| | 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 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 |
| |
|
