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)
rf.fit(features, 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)
cl.fit(data)

labels_data = {i: ([],[]) for i in range(-1, 2)}
for ex, label in zip(data, cl.labels):
    labels_data[label][0].append(ex[0])
    labels_data[label][1].append(ex[1])

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

plt.show()

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+https://github.com/amstuta/pylearning.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 request.