numbasom 0.1.0
A Fast Self-Organizing Map Python Library Implemented in Numba
Requires Python: >=3.9
Keywords: AI, ML, SOM, kohonen, numba, python, self-organizing map

# Welcome to NumbaSOM
> A fast Self-Organizing Map Python library implemented in Numba.


This is a **fast and simple to use** SOM library. It utilizes online training (one data point at the time) rather than batch training. The implemented topologies are a simple 2D lattice or a torus.

## How to Install

To install this package with pip run:

`pip install numbasom`

To install this package with conda run:

`conda install -c conda-forge numbasom`

## How to use

To import the library you can safely use:

```python
from numbasom import *
```

A **Self-Organizing Map** is often used to show the underlying structure in data. To show how to use the library, we will train it on 200 random 3-dimensional vectors (so we can render them as colors):

#### Create 200 random colors

```python
import numpy as np
data = np.random.random([200,3])
```

#### Initialize the library

We initalize a map with 50 rows and 100 columns. The default topology is a 2D lattice. We can also train it on a torus by setting `is_torus=True`

```python
som = SOM(som_size=(50,100), is_torus=False)
```

#### Train the SOM

We will adapt the lattice by iterating 10.000 times through our data points. If we set `normalize=True`, data will be normalized before training. 

```python
lattice = som.train(data, num_iterations=15000)
```

    SOM training took: 0.366633 seconds.


#### To access an individual cell type

```python
lattice[5,3]
```




    array([0.92550425, 0.20740594, 0.92610555])



#### To access multiple cells, slicing works

```python
lattice[1::6,1]
```




    array([[0.7473038 , 0.09876245, 0.93051731],
           [0.93542156, 0.18717452, 0.87611239],
           [0.9486485 , 0.11080808, 0.57557379],
           [0.87873391, 0.13527156, 0.368202  ],
           [0.78669284, 0.11830203, 0.2634972 ],
           [0.68213238, 0.06408478, 0.33050376],
           [0.54769163, 0.05391318, 0.31153485],
           [0.63722088, 0.12484291, 0.0684501 ],
           [0.64172725, 0.01517416, 0.09549566]])



The shape of the lattice should be (50, 100, 3)

```python
lattice.shape
```




    (50, 100, 3)



#### Visualizing the lattice

Since our lattice is made of 3-dimensional vectors, we can represent it as a lattice of colors.

```python
import matplotlib.pyplot as plt

plt.imshow(lattice)
plt.show()
```


![png](docs/images/output_26_0.png)


#### Compute U-matrix

Since the most of the data will not be 3-dimensional, we can use the `u_matrix` (unified distance matrix by Alfred Ultsch) to visualise the map and the clusters emerging on it. 

```python
um = u_matrix(lattice)
```

Each cell of the lattice is just a single value, thus the shape is:

```python
um.shape
```




    (50, 100)



#### Plot U-matrix

The library contains a function `plot_u_matrix` that can help visualise it.

```python
plot_u_matrix(um, fig_size=(6.2,6.2))
```


![png](docs/images/output_34_0.png)


#### Project on the lattice

To project data on the lattice, use `project_on_lattice` function.

Let's project a couple of predefined color on the trained lattice and see in which cells they will end up:

```python
colors = np.array([[1.,0.,0.],[0.,1.,0.],[0.,0.,1.],[1.,1.,0.],[0.,1.,1.],[1.,0.,1.],[0.,0.,0.],[1.,1.,1.]])
color_labels = ['red', 'green', 'blue', 'yellow', 'cyan', 'purple','black', 'white']
```

```python
projection = project_on_lattice(colors, lattice, additional_list=color_labels)

for p in projection:
    if projection[p]:
        print (p, projection[p][0])
```

    Projecting on SOM took: 0.158945 seconds.
    (0, 85) blue
    (2, 39) white
    (5, 1) purple
    (10, 60) cyan
    (41, 59) green
    (49, 12) red
    (49, 40) yellow
    (49, 96) black


#### Find every cell's closest vector in the data

To find every cell's closes vector in the provided data, use `lattice_closest_vectors` function.

We can again use the colors example:

```python
closest = lattice_closest_vectors(colors, lattice, additional_list=color_labels)
```

    Finding closest data points took: 0.003056 seconds.


We can ask now to which value in `color_labels` are out lattice cells closest to:

```python
closest[(1,1)]
```




    ['purple']



```python
closest[(40,80)]
```




    ['green']



We can find the closest vectors without supplying an additional list. Then we get the association between the lattice and the data vectors that we can display as colors.

```python
closest_vec = lattice_closest_vectors(colors, lattice)
```

    Finding closest data points took: 0.003491 seconds.


We take the values of the `closest_vec` vector and reshape it into a numpy vector `values`.

```python
values = np.array(list(closest_vec.values())).reshape(50,100,-1)
```

We can now visualise the projection of our 8 hard-coded colors onto the lattice:

```python
plt.imshow(values)
plt.show()
```


![png](docs/images/output_52_0.png)


#### Compute how each data vector 'activates' the lattice

We can use the function `lattice_activations`:

```python
activations = lattice_activations(colors, lattice)
```

    Computing SOM activations took: 0.000484 seconds.


Now we can show how the vector `blue: [0.,0.,1.]`  activates the lattice:

```python
plt.imshow(activations[2])
plt.show()
```


![png](docs/images/output_57_0.png)


If we wish to scale the higher values up, and scale down the lower values, we can use the argument `exponent` when computing the activations:

```python
activations = lattice_activations(colors, lattice, exponent=8)
```

    Computing SOM activations took: 0.000838 seconds.


```python
plt.imshow(activations[2])
plt.show()
```


![png](docs/images/output_60_0.png)