semvec/pypi-packages huggingface dataset:
ml-solution 0.0.3
A mechine learning pipeline lib.
Requires Python: >=3.10

# ML Solution

[pypi](https://pypi.org/project/ml-solution/) [github](https://github.com/JiahongZhang/ml_solution) [modelscope](https://modelscope.cn/models/hugo42/ml_solution/summary)

This package is used to quickly build a pipline for mechine learning.

## Quick Start

### Installation

Easy installation with pip:

```bash
pip install ml_solution
```

### Code Sample

The code below shows how to use ml_solution to build flexible deep learning model frame quickly:

```python
import torch
from torch import optim
import torch.nn as nn
from dataset import creat_loader
import modeling
from ml_solution.dl_tools import engine, engine_utils, train_utils
from ml_solution import data_utils
from transformers import XLMRobertaTokenizer

train_config = data_utils.json_load('./train_config.json')
dataset_config = data_utils.json_load('./dataset_config.json')

train_loader = creat_loader(dataset_config['train_json_path'],)
valid_loader = creat_loader(dataset_config['valid_json_path'])
dataloaders = {
    'train':train_loader,
    'valid':valid_loader
}

model = modeling.get_model()
optimizer = optim.Adam(model.parameters(), lr=train_config['lr'])
criterion = train_utils.DictInputWarpper(nn.CrossEntropyLoss(), 'logit', 'label')

metric_grader = engine_utils.ConfusionMetrics(
    num_classes=4, 
    metrics_list=train_config['metrics_list']
    )
loss_grader = engine_utils.LossRecorder()
computers = {
    'conf_metrics': metric_grader, 
    'loss': loss_grader
}
grader = engine_utils.Grader(computers)


wandb_init_config = data_utils.json_manipulate_keys(
    train_config, 
    ['lr', 'batch_size', "architecture"], 
    keep=True
    )
wandb_init_config['criterion'] = criterion.module.__class__.__name__
wandb_init_config['optimizer'] = optimizer.__class__.__name__
logger = engine_utils.WandbLogger(
    config=wandb_init_config, project=train_config['project'])

handler = engine.HandlerSaveModel(
    metric_name="ACC", 
    log_root=train_config['log_root'], 
    version=logger.version,
    ideal_th=5
    )

trainer = engine.TorchTrainer(
    model, dataloaders, criterion, 
    optimizer, device=device, mix_pre=train_config['mix_pre']
    )

train_pipeline = engine.TrainPipeline(
    trainer, grader, logger, 
    handler=handler
    )

train_pipeline.train_epoches(train_config['epoches'])


```
gcrkrause-pylistenbrainz2 0.5.5a7
A simple ListenBrainz client library for Python
Requires Python: >=3.8

# pylistenbrainz

*pylistenbrainz* is a simple Python library for the
[ListenBrainz Web API](https://listenbrainz.readthedocs.io/en/production/dev/api/).

pylistenbrainz will help you start getting data from and submitting data to
[ListenBrainz](https://listenbrainz.org>) very quickly.

Here's an example of getting the listening history of a ListenBrainz user::

``` python
import pylistenbrainz

client = pylistenbrainz.ListenBrainz()
listens = client.get_listens(username='iliekcomputers')
for listen in listens:
    print("Track name:", listen.track_name)
    print("Artist name:", listen.artist_name)
```

Here's another quick example of how to submit a listen to ListenBrainz::

``` python
import pylistenbrainz
import time

auth_token = input('Please enter your auth token: ')

listen = pylistenbrainz.Listen(
    track_name="Fade",
    artist_name="Kanye West",
    release_name="The Life of Pablo",
    listened_at=int(time.time()),
)

client = pylistenbrainz.ListenBrainz()
client.set_auth_token(auth_token)
response = client.submit_single_listen(listen)
```

More detailed documentation is available
at [Read The Docs](https://pylistenbrainz.readthedocs.io/en/latest/).

## Features

pylistenbrainz provides easy access to all ListenBrainz endpoints, handles
ratelimits automatically and supports the ListenBrainz authorization flow.

For details on the API endpoints that can be used via pylistenbrainz, take
a look at the [ListenBrainz API Documentation](https://listenbrainz.readthedocs.io/en/production/dev/api/).

## Installation

Install or upgrade pylistenbrainz with:

    pip install pylistenbrainz --upgrade

## Support

You can ask questions about how to use pylistenbrainz on IRC (freenode #metabrainz).
You can also email me at `iliekcomputers [at] gmail [dot] com`.

If you have found a bug or have a feature request, let me know by opening an issue (or a pull request).

## License

```
pylistenbrainz - A simple client library for ListenBrainz
Copyright (C) 2020 Param Singh <iliekcomputers@gmail.com>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
```
gcrkrause-pylistenbrainz 0.5.5a4
A simple ListenBrainz client library for Python
Requires Python: >=3.8

# pylistenbrainz

*pylistenbrainz* is a simple Python library for the
[ListenBrainz Web API](https://listenbrainz.readthedocs.io/en/production/dev/api/).

pylistenbrainz will help you start getting data from and submitting data to
[ListenBrainz](https://listenbrainz.org>) very quickly.

Here's an example of getting the listening history of a ListenBrainz user::

``` python
import pylistenbrainz

client = pylistenbrainz.ListenBrainz()
listens = client.get_listens(username='iliekcomputers')
for listen in listens:
    print("Track name:", listen.track_name)
    print("Artist name:", listen.artist_name)
```

Here's another quick example of how to submit a listen to ListenBrainz::

``` python
import pylistenbrainz
import time

auth_token = input('Please enter your auth token: ')

listen = pylistenbrainz.Listen(
    track_name="Fade",
    artist_name="Kanye West",
    release_name="The Life of Pablo",
    listened_at=int(time.time()),
)

client = pylistenbrainz.ListenBrainz()
client.set_auth_token(auth_token)
response = client.submit_single_listen(listen)
```

More detailed documentation is available
at [Read The Docs](https://pylistenbrainz.readthedocs.io/en/latest/).

## Features

pylistenbrainz provides easy access to all ListenBrainz endpoints, handles
ratelimits automatically and supports the ListenBrainz authorization flow.

For details on the API endpoints that can be used via pylistenbrainz, take
a look at the [ListenBrainz API Documentation](https://listenbrainz.readthedocs.io/en/production/dev/api/).

## Installation

Install or upgrade pylistenbrainz with:

    pip install pylistenbrainz --upgrade

## Support

You can ask questions about how to use pylistenbrainz on IRC (freenode #metabrainz).
You can also email me at `iliekcomputers [at] gmail [dot] com`.

If you have found a bug or have a feature request, let me know by opening an issue (or a pull request).

## License

```
pylistenbrainz - A simple client library for ListenBrainz
Copyright (C) 2020 Param Singh <iliekcomputers@gmail.com>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
```
rosalie 0.0.15
A simple simulation tool to compare experiment evaluation methods.
Requires Python: >=3.7

# rosalie

A simple simulation tool to compare experiment evaluation methods.

## Installation

The package is currently hosted on [Test PyPI](https://test.pypi.org/) and can be installed using:

```sh
python -m pip install --index-url https://test.pypi.org/simple/ rosalie
```

You also have to install dependencies if you haven't already (packages on Test PyPI can differ from those on live PyPI, so dependencies cannot be installed automatically):

```sh
python -m pip install altair
python -m pip install numpy
python -m pip install pandas
python -m pip install tqdm
```

## Developer workflow

- Published version lives on `main`.
- Development version lives on `dev`.
- New features are separate branches off `dev`.
- To publish a new feature, merge it into `dev` for testing, then merge into `main` to publish.


## For the curious

In [Three Little Nuts for Cinderella](https://en.wikipedia.org/wiki/T%C5%99i_o%C5%99%C3%AD%C5%A1ky_pro_Popelku), "Rosalie" is Cinderella's wise owl friend that helps her make good choices. The hope is that this package will do the same for its users.