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aggregate_results.py
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aggregate_results.py
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# Copyright Software Engineering Analytics Lab (SEAL), Wayne State University, 2023
# Authors: Jaydeb Sarker <[email protected]> and Amiangshu Bosu <[email protected]>
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
##this code is to aggregate the results from validation set and finding best threshold.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
path="./threshold_variation_results_with_validation_set/"
#path="./"
files =[
path+"cross-validation-BERT-bert.csv",
path+"cross-validation-XLNET-xlnet.csv",
path+"cross-validation-DBERT-dbert.csv",
path+"cross-validation-ALBERT-albert.csv",
path+"cross-validation-ROBERTA-roberta.csv"
]
for datafile in files:
dataframe =pd.read_csv(datafile)
aggregateDF=dataframe.groupby("Threshold").mean()
plt.plot(aggregateDF["precision_1"], label ="Precision toxic", linestyle="-.")
plt.plot(aggregateDF["recall_1"], label ="Recall toxic", linestyle="--")
plt.plot(aggregateDF["f-score_1"], label ="F1-score toxic", linestyle=":")
xmax = np.argmax(aggregateDF["f-score_1"])
xmax=(float)(xmax/100)
y_max=max(aggregateDF["f-score_1"])
print(y_max)
print(xmax)
#print(aggregateDF["f-score_1"].shape)
plt.legend()
#plt.title("")
text = "Threshold={:.2f}, F1={:.2f}".format(xmax, y_max)
#if not ax:
#ax = plt.gca()
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
arrowprops = dict(arrowstyle="->", connectionstyle="angle,angleA=0,angleB=60")
kw = dict(xycoords='data', textcoords="axes fraction",
arrowprops=arrowprops, bbox=bbox_props, ha="right", va="top")
plt.annotate(text, xy=(xmax, y_max), xytext=(0.6, 0.93), **kw)
plt.xlabel('Threshold' )
plt.ylabel('Score')
#plt.annotate('max',xy=(xmax,y_max))
plt.show()
aggregateDF.to_excel(datafile+".xlsx")