Note
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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
Read dataset from file.
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'))
Make a simple plot of the raw data
X_csr_DV = dataset.getSparseDocTypeCountMatrix()
bnpy.viz.BarsViz.show_square_images(
X_csr_DV[:10].toarray(), vmin=0, vmax=5)
pylab.tight_layout()
Setup: Function to show bars from start to end of training run
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()
Using 10 clusters and the ‘randexamples’ initialization procedure.
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)
First, we can plot the loss function over time We’ll skip the first few iterations, since performance is quite bad.
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()
Show the clusters over time
show_bars_over_time(info_dict['task_output_path'])
Using 10 clusters and the ‘randexamples’ initialization procedure.
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'])
Total running time of the script: ( 0 minutes 0.000 seconds)