callbacks.py

import numpy as np
import math

from utils.data_processing.synthetic import synthetic_dataset

from bokeh.io import curdoc, show, output_notebook
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, Select, Slider, Plot, Scatter
from bokeh.palettes import Spectral6
from bokeh.plotting import figure

spectral = np.hstack([Spectral6] * 20)
n_clusters_p_class = 1

def update_samples_or_dataset(attrname, 
                              old, 
                              new, 
                            #   dataset_select,
                            #   samples_slider,
                            #   classes_slider,
                            #   features_slider,
                            #   inf_slider,
                            #   source
                              ):
    global x, y

    dataset = dataset_select.value
    n_samples = int(samples_slider.value)
    n_classes = int(classes_slider.value)
    n_features = int(features_slider.value)
    n_inf = int(inf_slider.value)

    if n_inf > n_features:
        n_features = n_inf
        features_slider.update(value=n_inf)
    
    if n_classes * n_clusters_p_class > 2**n_inf:
        
        # n_inf = math.floor(math.sqrt(n_classes*n_clusters_p_class)) + n_classes % 2

        n_inf = (math.ceil(math.log2(n_classes)))
        n_features = n_inf
        # print("this is v", n_inf)
        
        inf_slider.update(value=n_inf)
        features_slider.update(value=n_features)

    x, y = synthetic_dataset(dataset, n_samples, n_inf, n_features, n_classes)
    colors = [spectral[i] for i in y]

    source.data = dict(colors=colors, x=x[:, 0], y=x[:, 1])