semvec/pypi-packages huggingface dataset:
clustutils4r 1.0.2
Wrapper around some basic sklearn utilities for clustering.
Requires Python: >=3.8

# Clustering Utilities

This packages provides a simple convenience wrapper around some basic sklearn utilities for clustering. The only function available is `eval_clustering()`.

## Installation
`pip install clustutils4r`

## Available Parameters

`model`: Clustering model object (untrained)

`X`: Numpy array containing preprocessed, normalized, complete dataset features

`gt_labels`: Numpy array containing encoded ground-truth labels for `X` (often not available)

`num_runs`: No. of times to fit a model

`best_model_metric`: Metric to use to choose the best model

`make_silhoutte_plots`: Whether to make silhouette plots for the best model (default = `False`).

`embed_data_in_2d`: Whether to compute TSNE embeddings of the `X` to plotted alongside silhouette plot or plot the first 2 features (default = `False`).

`save_dir`: location to store results; directory will be created if it does not exist

`save`: set True if you want to save all results in save_dir; defaults to False

`show`: display all results; useful in notebooks; defaults to False

## Example Usage
```python
import os
import numpy as np
from sklearn.datasets import make_blobs, load_iris, load_digits
from eval_clustering import eval_clustering

## For testing purposes
rng = np.random.RandomState(0)
n_samples=1000
X, y = make_blobs(n_samples=n_samples, centers=5, n_features=2, cluster_std=0.60, random_state=rng)

save_dir = "results"
os.makedirs(save_dir, exist_ok=True)

best_model, grid_search_results = eval_clustering(
                                       X=X,                                               # dataset to cluster
                                       gt_labels=y,                                       # ground-truth labels; often these aren't available so don't pass this argument
                                       num_runs=10,                                       # number of times to fit a model
                                       best_model_metric="FMI",                           # metric to use to choose the best model
                                       make_silhoutte_plots=True, embed_data_in_2d=False, # whether to make silhouette plots
                                       show=False,                                        # whether to display the plots; this is used in a notebook
                                       save=True, save_dir="results"                      # whether to save the plots
                                    )

```

![grid_search_results](tests/example_clustering/results/models/AgglomerativeClustering/parcoord_plot.png)
![sil](tests/example_clustering/results/models/AgglomerativeClustering/silhouette_plots/5_silhouette_plot.png)
thinkcol_llm_client 0.1.3
Package for calling llm clients asynchronously
Requires Python: <4.0,>=3.9

# LLM Client 
Internal package for making multi-threaded calls to LLMs.

# Supported Models
Cohere Command, Cohere Embed, OpenAI Models, Titan Embed, Mixtral

## Install
```
pip install thinkcol_llm_client
```

## Usage: 
### Normal Usage
```
from thinkcol_llm_client import OpenAIClient, BedrockClient
from thinkcol_llm_client import Anthropic

texts = ["Hello", "Text 1", "ThinkCol"]

client = OpenAIClient()
client.embed(texts)

questions = ["How do I implement best practices in data science projects" for _ in range(500)]
client.invoke(questions)

anthropic_client = BedrockClient(provider = Anthropic())
anthropic_client.invoke(questions)
```

### Providers
The list of providers for bedrock models can be found in bedrock/clients. Support for new providers/models can be added in this file.

## Tests
Unit tests for each provider and clients can be added in tests/unit/test_clients.py
kam12-baseoperations 3.0
Your short package description here

KAM12-BASEACTIONS

Description

This is a base-operation for generating Report for KAM12 CUSTOMS AND EXCISE OFFICE. It's developed based on my own experience, so some logic may not worked properly. This library works along with pandas, numpy, and openpyxl.

Requirements

- pandas
- numpy
- openpyxl

Installation

Global install: `pip install kam12-baseactions`
Virtual environment install: `pipenv install kam12-baseactions`