captn-client 2022.11.0

Capt’n client

Capt’n python client 2022.11.0

Docs

For full documentation, Please follow the below 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 first create a developer account. Please fill out the signup form below to get one:

• https://bit.ly/3I4cNuv

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 captn 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 CAPTN_SERVICE_USERNAME, CAPTN_SERVICE_PASSWORD, and CAPTN_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:

1. 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.

2. 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:

1. 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.

2. 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.

3. 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 captn 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 captn services to train a model and make predictions.

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

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

# Authenticate
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.

# Before you can use the data to train a model, it must be uploaded to the
# captn server. Run the following command to upload the data to the
# captn server for further processing.
data_blob = DataBlob.from_s3(
    uri="s3://test-airt-service/sample_gaming_130k"
)

# Display the upload progress
data_blob.progress_bar()

100%|██████████| 1/1 [00:35<00:00, 35.44s/it]

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:

# Add header rows
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.to_datasource method which preprocesses the data and stores it in captn server.

# Preprocess and prepare the data for training
data_source = data_blob.to_datasource(
    file_type="csv",
    index_column="game_name",
    sort_by="date",
    **kwargs
)

# Display the data preprocessing progress
data_source.progress_bar()

100%|██████████| 1/1 [00:55<00:00, 55.66s/it]

# 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())

                  date platform          user_type            network  \
game_name                                                               
game_name_0 2021-03-15      ios      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15      ios      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15      ios      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15      ios      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15      ios      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15  android  googleadwords_int  googleadwords_int   
game_name_0 2021-03-15  android  googleadwords_int  googleadwords_int   
game_name_0 2021-03-15  android         moloco_int         moloco_int   
game_name_0 2021-03-15  android      jetfuelit_int      jetfuelit_int   
game_name_0 2021-03-15  android      jetfuelit_int      jetfuelit_int   

                 campaign       adgroup  installs       spend  revenue_0  \
game_name                                                                  
game_name_0    campaign_0   adgroup_541         1    0.600000   0.000000   
game_name_0    campaign_0  adgroup_2351         2    4.900000   0.000000   
game_name_0    campaign_0   adgroup_636         3    7.350000   0.000000   
game_name_0    campaign_0   adgroup_569         1    0.750000   0.000000   
game_name_0    campaign_0   adgroup_243         2    3.440000   0.000000   
game_name_0  campaign_283  adgroup_1685        11    0.000000   0.000000   
game_name_0    campaign_2    adgroup_56        32   30.090000   0.000000   
game_name_0  campaign_191          None       291  503.480011  34.701553   
game_name_0    campaign_0   adgroup_190         4    2.740000   0.000000   
game_name_0    campaign_0   adgroup_755         8   11.300000  13.976003   

             revenue_1  ...  revenue_23  revenue_24  revenue_25  revenue_26  \
game_name               ...                                                   
game_name_0   0.018173  ...    0.018173    0.018173    0.018173    0.018173   
game_name_0   0.034000  ...    0.034000    6.034000    6.034000    6.034000   
game_name_0   0.000000  ...   12.112897   12.112897   12.112897   12.112897   
game_name_0   0.029673  ...    0.029673    0.029673    0.029673    0.029673   
game_name_0   0.027981  ...    0.042155    0.042155    0.042155    0.042155   
game_name_0   0.097342  ...    0.139581    0.139581    0.139581    0.139581   
game_name_0   0.802349  ...    2.548253    2.548253    2.771138    2.805776   
game_name_0  63.618111  ...  116.508331  117.334709  117.387489  117.509506   
game_name_0   0.000000  ...    0.000000    0.000000    0.000000    0.000000   
game_name_0  14.358793  ...   14.338905   14.338905   14.338905   14.338905   

             revenue_27  revenue_28  revenue_29  revenue_30  revenue_60  \
game_name                                                                 
game_name_0    0.018173    0.018173    0.018173    0.018173    0.018173   
game_name_0    6.034000    6.034000    6.034000    6.034000    6.034000   
game_name_0   12.112897   12.112897   12.112897   12.112897   12.112897   
game_name_0    0.029673    0.029673    0.029673    0.029673    0.029673   
game_name_0    0.042155    0.042155    0.042155    0.042155    0.042155   
game_name_0    0.139581    0.139581    0.139581    0.139581    0.139581   
game_name_0    2.805776    2.805776    2.805776    2.805776    2.805776   
game_name_0  118.811417  118.760765  119.151291  119.350220  139.069443   
game_name_0    0.000000    0.000000    0.000000    0.000000    0.000000   
game_name_0   14.338905   14.338905   14.338905   14.338905   14.338905   

             revenue_90  
game_name                
game_name_0    0.018173  
game_name_0   13.030497  
game_name_0   12.112897  
game_name_0    0.029673  
game_name_0    0.042155  
game_name_0    0.139581  
game_name_0    2.805776  
game_name_0  147.528793  
game_name_0    0.000000  
game_name_0   14.338905  

[10 rows x 41 columns]

2. Training

# Todo