airt-client 2022.3.1rc1

airt client

Python client for airt service 2022.3.1rc1

A python library encapsulating airt service REST API available at:

• https://api.airt.ai/

Docs

For full documentation, Please follow the below link:

• https://docs.airt.ai/

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:

• https://bit.ly/3hbXQLY

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:

• Tutorial with more elaborate example, and

• API with reference documentation.

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()

print(data_source.head())

100%|██████████| 1/1 [00:35<00:00, 35.35s/it]
100%|██████████| 1/1 [00:30<00:00, 30.32s/it]

                  event_time event_type  product_id          category_id  \
0  2019-11-06 06:51:52+00:00       view    26300219  2053013563424899933   
1  2019-11-05 21:25:44+00:00       view     2400724  2053013563743667055   
2  2019-11-05 21:27:43+00:00       view     2400724  2053013563743667055   
3  2019-11-05 19:38:48+00:00       view     3601406  2053013563810775923   
4  2019-11-05 19:40:21+00:00       view     3601406  2053013563810775923   
5  2019-11-06 05:39:21+00:00       view    15200134  2053013553484398879   
6  2019-11-06 05:39:34+00:00       view    15200134  2053013553484398879   
7  2019-11-05 20:25:52+00:00       view     1005106  2053013555631882655   
8  2019-11-05 23:13:43+00:00       view    31501222  2053013558031024687   
9  2019-11-06 07:00:32+00:00       view     1005115  2053013555631882655   

               category_code                      brand    price  \
0                       None                    sokolov    40.54   
1    appliances.kitchen.hood                      bosch   246.85   
2    appliances.kitchen.hood                      bosch   246.85   
3  appliances.kitchen.washer                       beko   195.60   
4  appliances.kitchen.washer                       beko   195.60   
5                       None                      racer    55.86   
6                       None                      racer    55.86   
7     electronics.smartphone                      apple  1422.31   
8                       None  dobrusskijfarforovyjzavod   115.18   
9     electronics.smartphone                      apple   915.69   

                           user_session  
0  d1fdcbf1-bb1f-434b-8f1a-4b77f29a84a0  
1  b097b84d-cfb8-432c-9ab0-a841bb4d727f  
2  b097b84d-cfb8-432c-9ab0-a841bb4d727f  
3  d18427ab-8f2b-44f7-860d-a26b9510a70b  
4  d18427ab-8f2b-44f7-860d-a26b9510a70b  
5  fc582087-72f8-428a-b65a-c2f45d74dc27  
6  fc582087-72f8-428a-b65a-c2f45d74dc27  
7  79d8406f-4aa3-412c-8605-8be1031e63d6  
8  e3d5a1a4-f8fd-4ac3-acb7-af6ccd1e3fa9  
9  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, 147.61it/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 [00:10<00:00,  3.38s/it]

              Score
user_id            
520088904  0.979853
530496790  0.979157
561587266  0.979055
518085591  0.978915
558856683  0.977960