-
Notifications
You must be signed in to change notification settings - Fork 3
/
plot.py
382 lines (344 loc) · 15.3 KB
/
plot.py
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d import axes3d
from sklearn.manifold import TSNE
import numpy as np
from tqdm import tqdm
from settings import PLOT_COLOR, PLOT_NAME, BETA_LIST, DEFAULT_BETA_TICKS
from my_utils import build_path
'''
Plot for data observation:
* plot_slate_response_distribution
Core plot function for evaluations:
* plot_standard_test_result
* plot_likelihood_test_result
* plot_latent
'''
# color templates of 6 types of responses
colors = ['#D98880', '#AF7AC5', '#5499C7', '#48C9B0', '#F4D03F', '#99A3A4']
# D = pickle.load(open("results/itemExchange/change_first_item.pkl", "rb"))
def plot_item_change(preds, targets):
'''
@input:
- preds: L * nChange
'''
fig, axs = plt.subplots(6, preds.shape[1], sharex='col', sharey='row', figsize = (2 * preds.shape[1], 6),
gridspec_kw={'hspace': 0, 'wspace': 0})
if preds.shape[1] == 1:
for i in range(6):
selectedPreds = preds[targets==i,0]
ax = axs[i]
ax.hist(selectedPreds, color = colors[i], bins = 100, label = i)
ax.set_yticklabels([])
axs[5].set_xlabel("Change no item", fontsize=18)
else:
for i in range(6):
selectedPreds = preds[targets==i]
for j in range(preds.shape[1]):
ax = axs[i,j]
ax.hist(selectedPreds[:,j], color = colors[i], bins = 100, label = i)
ax.set_yticklabels([])
axs[5,0].set_xlabel("Original", fontsize=18)
axs[5,5].set_xlabel("Perturb All", fontsize=18)
for j in range(preds.shape[1]-2):
axs[5,j+1].set_xlabel("Perturb " + str(j+1), fontsize=18)
plt.show()
fig.savefig("results/plots/change_first_item.pdf", bbox_inches='tight')
def plot_slate_response_distribution(responses, shape = (10,4), fig_name = "results/dist.pdf"):
respDist = np.zeros((responses.shape[0], 32))
for i in range(responses.shape[0]):
R = responses[i]
totalResp = 0
for v in R:
totalResp *= 2
totalResp += int(v)
respDist[i,totalResp] += 1
data = np.sum(respDist,0)
# plot
plt.figure(figsize = shape)
plt.bar(np.arange(len(data)), data)
plt.xticks(np.arange(len(data)), np.arange(respDist.shape[1]))
build_path(fig_name)
plt.savefig(fig_name)
plt.show()
def set_box_color(bp, color):
"""
box plot set color
"""
plt.setp(bp['boxes'], color=color)
plt.setp(bp['whiskers'], color=color)
plt.setp(bp['caps'], color=color)
plt.setp(bp['medians'], color=color)
'''
Plot latent distribution
'''
def plot_latent_box(reports, data_key = "", plot_key = "z"):
'''
@input:
- reports: {model_key: {beta: L * dim}}
- data_key: string for image saving
- plot_key: one of {"z": zList, "mu": muList, "logvar": logvarList, "prior_mu": pMuList, "prior_logvar": pLogvarList}
'''
assert plot_key == "z" or plot_key == "mu" or plot_key == "prior_mu"
nCol = 6
nRow = 6
modelKeys = list(reports.keys())
for j in range(len(modelKeys)):
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
betaList = sorted(list(modelReport.keys()))
# each model save a separate figure
fig, axs = plt.subplots(6,6, figsize = (15,15))
gs1 = gridspec.GridSpec(6,6)
gs1.update(hspace=0.0)
L = modelReport[betaList[0]][plot_key].shape[0]
# sample from results
filteredIndex = np.random.choice(L, min(max(500, int(0.005 * L)), L), replace = False)
for i in tqdm(range(len(betaList))):
beta = betaList[i]
data = modelReport[beta][plot_key]
resp = modelReport[beta]["resp"]
# TSNE with 2 components
tsne_embedded = TSNE(n_components=2).fit_transform(data[filteredIndex,:])
# plot
ax = axs[int(i/6),i%6]
ax.scatter(tsne_embedded[:,0], tsne_embedded[:,1], c = resp[filteredIndex], s = 2.0)
ax.set_title("Beta = %.5f"% beta)
ax.set_xticks([], [])
ax.set_yticks([], [])
# axs[5,5].legend(fontsize = 14)
# plt.show()
print("save figure \'results/plots/latent_" + plot_key + "_" + data_key + "_" + model_key + ".pdf\'")
savePath = "results/plots/latent_" + plot_key + "_" + data_key + "_" + model_key + ".pdf"
build_path(savePath)
fig.savefig(savePath, bbox_inches='tight')
plt.close()
def plot_latent_tsne(reports, data_key = "", plot_key = "z"):
'''
@input:
- reports: {model_key: {beta: L * dim}}
- data_key: string for image saving
- plot_key: one of {"z": zList, "mu": muList, "logvar": logvarList, "prior_mu": pMuList, "prior_logvar": pLogvarList}
'''
assert plot_key == "z" or plot_key == "mu" or plot_key == "prior_mu"
nCol = 6
nRow = 6
modelKeys = list(reports.keys())
for j in range(len(modelKeys)):
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
betaList = sorted(list(modelReport.keys()))
# each model save a separate figure
fig, axs = plt.subplots(6,6, figsize = (15,15))
gs1 = gridspec.GridSpec(6,6)
gs1.update(hspace=0.0)
L = modelReport[betaList[0]][plot_key].shape[0]
# sample from results
filteredIndex = np.random.choice(L, min(max(1000, int(0.005 * L)), L), replace = False)
for i in tqdm(range(len(betaList))):
beta = betaList[i]
data = modelReport[beta][plot_key]
resp = modelReport[beta]["resp"]
# TSNE with 2 components
tsne_embedded = TSNE(n_components=2).fit_transform(data[filteredIndex,:])
# plot
ax = axs[int(i/6),i%6]
# print(resp[filteredIndex])
# input()
ax.scatter(tsne_embedded[:,0], tsne_embedded[:,1], c = [colors[int(k)] for k in resp[filteredIndex]], s = 2.0)
ax.set_title("Beta = %.5f"% beta)
ax.set_xticks([],[])
ax.set_yticks([],[])
# axs[5,5].legend(fontsize = 14)
# plt.show()
bll = len(betaList)
ax = axs[int(bll/6), bll%6]
# scatter = ax.scatter([0 for i in range(6)], [0 for i in range(6)], c = [float(i) for i in range(6)], s = 0.1)
scl = [ax.scatter([i],[i], c = [colors[i]], s = 2.0, label = str(i)) for i in range(6)]
ax.set_xticks([],[])
ax.set_yticks([],[])
ax.legend()
print("save figure \'results/plots/latent_" + plot_key + "_" + data_key + "_" + model_key + ".pdf\'")
savePath = "results/plots/latent_" + plot_key + "_" + data_key + "_" + model_key + ".pdf"
build_path(savePath)
fig.savefig(savePath, bbox_inches='tight')
plt.close()
def plot_model(D, L, offset, color, ax, beta_list, detail, model_key = ""):
if len(model_key) == 0:
if detail:
bp = ax.boxplot(D, positions=[i * 2.0 + 0.4 + 1.7 * offset / L for i in range(len(beta_list))], \
widths=1.5/L, showfliers=False)
set_box_color(bp, color)
else:
ax.plot(np.arange(len(beta_list)) * 2.0 + 1.0, D, c = color)
else:
if detail:
a = 1.0 * offset / L
bp = ax.boxplot(D, positions=[i * 2.0 + 0.4 + 1.7 * offset / L for i in range(len(beta_list))], \
widths=1.5/L, showfliers=False)
ax.legend([bp["boxes"][0]], [model_key])
set_box_color(bp, color)
else:
ax.plot(np.arange(len(beta_list)) * 2.0 + 1.0, D, label = model_key, c = color)
def plot_likelihood_test_result(reports, detail = False, data_key = ""):
if detail:
fig, axs = plt.subplots(3,1, figsize = (16,6), sharex='col')
else:
fig, axs = plt.subplots(3,1, figsize = (8,6), sharex='col')
gs1 = gridspec.GridSpec(3,1)
gs1.update(hspace=0.0)
bpPlots = []
modelKeys = list(reports.keys())
for j in range(len(modelKeys)):
# plot for each model
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
betaList = sorted(list(modelReport.keys()))
recs = []
klds = []
vaes = []
for i in range(len(betaList)):
beta = betaList[i]
recs.append(modelReport[beta]["rec"])
klds.append(np.mean(np.log(modelReport[beta]["kld"])))
vaes.append(modelReport[beta]["vae"])
# box plot of ENC and diversity
plot_model(recs, len(modelKeys), j, model_color, axs[0], betaList, detail)
# plot of coverage
plot_model(klds, len(modelKeys), j, model_color, axs[1], betaList, detail = False, model_key = model_key)
# plot of ILD
plot_model(vaes, len(modelKeys), j, model_color, axs[2], betaList, detail)
axs[0].set_ylabel("Reconstruction Loss", fontsize = 12 + len(modelKeys))
axs[1].set_ylabel("Log KLD", fontsize = 12 + len(modelKeys))
axs[1].legend(fontsize = 12 + len(modelKeys))
axs[2].set_ylabel("VAE Loss", fontsize = 12 + len(modelKeys))
plt.xticks(np.arange(len(betaList)) * 2.0 + 1.0, DEFAULT_BETA_TICKS)
plt.xlim(0, len(betaList) * 2.0 + 1.0)
plt.show()
savePath = "results/plots/likelihood_test_" + data_key + ".pdf"
build_path(savePath)
fig.savefig(savePath, bbox_inches='tight')
def plot_standard_test_result(reports, detail = False, data_key = ""):
modelKeys = list(reports.keys())
betaList = sorted(list(reports[modelKeys[0]].keys()))
if detail:
fig, axs = plt.subplots(3,1, figsize = (int(5*len(modelKeys)*len(betaList)/35),6), sharex='col')
else:
fig, axs = plt.subplots(3,1, figsize = (int(3*len(modelKeys)*len(betaList)/35),6), sharex='col')
gs1 = gridspec.GridSpec(3,1)
gs1.update(hspace=0.0)
bpPlots = []
for j in range(len(modelKeys)):
# plot for each model
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
coverages = []
encs = []
diversities = []
for i in range(len(betaList)):
beta = betaList[i]
encs.append(modelReport[beta]["enc"][-1])
diversities.append(modelReport[beta]["diversity"][-1])
coverages.append(modelReport[beta]["coverage"][-1])
# box plot of ENC and diversity
plot_model(encs, len(modelKeys), j, model_color, axs[0], betaList, detail)
# plot of coverage
plot_model(coverages, len(modelKeys), j, model_color, axs[1], betaList, detail = False, model_key = model_key)
# plot of ILD
plot_model(diversities, len(modelKeys), j, model_color, axs[2], betaList, detail)
# # box plot of ENC and diversity
# ax = axs[0]
# bp = ax.boxplot(enc, positions=[i * 1.0 * len(modelKeys) + 0.4 + 0.8 * j], \
# widths=0.6, showfliers=False)
# set_box_color(bp, model_color)
# # plot of ILD
# ax = axs[2]
# bp = ax.boxplot(diversity, positions=[i * 1.0 * len(modelKeys) + 0.4 + 0.8 * j], \
# widths=0.6, showfliers=False)
# set_box_color(bp, model_color)
# # sp = ax.plot(np.arange(len(beta_list)) * 2.0 + 1.0, diversities, label = legends[j], c = colors[j])
# # plot of coverage
# ax = axs[1]
# cp = ax.plot(np.arange(len(betaList)) * 2.0 + 1.0, coverages, label = model_key, c = model_color)
axs[0].set_ylabel("ENC", fontsize = 12 + len(modelKeys))
# axs[0].legend([bpPlots[0]["boxes"][0], bpPlots[1]["boxes"][0]], legends)
axs[1].set_ylabel("Coverage", fontsize = 12 + len(modelKeys))
axs[1].legend(fontsize = 12 + len(modelKeys))
axs[2].set_ylabel("Diversity", fontsize = 12 + len(modelKeys))
# axs[2].legend()
plt.xticks(np.arange(len(betaList)) * 2.0 + 1.0, DEFAULT_BETA_TICKS, fontsize = 10 + len(modelKeys))
plt.xlim(0, len(betaList) * 2.0 + 1.0)
plt.show()
savePath = "results/plots/standard_test_" + data_key + ".pdf"
build_path(savePath)
fig.savefig(savePath, bbox_inches='tight')
def plot_ranking_test_result(reports, data_key, plot_key = "hit_rate", slate_size = 5):
'''
@input:
- plot_key: ["hit_rate", "recall", "p_hit_rate"], "p_hit_rate" means positional hit rate
'''
modelKeys = list(reports.keys())
betaList = sorted(list(reports[modelKeys[0]].keys()))
fig, axs = plt.subplots(slate_size, 1, figsize = (int(5*len(modelKeys)*len(betaList)/35),6), sharex='col')
gs1 = gridspec.GridSpec(slate_size, 1)
gs1.update(hspace=0.0)
for j in range(len(modelKeys)):
# plot for each model
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
# each model have a beta test
betaList = sorted(list(modelReport.keys()))
rs = np.zeros((len(betaList),slate_size))
for i in range(len(betaList)):
beta = betaList[i]
rs[i] = modelReport[beta][plot_key]
# box plot of ENC and diversity
for i in range(slate_size):
if i == 1:
axs[i].plot(np.arange(len(betaList)) * 2.0 + 1.0, rs[:,i], c = model_color, label = PLOT_NAME[model_key])
else:
axs[i].plot(np.arange(len(betaList)) * 2.0 + 1.0, rs[:,i], c = model_color)
for i in range(slate_size):
axs[i].set_ylabel("@" + str(i+1), fontsize = 12 + len(modelKeys))
# axs[0].legend([bpPlots[0]["boxes"][0], bpPlots[1]["boxes"][0]], legends)
# axs[1].set_ylabel("Coverage", fontsize = 14)
axs[1].legend(fontsize = 14)
# axs[2].set_ylabel("Diversity", fontsize = 14)
# axs[2].legend()
plt.xticks(np.arange(len(betaList)) * 2.0 + 1.0, DEFAULT_BETA_TICKS, fontsize = 12 + len(modelKeys))
plt.xlim(0, len(betaList) * 2.0 + 1.0)
plt.show()
savePath = "results/plots/ranking_test_" + data_key + "_" + plot_key + ".pdf"
build_path(savePath)
fig.savefig(savePath, bbox_inches='tight')
def plot_positional_ranking_test_result(reports, data_key, plot_key = "p_recall", slate_size = 5):
assert plot_key == "p_recall"
nCol = 6
nRow = 6
modelKeys = list(reports.keys())
for j in range(len(modelKeys)):
model_key = modelKeys[j]
modelReport = reports[model_key]
model_color = PLOT_COLOR[model_key]
fig, axs = plt.subplots(6,6, figsize = (15,15), sharex='col', sharey='row')
# each model save a separate figure
gs1 = gridspec.GridSpec(6,6)
gs1.update(hspace=0.0)
betaList = sorted(list(modelReport.keys()))
for i in tqdm(range(len(betaList))):
beta = betaList[i]
rs = modelReport[beta][plot_key]
ax = axs[int(i/6),i%6]
for j in range(rs.shape[0]):
ax.plot(rs[j], color = colors[j])
ax.set_title("Beta = %.5f"% beta)
print("save figure \'results/plots/ranking_" + data_key + "_" + plot_key + "_" + model_key + ".pdf\'")
savePath = "results/plots/ranking_" + data_key + "_" + plot_key + "_" + model_key + ".pdf"
buildPath(savePath)
fig.savefig(savePath, bbox_inches='tight')
plt.close()