File size: 5,474 Bytes
34393ef | 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 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
import argparse
sns.set()
sns.set_style('ticks')
colors = ["#3c5068", "#acbab6", "#dcd3cd", "#d4a6a6"]
params = {'legend.fontsize': 32,
'figure.figsize': (32, 10),
'axes.labelsize': 34,
'axes.titlesize':34,
'xtick.labelsize':34,
'ytick.labelsize':24}
#POSTER
params = {'legend.fontsize': 50,
'figure.figsize': (54, 18),
'axes.labelsize': 60,
'axes.titlesize':60,
'xtick.labelsize':60,
'ytick.labelsize':36}
plt.rcParams.update(params)
np.random.seed(25)
DISPLAY_DIFF = True
K = 100
PREFIX = ''
# MRL, TE
TYPE = 'MRL'
DISPLAY_DIFF = True
if TYPE == 'REGULAR':
PREFIX = 'outputs/'
elif TYPE == 'MIXED':
PREFIX = 'outputs_mixed/'
elif TYPE == 'GC_CONTROLED':
PREFIX = 'outputs/gc_'
if DISPLAY_DIFF:
TYPE = 'MMRL'
TITLE = "A"
init = []
with open(f'./../src/mrl_te_optimization/outputs/init_mrl_FMRL.txt') as f:
scores = f.readlines()
init = [float(score.replace('\n','')) for score in scores]
opt = []
with open(f'./../src/mrl_te_optimization/outputs/opt_mrl_FMRL.txt') as f:
scores = f.readlines()
opt = [float(score.replace('\n','')) for score in scores]
init = np.array(init)
opt = np.array(opt)
diffs = (opt - init)/init
print("FramePool MRL optimization:")
print(f"Average Opt: {np.average(opt)}")
print(f"Average Init: {np.average(init)}")
print(f"Max Opt: {np.max(opt)}")
print(f"Max Init: {np.max(init)}")
print(f"Max Increase (wrt Init) : {np.max(opt/init)}")
print(f"Average Increase (wrt Init) : {np.mean(opt/init)}")
print(f"Average Percent Increase (wrt Init): {np.average(diffs)}")
indices = np.argsort(opt)[::-1]
init_large = []
init_small = []
opt_large = []
opt_small = []
for i in range(len(indices)):
if diffs[indices[i]] >= 0:
init_small.append(init[indices[i]])
init_large.append(0)
opt_small.append(0)
opt_large.append(opt[indices[i]])
else:
init_large.append(init[indices[i]])
init_small.append(0)
opt_large.append(0)
opt_small.append(opt[indices[i]])
width = 1.0/(len(indices))
bins = [(i+1) * width for i in range(len(indices))]
ns = [i * width for i in range(len(indices))]
fig, axs = plt.subplots(1,2)
axs[0].bar(x=ns, bottom=0, width=width, height=opt_large, color=colors[0], edgecolor="white")
axs[0].bar(x=ns, bottom=0, width=width, height=opt_small, color=colors[0], edgecolor="white")
axs[0].bar(x=ns, bottom=0, width=width, height=init_small, color=colors[3], edgecolor="white")
axs[0].bar(x=ns, bottom=0, width=width, height=init_large, color=colors[3], edgecolor="white")
axs[0].set_title(TITLE,loc='left',weight='bold',fontsize=64)
axs[0].set_xticks([])
TYPE = "FMRL"
TITLE = "B"
init = []
with open(f'./../src/mrl_te_optimization/outputs/init_mrl_TE.txt') as f:
scores = f.readlines()
init = [float(score.replace('\n','')) for score in scores]
opt = []
with open(f'./../src/mrl_te_optimization/outputs/opt_mrl_TE.txt') as f:
scores = f.readlines()
opt = [float(score.replace('\n','')) for score in scores]
init = np.power(10,init)
init = np.array(init)
opt = np.power(10,opt)
opt = np.array(opt)
diffs = (opt - init)/init
print("MTtrans 3R TE optimization:")
print(f"Average Opt: {np.average(opt)}")
print(f"Average Init: {np.average(init)}")
print(f"Max Opt: {np.max(opt)}")
print(f"Max Init: {np.max(init)}")
print(f"Max Increase (wrt Init) : {np.max(opt/init)}")
print(f"Average Increase (wrt Init) : {np.mean(opt/init)}")
print(f"Max Increase (wrt Natural) : {np.max(opt/np.power(10,-0.63))}")
print(f"Average Percent Increase (wrt Init): {np.average(diffs)}")
indices = np.argsort(opt)[::-1]
init_large = []
init_small = []
opt_large = []
opt_small = []
for i in range(len(indices)):
if diffs[indices[i]] >= 0:
init_small.append(init[indices[i]])
init_large.append(0)
opt_small.append(0)
opt_large.append(opt[indices[i]])
else:
init_large.append(init[indices[i]])
init_small.append(0)
opt_large.append(0)
opt_small.append(opt[indices[i]])
axs[1].bar(x=ns, bottom=0, width=width, height=opt_large, color=colors[0], edgecolor="white")
axs[1].bar(x=ns, bottom=0, width=width, height=opt_small, color=colors[0], edgecolor="white")
axs[1].bar(x=ns, bottom=0, width=width, height=init_small, color=colors[3], edgecolor="white")
axs[1].bar(x=ns, bottom=0, width=width, height=init_large, color=colors[3], edgecolor="white")
axs[1].set_title(TITLE,loc='left',weight='bold',fontsize=64)
axs[1].set_xticks([])
orange_patch = mpatches.Patch(color=colors[3], label='Initial')
blue_patch = mpatches.Patch(color=colors[0], label='Optimized')
fig.legend(handles=[orange_patch,blue_patch],loc='upper right')
axs[0].set_ylabel('Predicted MRL')
axs[1].set_ylabel('Predicted TE')
axs[0].set_xlabel('UTR Samples')
axs[1].set_xlabel('UTR Samples')
fig.tight_layout()
plt.savefig(f'./plots/mrl_te_all.png')
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