<jupyter_start><jupyter_text>VisualizeVisualize an embedded feature spacing using TensorBoard. Use the outputs from `extract` to populate the features, metadata, and sprites parameters for `deepometry.visualize.make_projection`.Recall that the sprites image is an NxN grid of image data. The `sprites_dim` parameter tells TensorBoard how big (in pixels) each sprite is. For example, if a single sprit is 48x48 pixels, then `sprites_dim=48`.To launch TensorBoard, copy the output of the cell below and run it in your terminal.<jupyter_code>features = "data/features.tsv"
metadata = "data/metadata.tsv"
sprites = "data/sprites.png"
sprites_dim = 48

log_directory = deepometry.visualize.make_projection(
    features,
    metadata=metadata,
    sprites=sprites,
    sprites_dim=sprites_dim
)

print("tensorboard --logdir {:s}".format(log_directory))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08323831242873432, 'fraction_numerical': 0.009122006841505131, 'mean_word_length': 5.753846153846154, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '476019', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 281, 'n_tokens_neox': 263, 'n_words': 87, 'path': 'examples/visualize.ipynb', 'repo_name': 'broadinstitute/deepometry', 'size': '877'}