airt client
Project description
Python client for airt service (beta)
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:
The username, password, and server address can be specified explicitly when initializing the Client
object or it can be permanently stored in environment variables AIRT_SERVICE_USERNAME
, AIRT_SERVICE_PASSWORD
, and AIRT_SERVER_URL
.
Upon successful authentication, the airt services will be available to access.
Below is a minimal example of how to use the Client
to train a model and make a prediction using it. The example also assumes that the username, password, and server address for initializing the Client
object are already stored in the respective environment variables AIRT_SERVICE_USERNAME
, AIRT_SERVICE_PASSWORD
, and AIRT_SERVER_URL
.
For more information, please check:
0. Authenticate
from airt.client import Client, DataSource
client = Client()
1. Connect data
data_source_s3 = DataSource.s3(
client,
uri="s3://test-airt-service/ecommerce_behavior"
)
data_source_s3.pull().progress_bar(timeout=90)
print(data_source_s3.head())
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [01:25<00:00, 85.37s/it]
event_time event_type product_id category_id \
0 2019-11-01T00:00:00+00:00 view 1003461 2053013555631882655
1 2019-11-01T00:00:00+00:00 view 5000088 2053013566100866035
2 2019-11-01T00:00:01+00:00 view 17302664 2053013553853497655
3 2019-11-01T00:00:01+00:00 view 3601530 2053013563810775923
4 2019-11-01T00:00:01+00:00 view 1004775 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
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
2. Train
from datetime import timedelta
model = client.train(
data_source_s3,
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, 271.24it/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, 170.39it/s]
Score
user_id
520088904 0.979853
530496790 0.979157
561587266 0.979055
518085591 0.978915
558856683 0.977960
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