Implementation of the 2D self-organizing map, with support for NumPy arrays and Pandas DataFrames. Most features were implemented using NumPy, with Scikit-learn for standardization and PCA operations.
- Stepwise and batch training
- Random weight initialization
- Random sampling weight initialization
- Linear weight initialization (with PCA)
- Automatic selection of map size ratio (with PCA)
- Support for cyclic arrays, for toroidal or spherical maps
- Gaussian and Bubble neighborhood functions
- Support for custom decay functions
- Support for visualization (U-matrix, activation matrix)
- Support for supervised learning (label map)
- Support for NumPy arrays, Pandas DataFrames and regular lists of values
In the following code excerpt (also available in test.py) is an example of instantiation and training of a SOM with the Iris dataset:
# Import python_som import python_som # Import NumPy and Pandas for storing data import numpy as np import pandas as pd # Import libraries for plotting results import matplotlib.pyplot as plt import seaborn as sns # Load Iris dataset and columns of features and labels iris = sns.load_dataset('iris') target = iris.iloc[:, -1].to_numpy() iris = iris.iloc[:, :-1].to_numpy() # Transform labels into numeric codes for plotting tg = np.zeros(len(target), dtype=int) tg[target == 'setosa'] = 0 tg[target == 'versicolor'] = 1 tg[target == 'virginica'] = 2 # Instantiate SOM from python_som # Selecting shape automatically (providing dataset for constructor) # Using default decay and distance functions # Using gaussian neighborhood function # Using cyclic arrays in the vertical and horizontal directions som = python_som.SOM(x=20, y=None, input_len=iris.shape[1], learning_rate=0.5, neighborhood_radius=1.0, neighborhood_function='gaussian', cyclic_x=True, cyclic_y=True, data=iris) # Initialize weights of the SOM with linear initialization som.weight_initialization(mode='linear', data=iris) # Training SOM with default number of iterations # Using batch learning process som.train(data=iris, n_iteration=len(iris), mode='batch', verbose=True) # Calculating distance matrix for plotting umatrix = som.distance_matrix().T # Plotting U-matrix with seaborn/matplotlib plt.figure(figsize=som.get_shape()) plt.pcolor(umatrix, cmap='bone_r') markers = ['o', 's', 'D'] colors = ['C0', 'C1', 'C2'] for cnt, xx in enumerate(iris): w = som.winner(xx) # getting the winner plt.plot(w[0] + .5, w[1] + .5, markers[tg[cnt]], markerfacecolor='None', markeredgecolor=colors[tg[cnt]], markersize=12, markeredgewidth=2) plt.axis([0, som.get_shape()[0], 0, som.get_shape()[1]]) plt.show()The following image is generated from the previous test code, with the U-matrix of the trained SOM, and the distribution of the instances from the Iris dataset. In this graph, the instances are mapped to the self-organizing map, with color codes for each different label:
- Setosa: blue circle
- Versicolor: orange square
- Virginica: green diamond
The following are lists of public methods and functions currently available in the SOM class. The full documentation of each method can be found in the source code:
- _asymptotic_decay
- _linear_decay
- _exponential_decay
- _inverse_decay
- _euclidean_distance
- SOM
- SOM.get_shape
- SOM.get_weights
- SOM.set_learning_rate
- SOM.set_neighborhood_radius
- SOM.activate
- SOM.winner
- SOM.quantization
- SOM.quantization_error
- SOM.distance_matrix
- SOM.activation_matrix
- SOM.winner_map
- SOM.label_map
- SOM.train
- SOM.weight_initialization
This implemetation was based on the following paper, by Professor Teuvo Kohonen:
Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks, Volume 37, 2013, Pages 52-65, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2012.09.018.