def plotCovMatFromHModel(hmodel,
compListToPlot=None,
compsToHighlight=None,
proba_thr=0.001,
ax_handle=None,
**kwargs):
''' Plot square image of covariance matrix for each component.
Parameters
-------
hmodel : bnpy HModel object
compListToPlot : array-like of integer IDs of components within hmodel
compsToHighlight : int or array-like
integer IDs to highlight
if None, all components get unique colors
if not None, only highlighted components get colors.
proba_thr : float
Minimum weight assigned to component in order to be plotted.
All components with weight below proba_thr are ignored.
'''
nRow = 2
nCol = int(np.ceil(hmodel.obsModel.K / 2.0))
if ax_handle is None:
ax_handle = pylab.subplots(nrows=nRow,
ncols=nCol,
figsize=(nCol * 2, nRow * 2))
else:
pylab.subplots(nrows=nRow, ncols=nCol, num=ax_handle.number)
if compsToHighlight is not None:
compsToHighlight = np.asarray(compsToHighlight)
if compsToHighlight.ndim == 0:
compsToHighlight = np.asarray([compsToHighlight])
else:
compsToHighlight = list()
if compListToPlot is None:
compListToPlot = np.arange(0, hmodel.obsModel.K)
if hmodel.allocModel.K == hmodel.obsModel.K:
w = hmodel.allocModel.get_active_comp_probs()
else:
w = np.ones(hmodel.obsModel.K)
colorID = 0
for plotID, kk in enumerate(compListToPlot):
if w[kk] < proba_thr and kk not in compsToHighlight:
Sigma = getEmptyCompSigmaImage(hmodel.obsModel.D)
clim = [0, 1]
else:
Sigma = hmodel.obsModel.get_covar_mat_for_comp(kk)
clim = [-.25, 1]
pylab.subplot(nRow, nCol, plotID + 1)
pylab.imshow(Sigma, interpolation='nearest', cmap='hot', clim=clim)
pylab.xticks([])
pylab.yticks([])
pylab.xlabel('%.2f' % (w[kk]))
if kk in compsToHighlight:
pylab.xlabel('***')
for emptyID in range(plotID + 1, nRow * nCol):
aH = pylab.subplot(nRow, nCol, emptyID + 1)
aH.axis('off')
def plotManyPanelsByPVar(jpathPattern='/tmp/',
pvar=None,
pvals=None,
W=5,
H=4,
savefilename=None,
doShowNow=False,
**kwargs):
''' Create line plots for jobs matching pattern and provided kwargs
'''
if pvar is None:
jpathList = [jpathPattern]
pvar = None
pvals = [None]
else:
prefixfilepath = os.path.sep.join(jpathPattern.split(os.path.sep)[:-1])
PPListMap = makePPListMapFromJPattern(jpathPattern)
if pvals is None:
pvals = PPListMap[pvar]
else:
pvals = [p for p in pvals if p in PPListMap[pvar]]
jpathList = makeListOfJPatternsWithSpecificVals(
PPListMap,
prefixfilepath=prefixfilepath,
key=pvar,
vals=pvals,
**kwargs)
nrows = 1
ncols = len(pvals)
pylab.subplots(nrows=nrows, ncols=ncols, figsize=(ncols * W, nrows * H))
axH = None
for panelID, panel_jobPattern in enumerate(jpathList):
axH = pylab.subplot(nrows, ncols, panelID + 1, sharey=axH, sharex=axH)
# Only show legend on first plot
if panelID > 0 and 'loc' in kwargs:
kwargs['loc'] = None
kwargs['doShowNow'] = False
plotMultipleLinesByLVar(panel_jobPattern, **kwargs)
if pvar is not None:
pylab.title('%s=%s' % (pvar, pvals[panelID]))
pylab.subplots_adjust(bottom=0.15, wspace=0.5)
if savefilename is not None:
try:
pylab.show(block=False)
except TypeError:
pass # when using IPython notebook
pylab.savefig(savefilename, bbox_inches='tight', pad_inches=0)
elif doShowNow:
try:
pylab.show(block=True)
except TypeError:
pass # when using IPython notebook
Info = dict(
nrows=nrows,
ncols=ncols,
)
return Info
lapFrac=aiter)
if len(MInfo['m_UIDPairs']) > 0:
for (uidA, uidB) in MInfo['m_UIDPairs']:
combinedSS.mergeComps(uidA=uidA, uidB=uidB)
trainSS.mergeComps(uidA=uidA, uidB=uidB)
combinedModel.update_global_params(combinedSS)
print()
print("Plotting final combined model!")
print("Each plot shows 25 samples of image patches from that cluster")
PRNG = np.random.RandomState(0)
for k in range(trainSS.K):
Sigma_k = combinedModel.obsModel.get_covar_mat_for_comp(k)
X_k = PRNG.multivariate_normal(np.zeros(64), Sigma_k, size=25)
figH, axList = pylab.subplots(nrows=5, ncols=5)
figH.canvas.set_window_title('Cluster %d: Sample patches' % (k))
ii = 0
for r in range(5):
for c in range(5):
axList[r, c].imshow(X_k[ii].reshape((8, 8)),
interpolation='nearest',
cmap='gray_r',
vmin=-0.1,
vmax=0.1)
ii += 1
axList[r, c].set_xticks([])
axList[r, c].set_yticks([])
pylab.show()
def plotCompsAsSquareImages(
phi,
compsToHighlight=None,
compListToPlot=None,
activeCompIDs=None,
xlabels=[],
Kmax=50,
W=1,
H=1,
figH=None,
vocabList=None, # catchall
**kwargs):
curImshowArgs = dict(**imshowArgs)
curImshowArgs.update(kwargs)
if len(xlabels) > 0:
H = 1.5 * H
K, V = phi.shape
sqrtV = int(np.sqrt(V))
assert np.allclose(sqrtV, np.sqrt(V))
if compListToPlot is None:
compListToPlot = np.arange(0, K)
if activeCompIDs is None:
activeCompIDs = np.arange(0, K)
compsToHighlight = np.asarray(compsToHighlight)
if compsToHighlight.ndim == 0:
compsToHighlight = np.asarray([compsToHighlight])
# Create Figure
Kplot = np.minimum(len(compListToPlot), Kmax)
ncols = 3 # int(np.ceil(Kplot / float(nrows)))
nrows = int(np.ceil(Kplot / float(ncols)))
if figH is None:
# Make a new figure
figH, ha = pylab.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * W, nrows * H))
else:
# Use existing figure
# TODO: Find a way to make this call actually change the figsize
figH, ha = pylab.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * W, nrows * H),
num=figH.number)
for plotID, compID in enumerate(compListToPlot):
if plotID >= Kmax:
print('DISPLAY LIMIT EXCEEDED. Showing %d/%d components' \
% (plotID, len(activeCompIDs)))
break
if compID not in activeCompIDs:
aH = pylab.subplot(nrows, ncols, plotID + 1)
aH.axis('off')
continue
kk = np.flatnonzero(compID == activeCompIDs)[0]
phiIm = np.reshape(phi[kk, :], (sqrtV, sqrtV))
ax = pylab.subplot(nrows, ncols, plotID + 1)
pylab.imshow(phiIm, aspect=1.0, **curImshowArgs)
pylab.xticks([])
pylab.yticks([])
# Draw colored border around highlighted topics
if compID in compsToHighlight:
[i.set_color('green') for i in ax.spines.values()]
[i.set_linewidth(3) for i in ax.spines.values()]
if xlabels is not None:
if len(xlabels) > 0:
pylab.xlabel(xlabels[plotID], fontsize=15)
# Disable empty plots!
for kdel in range(plotID + 2, nrows * ncols + 1):
aH = pylab.subplot(nrows, ncols, kdel)
aH.axis('off')
# Fix margins between subplots
pylab.subplots_adjust(wspace=0.04,
hspace=0.04,
left=0.01,
right=0.99,
top=0.99,
bottom=0.01)
return figH
def plotCompsFromWordCounts(WordCounts=None,
topics_KV=None,
vocabList=None,
compListToPlot=None,
compsToHighlight=None,
xlabels=None,
wordSizeLimit=10,
Ktop=10,
Kmax=32,
H=2.5,
W=2.0,
figH=None,
ncols=10,
ax_list=None,
fontsize=10,
proba_fmt_str="%.4f",
**kwargs):
''' Create subplots of top 10 words from each topic, from word count array.
Post Condition
--------------
Current matplotlib figure has subplot for each topic.
'''
if vocabList is None:
raise ValueError('Missing vocabList. Cannot display topics.')
if WordCounts is not None:
WordCounts = np.asarray(WordCounts, dtype=np.float64)
if WordCounts.ndim == 1:
WordCounts = WordCounts[np.newaxis, :]
K, vocab_size = WordCounts.shape
N = np.sum(WordCounts, axis=1)
else:
topics_KV = np.asarray(topics_KV, dtype=np.float64)
K, vocab_size = topics_KV.shape
if compListToPlot is None:
compListToPlot = np.arange(0, K)
Kplot = np.minimum(len(compListToPlot), Kmax)
if len(compListToPlot) > Kmax:
print('DISPLAY LIMIT EXCEEDED. Showing %d/%d components' \
% (Kplot, len(compListToPlot)))
compListToPlot = compListToPlot[:Kplot]
# Parse comps to highlight
compsToHighlight = np.asarray(compsToHighlight)
if compsToHighlight.ndim == 0:
compsToHighlight = np.asarray([compsToHighlight])
nrows = int(np.ceil(Kplot / float(ncols)))
# Create Figure
if ax_list is None:
fig_h, ax_list = pylab.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * W, nrows * H))
if isinstance(ax_list, np.ndarray):
ax_list = ax_list.flatten().tolist()
elif str(type(ax_list)).count("matplotlib"):
ax_list = [ax_list] # degenerate case where subplots returns single ax
assert isinstance(ax_list, list)
n_images_viewable = len(ax_list)
n_images_to_plot = len(compListToPlot)
for plotID, compID in enumerate(compListToPlot):
cur_ax_h = ax_list[plotID] #pylab.subplot(nrows, ncols, plotID + 1)
topicMultilineStr = ''
if WordCounts is None:
topIDs = np.argsort(-1 * topics_KV[compID])
else:
topIDs = np.argsort(-1 * WordCounts[compID])
for wID in topIDs[:Ktop]:
if WordCounts is not None and WordCounts[compID, wID] > 0:
wctStr = count2str(WordCounts[compID, wID])
topicMultilineStr += '%s %s\n' % (
wctStr, vocabList[wID][:wordSizeLimit])
else:
topicMultilineStr += (proba_fmt_str + " %s\n") % (
topics_KV[compID, wID], vocabList[wID][:wordSizeLimit])
cur_ax_h.text(0,
0,
topicMultilineStr,
fontsize=fontsize,
family=u'monospace')
cur_ax_h.set_xlim([0, 1])
cur_ax_h.set_ylim([0, 1])
cur_ax_h.set_xticks([])
cur_ax_h.set_yticks([])
# Draw colored border around highlighted topics
if compID in compsToHighlight:
[i.set_color('green') for i in ax.spines.values()]
[i.set_linewidth(3) for i in ax.spines.values()]
if xlabels is not None:
if len(xlabels) > 0:
cur_ax_h.set_xlabel(xlabels[plotID], fontsize=11)
# Disable empty plots
for k, ax_h in enumerate(ax_list[n_images_to_plot:]):
ax_h.axis('off')
return figH, ax_list
def plotExampleBarsDocs(Data,
docIDsToPlot=None,
figID=None,
vmax=None,
nDocToPlot=16,
doShowNow=False,
seed=0,
randstate=np.random.RandomState(0),
xlabels=None,
W=1,
H=1,
**kwargs):
kwargs['vmin'] = 0
kwargs['interpolation'] = 'nearest'
if vmax is not None:
kwargs['vmax'] = vmax
if seed is not None:
randstate = np.random.RandomState(seed)
V = Data.vocab_size
sqrtV = int(np.sqrt(V))
assert np.allclose(sqrtV * sqrtV, V)
if docIDsToPlot is not None:
nDocToPlot = len(docIDsToPlot)
else:
size = np.minimum(Data.nDoc, nDocToPlot)
docIDsToPlot = randstate.choice(Data.nDoc, size=size, replace=False)
ncols = 5
nrows = int(np.ceil(nDocToPlot / float(ncols)))
if vmax is None:
DocWordArr = Data.getDocTypeCountMatrix()
vmax = int(np.max(np.percentile(DocWordArr, 98, axis=0)))
if figID is None:
figH, ha = pylab.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * W, nrows * H))
for plotPos, docID in enumerate(docIDsToPlot):
start = Data.doc_range[docID]
stop = Data.doc_range[docID + 1]
wIDs = Data.word_id[start:stop]
wCts = Data.word_count[start:stop]
docWordHist = np.zeros(V)
docWordHist[wIDs] = wCts
squareIm = np.reshape(docWordHist, (sqrtV, sqrtV))
pylab.subplot(nrows, ncols, plotPos + 1)
pylab.imshow(squareIm, **kwargs)
pylab.axis('image')
pylab.xticks([])
pylab.yticks([])
if xlabels is not None:
pylab.xlabel(xlabels[plotPos])
# Disable empty plots!
for kdel in range(plotPos + 2, nrows * ncols + 1):
aH = pylab.subplot(nrows, ncols, kdel)
aH.axis('off')
# Fix margins between subplots
pylab.subplots_adjust(wspace=0.04,
hspace=0.04,
left=0.01,
right=0.99,
top=0.99,
bottom=0.01)
if doShowNow:
pylab.show()
def show_square_images(topics_KV=None,
xlabels=[],
max_n_images=50,
ncols=5,
ax_list=None,
im_width=1,
im_height=1,
fontsize=10,
**kwargs):
''' Show provided vectors as square images
Post Condition
--------------
Provided axes have plots updated.
'''
global imshowArgs
local_imshowArgs = dict(**imshowArgs)
for key in local_imshowArgs:
if key in kwargs:
local_imshowArgs[key] = kwargs[key]
K, V = topics_KV.shape
sqrtV = int(np.sqrt(V))
assert np.allclose(sqrtV, np.sqrt(V))
n_images_to_plot = np.minimum(K, max_n_images)
ncols = np.minimum(ncols, n_images_to_plot)
if ax_list is None:
# Make a new figure
nrows = int(np.ceil(n_images_to_plot / float(ncols)))
fig_h, ax_list = pylab.subplots(nrows=nrows,
ncols=ncols,
figsize=(ncols * im_width,
nrows * im_height))
if isinstance(ax_list, np.ndarray):
ax_list = ax_list.flatten().tolist()
elif str(type(ax_list)).count("matplotlib"):
ax_list = [ax_list] # degenerate case where subplots returns single ax
assert isinstance(ax_list, list)
n_images_viewable = len(ax_list)
# Plot each row as square image
for k, ax_h in enumerate(ax_list[:n_images_to_plot]):
cur_im_sVsV = np.reshape(topics_KV[k, :], (sqrtV, sqrtV))
ax_h.imshow(cur_im_sVsV, **local_imshowArgs)
ax_h.set_xticks([])
ax_h.set_yticks([])
if xlabels is not None:
if len(xlabels) > 0:
ax_h.set_xlabel(xlabels[k], fontsize=fontsize)
# Disable empty plots
for k, ax_h in enumerate(ax_list[n_images_to_plot:]):
ax_h.axis('off')
# Fix margins between subplots
#pylab.subplots_adjust(
# wspace=0.1,
# hspace=0.1 * nrows,
# left=0.001, right=0.999,
# bottom=0.1, top=0.999)
return ax_list