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Description:

airtclient 2023.3.0

Python client for airt service
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 first create a developer account. Please fill out the signup form below to get one:

https://bit.ly/3hbXQLY

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 calling Client.get_token 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 Client.get_token method or store them in the environment variables AIRT_SERVICE_USERNAME, AIRT_SERVICE_PASSWORD, and AIRT_SERVER_URL
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_mfa method, 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_mfa and 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 method User.disable_mfa method.
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_sso method 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_token with
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_token method to generate a new token and use it automatically
in all future interactions with the airt server.

For more information, please check:

Tutorial with more elaborate example, and

API with reference documentation.

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
#| include: false
# Do not remove "# hide" from this cell. Else this cell will appear in documentation

import os

# setting the environment variable
os.environ["AIRT_SERVICE_USERNAME"] = "johndoe"
os.environ["AIRT_SERVICE_PASSWORD"] = os.environ["AIRT_SERVICE_SUPER_USER_PASSWORD"]

# Importing necessary libraries
from airt.client import Client, DataSource, DataBlob

# 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

2. Train
# 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

3. Predict
# 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