semvec/pypi-packages huggingface dataset:
g2m-snowflake-sdk-python 1.0.15
Python SDK for the G2M Platform API
License: Apache 2.0

# Python SDK for the G2M Platform API

## Overview
This Python client will give you access to the G2M API. See files in the `examples` folder
for examples showing how to use the client. Note that a `client_id` should always be provided when querying the API; it is used for reporting purposes.
* For general information please see https://g2m.ai.
* For help and support see https://support.g2m.ai.
* For SDK reference documentation see 'Coming Soon'.

## Installation instructions
Getting the client set up will require the following:

1. Install the latest version of the client on your local machine:
```
pip install g2m-snowflake-sdk-python -U
```

2. Get an API username and password from your G2M admin (you may need SSO credentials from your local admin instead).

3. Confirm you are able to connect to the API, and check the API version
as follows from a Python session:
```
>>> from g2mclient import Analyzer
>>> analyzer = Analyzer(host="<your host>")
>>> analyzer.login(
    user="snowflakeuser",
    account="account-idenfitier",
    endpoint="xyz.snowflakecomputing.com",
    role="snowflakerole",
    snowflake_account_url="xyz.snowflakecomputing.com",
    lifetime=59,
    renewal_delay=54
)
Login successful
>>> Analyzer().version()
{'status': 200, 'response': {'version': 'x.x.xxx', 'tenant': <your tenant name>, 'copyright': '2024 (c) Go2Market Insights Inc. All rights reserved.'}}
```

## Testing instructions
If you are developing the SDK and would like to test the repo, clone it locally using git then 
run the following from the root directory:
```
python -m unittest tests.test_all -v   # all tests
python -m unittest tests.test_quick -v # quick tests
```
Make sure you update the `config.json` file first to include the name of your API tenant. 
To run a single test case do:
```
python -m unittest tests.test_all.PropensityTest.test_logistic_regression_classifier -v
```