# 01: Standard variational training for mixture model¶

How to train a mixture of multinomials.

```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 toy “bars” dataset from file as BINARY

```dataset_path = os.path.join(bnpy.DATASET_PATH, 'bars_one_per_doc')
os.path.join(dataset_path, 'dataset.npz'))

dataset.word_count = np.asarray(
dataset.word_count > 0, dtype=dataset.word_count.dtype)
```

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.colorbar()
#pylab.clabel('word count')
pylab.tight_layout()
```

Let’s do one single run of the VB algorithm.

Using 10 clusters and the ‘randexamples’ initialization procedure.

```trained_model, info_dict = bnpy.run(
dataset, 'FiniteTopicModel', 'Bern', 'VB',
output_path='/tmp/bars_one_per_doc/helloworld-lik=bernoulli-K=10/',
nLap=1000, convergeThr=0.0005,
K=10, initname='randexamples',
alpha=0.5, lambda1=0.1, lambda0=0.1)
```

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'][2:], info_dict['loss_history'][2:], 'k.-')
pylab.xlabel('num. laps')
pylab.ylabel('loss')
pylab.tight_layout()
```

Setup: Useful function to display learned bar structure over time.

```def show_bars_over_time(
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_topics_KV = cur_model.obsModel.Post.lam1 / (
trained_model.obsModel.Post.lam1 + trained_model.obsModel.Post.lam0)
# 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()
```

Show the clusters over time

```show_bars_over_time(info_dict['task_output_path'])
```

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

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