def show_clusters_over_time(task_output_path=None,
query_laps=[0, 1, 2, 5, 10, None],
nrows=2):
ncols = int(np.ceil(len(query_laps) // float(nrows)))
fig_handle, ax_handle_list = pylab.subplots(figsize=(FIG_SIZE[0] * ncols,
FIG_SIZE[1] * nrows),
nrows=nrows,
ncols=ncols,
sharex=True,
sharey=True)
for plot_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_handle = ax_handle_list.flatten()[plot_id]
bnpy.viz.PlotComps.plotCompsFromHModel(
cur_model,
Data=dataset,
) #ax_handle=cur_ax_handle)
cur_ax_handle.set_xticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_yticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_xlabel("lap: %d" % lap_val)
pylab.tight_layout()
pylab.savefig("results/covMat1.png")
pylab.waitforbuttonpress()
pylab.show()
def show_clusters_over_time(task_output_path=None,
query_laps=[0, 1, 2, 5, 10, None],
nrows=2):
''' Read model snapshots from provided folder and make visualizations
Post Condition
--------------
New matplotlib plot with some nice pictures.
'''
ncols = int(np.ceil(len(query_laps) // float(nrows)))
fig_handle, ax_handle_list = pylab.subplots(figsize=(FIG_SIZE[0] * ncols,
FIG_SIZE[1] * nrows),
nrows=nrows,
ncols=ncols,
sharex=True,
sharey=True)
for plot_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_handle = ax_handle_list.flatten()[plot_id]
bnpy.viz.PlotComps.plotCompsFromHModel(cur_model,
Data=dataset,
ax_handle=cur_ax_handle)
cur_ax_handle.set_xticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_yticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_xlabel("lap: %d" % lap_val)
pylab.tight_layout()
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()
def show_bars_over_time(task_output_path=None,
query_laps=[0, 1, 2, 5, None],
ncols=10):
''' Show square-image visualization of estimated topics over time.
Post Condition
--------------
New matplotlib figure with visualization (one row per lap).
'''
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()
def show_clusters_over_time(task_output_path=None,
query_laps=[0, 1, 2, 10, 20, None],
nrows=2):
'''
'''
ncols = int(np.ceil(len(query_laps) // float(nrows)))
fig_handle, ax_handle_list = plt.subplots(figsize=(FIG_SIZE[0] * ncols,
FIG_SIZE[1] * nrows),
nrows=nrows,
ncols=ncols,
sharex=True,
sharey=True)
for plot_id, lap_val in enumerate(query_laps):
cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val)
cur_ax_handle = ax_handle_list.flatten()[plot_id]
bnpy.viz.PlotComps.plotCompsFromHModel(cur_model,
dataset=dataset,
ax_handle=cur_ax_handle)
cur_ax_handle.set_title("lap: %d" % lap_val)
cur_ax_handle.set_xlabel(dataset.column_names[0])
cur_ax_handle.set_ylabel(dataset.column_names[1])
cur_ax_handle.set_xlim(data_ax_h.get_xlim())
cur_ax_handle.set_ylim(data_ax_h.get_ylim())
plt.tight_layout()
plt.show()
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.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()
def show_topic_proportions_over_time(task_output_path=None,
query_laps=[0, 1, 2, 5, None],
ncols=10):
'''
'''
fig = plt.figure()
fig_handle, ax_handles_RC = plt.subplots(figsize=(MED_FIG_SIZE[0] * ncols,
MED_FIG_SIZE[1]),
nrows=1,
ncols=len(query_laps),
sharey=True)
for col_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 = ax_handles_RC[col_id]
cur_proportions = cur_model.allocModel.E_beta()
cur_ax.set_title('Lap = %d' % lap_val)
cur_ax.plot(cur_proportions, marker='o')
cur_ax.set_ylabel('Proportions')
cur_ax.set_xlabel('Topic index')
plt.subplots_adjust(wspace=0.04,
hspace=0.1,
left=0.01,
right=0.99,
top=0.99,
bottom=0.1)
plt.tight_layout()
savefigpath = output_path + 'topic_proportions.png'
print("Will save figure to %s" % savefigpath)
plt.savefig(savefigpath)
return
def show_top_words_over_time(task_output_path=None,
vocabList=None,
query_laps=[0, 1, 2, 5, None],
ncols=10):
'''
'''
fig = plt.figure()
nrows = len(query_laps)
fig_handle, ax_handles_RC = plt.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)
cur_proportions = cur_model.allocModel.E_beta()
for i in range(np.amin([len(cur_ax_list), len(cur_proportions)])):
ax = cur_ax_list[i]
ax.set_title('Prop. = %.2f' % cur_proportions[i], fontsize=10)
plt.subplots_adjust(wspace=0.04,
hspace=0.1,
left=0.01,
right=0.99,
top=0.99,
bottom=0.1)
plt.tight_layout()
savefigpath = output_path + 'top_words.png'
print("Will save figure to %s" % savefigpath)
plt.savefig(savefigpath)
return
