Simple Self-Organizing Maps in Python
# SimpSOM (Simple Self-Organizing Maps)
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## Version 2.0.2
SimpSOM is a lightweight implementation of Kohonen Self-Organizing Maps (SOM) for Python 3, useful for unsupervised learning, clustering and dimensionality reduction.
To install this package, clone this repository and install it with python setup.py install. Alternatively you can download it from PyPI, to retrieve it just type pip install simpsom, but make sure the version you need is available on the database.
It allows you to build and train SOM on your dataset, save/load the trained network weights, and display or print graphs of the network with selected features. The function run_colors_example() will run a toy model, where a number of colors will be mapped from the 3D RGB space to the 2D network map and clustered according to their similarity in the origin space.
## What's New
Class and function names have been updated to adhere to PEP8.
Batch training has been added and is now the default algorithm.
A light parallelization is now possible with RAPIDS.
## Version compatibility
This version introduces a number of changes, while attempting to maintain the original philosophy of this project: a SOM library easy to understand and customize. Functions and classes names have been changed to improve readability. If you are migrating from an older version (<=1.3.4), please make sure to check the API first!
` - Numpy == 1.19.5 - Matplotlib == 3.3.3 - Sklearn == 0.22.2.post1 `
Older/newer versons may work, but make sure to test the library
## Example of Usage
Here is a quick example on how to use the library with an exemplary raw_data dataset:
import simpsom as sps
Build a network 20x20 with a weights format taken from the raw_data and activate Periodic Boundary Conditions. The weights will be initialized at ‘random’ rather than with ‘PCA’ and you can fix the random number generator seed for reproducibility with ‘random_seed’.
net = sps.SOMNet(20, 20, raw_data, PBC=True, init=’random’, random_seed=8)
By default the network will be trained with the batch training algorithm and 10xsamples number of epochs. No learning rate is needed.
Alternatively, all of these options can be set mantually. For example to train the network with online training (much slower!) for 1000 epochs and with in nitial learning rate of 0.01, and using cosine as a distance metric, use this instead:
net.train(train_algo=’online’, learning_rate=0.01, metric=’cosine’, epochs=1000)
Early stopping is also available, for the moment only a couple of rough convergence metrics are available, but more will be added in the future. For example, to check the convergence of the distance between data points and their bmus you can add early_stop=’bmudiff’. The patience and tolerance can also be set.
- net.train(epochs=100, early_stop=’bmudiff’,
The convergence trend can be plotted with
If you are encountering memory issues when running batch training, you can select the size of the mini batches with batch_size. This won’t affect the final result, since the full dataset will still used for the weights’ update.
Save the weights to file in the out_path directory. This flag can be provided to any other plotting function to save the plots as png files.
Information on each node is stored in the .nodeList attribute of the network. These include each node position in the hexagonal grid (.pos) or its weights (.weights), i.e. the position of the node in the features space.
position_node0 = net.node_list.pos weights_node0 = net.node_list_.weights
You can print the hexagonal network nodes and color them according to the any feature (here the feature at position 0) and according to the distance between each node and its neighbours. You can also project the data points on the new 2D network map.
net.nodes_graph(colnum=0, out_path=out_path) net.diff_graph(out_path=out_path) net.project(raw_data, labels=labels, out_path=out_path)
Finally, you can cluster the with a number of different methods. It’s important to note that only Quality Threshold (‘qthresh’) and Density Peak (‘dpeak’) are natively implemented and compatible with periodic boundary conditions. Deactivate PBC if you intend to use ‘MeanShift’, ‘DBSCAN’, ‘KMeans’, or your own clustering tool.
## A More Interesting Example: MNIST
Here is another example of SimpSOM capabilities: the library was used to try and reduce a MNIST handwritten digits dataset. A 50x50 nodes map was trained with 500 MINST landmark data points and 100000 epochs in total, starting from a 0.1 learning rate and without PCA Initialization.
Projecting a few of those points on the map gives the following result, showing a clear distinction between clusters of digits with a few exceptions. Similar shapes (such as 7 and 9) are mapped closed together, while relatively far from other more distinct digits. The accuracy of this mapping could be further improved by tweaking the map parameters and training.
## Running on GPU
If you have a CUDA compatible system, you can run this library on GPU just by activating the namesake flag when instatiating the SOMNet class.
net = sps.SOMNet(20, 20, raw_data, PBC=False, GPU=True)
To be able to run this option you will need the following RAPIDS libraries:
` - cupy == 8.60 - cuml == 0.18 `
Please note that newer versions may work, but these libraries are still in development and API breaking changes are to be expected, make sure to check the RAPIDS repositories for more information. These libraries do not need to be installed to run the CPU version of SimpSOM.
While both online and batch training can be run on GPU, the latter benefits greatly from this implementation (~10 times faster), as shown in the following plot.
Here, a map has been trained for 100 epochs on the MNIST dataset for different dataset sizes. The map size was set as (1.5*sqrt(N))^2 where N is the population size. The y axis is adjusted per data point evaluation. One epoch in batch training evaluates the distances between network nodes and each point in the dataset, while online training only evaluates one point per epoch. This means the impact on the map will be larger per training epoch with the batch algorithm, and the total number of training iterations should be adapted accordingly. As shown in this plot, all tested methods grow linearly with the map and dataset size, but the batch training implementation is a few order of magnitude faster than online training. On GPU it is further ten times faster. Make sure to select the algorithm that best suits your needs! These tests were run on a 32-cores Xeon Gold 6140 CPU and an 8-cores NVIDIA V100 32GB Telsa GPU.
See [here](https://simpsom.readthedocs.io/en/latest/) the full API documentation
If using this library, please cite the appropriate version from [Zenodo](https://zenodo.org/badge/latestdoi/91130860)
> Federico Comitani. (2021). SimpSOM (v2.0.1). Zenodo. https://doi.org/10.5281/zenodo.5788411
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