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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=np.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 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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