semvec/pypi-packages huggingface dataset:
pylint2codeclimate 1.3
pylint reporter class to export results in codeclimate format
License: MIT

pylint2codeclimate
==================

Pylint reporter class to export results in codeclimate format, originally from 
https://github.com/mikebryant/codeclimate-pylint/blob/master/codeclimate_reporter.py

It can be used in a gitlab-ci.yml stage like:

::

    codequality:
      stage: test
      script:
        - pip install pylint2codeclimate pylint
        - pylint -f pylint2codeclimate.CodeClimateReporter *.py > codeclimate.json
      artifacts:
        when: always
        paths: [codeclimate.json]
polaris-py 0.7.3
Polaris is a hyperparamter optimization library.
License: MIT

# Polaris : Hyperparamter optimization library.

[![Documentation Status](https://readthedocs.org/projects/polaris/badge/?version=latest)](https://polaris.readthedocs.io/en/latest/?badge=latest)

Polaris is a hyperparamter optimization library.  
We plan to support every state-of-art tuning method as follows.

-  Random Search
-  Bayesian Optimization
-  Tree-structured Parzen Estimators (TPE)

## Documentation
Polaris' documentation can be found at [https://polaris.readthedocs.io/](https://polaris.readthedocs.io/)

## Prerequisites
- Python >= 3.6
- RabbitMQ (Only for parallel execution)

## Installation

```shell
$ pip install polaris-py
```

## Examples

### Sequential Execution

```python
from polaris import Polaris, STATUS_SUCCESS, Trials, Bounds


def pseudo_train(params, exp_info):
    lr_squared = (params['lr'] - 0.006) ** 2
    weight_decay_squared = (params['weight_decay'] - 0.02) ** 2
    loss = lr_squared + weight_decay_squared
    return {
        'loss':  loss,
        'status': STATUS_SUCCESS,
    }


if __name__ == '__main__':
    bounds = [
        Bounds('lr', 0.001, 0.01),
        Bounds('weight_decay', 0.0002, 0.04),
    ]
    trials = Trials()
    polaris = Polaris(
            pseudo_train, bounds, 'bayesian_opt',
            trials, max_evals=100, exp_key='this_is_test')
    best_params = polaris.run()
    print(best_params)
```

### Parallel Execution

#### Single Process

1. Run `rabbitmq-server`
1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)
1. Run `polaris-worker --exp-key this_is_test`
1. Run codes as follows

### Multiple Processes (Use MPI)

1. Run `rabbitmq-server`
1. Set `RABBITMQ_URL` (ampq://guest:guest@localhost//)
1. Run `mpirun -n 4 polaris-worker --mpi --exp-key this_is_test`
1. Run codes as follows


```python

...

if __name__ == '__main__':
    bounds = [
        Bounds('lr', 0.001, 0.01),
        Bounds('weight_decay', 0.0002, 0.04),
    ]
    trials = Trials()
    polaris = Polaris(
            pseudo_train, bounds, 'bayesian_opt',
            trials, max_evals=100, exp_key='this_is_test')
    best_params = polaris.run_parallel()
    print(best_params)
```