airt client
Project description
Python client for airt service 2022.3.0rc1
A python library encapsulating airt service REST API available at:
Docs
For full documentation, Please follow the below 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
To access the airt service, you must create a developer account. Please fill out the signup form below to get one:
Upon successful verification, you will receive the username/password for the developer account in an email.
Finally, you need an application token to access all the APIs in airt service. Please call the Client.get_token
method with the username/password to get one. You
can either pass the username, password, and server address as parameters to the Client.get_token
method or store the same in the AIRT_SERVICE_USERNAME,
AIRT_SERVICE_PASSWORD, and AIRT_SERVER_URL environment variables.
Upon successful authentication, the airt services will be available to access.
For more information, please check:
Below is a minimal example explaining how to train a model and make predictions using airt services.
!!! info
In the below example, the username, password, and server address are stored in **AIRT_SERVICE_USERNAME**, **AIRT_SERVICE_PASSWORD**, and **AIRT_SERVER_URL** environment variables.
0. Get token
from airt.client import Client, DataSource, DataBlob
Client.get_token()
1. Connect data
# In this case, the input data is a CSV file strored in an AWS S3 bucket.
# Pulling the data into airt server
data_blob = DataBlob.from_s3(
uri="s3://test-airt-service/ecommerce_behavior_csv"
)
data_blob.progress_bar()
# Preprocessing the data
data_source = data_blob.from_csv(
index_column="user_id",
sort_by="event_time"
)
data_source.progress_bar()
display(data_source.head())
100%|██████████| 1/1 [00:35<00:00, 35.33s/it]
100%|██████████| 1/1 [00:30<00:00, 30.28s/it]
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
event_time | event_type | product_id | category_id | category_code | brand | price | user_session | |
---|---|---|---|---|---|---|---|---|
0 | 2019-11-06 06:51:52+00:00 | view | 26300219 | 2053013563424899933 | None | sokolov | 40.54 | d1fdcbf1-bb1f-434b-8f1a-4b77f29a84a0 |
1 | 2019-11-05 21:25:44+00:00 | view | 2400724 | 2053013563743667055 | appliances.kitchen.hood | bosch | 246.85 | b097b84d-cfb8-432c-9ab0-a841bb4d727f |
2 | 2019-11-05 21:27:43+00:00 | view | 2400724 | 2053013563743667055 | appliances.kitchen.hood | bosch | 246.85 | b097b84d-cfb8-432c-9ab0-a841bb4d727f |
3 | 2019-11-05 19:38:48+00:00 | view | 3601406 | 2053013563810775923 | appliances.kitchen.washer | beko | 195.60 | d18427ab-8f2b-44f7-860d-a26b9510a70b |
4 | 2019-11-05 19:40:21+00:00 | view | 3601406 | 2053013563810775923 | appliances.kitchen.washer | beko | 195.60 | d18427ab-8f2b-44f7-860d-a26b9510a70b |
5 | 2019-11-06 05:39:21+00:00 | view | 15200134 | 2053013553484398879 | None | racer | 55.86 | fc582087-72f8-428a-b65a-c2f45d74dc27 |
6 | 2019-11-06 05:39:34+00:00 | view | 15200134 | 2053013553484398879 | None | racer | 55.86 | fc582087-72f8-428a-b65a-c2f45d74dc27 |
7 | 2019-11-05 20:25:52+00:00 | view | 1005106 | 2053013555631882655 | electronics.smartphone | apple | 1422.31 | 79d8406f-4aa3-412c-8605-8be1031e63d6 |
8 | 2019-11-05 23:13:43+00:00 | view | 31501222 | 2053013558031024687 | None | dobrusskijfarforovyjzavod | 115.18 | e3d5a1a4-f8fd-4ac3-acb7-af6ccd1e3fa9 |
9 | 2019-11-06 07:00:32+00:00 | view | 1005115 | 2053013555631882655 | electronics.smartphone | apple | 915.69 | 15197c7e-aba0-43b4-9f3a-a815e31ade40 |
2. Train
from datetime import timedelta
model = data_source.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, 135.21it/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, 112.70it/s]
Score
user_id
520088904 0.979853
530496790 0.979157
561587266 0.979055
518085591 0.978915
558856683 0.977960
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