airt client
Project description
Python client for airt service 2022.1.0rc1
A python library encapsulating airt service REST API available at:
Docs
Full documentation can be found at the following link:
How to install
If you don't have the airt library already installed, please install it using pip.
pip install airt-client
How to use
Before you can use the service, you must acquire a username and password for your developer account. Please fill in the following form to get one:
Upon successfully receiving a username/password pair, you have to call the Client.get_token
method in the Client
class for getting an application token.
The username, password, and server address can either be passed explicitly while calling the Client.get_token
method or stored in environment variables AIRT_SERVICE_USERNAME, AIRT_SERVICE_PASSWORD, and AIRT_SERVER_URL. After successful authentication, you will receive an application token and will be able to access airt services.
Below is a minimal example explaining how to train a model and make predictions using airt services.
The example assumes the username, password, and server address required for authenticating the Client
is stored in the environment variables AIRT_SERVICE_USERNAME, AIRT_SERVICE_PASSWORD, and AIRT_SERVER_URL respectively.
For more information, please check:
0. Get token
from airt.client import Client, DataSource
Client.get_token()
1. Connect data
data_source_s3 = DataSource.s3(
uri="s3://test-airt-service/ecommerce_behavior"
)
data_source_s3.pull().progress_bar()
print(data_source_s3.head())
100%|██████████| 1/1 [03:11<00:00, 192.00s/it]
event_time event_type product_id category_id \
0 2019-11-01 00:00:00+00:00 view 1003461 2053013555631882655
1 2019-11-01 00:00:00+00:00 view 5000088 2053013566100866035
2 2019-11-01 00:00:01+00:00 view 17302664 2053013553853497655
3 2019-11-01 00:00:01+00:00 view 3601530 2053013563810775923
4 2019-11-01 00:00:01+00:00 view 1004775 2053013555631882655
5 2019-11-01 00:00:01+00:00 view 1306894 2053013558920217191
6 2019-11-01 00:00:01+00:00 view 1306421 2053013558920217191
7 2019-11-01 00:00:02+00:00 view 15900065 2053013558190408249
8 2019-11-01 00:00:02+00:00 view 12708937 2053013553559896355
9 2019-11-01 00:00:02+00:00 view 1004258 2053013555631882655
category_code brand price user_id \
0 electronics.smartphone xiaomi 489.07 520088904
1 appliances.sewing_machine janome 293.65 530496790
2 None creed 28.31 561587266
3 appliances.kitchen.washer lg 712.87 518085591
4 electronics.smartphone xiaomi 183.27 558856683
5 computers.notebook hp 360.09 520772685
6 computers.notebook hp 514.56 514028527
7 None rondell 30.86 518574284
8 None michelin 72.72 532364121
9 electronics.smartphone apple 732.07 532647354
user_session
0 4d3b30da-a5e4-49df-b1a8-ba5943f1dd33
1 8e5f4f83-366c-4f70-860e-ca7417414283
2 755422e7-9040-477b-9bd2-6a6e8fd97387
3 3bfb58cd-7892-48cc-8020-2f17e6de6e7f
4 313628f1-68b8-460d-84f6-cec7a8796ef2
5 816a59f3-f5ae-4ccd-9b23-82aa8c23d33c
6 df8184cc-3694-4549-8c8c-6b5171877376
7 5e6ef132-4d7c-4730-8c7f-85aa4082588f
8 0a899268-31eb-46de-898d-09b2da950b24
9 d2d3d2c6-631d-489e-9fb5-06f340b85be0
2. Train
from datetime import timedelta
model = data_source_s3.train(
client_column="user_id",
target_column="event_type",
target="*purchase",
predict_after=timedelta(hours=3),
)
model.progress_bar()
print(model.evaluate())
100%|██████████| 5/5 [00:00<00:00, 121.12it/s]
eval
accuracy 0.985
recall 0.962
precision 0.934
3. Predict
predictions = model.predict()
predictions.progress_bar()
print(predictions.to_pandas().head())
100%|██████████| 3/3 [00:00<00:00, 75.22it/s]
Score
user_id
520088904 0.979853
530496790 0.979157
561587266 0.979055
518085591 0.978915
558856683 0.977960
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