captn-client 2022.6.0rc1

Capt’n client

Capt’n python client 2022.6.0rc1

Docs

Full documentation can be found at the following link:

• https://docs.captn.ai/

How to install

If you don't have the captn library already installed, please install it using pip.

pip install captn-client

How to use

To access the captn service, you must create a developer account. Please fill out the signup form below to get one:

• https://bit.ly/3I4cNuv

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 captn 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 CAPTN_SERVICE_USERNAME, CAPTN_SERVICE_PASSWORD, and CAPTN_SERVER_URL environment variables.

After successful authentication, the captn services will be available to access.

As an extra layer of security, we also support Multi-Factor Authentication (MFA) for generating tokens.

Multi-Factor Authentication (MFA) can be used to help protect your account from unauthorized access by requiring you to enter an additional code when you request a new token.

Once authenticated successfully, activating MFA for your account is a simple two step process:

1. Enable MFA for your account by calling User.enable_mfa method which will generate a QR code. You can then use an authenticator app, such as Google Authenticator to scan the QR code.

2. Activate the MFA by calling User.activate_mfa method and passing the dynamically generated six-digit verification code from the authenticator app.

Once MFA is successfully activated, you need to pass the dynamically generated six-digit verification code along with the username/password to Client.get_token method for generating new tokens.

You can also disable MFA for you account anytime by calling User.disable_mfa method.

For more information, please check:

• Tutorial with more elaborate example, and

• API with reference documentation.

Below is a minimal example explaining how to load the data, train a model and make predictions using captn services.

!!! info

In the below example, the username, password, and server address are stored in **CAPTN_SERVICE_USERNAME**, **CAPTN_SERVICE_PASSWORD**, and **CAPTN_SERVER_URL** environment variables.

0. Get token

from captn.client import Client, DataBlob, DataSource

Client.get_token()

1. Connect and preprocess data

In our example, we will be using the captn APIs to load and preprocess a sample CSV file stored in an AWS S3 bucket.

data_blob = DataBlob.from_s3(
    uri="s3://test-airt-service/sample_gaming_130k/"
)
data_blob.progress_bar()

The sample data we used in this example doesn't have the header rows and their data types defined.

The following code creates the necessary headers along with their data types and reads only a subset of columns that are required for modeling:

prefix = ["revenue", "ad_revenue", "conversion", "retention"]
days = list(range(30)) + list(range(30, 361, 30))
dtype = {
    "date": "str",
    "game_name": "str",
    "platform": "str",
    "user_type": "str",
    "network": "str",
    "campaign": "str",
    "adgroup": "str",
    "installs": "int32",
    "spend": "float32",
}
dtype.update({f"{p}_{d}": "float32" for p in prefix for d in days})
names = list(dtype.keys())

kwargs = {"delimiter": "|", "names": names, "parse_dates": ["date"], "usecols": names[:42], "dtype": dtype}

Finally, the above variables are passed to the DataBlob.from_csv method which preprocesses the data and stores it in captn server.

data_source = data_blob.from_csv(
    index_column="game_name",
    sort_by="date",
    **kwargs
)

data_source.progress_bar()

print(data_source.head())

2. Training

# Todo