Feature request: plot graph to visualise distance

I’ve always been a fan of using networkx to create graphs for my vector embeddings. The networkx implementation of the kamada kawai graph layout allows you to input a dictionary containing the distance of each node to the others (eg: {0: {0:0, 1: .01}, 1: {1:0, 0:, 0.1}}), which it then tries to visualise in a good way. So what I often do is calculate for example the cosine distances to be used as input. This can give a nice visual on the similarity based on distance:

My quick code for this on the embedding set (which doesn’t work for the api you made but I’ll leave that to you 😃 ):

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def plot_graph(self):
        plt.figure(figsize=(10, 10))
        vectors = [token.vector for k, token in self.embeddings.items()]
        label_dict = {i: w for i, (w, _) in enumerate(self.embeddings.items())}
        dist = cosine_distances(np.array(vectors), np.array(vectors))
        # Greate graph
        G = nx.from_numpy_matrix(dist)
        distance = pd.DataFrame(dist).to_dict()
        # Chhange laytout positions of the graph
        pos = nx.kamada_kawai_layout(G, dist=distance)
        # Draw nodes and labels
        nx.draw_networkx_nodes(G, pos, node_color='b', alpha=0.5)
        nx.draw_networkx_labels(G, pos, labels=label_dict, font_color='w')

Let me know what you think! I like it when dealing with high dimensional vectors.

[Arsilla]

Literarily said so in the second sentence haha, so yes. Expect I don’t draw the edges, just the nodes. You could but then they would all just be connected to each other, which doesn’t add much to the visual.

wrt your last comment, I’ll see if I have some time tonight to do that.

[koaning]

this went in.