Python client for airt service¤
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 first create a developer account. Please fill out the signup form below to get one:
After successful verification, you will receive an email with the username and password for the developer account.
Once you have the credentials, use them to get an access token by
method. It is necessary to get an access token; otherwise, you won’t be
able to access all of the airt service’s APIs. You can either pass the
username, password, and server address as parameters to the
method or store them in the environment variables
AIRT_SERVICE_USERNAME, AIRT_SERVICE_PASSWORD, and
In addition to the regular authentication with credentials, you can also enable multi-factor authentication (MFA) and single sign-on (SSO) for generating tokens.
To help protect your account, we recommend that you enable multi-factor authentication (MFA). MFA provides additional security by requiring you to provide unique verification code (OTP) in addition to your regular sign-in credentials when performing critical operations.
Your account can be configured for MFA in just two easy steps:
To begin, you need to enable MFA for your account by calling the
User.enable_mfamethod, which will generate a QR code. You can then scan the QR code with an authenticator app, such as Google Authenticator and follow the on-device instructions to finish the setup in your smartphone.
Finally, activate MFA for your account by calling
User.activate_mfaand passing the dynamically generated six-digit verification code from your smartphone’s authenticator app.
You can also disable MFA for your account at any time by calling the
Single sign-on (SSO) can be enabled for your account in three simple steps:
Enable the SSO for a provider by calling the
User.enable_ssomethod with the SSO provider name and an email address. At the moment, we only support “google” and “github” as SSO providers. We intend to support additional SSO providers in future releases.
Before you can start generating new tokens with SSO, you must first authenticate with the SSO provider. Call the
Client.get_tokenwith the same SSO provider you have enabled in the step above to generate an SSO authorization URL. Please copy and paste it into your preferred browser and complete the authentication process with the SSO provider.
After successfully authenticating with the SSO provider, call the
Client.set_sso_tokenmethod to generate a new token and use it automatically in all future interactions with the airt server.
For more information, please check:
Here’s a minimal example showing how to use airt services to train a model and make predictions.
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¤
# Importing necessary libraries from airt.client import Client, DataBlob, DataSource # Authenticate Client.get_token()
1. Connect data¤
# The input data in this case is a CSV file stored in an AWS S3 bucket. Before # you can use the data to train a model, it must be uploaded to the airt server. # Run the following command to upload the data to the airt server for further # processing. data_blob = DataBlob.from_s3(uri="s3://test-airt-service/ecommerce_behavior_csv") # Display the upload progress data_blob.progress_bar() # Once the upload is complete, run the following command to preprocess and # prepare the data for training. data_source = data_blob.to_datasource( file_type="csv", index_column="user_id", sort_by="event_time" ) # Display the data preprocessing progress data_source.progress_bar() # When the preprocessing is finished, you can run the following command to # display the head of the data to ensure everything is fine. print(data_source.head())
100%|██████████| 1/1 [01:00<00:00, 60.62s/it] 100%|██████████| 1/1 [00:35<00:00, 35.39s/it] event_time event_type product_id \ user_id 10300217 2019-11-06 06:51:52+00:00 view 26300219 253299396 2019-11-05 21:25:44+00:00 view 2400724 253299396 2019-11-05 21:27:43+00:00 view 2400724 272811580 2019-11-05 19:38:48+00:00 view 3601406 272811580 2019-11-05 19:40:21+00:00 view 3601406 288929779 2019-11-06 05:39:21+00:00 view 15200134 288929779 2019-11-06 05:39:34+00:00 view 15200134 310768124 2019-11-05 20:25:52+00:00 view 1005106 315309190 2019-11-05 23:13:43+00:00 view 31501222 339186405 2019-11-06 07:00:32+00:00 view 1005115 category_id category_code \ user_id 10300217 2053013563424899933 None 253299396 2053013563743667055 appliances.kitchen.hood 253299396 2053013563743667055 appliances.kitchen.hood 272811580 2053013563810775923 appliances.kitchen.washer 272811580 2053013563810775923 appliances.kitchen.washer 288929779 2053013553484398879 None 288929779 2053013553484398879 None 310768124 2053013555631882655 electronics.smartphone 315309190 2053013558031024687 None 339186405 2053013555631882655 electronics.smartphone brand price \ user_id 10300217 sokolov 40.54 253299396 bosch 246.85 253299396 bosch 246.85 272811580 beko 195.60 272811580 beko 195.60 288929779 racer 55.86 288929779 racer 55.86 310768124 apple 1422.31 315309190 dobrusskijfarforovyjzavod 115.18 339186405 apple 915.69 user_session user_id 10300217 d1fdcbf1-bb1f-434b-8f1a-4b77f29a84a0 253299396 b097b84d-cfb8-432c-9ab0-a841bb4d727f 253299396 b097b84d-cfb8-432c-9ab0-a841bb4d727f 272811580 d18427ab-8f2b-44f7-860d-a26b9510a70b 272811580 d18427ab-8f2b-44f7-860d-a26b9510a70b 288929779 fc582087-72f8-428a-b65a-c2f45d74dc27 288929779 fc582087-72f8-428a-b65a-c2f45d74dc27 310768124 79d8406f-4aa3-412c-8605-8be1031e63d6 315309190 e3d5a1a4-f8fd-4ac3-acb7-af6ccd1e3fa9 339186405 15197c7e-aba0-43b4-9f3a-a815e31ade40
# We assume that the input data for training a model includes the client_column # target_column, and timestamp column, which specify the time of an event. from datetime import timedelta model = data_source.train( client_column="user_id", target_column="event_type", target="*purchase", predict_after=timedelta(hours=3), ) # Display model training progress model.progress_bar() # Once the model training is complete, call the following method to display # multiple evaluation metrics to evaluate the model's performance. print(model.evaluate())
100%|██████████| 5/5 [00:00<00:00, 126.62it/s] eval accuracy 0.985 recall 0.962 precision 0.934
# Finally, you can use the trained model to make predictions by calling the # method below. predictions = model.predict() # Display model prediction progress predictions.progress_bar() # If the dataset is small enough, you can use the following method to download # the prediction results as a pandas DataFrame. 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