Python¶
PLEASE READ THE ‘CONCEPTS’ DOCUMENTATION TAB FIRST IF IT’S YOUR FIRST TIME USING THE CLIENT
Install¶
The python client for bender is available with the pip install bender-client command.
Or you can just download it from it’s github repo if you want.
Import and Login¶
Make sure you already created a Bender account on the web client !
from benderclient import Bender
b = Bender()
When you create a new bender object, it will automatically try to connect to the bender API so make sure you have a working internet connection.
The log-in menu will be prompt in the python console and ask for your email and password : it will create a temporary file on your computer with your credentials so you won’t have to login each time.
When running the Bender ecosystem, you want to use your own API, so when calling b = Bender(host=’https://bender-api.dreem.com’) you can set a host parameter which is by default set to the adress of our servers.
Experiment methods¶
List experiments¶
Return a list of the connected user’s experiments.
b.list_experiments()
Prototype :
list_experiments()
Arguments :
none
Return :
array
[{ "name": "exp_1", "id": "0597ca48-6..." }, { "name": "exp_2", "id": "68hj547r5-8..." } ]
Set experiment¶
Setup current experiment for the connected user.
b.set_experiment(name, experiment_id)
Prototype :
set_experiment(name=None, experiment_id=None)
Arguments :
| Argument | Type | Description and Example |
| name | string | Give the name of the experiment you want to retrieve and set as current |
"my_experiment" |
||
| experiment_id | string | Give the id of the experiment you want to retrieve and set as current |
"0597ca48-66f7-42be-9021-12ec57d4251e" |
Return :
none
Create experiment¶
Create new experiment and setup current experiment for the connected user.
If experiment name already exists, the already existing one is set by default as current experiment.
b.create_experiment(name, metrics, description, dataset, dataset_parameters)
Prototype :
create_experiment(name, metrics, description=None, dataset=None, dataset_parameters=None)
Arguments :
| Argument | Type | Description and Example |
| name | string | Name of your experiment |
"my_experiment" |
||
| metrics | array | List of the performance metrics used to evaluate your experiment |
[ {"metric_name": "metric_a","type": "reward" }, {"metric_name": "metric_b","type": "loss" }] |
||
| description | string | Short description of the experiment’s purpose |
"This experiment is just a random algorithm." |
||
| dataset | string | Name the dataset you are using for your experiment |
"dataset_name.csv" |
||
| dataset | dict | An object describing your dataset |
{ "version": 0.1, "CV_folds": "10" } |
Return :
none
Get experiment¶
Return the current experiment.
b.get_experiment()
Prototype :
get_experiment()
Arguments :
none
Return :
dict
A full experiment object.
Delete experiment¶
Delete targeted experiment of the connected user.
b.delete_experiment(experiment_id)
Prototype :
delete_experiment(experiment_id=None)
Arguments :
| Argument | Type | Description and Example |
| experiment_id | string | Give the id of the experiment you want to retrieve and delete |
"0597ca48-66f7-42be-9021-12ec57d4251e" |
Return :
none
Algo methods¶
List algos¶
Return a list of the connected user’s algos.
b.list_algos()
Prototype :
list_algos()
Arguments :
none
Return :
array
[{ "name": "algo_1", "id": "0597ca48-6..." }, { "name": "algo_2", "id": "68hj547r5-8..." } ]
Set algo¶
Setup current algo for the connected user.
b.set_algo(name, algo_id)
Prototype :
set_algo(name=None, algo_id=None)
Arguments :
| Argument | Type | Description and Example |
| name | string | Give the name of the algo you want to retrieve and set as current |
"my_algo" |
||
| algo_id | string | Give the id of the algo you want to retrieve and set as current |
"0597ca48-66f7-42be-9021-12ec57d4251e" |
Return :
none
Create algo¶
Create new algo and setup current algo for the connected user.
If algo name already exists, the already existing one is set by default as current algo.
b.create_algo(name, parameters, description)
Prototype :
create_algo(name, hyperparameters, description=None)
Arguments :
| Argument | Type | Description and Example |
| name | string | Name of your algo |
"my_algo" |
||
| hyperparameters | array | List of the hyperparameters used by the algo |
[{"name": "param_name","category": "categorical", "search_space": { "values": [3, 5, 7] } }] |
||
| description | string | Short description of the algo’s principle |
"This algo is a useless one." |
Return :
none
Get algo¶
Return the current algo.
b.get_algo()
Prototype :
get_algo()
Arguments :
none
Return :
dict
A full algo object.
Delete algo¶
Delete targeted algo of the connected user.
b.delete_algo(algo_id)
Prototype :
delete_algo(algo_id=None)
Arguments :
| Argument | Type | Description and Example |
| algo_id | string | Give the id of the algo you want to retrieve and delete |
"0597ca48-66f7-42be-9021-12ec57d4251e" |
Return :
none
Trials methods¶
List trials¶
List all trials of the current algo.
b.list_trials()
Prototype :
list_trials()
Arguments :
none
Return :
array
An array of trials dict.
Create trial¶
Create new trial for the current algo.
b.create_trial(name, hyperparameters, description)
Prototype :
create_trial(results, hyperparameters, weight=1, comment=None)
Arguments :
| Argument | Type | Description and Example |
| results | dict | Array of obtained metrics |
{"metric1": 0.8, "metric2": 0.3} |
||
| hyperparameters | dict | List of the hyperparameters used by the algo |
{"param1": "value","param2": 3567, "param3": "another"} |
||
| weight | integer | The importance of your result |
0.5 |
||
| comment | string | Anything you want to say about this trial |
"This is a normal trial." |
Return :
none
Delete trial¶
Delete targeted trial from current algo.
b.delete_trial(trial_id)
Prototype :
delete_trial(trial_id=None)
Arguments :
| Argument | Type | Description and Example |
| trial_id | string | Give the id of the trial you want to retrieve and delete |
"0597ca48-66f7-42be-9021-12ec57d4251e" |
Return :
none