semvec/pypi-packages huggingface dataset:
lazylearn 0.0.4
lazy-learn is a high-level Python interface for automated machine learning (AutoML) for the lazy data scientist. While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system. lazy-learn aims at providing the most approachable and fastest access to building baseline models.
Requires Python: >=3.7


<img width="500" src="doc/logo/transparent_small.png">

**lazy-learn** is a high-level Python interface for automated machine learning (AutoML). While there are many AutoML libraries available each typically solves a niche area of the overall ML pipeline without providing a covering and approachable end-to-end system.

The aim of lazy-learn is exactly that. Given a dataset, lazy-learn will analyse types and distributions of attributes, preprocess, feature-engineer and ultimately train models to be used for further evaluation or inference. 

## Upcoming features

Current stable version is 0.0.3. The upcoming updates will support:
- Abstract construction of model architectures
- XGBoost, LightGBM, Adaboost and Catboost architectures
- Time partitioning of datasets (Added in 0.0.4)
- Automated Hyperparameter Optimisation (HPO) (Added in 0.0.4)
- Text features
- An interface to AutoGluon
- Outlier detection and handling
- Automated suggestions of performance metrics

## Usage

Using lazy-learn revolves around the `LazyLearner` class. You can think of it as a kind of project, and it is the wrapper for any experiment within lazy-learn. You can consider a simple example with the California Housing dataset:

```python
from lazylearn import LazyLearner
from sklearn.datasets import fetch_california_housing


# get some data
data = fetch_california_housing(as_frame=True)
df = data["data"]
df["MedHouseVal"] = data["target"]

# instantiate and run the LazyLearner
learner = LazyLearner()
learner.create_project(data=df, target="MedHouseVal")
learner.run_autopilot()

# evaluate results
print(learner.leaderboard())

```

## Installation

### Dependencies

lazy-learn requires:

- pandas
- scikit-learn
- xgboost

### User Installation 
```
pip install lazy-learn
```

## Help and Support
### Documentation

### Citation
loggingx 1.0.1
logging拓展功能模块
Requires Python: >=3.7

# loggingx

logging拓展功能模块。

[Installation](#installation) |  [Usage](#usage)
***

## Installation

安装loggingx，只需要执行：

``` bash
pip install loggingx
```

loggingx部分功能需要依赖消息队列，需要安装相关依赖，可以通过以下命令安装必要的依赖：

``` bash
pip install "loggingx[queue]"
```

## Usage

loggingx在使用上基本和logging一致。

### handlers.RedisStreamHandler

将日志消息发布到redis stream消息队列，配合[handlers.RedisStreamListener](#handlersredisstreamlistener)可以解决多进程写日志文件不安全的问题。

``` python
# 示例一：直接使用

logger = logging.getLogger('test')
logger.setLevel(logging.DEBUG)
rsh = RedisStreamHandler('redis://redis:6379/0')
logger.addHandler(rsh)

# 示例二：在配置文件中使用
LOGGING_CONFIG = {
   'version': 1,
   'disable_existing_loggers': False,
   'formatters': {
      'verbose': {
         'format': '%(asctime)s.%(msecs)03d %(levelname)-8s %(processName)s:%(threadName)s %(pathname)s:%(lineno)d - %(message)s',
         'datefmt': '%Y-%m-%d %H:%M:%S'
      },
      'simple': {
         'format': '{name:s}: {asctime:s} {levelname} {message}',
         'style': '{',
         'datefmt': '%Y-%m-%d %H:%M:%S'
      },
   },
   'handlers': {
      'console': {
         'class': 'logging.StreamHandler',
         'level': 'INFO',
         'formatter': 'simple'
      },
      'redis_stream': {
         'class': 'loggingx.handlers.RedisStreamHandler',
         'level': 'INFO',
         'redis_url': 'redis://redis:6379/0'
      }
   },
   'loggers': {
      'cloud': {
         'handlers': ['console'],
         'propagate': True,
      },
      'cloud.request': {
         'handlers': ['redis_stream'],
         'level': 'INFO',
         'propagate': True,
      }
    },
}

logging.config.dictConfig(LOGGING_CONFIG)
```

在多进程服务中使用RedisStreamHandler，然后单独启动一个进程运行RedisStreamListener，可以保证日志消息完整传递到下游处理器。

完整示例可以参考[examples](examples/redis_stream_handler.py)。

### handlers.RedisStreamListener

消费redis stream消息队列的日志消息，传递到下游handlers。

RedisStreamListener不属于处理器，需要和[handlers.RedisStreamHandler](#handlersredisstreamhandler)配合工作。

``` python
# 创建TimedRotatingFileHandler实例
fh = logging.handlers.TimedRotatingFileHandler('logs/cloud.log', when='M', backupCount=5)
fh.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
# 使用RedisStreamListener消费RedisStreamHandler消息，传递到TimedRotatingFileHandler
rsl = RedisStreamListener('redis://redis:6379/0', fh)
rsl.start()
```
md-inputs 1.1
Python-mardown extension for inputs(text, select, checkbox...) in markdown document


# md_inputs

`md_inputs` is a python-mardown extension for inputs(text, select, checkbox...) in markdown document

# Usage

Use [[ text ]] to make a inline input element.

Text must contion attribute: `type` at least, like a html tag, your can add any attribute into the input element.

The `value` attribute is used to set the `checkbox` `radio` `select` input type value(`selected` or `checked` in given index(start from 0 for `selected`), "1" means `checked`)

If the `type` is `select`, the `opt` and `opt_value` attribute is used to set select options. `opt` means the display text of selections, each item use `:` to seperate. `opt_value` is the value of options, the separated item count of the `opt_value` must be the same of the `opt`. if the `opt_value` has not given, md_inputs will use the `opt` instead the `opt_value`.

# Example

## select

### markdown:

````
[[type='select' opt='eopt1:eopt2' class='dfaj' id='eid1']]
````

### html:

```` html
<p><span><select class='dfaj' id='eid1' ><option value='eopt1'>eopt1</option><option value='eopt1'>eopt2</option></select></span></p>
````

### markdown:

````
[[type='select' opt='eopt1:eopt2' opt_value="1:2" class='dfaj' id='eid1' value="1"]]
````

### html:

```` html
<p><span><select class='dfaj' id='eid1' value='1' ><option value='1' >eopt1</option><option value='2' selected>eopt2</option></select></span></p>
````

## input

markdown:

````
[[type='input' value="hello" id='edit1']]
````

html:

```` html
<p><span><input type='input' value='hello' id='edit1' /></span></p>
````

## checkbox

markdown:

````
[[type='checkbox' value="1"]]
````

html:

```` html
<p><span><input type='checkbox' value='1' checked /></span></p>
````