Package: captn-client
Capt’n python clientDocsFor full documentation, Please follow the below link:https://docs.captn.ai/How to installIf you don’t have the captn library already installed, please install it
using pip.pip install captn-clientHow to useTo access the captn service, you must first create a developer account.
Please fill out the signup form below to get one:https://bit.ly/3I4cNuvAfter 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
callingClient.get_tokenmethod. 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 theClient.get_tokenmethod or store them in the
environment variablesCAPTN_SERVICE_USERNAME,CAPTN_SERVICE_PASSWORD, andCAPTN_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 theUser.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 callingUser.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
methodUser.disable_mfamethod.Single sign-on (SSO) can be enabled for your account in three simple
steps:Enable the SSO for a provider by calling theUser.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 theClient.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 theClient.set_sso_tokenmethod to generate a new token and use it
automatically in all future interactions with the captn server.For more information, please check:Tutorialwith more elaborate
example, andAPIwith 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 inCAPTN_SERVICE_USERNAME,CAPTN_SERVICE_PASSWORD, andCAPTN_SERVER_URLenvironment variables.0. Get token# Importing necessary librariesfromcaptn.clientimportClient,DataBlob,DataSource# AuthenticateClient.get_token()1. Connect and preprocess dataIn 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 progressdata_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 rowsprefix=["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"forpinprefixfordindays})names=list(dtype.keys())kwargs={"delimiter":"|","names":names,"parse_dates":["date"],"usecols":names[:42],"dtype":dtype,}Finally, the above variables are passed to theDataBlob.to_datasourcemethod which preprocesses the data and stores it in captn server.# Preprocess and prepare the data for trainingdata_source=data_blob.to_datasource(file_type="csv",index_column="game_name",sort_by="date",**kwargs)# Display the data preprocessing progressdata_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