Skip to main content

Simple high-level library to use machine learning algorithms

Project description

## Pylearning: python machine learning library


Pylearning is a high-level machine learning package designed to easily prototype
and implement data analysis programs.

The library includes the following algorithms:
- Regression:
- Decision tree regressor
- Random forest regressor
- Nearest neighbours regressor
- Classification:
- Decision tree classifier
- Random forest classifier
- Nearest neighbours classifier
- Clustering:
- K-means
- DBSCAN (density-based clustering)

The two random forests algorithms use multithreading to train the trees in a
parallelized fashion.
This package is compatible with Python3+.

### Basic usage

All the algorithms available use the same simple interface described in the
examples below.

# Basic regression example using a random forest

from pylearning.ensembles import RandomForestRegressor

# Load the training dataset
features, targets = ...

rf = RandomForestRegressor(nb_trees=10, nb_samples=100, max_depth=20), targets)

# Load a testing sample
test_feature, test_target = ...

value_predicted = rf.predict(test_feature, test_target)

# Clustering example using DBSCAN algorithm

import matplotlib.pyplot as plt
from pylearning.clustering import DBSCAN
from sklearn.datasets import make_circles

# Load a dataset composed of two circles
data = make_circles(n_samples=1000, noise=0.05, factor=0.3)[0]

cl = DBSCAN(epsilon=0.2)

labels_data = {i: ([],[]) for i in range(-1, 2)}
for ex, label in zip(data, cl.labels):

colors = ['g','b']
for label, values in labels_data.items():
if label == -1:
plt.scatter(values[0], values[1], color='black')
plt.scatter(values[0], values[1], color=colors[label], s=50)

![Alt text](resources/dbscan.png?raw=true)

A complete documentation of the API is available [here](

### Installation

Pylearning requires to have numpy installed. It can be installed simply using Pypy:
# for the stable version
pip3 install pylearning

# for the latest version
pip3 install git+

### Further improvements

The core functionalities of the different algorithms are
implemented in this project, however there are many possible improvements:
- gini criterion for splitting nodes (Decision trees)
- pruning (Decision trees)
- ability to split a node into an arbitrary number of child nodes (Decision trees)
- optimizations to reduce time and memory consumption
- better compatibility with pandas DataFrame
- addition of new algorithms (density-based clustering, SVM, neural networks, ...)

If you wish, you're welcome to participate in the project or to make suggestions !
To do so, you can simply open an issue or fork the project and then create a pull

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

pylearning-3.2.2b1-py3-none-any.whl (20.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page