semvec/pypi-packages huggingface dataset:
llama-index-embeddings-google 0.4.2
llama-index embeddings google integration
Requires Python: <4.0,>=3.9

# LlamaIndex Embeddings Integration: Google

**NOTE:** This package is deprecated. Please use the `GoogleGenAIEmbedding` class instead, detailed [here](https://github.com/run-llama/llama_index/blob/main/docs/examples/embeddings/google_genai.ipynb).
llama-index-embeddings-gemini 0.4.2
llama-index embeddings gemini integration
Requires Python: <4.0,>=3.9

# LlamaIndex Embeddings Integration: Gemini

**NOTE:** This package is deprecated. Please use the `GoogleGenAIEmbedding` class instead, detailed [here](https://github.com/run-llama/llama_index/blob/main/docs/examples/embeddings/google_genai.ipynb).
sci-fab 2.0.1
Build system for scientific software
Requires Python: <4,>=3.9

Fab - A Build System for Tomorrow
=================================

.. image:: https://github.com/MetOffice/fab/actions/workflows/build.yml/badge.svg
   :target: https://github.com/MetOffice/fab/actions/workflows/build.yml
   :alt: testing

.. image:: https://github.com/MetOffice/fab/actions/workflows/documentation.yml/badge.svg
   :target: https://github.com/MetOffice/fab/actions/workflows/documentation.yml
   :alt: documentation

The "Fab" project aims to provide the means to quickly and easily compile
software in a way tailored for scientific software development. It aims to be
quick both in terms of use and operation. Meanwhile ease should mean the
simple things are simple and the complicated things are possible.

Fab is not intended to replace existing tools for compiling general
application software. It targets different problems to, for instance, CMake
derived build systems. This means that if your usage falls outside the focus
of development you shouldn't expect high priority on your feature requests.

Licence
~~~~~~~

The software is made available under a 3-clause BSD licence.

Installation
~~~~~~~~~~~~

The tool is easily installed using `pip install sci-fab`.

Usage
~~~~~

Fab offers two modes of operation. In "zero configuration" mode it is used
directly as a tool by running `fab`. This examines the currently selected
directory and tries to build whatever it finds there.

In "framework" mode it offers a library of building-blocks which a developer
can use to create a build system customised to the needs of their software.
exomiser-ml 0.1.17
A machine learning pipeline for variant prioritisation using Exomiser output.
Requires Python: >=3.12

# Exomiser ML

Exomiser-ML is a Python-based machine learning pipeline designed to enhance variant prioritisation by integrating various Exomiser scores. It supports classification using Logistic Regression, Random Forest, and XGBoost, and includes utilities for feature extraction, data splitting, model training, and post-processing.

🚀 Features 
* Extracts features from Exomiser variant TSV result files and Phenopackets.
* Supports multiple classifiers: Logistic Regression, Random Forest, and XGBoost.
* Provides CLI commands for training, and full pipeline execution.
* Generates metadata and post-processed results compatible with PhEval for benchmarking.

📦 Installation

Ensure you have Python 3.12 or higher installed.

```bash
pip install exomiser-ml
```

🧪 Usage

The package provides several CLI commands:

1. run-model

Trains and evaluates a model using provided training data and test directory.

```bash
run-model \
  --training-data path/to/train.tsv \
  --test-dir path/to/test_dir \
  --features FEATURE1 FEATURE2 ... \
  --output-dir path/to/output \
  --phenopacket-dir path/to/phenopackets \
  --model MODEL_TYPE
```

Parameters:
* --training-data: Path to the training data TSV file.
* --test-dir: Directory containing test data files.
* --features: List of features to extract.
* --output-dir: Directory to save outputs.
* --phenopacket-dir: Directory containing Phenopacket JSON files.
* --model: Model type to use. Choices: LOGISTIC_REGRESSION, RANDOM_FOREST, XGBOOST_CLASSIFIER

2. run-pipeline

Executes the full pipeline: feature extraction, data splitting, training, evaluation, and post-processing.

```bash
run-pipeline \
  --phenopacket-dir path/to/phenopackets \
  --result-dir path/to/exomiser_results \
  --output-dir path/to/output \
  --features FEATURE1 FEATURE2 ... \
  --test-size 0.2 \
  --model MODEL_TYPE
```

Parameters:
* --phenopacket-dir: Directory containing Phenopacket JSON files.
* --result-dir: Directory containing Exomiser result TSV files.
* --output-dir: Directory to save outputs.
* --features: List of features to extract.
* --test-size: Proportion of data to use for testing (e.g., 0.2 for 20%).
* --model: Model type to use. Choices: LOGISTIC_REGRESSION, RANDOM_FOREST, XGBOOST_CLASSIFIER.

3. add-features

Adds features to Exomiser results.

```bash
add-features \
  --phenopacket-dir path/to/phenopackets \
  --result-dir path/to/exomiser_results \
  --output-dir path/to/output
```

4. split-data

Splits data (Exomiser TSV results) into training and testing sets.

```bash
split-data \
  --input-dir path/to/input_data \
  --test-size 0.2 \
  --output-dir path/to/output
```

5. post-process

Post-processes test results for downstream benchmarking with PhEval.

```bash
post-process \
  --test-dir path/to/test_results \
  --phenopacket-dir path/to/phenopackets \
  --output-dir path/to/output \
  --score NEW_SCORE
```