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import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import umap
matplotlib.use("Agg")
colormap = (
np.array(
[
[76, 255, 0],
[0, 127, 70],
[255, 0, 0],
[255, 217, 38],
[0, 135, 255],
[165, 0, 165],
[255, 167, 255],
[0, 255, 255],
[255, 96, 38],
[142, 76, 0],
[33, 0, 127],
[0, 0, 0],
[183, 183, 183],
],
dtype=float,
)
/ 255
)
def plot_embeddings(embeddings, num_classes_in_batch):
num_utter_per_class = embeddings.shape[0] // num_classes_in_batch
# if necessary get just the first 10 classes
if num_classes_in_batch > 10:
num_classes_in_batch = 10
embeddings = embeddings[: num_classes_in_batch * num_utter_per_class]
model = umap.UMAP()
projection = model.fit_transform(embeddings)
ground_truth = np.repeat(np.arange(num_classes_in_batch), num_utter_per_class)
colors = [colormap[i] for i in ground_truth]
fig, ax = plt.subplots(figsize=(16, 10))
_ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
plt.gca().set_aspect("equal", "datalim")
plt.title("UMAP projection")
plt.tight_layout()
plt.savefig("umap")
return fig
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