semvec/pypi-packages huggingface dataset:
logfile-metrics 1.0.0
Parse the logfile(s) and output the results in metrics file format for Prometheus.
Requires Python: >=3.8,<4.0

# logfile_metrics - Сканирование лог-файлов с целью формирования метрик для Prometheus.

## Steps to release a new package version
* Run tests, linter, formatter, ...: `poetry run pytest (pylint, black, ...)`
* Bump project's version: `poetry version ...`
* Build: `poetry build`
* Publish: `poetry publish`
aws-framework 0.0.2
A Demo Framework for the AWS Workshop
Requires Python: >=3.7,<4.0

# AWS Framework

## Description

Async AWS Framework for Python 3.6+

## Installation

```bash
pip install aws-framework
```

## Tech stack

- Python 3.6+
- [AWS Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html)
- [Aiohttp](https://docs.aiohttp.org/en/stable/)
- [Pydantic](https://pydantic-docs.helpmanual.io/)
pipeline-viz 0.1.2
Visualization tool for sklearn pipelines


A Python package that provides a convenient way to visualize Scikit-learn machine learning pipelines. It utilizes libraries such as NetworkX, Matplotlib, and Plotly to generate clear, interactive, and insightful visual representations of your ML pipelines

## Installation

You can install Visualize Pipeline using pip:

```python
pip install visualize-pipeline

```
## Dependencies

Visualize Pipeline depends on the following Python libraries:

--NetworkX
--Matplotlib
--Plotly
--Scikit-Learn

### You can install these dependencies using pip:
```python
pip install networkx matplotlib plotly scikit-learn

```
## Usage

### Basic Example
Here's a basic example of how to use Visualize Pipeline:

```python
from visualize_pipeline import visualize_pipeline
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# Create a simple pipeline
pipe = Pipeline([
    ('scale', StandardScaler()),
    ('clf', LogisticRegression())
])

# Visualize the pipeline
graph = visualize_pipeline(pipe)

# Save the graph to an HTML file
convert_graph_to_plot(graph, 'pipeline.html')

```

This will create an interactive HTML file pipeline.html with the visualization of your pipeline.

### Example in Google Colab with a complex pipeline

```python
pipe = Pipeline(steps=[
    ('scaler', StandardScaler()),
    ('classifier', LogisticRegression())])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), [0, 1]),
        ('cat', OneHotEncoder(), [2, 3])])

complex_pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', pipe)])

graph = visualize_pipeline(complex_pipeline)
# fig = convert_graph_to_plot(graph)

# Convert the graph to a plotly figure
fig = convert_graph_to_plot(graph, filename='pipeline.html')
from IPython.display import HTML

# Display the HTML file
display(HTML('pipeline.html'))

```

### Visual Example

![Pipeline Visualization Example 1](https://raw.githubusercontent.com/pritiyadav888/visualize_pipeline/main/images/Screenshot%202023-06-21%20at%2010.22.21%20PM.png)

![Pipeline Visualization Example 2](https://raw.githubusercontent.com/pritiyadav888/visualize_pipeline/main/images/Screenshot%202023-06-21%20at%2010.22.27%20PM.png)


## Scope

Visualize Pipeline currently supports Scikit-Learn's Pipeline, FeatureUnion, and ColumnTransformer classes. It can visualize pipelines with nested pipelines and feature unions/column transformers.

The package is meant for visualizing the structure of your pipelines and does not show the actual data flow or transformations in the pipeline.

### Contributing
Contributions are welcome! Please open an issue or submit a pull request on the GitHub repository.