captn-client 2022.5.0rc0

Capt’n client

Capt’n python client 2022.5.0rc0

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.

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()

100%|██████████| 1/1 [01:40<00:00, 100.97s/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:

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()

100%|██████████| 1/1 [00:40<00:00, 40.33s/it]

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