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recreate_table.py
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recreate_table.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This code is part of the paper :
# IJCAI 2020 paper "Metric Learning in Optimal Transport for Domain Adaptation"
# Written by Tanguy Kerdoncuff
# If there is any bug, don't hesitate to send me a mail to my personal email:
import pandas as pd
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# ----------------- Comparaison between cheating cross validation and our method ------------
def best_test_cheat(name="OT", source="amazon", target="caltech10", feature="surf_SA",
reformat_pickle=False, pickle_file="pickle"):
if reformat_pickle:
list_iter = []
pickle_in = open(pickle_file + "/" + feature + "/" + feature + name + source + target + ".pickle", "rb")
error = 0
while True:
try:
pickle_loaded = pickle.load(pickle_in)
if type(pickle_loaded) is list:
list_iter = list_iter + pickle_loaded
else:
list_iter.append(pickle_loaded)
except:
error += 1
if error > 100:
break
if len(list_iter) == 1:
list_iter = list_iter[0]
pickle_in.close()
pickle_out = open(pickle_file + "/" + feature + "/" + feature + name + source + target + ".pickle", "wb")
pickle.dump(list_iter, pickle_out)
pickle_out.close()
pickle_in = open(pickle_file + "/" + feature + "/" + feature + name + source + target + ".pickle", "rb")
list_iter = pickle.load(pickle_in)
pickle_in.close()
best_target_result_cheat = 0
for iter_i in list_iter:
if np.mean(iter_i["target_result"]) > best_target_result_cheat:
best_target_result_cheat = np.mean(iter_i["target_result"])
best_source_result = 0
for iter_i in list_iter:
if np.mean(iter_i["result"]) > best_source_result:
best_source_result = np.mean(iter_i["result"])
best_target_result = np.mean(iter_i["target_result"])
return best_target_result, best_target_result_cheat, len(list_iter)
def create_latex_cheat(names, feature, number_dataset=12, reformat_pickle=False, cheat=True,
pickle_file="pickle"):
latex_tabular = {"dataset": []}
nb_iter_cross = np.zeros((len(names), number_dataset))
mean = np.zeros((len(names), number_dataset + 1))
mean_cheat = np.zeros((len(names), number_dataset + 1))
if number_dataset == 12:
datasets_s = ["amazon", "caltech10", "dslr", "webcam"]
datasets_t = ["amazon", "caltech10", "dslr", "webcam"]
else:
datasets_s = ["amazon", "dslr", "webcam"]
datasets_t = ["amazon", "dslr", "webcam"]
i = 0
for name in names:
dataset_j = 0
for source in datasets_s:
for target in datasets_t:
if target != source:
mean[i, dataset_j], mean_cheat[i, dataset_j], nb_iter_cross[i, dataset_j] = \
best_test_cheat(name=name,
source=source,
target=target,
feature=feature,
reformat_pickle=reformat_pickle,
pickle_file=pickle_file)
dataset_j += 1
if name == names[0]:
latex_tabular["dataset"].append(str(source[0].upper()) +
"$\rightarrow$" + str(target[0].upper()))
i += 1
mean_cheat[:, number_dataset] = np.mean(mean_cheat[:, :number_dataset], axis=1)
mean[:, number_dataset] = np.mean(mean[:, :number_dataset], axis=1)
for i, name in enumerate(names):
if cheat:
latex_tabular[name] = list(np.round(mean_cheat[i, :], 1))
else:
latex_tabular[name] = list(np.round(mean[i, :], 1))
latex_tabular[name].append(np.round(np.mean(nb_iter_cross[i, :]), 1))
if cheat:
textbf = np.argmax(mean_cheat, axis=0)
else:
textbf = np.argmax(mean, axis=0)
i = 0
for name in names:
for j in range(len(textbf)):
if textbf[j] == i:
latex_tabular[name][j] = "\textbf{" + str(latex_tabular[name][j]) + '}'
i += 1
latex_tabular["dataset"].append("AVG")
latex_tabular["dataset"].append("nb")
df = pd.DataFrame(latex_tabular)
print(df.to_latex(index=False, escape=False))
def creat_all_cheat_tables(names="NA,SA,CORAL,TCA,OT,OTDA,OTDA_pca,MLOT_id,MLOT",
features="surf_SA,decaf6_SA,office31fc7_SA",
reformat_pickle=False):
names = names.split(",")
features = features.split(",")
for feature in features:
if "office" in feature:
number_dataset = 6
else:
number_dataset = 12
for cheat in [True, False]:
print("\\newpage", "Feature :$", feature, "$ Cheat ", cheat, "\\newline")
create_latex_cheat(names,
feature,
number_dataset=number_dataset,
reformat_pickle=reformat_pickle,
cheat=cheat,
pickle_file="pickle")
def average_result():
"""
:return: Display the average result that are hard coded in the function. Quite ugly...
"""
surf = np.array([31.4,
44.8,
45.8,
1.,
35.7,
44.5,
42.2,
48.2,
48.8,
0.9,
47.0,
49.7,
0.8])
decaf6 = np.array([71.0,
79.4,
83.7,
0.5,
77.2,
83.4,
83.9,
83.2,
82.6,
0.5,
78.2,
84.7,
0.3])
decaf7 = np.array([64.3,
64.7,
66.5,
0.2,
64.1,
64.1,
65.3,
65.3,
65.2,
0.1,
64.4,
66.2,
0.1])
a = (surf * 12 + decaf6 * 12 + decaf7 * 6) / (12 + 12 + 6)
a = list(a)
i = 0
print("AVG & ", end="")
while i < len(a):
# print(i)
if i in [2, 8, 11]:
if i == 10:
print(round(a[i], 1), "$\pm$", round(a[i + 1], 1), end="")
else:
print(round(a[i], 1), "$\pm$", round(a[i + 1], 1), "& ", end="")
i += 2
else:
print(round(a[i], 1), "& ", end="")
i += 1
print("\\\\")
# ------------------- Complex Cross validation --------------------
def load_pickle_cross(name, rule, features, number_dataset=6, average=True):
pickle_in = open("./pickle_latex/" + name + rule + features + ".pickle", "rb")
specific_run = pickle.load(pickle_in)[:number_dataset,:]
a = pickle.load(pickle_in)
average_run = pickle.load(pickle_in)
pickle_in.close()
name_line = []
keys_dataset = list(average_run.keys())
name_aux = list(average_run[list(average_run.keys())[0]].keys())[0]
if average:
mean = []
std = []
for key in keys_dataset:
mean.append(average_run[key][name_aux]["mean"])
std.append(average_run[key][name_aux]["std"])
average_run[key][name_aux]["mean"] = round(average_run[key][name_aux]["mean"], 1)
average_run[key][name_aux]["std"] = round(average_run[key][name_aux]["std"], 1)
if name not in ["MLOT_id", "OTDA", "OT", "NA", "Tused", "TCA", "CORAL", "JDA", "JDOT", "JDOTSVM", "LMNN",
"JDOTe", "JDOTSVMe"]:
name_line.append(str(average_run[key][name_aux]["mean"])[:4] +
" $\pm$ " + str(average_run[key][name_aux]["std"])
[:min(4,len(str(average_run[key][name_aux]["std"])))])
else:
name_line.append(str(average_run[key][name_aux]["mean"])[:4])
mean_mean = round(np.mean(mean), 1)
std_mean = round(np.mean(std) ,1)
if name not in ["MLOT_id", "OTDA", "OT", "NA", "Tused", "TCA", "CORAL", "JDA", "JDOT", "JDOTSVM", "LMNN",
"JDOTe", "JDOTSVMe"]:
a = [str(mean_mean)[:4] + "$\pm$" + str(std_mean)[:min(4, len(str(std_mean)))]]
return name_line + a, keys_dataset, mean
else:
return name_line + [str(mean_mean)[:4]], keys_dataset, mean
else:
specific_run = np.array(specific_run)
for i in range(len(specific_run)):
if name not in ["MLOT_id", "OTDA", "OT", "NA", "Tused", "TCA", "CORAL", "JDA", "JDOT", "JDOTSVM", "LMNN",
"JDOTe", "JDOTSVMe"]:
name_line.append(str(round(specific_run[i, 0], 1))[:4] +
"$\pm$" + str(round(specific_run[i, 1], 1))
[:min(4,len(str(round(specific_run[i, 1], 1))))])
else:
name_line.append(str(round(specific_run[i, 0], 1))[:4])
mean_mean = round(np.mean(specific_run[:, 0]), 1)
std_mean = round(np.mean(specific_run[:, 1]), 1)
if name not in ["MLOT_id", "OTDA", "OT", "NA", "Tused", "TCA", "CORAL", "JDA", "JDOT", "JDOTSVM", "LMNN",
"JDOTe", "JDOTSVMe"]:
a = [str(mean_mean)[:4] + "$\pm$" + str(std_mean)[:min(4, len(str(std_mean)))]]
return name_line + a, keys_dataset, specific_run[:, 0]
else:
return name_line + [str(mean_mean)[:4]], keys_dataset, specific_run[:, 0]
def create_latex(names, rule, features, average, number_dataset=12):
latex_tabular = {"dataset": []}
mean = np.zeros((len(names), number_dataset))
i = 0
for name in names:
latex_tabular[name], latex_tabular["dataset"], mean[i, :] = load_pickle_cross(name, rule, features,
number_dataset=number_dataset,
average=average)
i += 1
textbf = np.argmax(mean, axis=0)
textbf_mean = np.array([np.argmax(np.mean(mean, axis=1))])
textbf = np.concatenate((textbf, textbf_mean))
i = 0
for name in names:
for j in range(len(textbf)):
if textbf[j] == i:
if name in ["MLOT_id", "OTDA", "OT", "NA", "Tused", "TCA", "CORAL", "JDA", "JDOT", "JDOTSVM",
"LMNN", "JDOTe", "JDOTSVMe"]:
latex_tabular[name][j] = '\textbf{' + latex_tabular[name][j] + '}'
else:
latex_tabular[name][j] = '\textbf{' + latex_tabular[name][j].split("$\pm$")[0] + '}' + \
"$\pm$" + latex_tabular[name][j].split("$\pm$")[1]
i += 1
for i in range(len(latex_tabular["dataset"])):
latex_tabular["dataset"][i] = latex_tabular["dataset"][i][0] + \
"$\rightarrow$" + \
latex_tabular["dataset"][i][-1]
latex_tabular["dataset"].append("AVG")
df = pd.DataFrame(latex_tabular)
print(df.to_latex(index=False, escape=False))
def creat_latex_tables(name="NA,CORAL,SA,TCA,OT,OTDA,OTDA_pca,MLOT_id,MLOT",
rules="max",
features="surf_SA"):
for feature in features.split(","):
if "office" in feature:
number_dataset = 6
else:
number_dataset = 12
for rule in rules.split(","):
for average in [False]:
print("\\newpage", "Feature :", feature, "Rule :", rule, "Average :",
average, "\\newline")
create_latex(name.split(","), rule, feature, average, number_dataset=number_dataset)
# --------------------- Display the images --------------------------
def load_pickle(path, features):
pickle_in = open("./pickle_specific/" + path + features + ".pickle", "rb")
param = []
result = []
while True:
try:
param.append(pickle.load(pickle_in))
result.append(pickle.load(pickle_in))
except:
break
pickle_in.close()
return param, result
def plot_image(param, result, comparaison, color_map='Greys', save_image=False):
unique_params_X = np.sort(np.unique(param[:, 0]))
unique_params_Y = np.sort(np.unique(param[:, 1]))
result_mean = np.zeros((len(unique_params_X), len(unique_params_Y)))
for i in range(len(unique_params_X)):
for j in range(len(unique_params_Y)):
for k in range(len(result)):
if param[k, 0] == unique_params_X[i]:
if param[k, 1] == unique_params_Y[j]:
if comparaison:
result_mean[i, j] = result[k, 1] - result[k, 0]
else:
result_mean[i, j] = result[k, 1]
X, Y = unique_params_X, unique_params_Y
Z = np.transpose(result_mean)
fig, ax = plt.subplots(figsize=(10, 5))
ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
if comparaison:
im = ax.imshow(Z, cmap=color_map, vmin=0, vmax=6, extent=(-0.5, 6.5, -0.5, 4.5))
else:
im = ax.imshow(Z, cmap=color_map, vmin=43, vmax=55, extent=(-0.5, 6.5, -0.5, 4.5))
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(len(X)))
if comparaison:
ax.set_yticks(np.arange(len(Y))[::-1])
else:
ax.set_yticks([])
ax.set_xticklabels(X, size=20)
if comparaison:
ax.set_yticklabels(Y, size=20)
else:
ax.set_yticklabels([])
ax.set_xlabel('Entropy regularization', size=20)
if comparaison:
ax.set_ylabel('Classes regularization', size=20)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
for i in range(len(Y)):
for j in range(len(X)):
if comparaison:
text = ax.text(j, i, "+" + str(round(Z[(len(Y) - 1) - i, j], 1)), # from top to bottom
ha="center", va="center", color="k", size=20)
else:
text = ax.text(j, i, str(round(Z[(len(Y) - 1) - i, j], 1)),
ha="center", va="center", color="k", size=20)
fig.tight_layout()
form = "pdf"
if save_image:
if comparaison:
plt.savefig("./PDF/MLOT_comparaison_heatmap." + form, format=form, bbox_inches='tight')
else:
plt.savefig("./PDF/MLOT_alone_heatmap." + form, format=form, bbox_inches='tight')
plt.show()
def organise_param_result(param, result):
param_list = np.zeros((len(param), 2))
result_list = np.zeros((len(param), 12, 2))
for i in range(len(param)):
param_list[i, 0] = param[i]["reg_e"]
param_list[i, 1] = param[i]["reg_cl"]
k = 0
for key_dataset in result[i]:
j = 0
# Different names can appear here.
if "MLOT_SS_TT" in list(result[i][key_dataset].keys()) and "OT" in list(result[i][key_dataset].keys()):
list_key_algo = ["OT", "MLOT_SS_TT"]
else:
list_key_algo = ["OTDA", "MLOT"]
for key_algo in list_key_algo:
result_list[i, k, j] = result[i][key_dataset][key_algo]["mean"]
j += 1
k += 1
result_list = np.mean(result_list, axis=1)
return param_list, result_list
def display_image(path="OTDAvsMLOT",
features="surf",
color_map="Greens",
comparaison=True,
save_image=False):
param, result = load_pickle(path, features)
param_list, result_list = organise_param_result(param, result)
plot_image(param=param_list, result=result_list, comparaison=comparaison, color_map=color_map,
save_image=save_image)