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Clustering via hierarchical agglomerative learning

Project description

Hierarchical Agglomerative Learning (HAL)

Package for performing clustering for high-dimensional data. This packages uses heavily scikit-learn and FFT accelerated t-SNE.

System requirement

  • Has been tested on latest version of OS X (Sierra and High Sierra) and Linux (Ubuntu v 16)
  • (Optional) The dynamical plotting requires Chrome, Safari or Firefox (without ad blockers !).

Requirement:

Python 3.6 or later versions.

Installing (once)

Activate an Anaconda Python 3 environment

conda config --add channels conda-forge
conda install cython numpy fftw scipy
pip install hal-x

Updating

For future versions of the package, you can upgrade using:

pip install hal-x --upgrade

Small example

from hal import HAL  # this imports the class HAL() 
from sklearn.datasets import make_blobs
import numpy as np

# Setting random seed, in case you want to re-run example but keep saved data in info_hal/ 
np.random.seed(0)

# Generate some data. 
X,y = make_blobs(10000,12,10) # 10 gaussians in 12 dimensions, 10000 data points

# The HAL constructor has many optional parameters (documentation coming soon)
model = HAL(clf_type='svm') # using linear SVMs (fastest) for agglomeration. Other options are 'rf' and 'nb' (random forest, and naive bayes)

# builds model -> will save data in file info_hal
model.fit(X)

# rendering of results using javascript (with optional feature naming)
feature_name = ['feat_%i'%i for i in range(12)]
model.plot_tree(feature_name = feature_name)

# Now that your model is fitted, can predict on data (either new or old), using a cross-validation score of 0.95
ypred = model.predict(X, cv=0.95)

# The fitted model information is in directory info_hal. To reload that information for later use, just:
model.load()

# To load t-SNE coordinates:
model.load('tsne')

Project details


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