semvec/pypi-packages huggingface dataset:
mlexplainer 1.0.2
Advanced Machine Learning package dedicated to model interpretability, with a primary focus on leveraging Shapley values for explaining complex predictive models.
License: MIT
Requires Python: <4.0,>=3.11

# MLExplainer

<p align="left">
    <img alt="PyPI" src="https://img.shields.io/pypi/v/mlexplainer">
    <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/mlexplainer">
    <img alt="License" src="https://img.shields.io/github/license/zachariebuisson1/mlexplainer">
</p>

Advanced Machine Learning package dedicated to model interpretability, with a primary focus on leveraging Shapley values for explaining complex predictive models.

# ReadTheDocs

Check out the package documentation on [ReadTheDocs](https://mlexplainer.readthedocs.io/en/latest/index.html)!

# Demo

Check out the demo on [Streamlit](https://mlexplainer.streamlit.app/)

# Install

**MLExplainer** can be installed from [PyPI](https://pypi.org/project/mlexplainer/):

<pre>
pip install mlexplainer
</pre>

# Why MLExplainer ?

**MlExplainer** is a tool designed to tackle a major challenge in advanced Machine Learning for tabular data.

Indeed, advanced Machine Learning models - such as Boosting algorithms - are often perceived as black boxes. 
Today, our goal is to provide a way to demystify these models by leveraging mathematical concepts, making it possible to clearly explain how they work to any audience.

Key Features :

* **SHAP Integration**: Built-in support for SHAP explainers with optimized workflows
* **Multiple Classification Types**: Support for binary and multilabel classification tasks
* **Automatic Feature Detection**: Intelligent categorization of numerical, categorical, and string features
* **Rich Visualizations**: Integrated plotting for feature-target relationships and SHAP value distributions
* **Validation Tools**: Built-in interpretation consistency validation
* **Modern Architecture**: Clean, extensible design with proper abstractions

# About

If you encounter any issues with the code or have suggestions for improvement, feel free to open an issue or contact me.
replicated 0.1.0a2
Python SDK for Replicated customer, metrics, and instance insights
License: MIT
Requires Python: >=3.8
Keywords: replicated, sdk, metrics, customers

# Replicated Python SDK

A Python SDK for embedding Replicated customer, custom metrics, and instance insights data in your applications.

## Installation

Install the SDK via pip:

```bash
pip install --upgrade replicated
```

## Usage

### Sync Example

```python
from replicated import ReplicatedClient, InstanceStatus

client = ReplicatedClient(
    publishable_key="replicated_pk_...", 
    app_slug="my-app"
)

# Create a customer (or fetch an existing one)
customer = client.customer.get_or_create(email_address="xxx@yyy.com")

# Get or create the associated instance
instance = customer.get_or_create_instance()

# Use the instance for metrics and status reporting
instance.send_metric("cpu_usage", 0.83)
instance.set_status(InstanceStatus.RUNNING)
instance.set_version("1.2.0")
```

### Custom State Directory

By default, the SDK stores state in platform-specific directories. You can override this for testing, containerization, or custom deployments:

```python
from replicated import ReplicatedClient

# Use a custom directory (supports ~ and relative paths)
client = ReplicatedClient(
    publishable_key="replicated_pk_...",
    app_slug="my-app",
    state_directory="/var/lib/my-app/replicated-state"
)

# Or use a relative path (will be resolved to absolute)
client = ReplicatedClient(
    publishable_key="replicated_pk_...",
    app_slug="my-app",
    state_directory="./local-state"
)
```

**When to use custom state directories:**
- Testing with temporary directories
- Docker containers with mounted volumes
- Multi-tenant applications requiring isolated state
- Development with project-local state

### Async Example

```python
from replicated import AsyncReplicatedClient, InstanceStatus

async def main():
    async with AsyncReplicatedClient(
        publishable_key="replicated_pk_...", 
        app_slug="my-app"
    ) as client:
        customer = await client.customer.get_or_create(email_address="xxx@yyy.com")
        instance = await customer.get_or_create_instance()

        await instance.send_metric("cpu_usage", 0.83)
        await instance.set_status(InstanceStatus.RUNNING)
        await instance.set_version("1.2.0")
```

## Documentation

For detailed documentation, visit [docs.replicated.com/sdk/python](https://docs.replicated.com/sdk/python).

## License

MIT License
tfviz 1.0.0
Real-time visualization for TensorFlow training metrics
License: MIT
Requires Python: >=3.7
Keywords: tensorflow, visualization, machine-learning, plotting, metrics

# TFViz

Real-time visualization for TensorFlow training metrics with automatic color coding and professional plotting capabilities.

## Features

- **Real-time plotting** with configurable update frequency
- **Automatic metric categorization** (loss, accuracy, precision, recall, etc.)
- **Thread-safe operations** for async plotting
- **Multiple visualization themes** (dark, light, minimal)
- **TensorFlow Callback integration** for seamless training monitoring
- **Configurable plot styles** (line, scatter, bar, histogram)
- **Export/import capabilities** for metric data
- **Professional API** with type hints and error handling
- **Distinct color coding** for training vs validation metrics

## Quick Start

```python
from tfviz import TFVizCallback

callback = TFVizCallback()
model.fit(X_train, y_train, callbacks=[callback])
```

## Installation

```bash
pip install tfviz
```

## Basic Usage

### Simple Training Visualization

```python
import tensorflow as tf
from tfviz import TFVizCallback

# Load your data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Create your model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Add TFViz callback
callback = TFVizCallback()

# Train with visualization
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test), callbacks=[callback])
```

### Advanced Configuration

```python
from tfviz import TFVizCallback, TFVizConfig
from tfviz.config import Theme, PlotStyle

# Custom configuration
config = TFVizConfig(
    theme=Theme.DARK,
    plot_config=PlotConfig(style=PlotStyle.LINE),
    update_config=UpdateConfig(frequency=5)
)

callback = TFVizCallback(config=config)
model.fit(X_train, y_train, callbacks=[callback])
```

## Library Structure

```
tfviz/
├── __init__.py          # Main package exports
├── config.py            # Configuration classes and settings
├── metrics.py           # Metrics tracking and management
├── visualizer.py        # Visualization engine and plotting
└── callback.py          # TensorFlow callback integration
```

## Components

### TFVizConfig
Configuration management with themes, plot styles, and update settings.

### MetricsTracker
Thread-safe metrics collection with automatic categorization and statistics.

### VisualizationEngine
Matplotlib-based plotting engine with async capabilities and multiple themes.

### TFVizCallback
TensorFlow callback for automatic metric collection and visualization during training.

## Color Coding

The library automatically assigns distinct colors to different metrics:
- **Training Loss**: Red
- **Validation Loss**: Blue
- **Training Accuracy**: Green
- **Validation Accuracy**: Orange

## Requirements

- Python 3.7+
- TensorFlow 2.x
- Matplotlib
- NumPy

## License

MIT License
custom-parser-hiba-khabar 1.0.0
A simple IPv4 log parser using regex, created by hiba khabar, file handling, and data structures.
License: MIT
Requires Python: >=3.8

# IPv4 Log Parser - HIBA KHABAR

A simple log parser for IPv4 addresses in CIDR notation.

## Installation
```bash
pip install ipv4-log-parser
junglescout-client 0.6.0
Jungle Scout API Client
License: MIT
Requires Python: <4.0,>=3.9

# Jungle Scout Python Client

The official Jungle Scout API Python Client with both sync and async support.
Documentation for this client is available on
[Read the Docs](https://jungle-scout-junglescout-python-client.readthedocs-hosted.com).

See the [Jungle Scout API Documentation](https://developer.junglescout.com) and
the [Jungle Scout Postman Collection](https://postman.junglescout.com) for more
information about the Jungle Scout API.

## Rate Limits

The current API request limit per minute is 300, and our burst limit per second is 15.

## Installation

```bash
pip install junglescout-client
```

## Usage

Here's a quick example using sync client to get keywords by ASIN:

```python
from junglescout import ClientSync
from junglescout.models.parameters import Marketplace, ApiType, FilterOptions, Sort

API_KEY_NAME = "api_key_name"
API_KEY = "api_key"

client = ClientSync(
    api_key_name=API_KEY_NAME,
    api_key=API_KEY,
    marketplace=Marketplace.US,
    api_type=ApiType.JS
)

filter_options = FilterOptions(min_monthly_search_volume_exact=150)

keywords = client.keywords_by_asin(
    asin='B005IHSKYS',
    filter_options=filter_options,
    sort_option=Sort.MONTHLY_SEARCH_VOLUME_EXACT_MATCH
)

client.close()
```

See the
[documentation](https://jungle-scout-junglescout-python-client.readthedocs-hosted.com)
for information on the available methods and parameters.

### Pydantic Models

This project defines API model objects using
[Pydantic](https://docs.pydantic.dev/latest). Autocomplete for model parameters
during instantiation should work by default in most modern IDEs. For Jetbrains
IDEs, the [Pydantic](https://plugins.jetbrains.com/plugin/12861-pydantic) plugin
is required for autocomplete. See the following links for more information:

- [pydantic/pull/2721](https://github.com/pydantic/pydantic/pull/2721)
- [pydantic/issues/650](https://github.com/pydantic/pydantic/issues/650)

## Contributing

Contributions are welcome! Please read the
[contributing guidelines](CONTRIBUTING.md) for more information.