables:
mysql> show tables;
+---------------------+
| Tables_in_mhc_rec |
+---------------------+
| activity_completion |
| activity_features |
| activity_metadata |
| user_fitness |
| user_preferences |
+---------------------+
5 rows in set (0.00 sec)
mysql> describe activity_metadata;
+---------------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+---------------------+--------------+------+-----+---------+-------+
| activity_hash | varchar(255) | YES | | NULL | |
| activity_name | varchar(255) | YES | | NULL | |
| activity_category | varchar(255) | YES | | NULL | |
| min_fitness_level | int(11) | YES | | NULL | |
| max_fitness_level | int(11) | YES | | NULL | |
| target_exertion_min | int(11) | YES | | NULL | |
| target_exertion_max | int(11) | YES | | NULL | |
+---------------------+--------------+------+-----+---------+-------+
7 rows in set (0.00 sec)
mysql> describe activity_features;
+---------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+---------------+--------------+------+-----+---------+-------+
| activity_hash | varchar(255) | YES | | NULL | |
| feature | varchar(255) | YES | | NULL | |
| feature_value | varchar(255) | YES | | NULL | |
+---------------+--------------+------+-----+---------+-------+
3 rows in set (0.00 sec)
mysql> describe activity_completion;
+-------------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------------------+--------------+------+-----+---------+-------+
| user | varchar(255) | YES | | NULL | |
| date | date | YES | | NULL | |
| activity_hash | varchar(255) | YES | | NULL | |
| activity_category | varchar(255) | YES | | NULL | |
| rating | int(11) | YES | | NULL | |
| exertion | int(11) | YES | | NULL | |
| attempted | tinyint(1) | YES | | NULL | |
+-------------------+--------------+------+-----+---------+-------+
7 rows in set (0.00 sec)
mysql> describe user_fitness;
+---------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+---------------+--------------+------+-----+---------+-------+
| user | varchar(255) | YES | | NULL | |
| date | date | YES | | NULL | |
| fitness_score | double | YES | | NULL | |
+---------------+--------------+------+-----+---------+-------+
3 rows in set (0.00 sec)
mysql> describe user_preferences;
+------------+--------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+------------+--------------+------+-----+---------+-------+
| user | varchar(255) | YES | | NULL | |
| feature | varchar(255) | YES | | NULL | |
| value | varchar(255) | YES | | NULL | |
| importance | int(11) | YES | | NULL | |
+------------+--------------+------+-----+---------+-------+
4 rows in set (0.00 sec)
python update_user.py --help
usage: update_user.py [-h] [-user USER] [-user_pref_file USER_PREF_FILE]
[--user_fitness_level {1,2,3,4,5}]
updated user information
optional arguments:
-h, --help show this help message and exit
-user USER unique user identifier
-user_pref_file USER_PREF_FILE
tab-separated file with columns Feature, Value,
Importance
--user_fitness_level {1,2,3,4,5}
fitness level 1 (lowest) - 5 (highest)
Execute the script update_user.sh for an example of the input values
python recommend.py --help
usage: recommend.py [-h] [-user USER]
[-activity_category {lifestyle,training,videos,skip}]
[--outf OUTF] [--n N]
recommend activity
optional arguments:
-h, --help show this help message and exit
-user USER
-activity_category {lifestyle,training,videos,skip}
--outf OUTF if None, print to console, else write string of
recommended activities to a file
--n N Number of activities to recommend
Execute recommend.sh as an example
python rate.py --help
usage: rate.py [-h] [-user USER] [-activity_hash ACTIVITY_HASH]
[-activity_category {lifestyle,training,videos,skip}]
[-rating {-1,0,1}]
[-exertion {6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}]
[-attempted {True,False}]
[--exertion_mismatch_thresh_to_adjust_fitness EXERTION_MISMATCH_THRESH_TO_ADJUST_FITNESS]
rank an exercise activity -1,0,1
optional arguments:
-h, --help show this help message and exit
-user USER
-activity_hash ACTIVITY_HASH
-activity_category {lifestyle,training,videos,skip}
-rating {-1,0,1}
-exertion {6,7,8,9,10,11,12,13,14,15,16,17,18,19,20}
-attempted {True,False}
--exertion_mismatch_thresh_to_adjust_fitness EXERTION_MISMATCH_THRESH_TO_ADJUST_FITNESS
if user's reported exertion differs from the expected
exertion for the ativity by this much (or greater),
adjust the user's fitness score
Execute rate.sh as an example.