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plot_fig_together.py
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plot_fig_together.py
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import argparse
import json, math, seaborn
import statistics
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from typing import List
# del matplotlib.font_manager.weight_dict['roman']
from attention_y_entropy import font_size
from plot_figures import _read_data, _read_data_position_fig2, normalize_figure1
matplotlib.font_manager._rebuild()
GLOBAL_FIGURE_WIDTH = 8
dpi = 800
# plt.rcParams["font.weight"] = "light"
plt.rcParams.update({'font.size': 14})
# plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['font.family'] = 'DejaVu Sans'
dir_datadrive = '/mnt/data0/jcxu/data/prob_gpt'
# Density: x: entropy, two plots: is bigram or not
FIG_SIZE_x = GLOBAL_FIGURE_WIDTH
FIG_SIZE_y = 3
ysize_fig1 = 4
ysize_figure2 = 4
ysize_figure3 = 4
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
def plot_fig1_single(this_fig, spec, dir, spec_name, SEPS, max_lim=5, bins_num=15, x_ticklabel_vis=True,
y_ticklabel_vis=True,
y_namelabel_vis=True, showLegend=False, data_name="", ylim=500, title=""):
axes1 = this_fig.add_subplot(spec)
ExistingBigram = "Existing Bigrams"
NovelBigram = "Novel Bigrams"
linewidth = 1.5
_, cnndm_bigram_entropies, cnndm_not_bigram_entropies = _read_data(dir, spec_name, SEPS)
cnndm_bigram_entropies, cnndm_not_bigram_entropies = normalize_figure1(cnndm_bigram_entropies,
cnndm_not_bigram_entropies)
# axes1 = plt.subplot(locid)
# axes = fig.add_axes([0., 0.3, 0.84, 0.66])
# sns.distplot(x=ykey, data=df, hist=False, rug=True)
# axes = sns.distplot(bigram_entropies,rug=True)
# axes = sns.kdeplot(bigram_entropies, shade=True,cumulative=True)
# axes = sns.distplot(bigram_entropies,rug=True)
color = sns.color_palette("coolwarm", 7)
color = ['#333f48', '#bf5700']
if showLegend:
axes1 = sns.distplot(cnndm_bigram_entropies, bins=bins_num,
hist_kws={'range': [0, max_lim],
# "histtype": "step", "linewidth": linewidth,
# "alpha": 1,
# "color" :'none',
"edgecolor": color[0],
# "hatch": "-",
# "color": color[0]
},
hist=True,
kde=False,
color=color[0],
label=f"{ExistingBigram}",
)
axes1.legend(frameon=True)
else:
axes1 = sns.distplot(cnndm_bigram_entropies, bins=bins_num,
hist_kws={'range': [0, max_lim],
# "histtype": "step", "linewidth": linewidth,
# "color": 'none',
"edgecolor": color[0], "hatch": "-",
# "color": color[0]
},
hist=True,
kde=False,
color=color[0],
# label=f"{ExistingBigram}"
)
if title != "":
axes1.set_title(title)
if y_namelabel_vis:
axes1.set_ylabel(data_name)
plt.ylim(0, ylim)
if showLegend:
axes1 = sns.distplot(cnndm_not_bigram_entropies, bins=bins_num, hist_kws={'range': [0, max_lim]},
hist=True,
kde=False,
color=color[-1],
label=f"{NovelBigram}"
)
axes1.legend(frameon=True)
else:
axes1 = sns.distplot(cnndm_not_bigram_entropies, bins=bins_num, hist_kws={'range': [0, max_lim]},
hist=True,
kde=False,
color=color[-1],
# label=f"{NovelBigram}"
)
plt.axvline(statistics.median(cnndm_bigram_entropies), color=color[0], linestyle='dashed', linewidth=linewidth)
plt.axvline(statistics.median(cnndm_not_bigram_entropies), color=color[-1], linestyle='dashed', linewidth=linewidth)
# axes.legend(prop={'size': 10})
# print(f"{statistics.median(cnndm_bigram_entropies), statistics.mean(cnndm_not_bigram_entropies),}")
print(f"{statistics.median(cnndm_bigram_entropies), statistics.median(cnndm_not_bigram_entropies)}")
# axes1.set_xticks(list(np.arange(0, 5, 0.5)))
# axes1.set_xticklabels(list(np.arange(0, 5, 1)))
# axes1.set_xticklabels(list(np.arange(0.5, 5.5, 1)), minor=True)
axes1.xaxis.set_major_locator(MultipleLocator(1))
axes1.xaxis.set_major_formatter(FormatStrFormatter('%d'))
# For the minor ticks, use no labels; default NullFormatter.
axes1.xaxis.set_minor_locator(MultipleLocator(0.5))
if not x_ticklabel_vis:
plt.setp(axes1.get_xticklabels(), visible=False)
if not y_ticklabel_vis:
plt.setp(axes1.get_yticklabels(), visible=False)
return axes1
def draw_x_entropy_y_bigram_count(dir, SEPS=10, FIG_SIZE_x=10, FIG_SIZE_y=3, bins_num=18,
cnndm_spec_name='cnndm', xsum_spec_name='xsum'):
ExistingBigram = "Existing Bigrams"
NovelBigram = "Novel Bigrams"
linewidth = 1.5
_, cnndm_bigram_entropies, cnndm_not_bigram_entropies = _read_data(dir, cnndm_spec_name, SEPS)
cnndm_bigram_entropies, cnndm_not_bigram_entropies = normalize_figure1(cnndm_bigram_entropies,
cnndm_not_bigram_entropies)
axes1 = plt.subplot(211)
# axes = fig.add_axes([0., 0.3, 0.84, 0.66])
# sns.distplot(x=ykey, data=df, hist=False, rug=True)
# axes = sns.distplot(bigram_entropies,rug=True)
# axes = sns.kdeplot(bigram_entropies, shade=True,cumulative=True)
# axes = sns.distplot(bigram_entropies,rug=True)
data = 'CNN/DM'
max_lim = 6
color = sns.color_palette("coolwarm", 7)
axes1 = sns.distplot(cnndm_bigram_entropies, bins=bins_num,
hist_kws={'range': [0, max_lim]},
hist=True,
kde=False,
color=color[0],
label=f"{ExistingBigram}"
)
# axes1.set_title("CNN/DM")
axes1.set_ylabel('CNN/DM')
axes1 = sns.distplot(cnndm_not_bigram_entropies, bins=bins_num, hist_kws={'range': [0, max_lim]},
hist=True,
kde=False,
color=color[-1],
label=f"{NovelBigram}"
)
plt.axvline(statistics.median(cnndm_bigram_entropies), color=color[0], linestyle='dashed', linewidth=linewidth)
plt.axvline(statistics.median(cnndm_not_bigram_entropies), color=color[-1], linestyle='dashed', linewidth=linewidth)
# axes.legend(prop={'size': 10})
# axes1.legend(frameon=False)
# print(f"{statistics.median(cnndm_bigram_entropies), statistics.mean(cnndm_not_bigram_entropies),}")
print(f"{statistics.median(cnndm_bigram_entropies), statistics.median(cnndm_not_bigram_entropies),}")
plt.setp(axes1.get_xticklabels(), visible=False)
# axes = sns.distplot(not_bigram_entropies,rug=True)
# axes1.set_title('CNN\DM',loc='left')
# axes1.set_xlabel('Entropy')
_, xsum_bigram_entropies, xsum_not_bigram_entropies = _read_data(dir, xsum_spec_name, SEPS)
xsum_bigram_entropies, xsum_not_bigram_entropies = normalize_figure1(xsum_bigram_entropies,
xsum_not_bigram_entropies)
# axes = fig.add_axes([0., 0.3, 0.84, 0.66])
with sns.color_palette("Set2"):
axes2 = plt.subplot(212, sharex=axes1)
# axes2.set_title("XSum",loc='left')
axes2.set_ylabel('XSum')
data = 'XSum'
axes2 = sns.distplot(xsum_bigram_entropies, bins=bins_num,
hist_kws={'range': [0, max_lim], }, hist=True, kde=False,
# label=f"{ExistingBigram}",
color=color[0]
)
axes2 = sns.distplot(xsum_not_bigram_entropies, bins=bins_num,
hist_kws={'range': [0, max_lim], }, hist=True, kde=False,
# label=f"{NovelBigram}",
color=color[-1]
)
plt.axvline(statistics.median(xsum_bigram_entropies), color=color[0], linestyle='dashed', linewidth=linewidth)
plt.axvline(statistics.median(xsum_not_bigram_entropies), color=color[-1], linestyle='dashed',
linewidth=linewidth)
# axes.legend(prop={'size': 10})
# axes2.legend()
# axes = sns.distplot(not_bigram_entropies,rug=True)
# axes2.set_title('XSum')
axes2.set_xlabel('Entropy')
print(f"{statistics.median(xsum_bigram_entropies), statistics.mean(xsum_not_bigram_entropies),}")
print(f"{statistics.median(xsum_bigram_entropies), statistics.median(xsum_not_bigram_entropies),}")
import matplotlib.gridspec as gridspec
def draw_fig_1(cnndm_peg, xsum_peg, cnndm_bart, xsum_bart):
fig = plt.figure(figsize=(FIG_SIZE_x, ysize_fig1))
spec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)
# plt.rcParams["font.weight"] = "light"
# plt.rcParams.update({'font.size': 15})
# plt.rcParams["font.family"] = "Times New Roman"
# cnndm_spec_name = 'd_cnn_dailymail-m_ymail-full1'
# xsum_spec_name = 'd_xsum-m_-xsum-full1'
plot_fig1_single(fig, spec2[0, 0], dir=dir_datadrive, spec_name=cnndm_peg, SEPS=10,
# data_name='CNN/DM',
x_ticklabel_vis=False,
y_ticklabel_vis=False,
ylim=3000, title="PEGASUS")
plot_fig1_single(fig, spec2[0, 1], dir=dir_datadrive, spec_name=cnndm_bart, SEPS=10, y_ticklabel_vis=False,
x_ticklabel_vis=False, ylim=3000,
showLegend=True,
title="BART")
plot_fig1_single(fig, spec2[1, 0], dir=dir_datadrive, spec_name=xsum_peg, SEPS=10,
# data_name='XSum',
y_ticklabel_vis=False,
ylim=1200)
plot_fig1_single(fig, spec2[1, 1], dir=dir_datadrive, spec_name=xsum_bart, SEPS=10, y_namelabel_vis=False,
y_ticklabel_vis=False, ylim=1200)
# fig.text(0.5, 0.01, 'Prediction Entropy', ha='center', fontsize=font_size)
# fig.text(0.0, 0.5, 'Count', va='center', rotation='vertical', fontsize=font_size)
# ax = fig.add_subplot(111)
# fig.subplots_adjust(top=0.85)
# ax.set_title('axes title')
# ax.axis('off')
# ax.set_xlabel('xlabel')
# ax.set_ylabel('ylabel')
fig.tight_layout()
# plt.subplots_adjust(left=0.2,bottom=0.5, right=0.6, top=0.9)
# plt.xlabel('asdasdasd',loc='left')
plt.savefig(f"x_entropy-y_bigram-grid.pdf", dpi=dpi)
plt.show()
plt.close()
from matplotlib.axes._axes import Axes
import numpy as np
def plot_single_fig2(this_fig, spec, dir, spec_name, SEPS,
x_ticklabel_vis=True,
y_ticklabel_vis=True,
data_name="", model_name="", xlabel="",
ylim=7
):
axes1 = this_fig.add_subplot(spec)
cnndm_df = _read_data_position_fig2(dir, spec_name, SEPS)
colorblind = sns.color_palette("coolwarm", 10)[::-1]
keys = ['Relative Position', 'Entropy']
# axes = fig.add_axes([0.15, 0.3, 0.84, 0.66])
# sns.distplot(x=ykey, data=df, hist=False, rug=True)
# axes = sns.kdeplot(bigram_entropies)
# axes = sns.kdeplot(not_bigram_entropies)
#
# axes1: Axes = plt.subplot(121)
sns.boxplot(x=keys[0], y=keys[1], data=cnndm_df,
fliersize=0,
# palette='coolwarm',
# color=colorblind[3],
palette=colorblind,
# notch=True,
)
# axes1.tick_params(which='major', length=5)
# axes1.set_xticks(list(np.arange(0,1,0.1)))
# axes1.set_xticklabels(list(np.arange(0, 1, 0.1)))
# axes1.xaxis.set_major_locator(MultipleLocator(0.2))
#
# # For the minor ticks, use no labels; default NullFormatter.
# axes1.xaxis.set_minor_locator(MultipleLocator(0.1))
# for box in axes1['boxes']:
# # change outline color
# # box.set(color='#7570b3', linewidth=2)
# # change fill color
# box.set(edgecolor='white')
if not x_ticklabel_vis:
plt.setp(axes1.get_xticklabels(), visible=False)
if not y_ticklabel_vis:
plt.setp(axes1.get_yticklabels(), visible=False)
if model_name != "":
axes1.set_title(model_name)
if xlabel != "":
axes1.set_xlabel(xlabel)
else:
axes1.set_xlabel("")
if data_name != "":
axes1.set_ylabel(data_name)
else:
axes1.set_ylabel("")
axes1.set_ylim(0, ylim)
def draw_fig_2(cnndm_peg, xsum_peg, cnndm_bart, xsum_bart):
fig = plt.figure(figsize=(FIG_SIZE_x, ysize_figure2 + 2))
spec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)
# plt.rcParams["font.weight"] = "light"
# plt.rcParams.update({'font.size': 15})
# plt.rcParams["font.family"] = "Times New Roman"
"""
ax = fig.add_subplot(111) # The big subplot
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top=False, bottom=False, left=False, right=False)
# ax.set_ylabel("Entropy")
ax.set_xlabel('Relative Position')
"""
fig.text(0.5, 0.0, 'Relative Position', ha='center', fontsize=font_size)
fig.text(0.0, 0.5, 'Entropy', va='center', rotation='vertical', fontsize=font_size)
# cnndm_spec_name = 'd_cnn_dailymail-m_ymail-full1'
# xsum_spec_name = 'd_xsum-m_-xsum-full1'
plot_single_fig2(fig, spec2[0, 0], dir=dir_datadrive, spec_name=cnndm_peg, SEPS=10,
x_ticklabel_vis=False,
y_ticklabel_vis=True,
data_name="CNN/DM", model_name="PEGASUS", ylim=8
)
plot_single_fig2(fig, spec2[0, 1], dir=dir_datadrive, spec_name=cnndm_bart, SEPS=10,
x_ticklabel_vis=False,
y_ticklabel_vis=False,
model_name="BART", ylim=8
)
plot_single_fig2(fig, spec2[1, 0], dir=dir_datadrive, spec_name=xsum_peg, SEPS=10,
x_ticklabel_vis=True,
y_ticklabel_vis=True,
data_name="XSum",
)
plot_single_fig2(fig, spec2[1, 1], dir=dir_datadrive, spec_name=xsum_bart, SEPS=10,
x_ticklabel_vis=True,
y_ticklabel_vis=False,
)
fig.tight_layout()
plt.savefig(f"position_grid.pdf", dpi=dpi, bbox_inches='tight')
plt.show()
plt.close()
if __name__ == '__main__':
cnndm_peg = "d_cnn_dailymail-m_googlepegasuscnn_dailymail-full10.95"
xsum_peg = "d_xsum-m_googlepegasusxsum-full10.95"
cnndm_bart = "d_cnn_dailymail-m_facebookbartlargecnn-full10.95"
xsum_bart = 'd_xsum-m_facebookbartlargexsum-full10.95'
draw_fig_1(cnndm_peg, xsum_peg, cnndm_bart, xsum_bart)
draw_fig_2(cnndm_peg, xsum_peg, cnndm_bart, xsum_bart)