/
draw_pic.py
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/
draw_pic.py
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# -*- coding:utf-8 -*-
from pandas import DataFrame
from collections import defaultdict
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib_venn import venn2, venn2_circles, venn3, venn3_circles
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
def plot_line_chart():
x1 = [50, 100, 150, 200, 250, 300]
metrics = ["BLEU(%)", "METEOR(%)"]
markers = ["o", "s", "^", "x", "D"]
embed = [[2.26, 2.13, 2.19, 2.12, 2.17, 2.02],
[7.47, 7.15, 7.46, 7.39, 7.25, 7.28]]
embedData = np.array(embed)
for i in range(embedData.shape[0]):
y = embedData[i, :]
plt.plot(x1, y, label=metrics[i],
marker=markers[i], linewidth=1, markersize=7)
plt.xticks(fontsize=16)
plt.yticks(fontsize=12)
plt.xlabel("Embedding Size", fontsize=14)
plt.ylabel("BLEU/METEOR", fontsize=14)
plt.xticks(x1)
plt.ylim(0, 10)
handles, labels = plt.gca().get_legend_handles_labels()
order = [1, 2, 0]
plt.legend(loc="center right", fontsize=14, ncol=1)
plt.grid(True, linestyle="-.")
plt.savefig("Embedding_Size.eps", format="eps")
plt.show()
# def plot_histogram():
# files = ['/Users/kingxu/result/ASE18result/nmt_bleu.txt',
# '/Users/kingxu/result/ASE18result/nngen_bleu.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nmt_bleu.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nngen_bleu.txt',
# '/Users/kingxu/result/ASE18result/nmt_meteor.txt',
# '/Users/kingxu/result/ASE18result/nngen_meteor.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nmt_meteor.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nngen_meteor.txt']
# lists = [txt2list(f) for f in files]
# dicts = [list2dict(l) for l in lists]
# bleu_dicts = dicts[:4]
# meteor_dicts = dicts[4:]
# bleu_data = np.zeros((10, 4), int)
# meteor_data = np.zeros((10, 4), int)
# for i, j in enumerate(bleu_dicts):
# for a in range(10):
# bleu_data[a, i] = j[a]
# for i, j in enumerate(meteor_dicts):
# for a in range(10):
# meteor_data[a, i] = j[a]
# print(bleu_data, meteor_data)
# fig = plt.figure()
# ax = fig.add_subplot()
# ax.set_xlabel("METEOR Value", fontsize=14)
# ax.set_ylabel("Count Number", fontsize=14)
# ax.set_position([0.16, 0.26, 0.70, 0.62])
# df = DataFrame(meteor_data,
# index=['[0.0,0.1)', '[0.1,0.2)', '[0.2,0.3)', '[0.3,0.4)',
# '[0.4,0.5)', '[0.5,0.6)', '[0.6,0.7)', '[0.7,0.8)',
# '[0.8,0.9)', '[0.9,1.0]'],
# columns=['origin/nmt', 'origin/nngen', 'cleaned/nmt', 'cleaned/nngen'])
# df.columns.name = 'dataset/approach'
# # plt.xlabel("BLEU value", fontsize=14)
# # plt.ylabel("Number", fontsize=14)
# df.plot(ax=ax, kind='bar')
# plt.savefig("METEOR_dist.eps", format="eps")
# plt.show()
def plot_histogram():
files = ['/Users/kingxu/result/IJCAI19result/nmt_bleu.txt',
'/Users/kingxu/result/IJCAI19result/nngen_bleu.txt',
'/Users/kingxu/result/IJCAI19result/codisum_bleu.txt',
'/Users/kingxu/result/IJCAI19result/nmt_meteor.txt',
'/Users/kingxu/result/IJCAI19result/nngen_meteor.txt',
'/Users/kingxu/result/IJCAI19result/codisum_meteor.txt']
lists = [txt2list(f) for f in files]
dicts = [list2dict(l) for l in lists]
bleu_dicts = dicts[:3]
meteor_dicts = dicts[3:]
bleu_data = np.zeros((10, 3), int)
meteor_data = np.zeros((10, 3), int)
for i, j in enumerate(bleu_dicts):
for a in range(10):
bleu_data[a, i] = j[a]
for i, j in enumerate(meteor_dicts):
for a in range(10):
meteor_data[a, i] = j[a]
print(bleu_data, meteor_data)
fig = plt.figure()
ax = fig.add_subplot()
ax.set_xlabel("BLEU Value", fontsize=14)
ax.set_ylabel("Count Number", fontsize=14)
ax.set_position([0.16, 0.26, 0.70, 0.62])
df = DataFrame(bleu_data,
index=['[0.0,0.1)', '[0.1,0.2)', '[0.2,0.3)', '[0.3,0.4)',
'[0.4,0.5)', '[0.5,0.6)', '[0.6,0.7)', '[0.7,0.8)',
'[0.8,0.9)', '[0.9,1.0]'],
columns=['nmt', 'nngen', 'codisum'])
df.columns.name = 'approach'
# plt.xlabel("BLEU value", fontsize=14)
# plt.ylabel("Number", fontsize=14)
df.plot(ax=ax, kind='bar')
plt.savefig("BLEU_dist2.eps", format="eps")
plt.show()
def txt2list(in_file):
ret_list = []
with open(in_file) as f:
for num in f:
ret_list.append(float(num))
return ret_list
def list2dict(in_list):
ret_dict = defaultdict(int)
for i in in_list:
cate = int(i * 10)
cate = cate if cate != 10 else 9
ret_dict[cate] += 1
return ret_dict
def txt2dict(in_file):
ret_dict = dict()
with open(in_file) as f:
for i, num in enumerate(f):
ret_dict[i] = float(num)
return ret_dict
def dict2set(in_dict, threshold):
"""
Argments:
in_dict {dict}
threshold {str} -- "[0.1, 0.2]", "[0.1, 0.3)"
"""
start = threshold[0]
end = threshold[-1]
vs = [float(i) for i in threshold[1:-1].split(',')]
if start == '[':
if end == ']':
return set([i for i, j in in_dict.items() if vs[0] <= j <= vs[1]])
else:
return set([i for i, j in in_dict.items() if vs[0] <= j < vs[1]])
else:
if end == ']':
return set([i for i, j in in_dict.items() if vs[0] < j <= vs[1]])
else:
return set([i for i, j in in_dict.items() if vs[0] < j < vs[1]])
# def plot_venn():
# files = ['/Users/kingxu/result/ASE18result/nmt_bleu.txt',
# '/Users/kingxu/result/ASE18result/nngen_bleu.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nmt_bleu.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nngen_bleu.txt',
# '/Users/kingxu/result/ASE18result/nmt_meteor.txt',
# '/Users/kingxu/result/ASE18result/nngen_meteor.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nmt_meteor.txt',
# '/Users/kingxu/result/ASE18result/cleaned_nngen_meteor.txt']
# dicts = [txt2dict(f) for f in files]
# sets1 = [dict2set(d, "[0.0, 0.1)") for d in dicts]
# sets2 = [dict2set(d, "[0.9, 1.0]") for d in dicts]
# plt.figure()
# subsets = sets2[:2]
# venn2(subsets=subsets, set_labels=('NMT', 'NNGen'))
# venn2_circles(subsets=subsets, linestyle='dotted', linewidth=1.0)
# plt.savefig("BLEU1venn.eps", format="eps")
# plt.show()
def plot_venn():
files = ['/Users/kingxu/result/IJCAI19result/nmt_bleu.txt',
'/Users/kingxu/result/IJCAI19result/nngen_bleu.txt',
'/Users/kingxu/result/IJCAI19result/codisum_bleu.txt',
'/Users/kingxu/result/IJCAI19result/nmt_meteor.txt',
'/Users/kingxu/result/IJCAI19result/nngen_meteor.txt',
'/Users/kingxu/result/IJCAI19result/codisum_meteor.txt']
dicts = [txt2dict(f) for f in files]
sets1 = [dict2set(d, "[0.0, 0.1)") for d in dicts]
sets2 = [dict2set(d, "[0.9, 1.0]") for d in dicts]
plt.figure()
subsets = sets2[3:]
venn3(subsets=subsets, set_labels=('NMT', 'NNGen', 'CoDiSum'))
venn3_circles(subsets=subsets, linestyle='dotted', linewidth=1.0)
plt.savefig("METEOR1venn2.eps", format="eps")
plt.show()
def plot_box_chart():
tips = sns.load_dataset('tips')
sns.boxplot(x='day', y='total_bill', data=tips,
linewidth=2, # 线宽
width=0.8, # 箱之间的间隔比例
fliersize=3, # 异常点大小
palette='hls', # 设置调色板
whis=1.5, # 设置IQR
notch=True, # 设置是否以中值做凹槽
order={'Thur', 'Fri', 'Sat', 'Sun'}, # 筛选类别
)
# 可以添加散点图
sns.swarmplot(x='day', y='total_bill', data=tips, color='k',
size=3, alpha=0.8)
plt.show()
def plot_violin_chart():
tips = sns.load_dataset('tips')
sns.violinplot(x='day', y='total_bill', data=tips,
linewidth=2, # 线宽
width=0.8, # 箱之间的间隔比例
palette='hls', # 设置调色板
order={'Thur', 'Fri', 'Sat', 'Sun'}, # 筛选类别
scale='count', # 测度小提琴图的宽度： area-面积相同,count-按照样本数量决定宽度,width-宽度一样
gridsize=50, # 设置小提琴图的平滑度，越高越平滑
inner='box', # 设置内部显示类型 --> 'box','quartile','point','stick',None
# bw = 0.8 # 控制拟合程度，一般可以不设置
)
plt.show()
if __name__ == "__main__":
# plot_line_chart()
# plot_box_chart()
plot_violin_chart()