.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/06_we8there/run-03-demo=topic_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 ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_06_we8there_run-03-demo=topic_vb_single_run-model=hdp_topic+mult.py: ============================================= Standard variational training for topic model ============================================= .. GENERATED FROM PYTHON SOURCE LINES 8-18 .. code-block:: default import bnpy import numpy as np import os from matplotlib import pylab import seaborn as sns FIG_SIZE = (2, 2) SMALL_FIG_SIZE = (1.5, 1.5) .. GENERATED FROM PYTHON SOURCE LINES 19-20 Read text dataset from file .. GENERATED FROM PYTHON SOURCE LINES 20-32 .. code-block:: default dataset_path = os.path.join(bnpy.DATASET_PATH, 'we8there', 'raw') dataset = bnpy.data.BagOfWordsData.read_npz( os.path.join(dataset_path, 'dataset.npz'), vocabfile=os.path.join(dataset_path, 'x_csc_colnames.txt')) # Filter out documents with less than 20 words doc_ids = np.flatnonzero( dataset.getDocTypeCountMatrix().sum(axis=1) >= 20) dataset = dataset.make_subset(docMask=doc_ids, doTrackFullSize=False) .. GENERATED FROM PYTHON SOURCE LINES 33-37 Train LDA topic model --------------------- Using 10 clusters and a random initialization procedure. .. GENERATED FROM PYTHON SOURCE LINES 37-53 .. 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/we8there/helloworld-model=topic+mult-K=10/', nLap=10, convergeThr=0.01, K=10, initname='randomlikewang', alpha=0.5, lam=0.1, **local_step_kwargs) .. GENERATED FROM PYTHON SOURCE LINES 54-57 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 58-66 .. 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 67-68 Setup: Helper function to plot bars at each stage of training .. GENERATED FROM PYTHON SOURCE LINES 69-98 .. code-block:: default def show_top_words_over_time( task_output_path=None, vocabList=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) # Plot the current model cur_ax_list = ax_handles_RC[row_id].flatten().tolist() bnpy.viz.PrintTopics.plotCompsFromHModel( cur_model, vocabList=vocabList, fontsize=9, Ktop=7, ax_list=cur_ax_list) cur_ax_list[0].set_ylabel("lap: %d" % lap_val) pylab.subplots_adjust( wspace=0.04, hspace=0.1, left=0.01, right=0.99, top=0.99, bottom=0.1) pylab.tight_layout() .. GENERATED FROM PYTHON SOURCE LINES 99-100 Show the topics over time .. GENERATED FROM PYTHON SOURCE LINES 101-105 .. code-block:: default show_top_words_over_time( info_dict['task_output_path'], vocabList=dataset.vocabList) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_examples_06_we8there_run-03-demo=topic_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_vb_single_run-model=hdp_topic+mult.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: run-03-demo=topic_vb_single_run-model=hdp_topic+mult.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_