semvec/pypi-packages huggingface dataset:
parakeet-python 1.0.0
ParakeetSDK
Requires Python: >=3.6
Keywords: OpenAPI, OpenAPI-Generator, ParakeetSDK

# parakeet-python
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:

- API version: 1.0.0
- Package version: 1.0.0
- Build package: org.openapitools.codegen.languages.PythonClientCodegen

## Requirements.

Python 2.7 and 3.4+

## Installation & Usage
### pip install

If the python package is hosted on a repository, you can install directly using:

```sh
pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git
```
(you may need to run `pip` with root permission: `sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git`)

Then import the package:
```python
import parakeet
```

### Setuptools

Install via [Setuptools](http://pypi.python.org/pypi/setuptools).

```sh
python setup.py install --user
```
(or `sudo python setup.py install` to install the package for all users)

Then import the package:
```python
import parakeet
```

## Getting Started

Please follow the [installation procedure](#installation--usage) and then run the following:

```python
from __future__ import print_function

import time
import parakeet
from parakeet.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = parakeet.Configuration(
    host = "http://localhost"
)



# Enter a context with an instance of the API client
with parakeet.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = parakeet.DefaultApi(api_client)
    tts_request = parakeet.TtsRequest() # TtsRequest |  (optional)

    try:
        # Text To Speech Streaming
        api_response = api_instance.text_to_speech_streaming_v1_tts_streaming_post(tts_request=tts_request)
        pprint(api_response)
    except ApiException as e:
        print("Exception when calling DefaultApi->text_to_speech_streaming_v1_tts_streaming_post: %s\n" % e)
    
```

## Documentation for API Endpoints

All URIs are relative to *http://localhost*

Class | Method | HTTP request | Description
------------ | ------------- | ------------- | -------------
*DefaultApi* | [**text_to_speech_streaming_v1_tts_streaming_post**](docs/DefaultApi.md#text_to_speech_streaming_v1_tts_streaming_post) | **POST** /v1/tts_streaming | Text To Speech Streaming
*DefaultApi* | [**text_to_speech_v1_tts_post**](docs/DefaultApi.md#text_to_speech_v1_tts_post) | **POST** /v1/tts | Text To Speech


## Documentation For Models

 - [HttpValidationError](docs/HttpValidationError.md)
 - [SpeakerId](docs/SpeakerId.md)
 - [TtsRequest](docs/TtsRequest.md)
 - [ValidationError](docs/ValidationError.md)


## Documentation For Authorization

 All endpoints do not require authorization.

## Author
aion-ml-optimizer 0.1.0
An automated machine learning optimization package
Requires Python: >=3.8

# ML Optimizer

A Python package for automated machine learning optimization.

## Installation

```bash
pip install ml_optimizer
```

## Usage

```python
from ml_optimizer import EnhancedSequentialOptimizer
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

# Load data
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target

# Initialize optimizer
optimizer = EnhancedSequentialOptimizer(
    task_type='classification',
    target_column='target',
    scoring_metrics=['accuracy', 'precision', 'recall', 'f1'],
    generations=5,
    cv_folds=5
)

# Split and fit
train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
optimizer.fit(train_df)