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_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_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_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 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')