For full documentation, Please follow the below link:
If you don’t have the captn library already installed, please install it using pip.
pip install captn-client
To access the captn 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
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:
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 captn server.
For more information, please check:
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.
# Importing necessary libraries
from captn.client import Client, DataBlob, DataSource
# Authenticate
Client.get_token()
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]
# Todo
FAQs
Captn client library
We found that captn-client demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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