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import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import *
from sklearn.preprocessing import label_binarize
from coding.llh.static.config import Config
def draw_roc_auc_curve_total(input_dict, type):
plt.figure(figsize=(10, 6))
if type == "train":
i = 0
for label_name, values in input_dict.items():
fpr = values[0]
tpr = values[1]
thresholds = values[2]
plt.plot(
fpr,
tpr,
"o-",
color=Config.COLORS[i],
label=label_name+str(round(auc(fpr, tpr), 2))
)
i += 1
title = "Training roc-auc curve"
plt.title(title)
else:
i = 0
for label_name, values in input_dict.items():
fpr = values[0]
tpr = values[1]
thresholds = values[2]
plt.plot(
fpr,
tpr,
"o-",
color=Config.COLORS[i],
label=label_name + str(round(auc(fpr, tpr), 2))
)
i += 1
title = "Cross-validation roc-auc curve"
plt.title(title)
plt.xlabel("tpr")
plt.ylabel("fpr")
plt.legend()
plt.savefig("./diagram/{}.png".format(title), dpi=300)
plt.show() |