semvec/pypi-packages huggingface dataset:
DTAnalyze 1.0
Python Decision Tree Analysis
Keywords: Decision,Tree,RandomForest,GBM,Activation

# DTAnalyze
Functions to estimate importance of features in determining predictions for individual samples (aka "feature activations"). Fast `nogil` implementation in Cython.


## Example Usage
```
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from DTAnalyze.Activation import GetActivations

A = np.random.rand(256, 3)
Y = (2 * (A[:, 0] > 0.5) - (A[:, 1] < 0.5) - 
     (A[:, 2] > 0.5) + np.random.normal(0, 0.1, size=256))

rfr = RandomForestRegressor(n_jobs=4).fit(A, Y)

L1 = GetActivations(rfr, A)
```

## Install

`python setup.py build_ext`

Then copy build artifact into `DTAnalyze` (sub) folder and put that folder somewhere in your path.
torlib 0.0.4
A utility package that can help in many scenarios :D
Requires Python: >=3.6

# torlib

torlib is an open source Python utility package that can help in many scenarios :D

[![Package Version](https://img.shields.io/pypi/v/torlib.svg)](https://pypi.python.org/pypi/torlib/)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)



# Installation

This package can be installed from [PyPi](https://pypi.python.org/pypi/ChatterBot) by running:

```
pip install torlib
```

# Documentation

You can view the [documentation](https://noppadol-assava.github.io/torlib/) for torlib.

To build the documentation locally, you can run the following command on your terminal ([Sphinx](http://www.sphinx-doc.org/) is required):

```
sphinx-build -b html docs/ docs/_build/ -D language="en"
```

# Features

* [Crawler](https://noppadol-assava.github.io/torlib/en/master/crawler.html)

# Changelog

See release notes for changelog https://github.com/noppadol-assava/torlib/releases


# License

torlib is licensed under the [MIT License](https://opensource.org/licenses/MIT).