semvec/pypi-packages huggingface dataset:
araucanaxai 0.9.4
A novel approach for generating explanations of the predictions of a generic ML model
Requires Python: >=3.6
Keywords: xai,ai

# Auracana XAI

Tree-based local explanations of machine learning model predictions.
Implementation of the pipeline described in <a href="https://arxiv.org/abs/2110.08272">Parimbelli et al., 2021</a>

## About The Project

Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between performance and intelligibility is often to be faced, especially in high-stakes applications like medicine. This project propose a novel methodological approach for generating explanations of the predictions of a generic ML model, given a specific instance for which the prediction has been made, that can tackle both classification and regression tasks. Advantages of the proposed XAI approach include improved fidelity to the original model, the ability to deal with non-linear decision boundaries, and native support to both classification and regression problems.

**Keywords**: *explainable AI, explanations, local explanation, fidelity, interpretability, transparency, trustworthy AI, black-box, machine learning, feature importance, decision tree, CART, AIM*.

### Paper
The araucanaxai package implements the pipeline described in <a href="https://arxiv.org/abs/2110.08272">*Tree-based local explanations of machine learning model predictions - Parimbelli et al., 2021*</a>.
adversarial-test 0.1.4
Adversarial test for tabular data

# Adversarial test: simple way to know if your train data and test data are similar

We combine our train and test data, labeling them 0 for the training data and 1 for the test data, mix them up, then see if we are able to correctly re-identify them using a binary classifier.

If a classifier can identify whether a sample comes from train or test data set, we know that there's at least one feature in your data is shifted; use feature importance methods to point out the shifted feature(s)

# Get Started and Documentation
To install from pip:
```
pip install adversarial-test
```

Code example:
- [Using adversarial test with category features]()
- See more usages in [notebooks]() directory
simplerabbit 0.0.15
Simple RabbitMQ library
Requires Python: >=3.7

# Atom Rabbit Lib
Rabbit Lib is a Python library for sending and receiving messages using RabbitMQ. It includes two classes, RabbitSender and RabbitReceiver, that provide a simple interface for sending and receiving messages over a RabbitMQ broker.
## Installation
You can install Rabbit Lib using pip:
``` python
pip install rabbit_lib
```

Rabbit Lib requires Python 3.6 or later, as well as the pika library (version 1.2.0)

## Usage
Here's an example of how to use RabbitSender to send a message:
``` python
from rabbit_lib import RabbitSender

sender = RabbitSender('amqp://guest:guest@localhost:5672/', 'my_queue')
sender.send_message('Hello, world!')
```

And here's an example of how to use RabbitReceiver to receive messages:
``` python
from rabbit_lib import RabbitReceiver

receiver = RabbitReceiver('amqp://guest:guest@localhost:5672/', 'my_queue')
message = receiver.receive_message()
print(message)
```

## License
Rabbit Lib is licensed under the MIT License. See LICENSE for more information.

## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
abaxai-speech-client 0.1.2
A Python3 client to connect and use Abax.AI Speech services
Requires Python: >=3.6

## Project description

This is the speech client python package built by Abax.AI

It allows use to use speech to text services in both manners: offline and online

Online streaming will take audio stream in realtime, and return the transcripts on the fly

Offline transcribing will read your audio file, transcribe and return the transcripts in different formats.


## Examples

### Stream your audio file with python3

    import io
    from abaxai_sdk import streaming

    accessToken = 'YOUR_ACCESS_TOKEN'


    def get_wav_data(wavfile):
        for block in iter(lambda: wavfile.read(1280), b""):
            yield generate_block(block)

    def generate_block(block):
        return block

    def abaxai_streaming(stream_file):
        """Streams transcription of the given audio file."""

        client = streaming.SpeechClient()

        config = streaming.RecognitionConfig(
            encoding=streaming.AudioEncoding.LINEAR16,
            sample_rate_hertz=16000,
            language_code="en-US",
            model="basic_english",
        )

        streaming_config = streaming.StreamingRecognitionConfig(config, accessToken)

        # In practice, stream should be a generator yielding chunks of audio data.
        with io.open(stream_file, 'rb') as audiostream:
            data = get_wav_data(audiostream)
            requests = (
                streaming.StreamingRecognizeRequest(audio_content=chunk) for chunk in data
            )

            responses = client.streaming_recognize(
                            config=streaming_config,
                            requests=requests,
                    )
            
            print("\n\nFinal transcripts: \n")
            for response in responses:
                print(response)


    audio_file = "your_audio_file.wav"

    print("Streaming the audio file")
    abaxai_streaming(audio_file)

### Transcribe your audio file with python3

    from abaxai_sdk import transcribing

    accessToken = 'YOUR_ACCESS_TOKEN'

    def abaxai_transcribe(audio_filepath):
        """Streams transcription of the given audio file."""

        client = transcribing.SpeechClient()

        config = transcribing.RecognitionConfig(
            encoding=transcribing.AudioEncoding.LINEAR16,
            sample_rate_hertz=16000,
            language_code="en-US",
            model="wenet-english",
        )
        
        transcribing_config = transcribing.TranscribeConfig(config, accessToken)

        client.recognize(config=transcribing_config,
                            audiofilepath=audio_filepath,)
        
        # speechid = "<your_speech_id>"
        # client.get_transcription(speechid, accessToken)

        print("Done.")


    audio_file = "your_audio_file.wav"

    print("Transcribe the audio file")
    abaxai_transcribe(audio_file)


## Resources

Homepage: https://abax.ai/
iolsmp 1.0.0
Log linear model regression and dealing with zeros
Keywords: log linear,regression

The Zero-Inflated Log Linear Regression package provides a set of functions and utilities to handle zero-inflated cases in log linear regression models. It offers methods for estimating model parameters, calculating predicted values, and handling clustered data. This package is particularly useful when dealing with datasets where the dependent variable has excess zeros, allowing for more accurate and robust analysis of log linear regression models. It leverages the power of numpy, statsmodels, and pandas libraries to provide efficient and reliable computations for zero-inflated log linear regression analysis.