Visualizing GraphLab's LDA TopicModel with pyLDAvis

This is an example of how to use pyLDAvis helper functions to visualize a GraphLab Create Topic Model model. For our example model we will be extending the example provided by GraphLab's own documenation.


In [2]:
import graphlab as gl
import pyLDAvis
import pyLDAvis.graphlab

In [3]:
# turn on automatic rendering of visualizations
pyLDAvis.enable_notebook()

In [4]:
docs = gl.SArray('http://s3.amazonaws.com/GraphLab-Datasets/nytimes')


PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/dir_archive.ini to /var/tmp/graphlab-bmabey/7868/000000.ini
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/objects.bin to /var/tmp/graphlab-bmabey/7868/000001.bin
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.sidx to /var/tmp/graphlab-bmabey/7868/000002.sidx
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0000 to /var/tmp/graphlab-bmabey/7868/000003.0000
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0001 to /var/tmp/graphlab-bmabey/7868/000004.0001
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0002 to /var/tmp/graphlab-bmabey/7868/000005.0002
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0003 to /var/tmp/graphlab-bmabey/7868/000006.0003
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0004 to /var/tmp/graphlab-bmabey/7868/000007.0004
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0005 to /var/tmp/graphlab-bmabey/7868/000008.0005
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0006 to /var/tmp/graphlab-bmabey/7868/000009.0006
PROGRESS: Downloading http://s3.amazonaws.com/GraphLab-Datasets/nytimes/m_2ae8944a.0007 to /var/tmp/graphlab-bmabey/7868/000010.0007

In [7]:
gl.topic_model.create?

In [8]:
%%time
topic_model = gl.topic_model.create(docs, num_topics=15, num_iterations=3000)


PROGRESS: Learning a topic model
PROGRESS:        Number of documents     10000
PROGRESS:            Vocabulary size     63958
PROGRESS:    Running collapsed Gibbs sampling
PROGRESS: +-----------+---------------+----------------+-----------------+
PROGRESS: | Iteration | Elapsed Time  | Tokens/Second  | Est. Perplexity |
PROGRESS: +-----------+---------------+----------------+-----------------+
PROGRESS: | 10        | 4.30s         | 5.59845e+06    | 0               |
PROGRESS: | 20        | 8.08s         | 5.65802e+06    | 0               |
PROGRESS: | 30        | 11.87s        | 5.9078e+06     | 0               |
PROGRESS: | 40        | 15.64s        | 5.53945e+06    | 0               |
PROGRESS: | 50        | 19.58s        | 5.64386e+06    | 0               |
PROGRESS: | 60        | 23.63s        | 5.14035e+06    | 0               |
PROGRESS: | 70        | 28.19s        | 5.19469e+06    | 0               |
PROGRESS: | 80        | 32.55s        | 5.06062e+06    | 0               |
PROGRESS: | 90        | 36.71s        | 5.24635e+06    | 0               |
PROGRESS: | 100       | 40.63s        | 5.46609e+06    | 0               |
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PROGRESS: | 2970      | 22m 2s        | 5.00597e+06    | 0               |
PROGRESS: | 2980      | 22m 7s        | 4.7511e+06     | 0               |
PROGRESS: | 2990      | 22m 11s       | 4.57458e+06    | 0               |
PROGRESS: | 3000      | 22m 17s       | 3.65077e+06    | 0               |
PROGRESS: +-----------+---------------+----------------+-----------------+
CPU times: user 432 ms, sys: 647 ms, total: 1.08 s
Wall time: 22min 19s

In [58]:
pyLDAvis.graphlab.prepare(topic_model, docs)


Out[58]: