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توضیحات

Captn client library

ویژگی	مقدار
سیستم عامل	-
نام فایل	captn-client-2023.3.0rc0
نام	captn-client
نسخه کتابخانه	2023.3.0rc0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	airt.ai
ایمیل نویسنده	info@airt.ai
آدرس صفحه اصلی	https://github.com/airtai/captn-client
آدرس اینترنتی	https://pypi.org/project/captn-client/
مجوز	Creative Commons License

Capt’n python client ================  ## Docs For full documentation, Please follow the below link: - <a href="https://docs.captn.ai" target="_blank">https://docs.captn.ai/</a> ## How to install If you don’t have the captn library already installed, please install it using pip. ``` console 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](https://docs.captn.ai/Tutorial/) with more elaborate example, and - [API](https://docs.captn.ai/API/client/Client/) 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 ``` python # 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. ``` python # 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: ``` python # 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. ``` python # 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] ``` python # 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 ``` python # Todo ```

نیازمندی

مقدار	نام
==2023.3.0	airt-client
==1.27.81	awscli
==1.12.0	azure-identity
==21.0.0	azure-mgmt-storage
==1.7.4	bandit
==23.1.0	black
==1.26.81	boto3
==1.4.0	detect-secrets
==1.0.1	mypy
==2.1.1	mysqlclient
==0.1.0	nbdev-mkdocs
==0.25.0	openai
==3.1.1	pre-commit
==11.0.0	pyarrow
==7.2.1	pytest
==1.13.0	semgrep
==0.0.8	sqlmodel
==2.28.11.15	types-requests
==0.9.0.1	types-tabulate

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl captn-client-2023.3.0rc0:

pip install captn-client-2023.3.0rc0.whl

نصب پکیج tar.gz captn-client-2023.3.0rc0:

pip install captn-client-2023.3.0rc0.tar.gz