def tsne(data, featureDict, x, prop):
"""t-SNE methods
Args:
data (TYPE): Description
x (TYPE): Description
queryrate (TYPE): Description
plotsize (TYPE): Description
Returns:
TYPE: Description
"""
x = 'tsne-%s' % x
print "Computing t-SNE embedding"
t0 = time.clock()
X = np.array(data['data'])
model = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
# list, each of them is formatted as (x,y)
Y = model.fit_transform(X)
print "t-SNE embedding of the digits (time %.2fs)" % (time.clock() - t0)
func.matrixtofile(
Y, '%s/PointsPosition-1-in-%s-%s.csv' %
(prop['dic'], str(prop['queryrate']), str(x)))
result = {'data': Y, 'id': data['id']}
prop['text'] = x
drawFigure(result, featureDict, prop)
def pca(data, featureDict, x, prop):
"""PCA methods
Args:
data (TYPE): Description
x (TYPE): Description
queryrate (TYPE): Description
plotsize (TYPE): Description
Returns:
TYPE: Description
"""
x = 'PCA-%s' % x
print("Computing PCA projection")
t0 = time.clock()
pca = PCA(n_components=2)
X_pca = pca.fit_transform(data['data'])
print "Principal Components projection of the digits (time %.2fs)" % (
time.clock() - t0)
func.matrixtofile(
X_pca, '%s/PointsPosition-1-in-%s-%s.csv' %
(prop['dic'], str(prop['queryrate']), str(x)))
result = {'data': X_pca, 'id': data['id']}
prop['text'] = x
drawFigure(result, featureDict, prop)
def mds(data, featureDict, x, prop):
x = 'MDS-%s' % x
print "Computing MDS embedding"
t0 = time.clock()
clf = MDS(n_components=2, n_init=1, max_iter=100)
X_mds = clf.fit_transform(data['data'])
print("Done. Stress: %f" % clf.stress_)
print "MDS embedding of the digits (time %.2fs)" % (time.clock() - t0)
func.matrixtofile(
X_mds, '%s/PointsPosition-1-in-%s-%s.csv' %
(prop['dic'], str(prop['queryrate']), str(x)))
result = {'data': X_mds, 'id': data['id']}
prop['text'] = x
drawFigure(result, featureDict, prop)
def drawFigure(data, featureDict, prop):
"""Figures' drawing and saving
Args:
data (list): Dots list
plotsize (TYPE): Description
queryrate (TYPE): Description
Returns:
TYPE: Description
"""
# Init transformation
plotsize, queryrate, fthre = prop['plotsize'], str(prop['queryrate']), str(
prop['text'])
# matrixData 用于存储降维后点值的二维位置与对应 ID 信息
matrixDataRaw = np.asarray(np.array(data['data'])[:, 0:2])
matrixDataRes = []
# 相似度对应的 colormap 取值表
simcolormap = {
'4': '#1924B1',
'5': '#37B6CE',
'6': '#25D500',
'7': '#FFC700',
'8': '#FF8E00',
'9': '#FF1300'
}
# Scatter plot
X, Y = data['data'][:, 0], data['data'][:, 1]
whlC, gt11C, numC = [], [], []
for x in xrange(0, len(data['id'])):
whlSimVal = int(
math.floor(featureDict[str(data['id'][x])]['whlsim'] * 10))
gt11SimVal = int(
math.floor(featureDict[str(data['id'][x])]['gt11sim'] * 10))
whlC.append(simcolormap[str(whlSimVal)])
gt11C.append(simcolormap[str(gt11SimVal)])
totalNum = featureDict[str(data['id'][x])]['totalNum']
numC.append(func.calColorbyNum(int(totalNum)))
matrixDataRes.append([
data['id'][x], matrixDataRaw[x][0], matrixDataRaw[x][1], totalNum,
whlSimVal, gt11SimVal
])
textRecNum = '2D-ScatterData_1-in-%s_%s(byRecNum)' % (queryrate, fthre)
textAveSim = '2D-ScatterData_1-in-%s_%s(byAveSim)' % (queryrate, fthre)
func.matrixtofile(
matrixDataRes,
'%s/2D-ScatterData_1-in-%s_%s.csv' % (prop['dic'], queryrate, fthre))
scatterTC = TimeConsuming(textRecNum)
plt.figure()
plt.title(textRecNum)
plt.scatter(X, Y, s=plotsize, c=numC, lw=0)
classes = ['5', '10', '200', '800', '2000', '4000', '8500']
class_colours = [
'#000000', '#1924B1', '#37B6CE', '#25D500', '#FFC700', '#FF8E00',
'#FF1300'
]
recs = []
for i in range(0, len(class_colours)):
recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=class_colours[i]))
plt.legend(recs,
classes,
scatterpoints=1,
loc='lower left',
ncol=4,
fontsize=5)
img = plt.gcf()
img.savefig('%s/%s.png' % (prop['imgdic'], textRecNum), dpi=400)
plt.close()
scatterTC.end()
scatterTC2 = TimeConsuming(textAveSim)
plt.figure()
plt.title(textAveSim)
plt.scatter(X, Y, s=plotsize, c=whlC, lw=0)
whlClasses = [float(x) / 10 for x in simcolormap]
whlClassColours = [simcolormap[x] for x in simcolormap]
whlRecs = []
for i in range(0, len(whlClassColours)):
whlRecs.append(mpatches.Rectangle((0, 0), 1, 1, fc=whlClassColours[i]))
plt.legend(whlRecs,
whlClasses,
scatterpoints=1,
loc='lower left',
ncol=3,
fontsize=5)
img2 = plt.gcf()
img2.savefig('%s/%s.png' % (prop['imgdic'], textAveSim), dpi=400)
plt.close()
scatterTC2.end()
