L1AnomalyTSNE.py

from sklearn.manifold import TSNE

import L1AnomalyBase
import L1AnomalyPlot

class L1AnomalyTSNE(L1AnomalyBase):
    def __init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar = False):
        super().__init__(self, background_file, signal_files, signal_labels, blackbox_file, classVar)
        self.background_data = super.load_data(self, type = 0)
        #returns an array with four signals chronologically
        self.signal_data = super.load_data(self, type = 1)
        self.blackbox_data = super.load_data(self, type = 2)
        
        
    def setup_model(self, n_components, perplexity=30, early_exaggeration=12, learning_rate=200, n_iter=1000, random_state = None):
        self.model = TSNE(n_components = n_components, perplexity=perplexity, early_exaggeration=early_exaggeration,
                          learning_rate=learning_rate, n_iter=n_iter, random_state=random_state)
        
    def plot_bokeh(self, signal = 3):
        L1AnomalyPlot.StandardBokehSplit(self, RS=42, background_data = self.background_data,
                                        signal_data = self.signal_data[signal], blackbox_data = self.blackbox_data)
        L1AnomalyPlot.Bokeh(self, reducer = self.model, reducer_string = "TSNE")