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pretty_print.py
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import joblib
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation, rc
from matplotlib.lines import Line2D
class AxesDecorator():
def __init__(self, ax, size="5%", pad=0.05, ticks=[1,2,3], spacing=0.05,
color="k"):
self.divider= make_axes_locatable(ax)
self.ax = self.divider.new_vertical(size=size, pad=pad, sharex=ax, pack_start=True)
ax.figure.add_axes(self.ax)
self.ticks=np.array(ticks)
self.d = np.mean(np.diff(ticks))
self.spacing = spacing
self.get_curve()
self.color=color
for x0 in ticks:
self.plot_curve(x0)
self.ax.set_yticks([])
plt.setp(ax.get_xticklabels(), visible=False)
self.ax.tick_params(axis='x', which=u'both',length=0)
ax.tick_params(axis='x', which=u'both',length=0)
for direction in ["left", "right", "bottom", "top"]:
self.ax.spines[direction].set_visible(False)
self.ax.set_xlabel(ax.get_xlabel(), fontsize= 24)
ax.set_xlabel("")
self.ax.set_xticks(self.ticks)
def plot_curve(self, x0):
x = np.linspace(x0-self.d/1.*(1-self.spacing),x0+self.d/2.*(2-self.spacing), 50 )
self.ax.plot(x, self.curve, c=self.color)
def get_curve(self):
lx = np.linspace(-np.pi/2.+0.05, np.pi/2.-0.05, 25)
tan = np.tan(lx)*10
self.curve = np.hstack((tan[::-1],tan))
return self.curve
def bracket_X(ax, offset, pos=[0,0], color="black", scalex=1, scaley=1, text="",textkw = {}, linekw = {}, rotate=90):
x = np.array([0, 0.05, 0.45,0.5])
y = np.array([0,-0.01,-0.01,-0.02])
x = np.concatenate((x,x+0.5))
y = np.concatenate((y,y[::-1]))
ax.plot(x*scalex+pos[0], -y*scaley+pos[1], clip_on=False,
transform=ax.get_xaxis_transform(), **linekw)
ax.text(pos[0]+0.5*scalex+offset, (y.min()+0.042)*scaley+pos[1], text,
transform=ax.get_xaxis_transform(), size=48, rotation=rotate,
ha="center", va="bottom", color=color,weight="bold", **textkw)
def bracket_Y(ax, offset, color="black", textoffset=0, pos=[0,0], scalex=1, scaley=1, text="",textkw = {}, linekw = {}, rotate=0):
x = np.array([0, 0.05, 0.45,0.5])
y = np.array([0,-0.01,-0.01,-0.02])
x = np.concatenate((x,x+0.5))
y = np.concatenate((y,y[::-1]))
ax.plot(y*scaley+pos[1], x*scalex+pos[0], clip_on=False,
transform=ax.get_xaxis_transform(), **linekw)
ax.text((y.min()-0.005)*scaley+pos[1]+textoffset, pos[0]+0.5*scalex-offset, text,
transform=ax.get_xaxis_transform(), size=48, rotation=rotate,
ha="center", va="bottom", color=color,weight="bold", **textkw)
def pretty_print_mat(affinity):
scale = [6] * 8 + [2] * 12 + [1] * 24 + [1] * 18 + [2] * 13 + [2] * 13 + [3] * 6 + [3] * 6
arr = []
for ei, i in enumerate(scale):
for k in range(i):
lst = []
for ej, j in enumerate(scale):
for l in range(j):
lst.append(affinity[ei][ej])
arr.append(lst)
arr = np.array(arr)
arr_len = arr.shape[0]
fig = plt.figure(figsize=(100,120))
ax = fig.add_subplot(221, label='k')
cax = ax.matshow(arr, interpolation='nearest', vmin=0, vmax=0.1)
cb = fig.colorbar(cax, shrink=0.5, orientation='horizontal', pad=0.004)
cb.ax.tick_params(labelsize=60)
plt.yticks([])
plt.xlim(-0.5,arr_len - 0.5)
plt.ylim(arr_len - 0.5,-0.5)
ax.tick_params(axis="x", bottom=False, top=False, labelbottom=False, labeltop=False)
ax.tick_params(axis="y", bottom=False, top=False, labelbottom=False, labeltop=False)
bracket_X(ax,0.1,text="GloVe", color='green', scalex=5, pos=[0,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="BERT-E", color='green', scalex=5, pos=[6,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="FLAIR", color='green',scalex=5, pos=[12,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="POS", color='red', scalex=5, pos=[18,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="CHUNK", color='red',scalex=5, pos=[24,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="NER",color='blue', scalex=5, pos=[30,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="FRAME", color='blue', scalex=5, pos=[36,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="NWE", scalex=5, pos=[42,1.005], linekw=dict(color="black", lw=2) )
bracket_X(ax,0.1, text="GPT-2 S", color='orange', scalex=23, pos=[48,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,0.1, text="GPT-2 M", color='coral',scalex=23, pos=[72,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,0.1, text="Trans-XL", color='fuchsia', scalex=17, pos=[96,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,0.1, text="BERT", color='cadetblue', scalex=25, pos=[114,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,0.1, text="ALBERT", color='grey', scalex=25, pos=[140,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,-1, text="Eng$\Rightarrow$Zh", color='mediumvioletred', scalex=17, pos=[166,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_X(ax,0.1, text="Eng$\Rightarrow$De", color='olive', scalex=17, pos=[184,1.005], linekw=dict(color="black", lw=2), rotate=0 )
bracket_Y(ax, 0.047, text="Eng$\Rightarrow$De",color='olive', scaley=256, scalex=17/sum(scale), pos=[0.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.041, text="Eng$\Rightarrow$Zh",color='mediumvioletred', scaley=256, scalex=17/sum(scale), pos=[18.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.039, text="ALBERT",color='grey', scaley=256, scalex=25/sum(scale), pos=[36.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.026, text="BERT", color='cadetblue',scaley=256, scalex= 25/sum(scale), pos=[62.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.047, text="Trans-XL",color='fuchsia', scaley=256, scalex= 17/sum(scale), pos=[88.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.044, text="GPT-2 M", color='coral',scaley=256, scalex= 23/sum(scale), pos=[106.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.04, text="GPT-2 S", color='orange',scaley=256, scalex= 23/sum(scale), pos=[130.5/sum(scale),-1], linekw=dict(color="black", lw=2), rotate=90)
bracket_Y(ax, 0.01,textoffset=-3.5, text="NWE", scaley=256, scalex= 5/sum(scale), pos=[154.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-6, text="FRAME", color='blue', scaley=256, scalex= 5/sum(scale), pos=[160.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-3.5, text="NER",color='blue', scaley=256, scalex= 5/sum(scale), pos=[166.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-6,text="CHUNK", color='red', scaley=256, scalex= 5/sum(scale), pos=[172.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-3.5,text="POS", color='red', scaley=256, scalex= 5/sum(scale), pos=[178.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-5, text="FLAIR", color='green', scaley=256, scalex= 5/sum(scale), pos=[184.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-6, text="BERT-E", color='green', scaley=256, scalex= 5/sum(scale), pos=[190.5/sum(scale),-1], linekw=dict(color="black", lw=2))
bracket_Y(ax, 0.01,textoffset=-5, text="GloVe", color='green', scaley=256, scalex= 5/sum(scale), pos=[196.5/sum(scale),-1], linekw=dict(color="black", lw=2))
ax.text(55,-17,"Encoded Feature Space", size=108)
ax.text(-30,145,"Decoded Feature Space", size=108, rotation=90)
pos1 = ax.get_position().bounds
def pretty_print_mds(q1, l1, fname, writer='imagemagick'):
def plot_gradient(q1, start, end, color, alpha_range, labels2, texts1=[], offset=0):
alphas = np.linspace(alpha_range[0], alpha_range[1], end - start)
for ei, i in enumerate(range(start, end - 1)):
# Scale by l1
plt.plot([q1[i, -1] * l1[-1], q1[i + 1, -1] * l1[-1]], [q1[i, -2] * l1[-2], q1[i + 1, -2] * l1[-2]], \
[q1[i, -3] * l1[-3], q1[i + 1, -3] * l1[-3]], 'o-', color=color, alpha=alphas[ei], lw=2, ms=3)
return texts1
ax = Axes3D(plt.figure(figsize=(8,8)))
ax.set_xlim3d(-4, 4)
ax.set_ylim3d(-4, 4)
ax.set_zlim3d(-4, 4)
colors = dict(semantic='blue',
syntactic='red',
embedding='green',
lowlevel='purple',
other='black',
lm='orange',
lm2='brown',
lm_out="black",
mt='pink',
mt2='indigo',
bi_lm='yellow',
lm3='cyan',
bi_lm2="lime")
labels = np.array(['GloVe', "BERT-E", "FLAIR", 'POS', 'CHUNK', "NER", 'FRAME', "GloVe Next Word",
"", "", "", "", "", "", "GPT2-Small", "", "", "", "", "",
"", "", "", "", "", "GPT2-Medium", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "Transformer-XL", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "BERT", "", "", "", "", "", "", "",
"", "", "", "", "", "", "ALBERT", "", "", "", "", "", "",
"", "", "", "Eng → Zh", "", "",
"", "", "", "Eng → De", "", ""])
plot_gradient(q1, 8, 20, 'black', (0,0.7), labels)
texts = plot_gradient(q1, 8, 20, 'orange', (0.7,0.7), labels, offset=2, texts1=[])
plot_gradient(q1, 20, 44, 'black', (0,0.7), labels, texts1=[])
texts = plot_gradient(q1, 20, 44, 'coral', (0.7,0.7), labels, texts, offset=3)
plot_gradient(q1, 44, 62,"navy", (0,1), labels, texts1=[])
texts = plot_gradient(q1, 44, 62, "fuchsia", (0.7,0.7), labels, texts, offset=3)
plot_gradient(q1, 62, 75, "black", (0,1), labels, texts1=[])
texts = plot_gradient(q1, 62, 75, "cadetblue", (0.5,0.5), labels, texts)
plot_gradient(q1, 75, 88, "black", (0.3,1), labels, texts1=[])
texts = plot_gradient(q1, 75, 88, "gray", (1,0), labels, texts, offset=5)
plot_gradient(q1, 88, 94,"black", (0,1), labels, texts1=[])
texts = plot_gradient(q1, 88, 94,"mediumvioletred", (0.7, 0.7), labels, texts, offset=9)
plot_gradient(q1, 94, 100, 'green', (0,1), labels, texts1=[])
texts = plot_gradient(q1, 94, 100, 'olive', (1,0), labels, texts)
colors3d = ["green", "green", "green", "red", "red", "blue", "blue", "black"] + ["orange"] * 12 + ["coral"] * 24 + ["fuchsia"] * 18 + ["cadetblue"] * 13 + ["gray"] * 13 + ["mediumvioletred"] * 6 + ["olive"] * 6
for qx, qy, qz, ll, ei in zip(q1[:,-1]*l1[-1], q1[:,-2]*l1[-2], q1[:,-3]*l1[-3], labels, list(range(100))):
ax.plot([qx], [qy], [qz], 'o', color=colors3d[ei])
ax.text(qx, qy, qz, ll, color=colors3d[ei], weight="bold")
ax.view_init(45, 0)
def animfxn(f):
ax.view_init(25, f)
return []
anim = animation.FuncAnimation(plt.gcf(), animfxn, #init_func=lambda:None,
frames=360, interval=20, blit=True)
print("Writing gif...")
anim.save(fname, writer=writer, fps=60)