File size: 2,473 Bytes
49812da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import time
from sklearn.manifold import TSNE
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.patheffects as PathEffects
import matplotlib.pyplot as plt
import pickle,sys
def plot_scatter(x, colors, fileName):
# choose a color palette with seaborn.
num_classes = len(np.unique(colors))
print(num_classes)
palette = np.array(sns.color_palette("hls", num_classes))
# print(palette)
# create a scatter plot.
f = plt.figure(figsize=(16, 12))
# ax = plt.subplot(aspect='equal')
ax = plt.subplot()
# sc = ax.scatter(x[:,0], x[:,1], lw=0, s=40, c=df['label'], cmap=plt.cm.get_cmap('Paired'))
# sc = ax.scatter(x[:,0], x[:,1], c=palette[colors.astype(np.int)], cmap=plt.cm.get_cmap('Paired'))
sc = ax.scatter(x[:, 0], x[:, 1], c=palette[colors.astype(np.int)], cmap=plt.cm.get_cmap('Paired'))
plt.xlim(-25, 25)
plt.ylim(-25, 25)
ax.legend()
ax.axis('off')
ax.axis('tight')
# add the labels for each digit corresponding to the label
txts = []
for i in range(num_classes):
# Position of each label at median of data points.
xtext, ytext = np.median(x[colors == i, :], axis=0)
txt = ax.text(xtext, ytext, str(i), fontsize=24)
txt.set_path_effects([
PathEffects.Stroke(linewidth=5, foreground="w"),
PathEffects.Normal()])
txts.append(txt)
ax.grid(True)
plt.savefig(fileName + '.pdf', fmt='pdf', bbox_inches='tight')
plt.show()
def gettSNEResults(features,labels):
X = pd.DataFrame(features)
Y = pd.DataFrame(labels)
#X = X.sample(frac=0.1, random_state=10).reset_index(drop=True)
#Y = Y.sample(frac=0.1, random_state=10).reset_index(drop=True)
df = X
time_start = time.time()
tsne = TSNE(random_state=0)
tsne_results = tsne.fit_transform(df.values)
df['label'] = Y
return tsne_results,df['label']
featureFile = sys.argv[1] #eg. desEmbedding.pickle or synthesisFlow.pickle
labelFile = sys.argv[2] # designName or synthesisID
tSNE_diagName = sys.argv[3] # tSNE file name
with open(featureFile,'rb') as f:
featureEmbedding = pickle.load(f)
with open(labelFile,'rb') as f:
outputLabel = pickle.load(f)
labelDict = {}
labelList = []
for i in range(len(outputLabel)):
labelDict[outputLabel[i]] = i
labelList.append(i)
tsne_F,tsne_Y = gettSNEResults(featureEmbedding,labelList)
plot_scatter(tsne_F,tsne_Y,tSNE_diagName)
print(labelDict) |