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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

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pylearning-3.2.2b1-py3-none-any.whl (20.0 kB) Copy SHA256 hash SHA256 Wheel py3 Jul 2, 2017

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