captn-client 2022.3.0

Capt’n client

Capt’n python client 2022.3.0

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

import json
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 [01:35<00:00, 95.72s/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_json=json.dumps(kwargs)
)

data_source.progress_bar()

100%|██████████| 1/1 [00:45<00:00, 45.39s/it]

data_source.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	date	platform	user_type	network	campaign	adgroup	installs	spend	revenue_0	revenue_1	...	revenue_23	revenue_24	revenue_25	revenue_26	revenue_27	revenue_28	revenue_29	revenue_30	revenue_60	revenue_90
0	2021-03-15	ios	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_541	1	0.600000	0.000000	0.018173	...	0.018173	0.018173	0.018173	0.018173	0.018173	0.018173	0.018173	0.018173	0.018173	0.018173
1	2021-03-15	ios	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_2351	2	4.900000	0.000000	0.034000	...	0.034000	6.034000	6.034000	6.034000	6.034000	6.034000	6.034000	6.034000	6.034000	13.030497
2	2021-03-15	ios	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_636	3	7.350000	0.000000	0.000000	...	12.112897	12.112897	12.112897	12.112897	12.112897	12.112897	12.112897	12.112897	12.112897	12.112897
3	2021-03-15	ios	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_569	1	0.750000	0.000000	0.029673	...	0.029673	0.029673	0.029673	0.029673	0.029673	0.029673	0.029673	0.029673	0.029673	0.029673
4	2021-03-15	ios	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_243	2	3.440000	0.000000	0.027981	...	0.042155	0.042155	0.042155	0.042155	0.042155	0.042155	0.042155	0.042155	0.042155	0.042155
5	2021-03-15	android	googleadwords_int	googleadwords_int	campaign_283	adgroup_1685	11	0.000000	0.000000	0.097342	...	0.139581	0.139581	0.139581	0.139581	0.139581	0.139581	0.139581	0.139581	0.139581	0.139581
6	2021-03-15	android	googleadwords_int	googleadwords_int	campaign_2	adgroup_56	32	30.090000	0.000000	0.802349	...	2.548253	2.548253	2.771138	2.805776	2.805776	2.805776	2.805776	2.805776	2.805776	2.805776
7	2021-03-15	android	moloco_int	moloco_int	campaign_191	None	291	503.480011	34.701553	63.618111	...	116.508331	117.334709	117.387489	117.509506	118.811417	118.760765	119.151291	119.350220	139.069443	147.528793
8	2021-03-15	android	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_190	4	2.740000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
9	2021-03-15	android	jetfuelit_int	jetfuelit_int	campaign_0	adgroup_755	8	11.300000	13.976003	14.358793	...	14.338905	14.338905	14.338905	14.338905	14.338905	14.338905	14.338905	14.338905	14.338905	14.338905

10 rows × 41 columns

2. Training

# Todo