.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/04_bars_one_per_doc/run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_04_bars_one_per_doc_run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_04_bars_one_per_doc_run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py:


=================================================
03: Standard variational training for topic model
=================================================

.. GENERATED FROM PYTHON SOURCE LINES 8-19

.. code-block:: default

    import bnpy
    import numpy as np
    import os

    from matplotlib import pylab
    import seaborn as sns

    FIG_SIZE = (3, 3)
    SMALL_FIG_SIZE = (1,1)
    pylab.rcParams['figure.figsize'] = FIG_SIZE


.. GENERATED FROM PYTHON SOURCE LINES 20-21

Read dataset from file.

.. GENERATED FROM PYTHON SOURCE LINES 21-26

.. code-block:: default


    dataset_path = os.path.join(bnpy.DATASET_PATH, 'bars_one_per_doc')
    dataset = bnpy.data.BagOfWordsData.read_npz(
        os.path.join(dataset_path, 'dataset.npz'))


.. GENERATED FROM PYTHON SOURCE LINES 27-28

Make a simple plot of the raw data

.. GENERATED FROM PYTHON SOURCE LINES 29-34

.. code-block:: default

    X_csr_DV = dataset.getSparseDocTypeCountMatrix()
    bnpy.viz.BarsViz.show_square_images(
        X_csr_DV[:10].toarray(), vmin=0, vmax=5)
    pylab.tight_layout()


.. GENERATED FROM PYTHON SOURCE LINES 35-36

Setup: Function to show bars from start to end of training run

.. GENERATED FROM PYTHON SOURCE LINES 37-60

.. code-block:: default


    def show_bars_over_time(
            task_output_path=None,
            query_laps=[0, 1, 2, 5, None],
            ncols=10):
        '''
        '''
        nrows = len(query_laps)
        fig_handle, ax_handles_RC = pylab.subplots(
            figsize=(SMALL_FIG_SIZE[0] * ncols, SMALL_FIG_SIZE[1] * nrows),
            nrows=nrows, ncols=ncols, sharex=True, sharey=True)
        for row_id, lap_val in enumerate(query_laps):
            cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val)
            cur_topics_KV = cur_model.obsModel.getTopics()
            # Plot the current model
            cur_ax_list = ax_handles_RC[row_id].flatten().tolist()
            bnpy.viz.BarsViz.show_square_images(
                cur_topics_KV,
                vmin=0.0, vmax=0.06,
                ax_list=cur_ax_list)
            cur_ax_list[0].set_ylabel("lap: %d" % lap_val)
        pylab.tight_layout()


.. GENERATED FROM PYTHON SOURCE LINES 61-65

Train LDA topic model
---------------------

Using 10 clusters and the 'randexamples' initialization procedure.

.. GENERATED FROM PYTHON SOURCE LINES 65-81

.. code-block:: default


    local_step_kwargs = dict(
        # perform at most this many iterations at each document
        nCoordAscentItersLP=100,
        # stop local iters early when max change in doc-topic counts < this thr
        convThrLP=0.001,
        )

    trained_model, info_dict = bnpy.run(
        dataset, 'FiniteTopicModel', 'Mult', 'VB',
        output_path='/tmp/bars_one_per_doc/helloworld-model=topic+mult-K=10/',
        nLap=100, convergeThr=0.01,
        K=10, initname='randomlikewang',
        alpha=0.5, lam=0.1,
        **local_step_kwargs)


.. GENERATED FROM PYTHON SOURCE LINES 82-85

First, we can plot the loss function over time
We'll skip the first few iterations, since performance is quite bad.


.. GENERATED FROM PYTHON SOURCE LINES 86-93

.. code-block:: default


    pylab.figure(figsize=FIG_SIZE)
    pylab.plot(info_dict['lap_history'][1:], info_dict['loss_history'][1:], 'k.-')
    pylab.xlabel('num. laps')
    pylab.ylabel('loss')
    pylab.tight_layout()


.. GENERATED FROM PYTHON SOURCE LINES 94-95

Show the clusters over time

.. GENERATED FROM PYTHON SOURCE LINES 96-99

.. code-block:: default

    show_bars_over_time(info_dict['task_output_path'])



.. GENERATED FROM PYTHON SOURCE LINES 100-104

Train LDA topic model with restarts
-----------------------------------

Using 10 clusters and the 'randexamples' initialization procedure.

.. GENERATED FROM PYTHON SOURCE LINES 104-125

.. code-block:: default


    r_local_step_kwargs = dict(
        # perform at most this many iterations at each document
        nCoordAscentItersLP=100,
        # stop local iters early when max change in doc-topic counts < this thr
        convThrLP=0.001,
        # perform restart proposals at each document
        restartLP=1,
        restartNumItersLP=5,
        restartNumTrialsLP=5,
        )

    r_trained_model, r_info_dict = bnpy.run(
        dataset, 'FiniteTopicModel', 'Mult', 'VB',
        output_path='/tmp/bars_one_per_doc/helloworld-model=topic+mult-K=10-localstep=restarts/',
        nLap=100, convergeThr=0.01,
        K=10, initname='randomlikewang',
        alpha=0.5, lam=0.1,
        **r_local_step_kwargs)

    show_bars_over_time(r_info_dict['task_output_path'])


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_04_bars_one_per_doc_run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py <run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.ipynb <run-03-demo=topic_model_vb_single_run-model=hdp_topic+mult.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_