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