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NaiveBayesClassifierUtils.py
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NaiveBayesClassifierUtils.py
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"""
Author: Razine Ahmed Bensari, [email protected]
Author: Annes Cherrid, [email protected]
Class with helper function for Naive Bayes classifers.
Helps clean the data and provide insight on the classifier and produce output files required
"""
from enum import Enum
from NBLanguageClassifier import NBLanguageClassifier
from BYONBC import BYONBC
import time
class Language(Enum):
EU = "eu"
CA = "ca"
GL = "gl"
ES = "es"
EN = "en"
PT = "pt"
def cleanTrainingData():
print("Creating training files for each classes")
fileName_EU = "./training_files/" + Language.EU.value + "_training-tweets.txt"
fileName_CA = "./training_files/" + Language.CA.value + "_training-tweets.txt"
fileName_GL = "./training_files/" + Language.GL.value + "_training-tweets.txt"
fileName_ES = "./training_files/" + Language.ES.value + "_training-tweets.txt"
fileName_EN = "./training_files/" + Language.EN.value + "_training-tweets.txt"
fileName_PT = "./training_files/" + Language.PT.value + "_training-tweets.txt"
fEU = open(fileName_EU, "w")
fCA = open(fileName_CA, "w")
fGL = open(fileName_GL, "w")
fES = open(fileName_ES, "w")
fEN = open(fileName_EN, "w")
fPT = open(fileName_PT, "w")
with open("./OriginalDataSet/training-tweets.txt", "r") as file:
for line in file:
tweetArray = line.split("\t")
if tweetArray[2] == "eu":
fEU.write(line)
if tweetArray[2] == "ca":
fCA.write(line)
if tweetArray[2] == "gl":
fGL.write(line)
if tweetArray[2] == "es":
fES.write(line)
if tweetArray[2] == "en":
fEN.write(line)
if tweetArray[2] == "pt":
fPT.write(line)
fEU.close()
fCA.close()
fGL.close()
fES.close()
fEN.close()
fPT.close()
def trainBYONBC():
TweetClassifier = BYONBC()
TweetClassifier.trainClassifier()
TweetClassifier.testClassifier() # Change test file name here
def getTweetCount(filename):
tweetCount = {"eu": 0, "ca": 0, "gl": 0, "es": 0, "en": 0, "pt": 0}
with open(filename, "r") as file:
for line in file:
tweetArray = line.split("\t")
lang = tweetArray[2]
tweetCount[lang] += 1
return tweetCount
def trainAllNBLanguageClassifier():
startP = time.time()
for v in range(3):
startV = time.time()
for n in range(1, 4):
startN = time.time()
for d in range(0, 11):
startD = time.time()
delta = d * 0.1
TweetClassifier = NBLanguageClassifier(v, n, round(delta, 1))
TweetClassifier.trainClassifier()
TweetClassifier.testClassifier()
stopD = time.time()
d = round(delta, 1)
print("\t\t\tIn V = " + str(v) + ", N = " + str(n) + ", D = " + str(
d) + ". Time (s) to iterate over D is " + str(round(stopD - startD, 1)))
stopN = time.time()
print("\t\tTime (s) to iterate over N = " + str(n) + ", " + str(round(stopN - startN, 1)))
stopV = time.time()
print("\tTime (s) to iterate over V = " + str(v) + ", " + str(round(stopV - startV, 1)))
stopP = time.time()
print("Time for the whole process: " + str(round(stopP - startP, 1)))
def generateEvalFileDemo(v, n, d):
trace_file_name = "./trace_files/trace_" + str(v) + "_" + str(n) + "_" + str(d) + ".txt"
eval_file_name = "./eval_files/eval_" + str(v) + "_" + str(n) + "_" + str(d) + ".txt"
f = open(eval_file_name, "w")
print("Doing this trace file: " + trace_file_name)
with open(trace_file_name, "r") as file:
eu_P = {"correct": 0, "wrong": 0, "actual": 0}
ca_P = {"correct": 0, "wrong": 0, "actual": 0}
gl_P = {"correct": 0, "wrong": 0, "actual": 0}
es_P = {"correct": 0, "wrong": 0, "actual": 0}
en_P = {"correct": 0, "wrong": 0, "actual": 0}
pt_P = {"correct": 0, "wrong": 0, "actual": 0}
num_correct = 0
num_wrong = 0
for line in file:
trace_array = line.split(" ")
tweet_id = trace_array[0]
most_likely_class = trace_array[1]
score = trace_array[2]
actual_class = trace_array[3]
result = trace_array[4].rstrip() # correct or wrong
if result == "correct":
num_correct += 1
if most_likely_class == "eu":
eu_P["correct"] += 1
elif most_likely_class == "ca":
ca_P["correct"] += 1
elif most_likely_class == "gl":
gl_P["correct"] += 1
elif most_likely_class == "es":
es_P["correct"] += 1
elif most_likely_class == "en":
en_P["correct"] += 1
elif most_likely_class == "pt":
pt_P["correct"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
else:
num_wrong += 1
if most_likely_class == "eu":
eu_P["wrong"] += 1
elif most_likely_class == "ca":
ca_P["wrong"] += 1
elif most_likely_class == "gl":
gl_P["wrong"] += 1
elif most_likely_class == "es":
es_P["wrong"] += 1
elif most_likely_class == "en":
en_P["wrong"] += 1
elif most_likely_class == "pt":
pt_P["wrong"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
tweetCount = getTweetCount("./OriginalDataSet/test-tweets-given.txt")
accuracy = round((num_correct / (num_correct + num_wrong)), 4)
eu_precision = round((eu_P["correct"] / (eu_P["correct"] + eu_P["wrong"])), 4)
ca_precision = round((ca_P["correct"] / (ca_P["correct"] + ca_P["wrong"])), 4)
if gl_P["correct"] == 0 and gl_P["wrong"] == 0:
gl_precision = .000
else:
gl_precision = round((gl_P["correct"] / (gl_P["correct"] + gl_P["wrong"])), 4)
es_precision = round((es_P["correct"] / (es_P["correct"] + es_P["wrong"])), 4)
en_precision = round((en_P["correct"] / (en_P["correct"] + en_P["wrong"])), 4)
pt_precision = round((pt_P["correct"] / (pt_P["correct"] + pt_P["wrong"])), 4)
eu_recall = round((eu_P["correct"] / (eu_P["actual"])), 4)
ca_recall = round((ca_P["correct"] / (ca_P["actual"])), 4)
gl_recall = round((gl_P["correct"] / (gl_P["actual"])), 4)
es_recall = round((es_P["correct"] / (es_P["actual"])), 4)
en_recall = round((en_P["correct"] / (en_P["actual"])), 4)
pt_recall = round((pt_P["correct"] / (pt_P["actual"])), 4)
eu_f1 = round((2 * ((eu_precision * eu_recall) / (eu_precision + eu_recall))), 4)
ca_f1 = round((2 * ((ca_precision * ca_recall) / (ca_precision + ca_recall))), 4)
if (gl_precision + gl_recall) == 0:
gl_f1 = 0
else:
gl_f1 = round((2 * ((gl_precision * gl_recall) / (gl_precision + gl_recall))), 4)
es_f1 = round((2 * ((es_precision * es_recall) / (es_precision + es_recall))), 4)
en_f1 = round((2 * ((en_precision * en_recall) / (en_precision + en_recall))), 4)
pt_f1 = round((2 * ((pt_precision * pt_recall) / (pt_precision + pt_recall))), 4)
macro_f1 = round(((eu_f1 + ca_f1 + gl_f1 + es_f1 + en_f1 + pt_f1) / 6), 4)
weighted_avg = round((((eu_f1 * tweetCount["eu"]) + (ca_f1 * tweetCount["ca"]) + (gl_f1 * tweetCount["gl"]) + (
es_f1 * tweetCount["es"]) + (en_f1 * tweetCount["en"]) + (pt_f1 * tweetCount["pt"])) / sum(tweetCount.values())), 4)
f.write(str(accuracy) + "\n")
f.write(str(eu_precision) + " " + str(ca_precision) + " " + str(gl_precision) + " " + str(
es_precision) + " " + str(en_precision) + " " + str(pt_precision) + "\n")
f.write(str(eu_recall) + " " + str(ca_recall) + " " + str(gl_recall) + " " + str(
es_recall) + " " + str(en_recall) + " " + str(pt_recall) + "\n")
f.write(str(eu_f1) + " " + str(ca_f1) + " " + str(gl_f1) + " " + str(
es_f1) + " " + str(en_f1) + " " + str(pt_f1) + "\n")
f.write(str(macro_f1) + " " + str(weighted_avg))
f.close()
def runDemoTest(BYONBC):
"""
:param BYONBC:
:return:
To run for demo, change path in *.testClassifier() to the new input test file
also, change path in *.testClassifier() to the new input test file trainBYONBC --> testClassifier()
"""
TweetClassifier1 = NBLanguageClassifier(0, 1, 0.0)
print("Training Tweet Language Classifier: v = 0, n = 1, d = 0.0")
TweetClassifier1.trainClassifier() # Change test file name here
print("Testing Tweet Language Classifier: v = 0, n = 1, d = 0.0")
TweetClassifier1.testClassifier()
print("Generating eval file for Tweet Language Classifier: v = 0, n = 1, d = 0.0")
generateEvalFileDemo(0, 1, 0.0)
print("\n\n\n")
TweetClassifier2 = NBLanguageClassifier(1, 2, round(0.5, 1))
print("Training Tweet Language Classifier: v = 1, n = 2, d = 0.5")
TweetClassifier2.trainClassifier()
print("Testing Tweet Language Classifier: v = 1, n = 2, d = 0.5")
TweetClassifier2.testClassifier()
print("Generating eval file for Tweet Language Classifier: v = 1, n = 2, d = 0.5")
generateEvalFileDemo(1, 2, round(0.5, 1))
print("\n\n\n")
TweetClassifier3 = NBLanguageClassifier(1, 3, 1.0)
print("Training Tweet Language Classifier: v = 1, n = 3, d = 1.0")
TweetClassifier3.trainClassifier()
print("Testing Tweet Language Classifier: v = 1, n = 3, d = 1.0")
TweetClassifier3.testClassifier()
print("Generating eval file for Tweet Language Classifier: v = 1, n = 3, d = 1.0")
generateEvalFileDemo(1, 3, 1.0)
print("\n\n\n")
TweetClassifier4 = NBLanguageClassifier(2, 2, round(0.3, 1))
print("Training Tweet Language Classifier: v = 2, n = 2, d = 0.3")
TweetClassifier4.trainClassifier()
print("Testing Tweet Language Classifier: v = 2, n = 2, d = 0.3")
TweetClassifier4.testClassifier()
print("Generating eval file for Tweet Language Classifier: v = 2, n = 2, d = 0.3")
generateEvalFileDemo(2, 2, round(0.3, 1))
print("\n\n\n")
print("Training & testing Tweet Language Classifier BYOM.")
trainBYONBC() # Change test file name here
print("Generating eval file for Tweet Language Classifier BYOM.")
generateBYONBCEvalFiles()
print("\n\n\n")
# 1. your BYOM
# 2. the required model with V=0 n=1 d=0
# 3. the required model with V=1 n=2 d=0.5
# 4. the required model with V=1 n=3 d=1
# 5. the required model with V=2 n=2 d=0.3
def generateBYONBCEvalFiles():
trace_file_name = "./trace_files/trace_BYONBC.txt"
eval_file_name = "./eval_files/eval_BYONBC.txt"
f = open(eval_file_name, "w")
print("Doing this trace file: " + trace_file_name)
with open(trace_file_name, "r") as file:
eu_P = {"correct": 0, "wrong": 0, "actual": 0}
ca_P = {"correct": 0, "wrong": 0, "actual": 0}
gl_P = {"correct": 0, "wrong": 0, "actual": 0}
es_P = {"correct": 0, "wrong": 0, "actual": 0}
en_P = {"correct": 0, "wrong": 0, "actual": 0}
pt_P = {"correct": 0, "wrong": 0, "actual": 0}
num_correct = 0
num_wrong = 0
for line in file:
trace_array = line.split(" ")
tweet_id = trace_array[0]
most_likely_class = trace_array[1]
score = trace_array[2]
actual_class = trace_array[3]
result = trace_array[4].rstrip() # correct or wrong
if result == "correct":
num_correct += 1
if most_likely_class == "eu":
eu_P["correct"] += 1
elif most_likely_class == "ca":
ca_P["correct"] += 1
elif most_likely_class == "gl":
gl_P["correct"] += 1
elif most_likely_class == "es":
es_P["correct"] += 1
elif most_likely_class == "en":
en_P["correct"] += 1
elif most_likely_class == "pt":
pt_P["correct"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
else:
num_wrong += 1
if most_likely_class == "eu":
eu_P["wrong"] += 1
elif most_likely_class == "ca":
ca_P["wrong"] += 1
elif most_likely_class == "gl":
gl_P["wrong"] += 1
elif most_likely_class == "es":
es_P["wrong"] += 1
elif most_likely_class == "en":
en_P["wrong"] += 1
elif most_likely_class == "pt":
pt_P["wrong"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
tweetCount = getTweetCount("./OriginalDataSet/test-tweets-given.txt")
accuracy = round((num_correct / (num_correct + num_wrong)), 4)
eu_precision = round((eu_P["correct"] / (eu_P["correct"] + eu_P["wrong"])), 4)
ca_precision = round((ca_P["correct"] / (ca_P["correct"] + ca_P["wrong"])), 4)
if gl_P["correct"] == 0 and gl_P["wrong"] == 0:
gl_precision = .000
else:
gl_precision = round((gl_P["correct"] / (gl_P["correct"] + gl_P["wrong"])), 4)
es_precision = round((es_P["correct"] / (es_P["correct"] + es_P["wrong"])), 4)
en_precision = round((en_P["correct"] / (en_P["correct"] + en_P["wrong"])), 4)
pt_precision = round((pt_P["correct"] / (pt_P["correct"] + pt_P["wrong"])), 4)
eu_recall = round((eu_P["correct"] / (eu_P["actual"])), 4)
ca_recall = round((ca_P["correct"] / (ca_P["actual"])), 4)
gl_recall = round((gl_P["correct"] / (gl_P["actual"])), 4)
es_recall = round((es_P["correct"] / (es_P["actual"])), 4)
en_recall = round((en_P["correct"] / (en_P["actual"])), 4)
pt_recall = round((pt_P["correct"] / (pt_P["actual"])), 4)
eu_f1 = round((2 * ((eu_precision * eu_recall) / (eu_precision + eu_recall))), 4)
ca_f1 = round((2 * ((ca_precision * ca_recall) / (ca_precision + ca_recall))), 4)
if (gl_precision + gl_recall) == 0:
gl_f1 = 0
else:
gl_f1 = round((2 * ((gl_precision * gl_recall) / (gl_precision + gl_recall))), 4)
es_f1 = round((2 * ((es_precision * es_recall) / (es_precision + es_recall))), 4)
en_f1 = round((2 * ((en_precision * en_recall) / (en_precision + en_recall))), 4)
pt_f1 = round((2 * ((pt_precision * pt_recall) / (pt_precision + pt_recall))), 4)
macro_f1 = round(((eu_f1 + ca_f1 + gl_f1 + es_f1 + en_f1 + pt_f1) / 6), 4)
weighted_avg = round((((eu_f1 * tweetCount["eu"]) + (ca_f1 * tweetCount["ca"]) + (gl_f1 * tweetCount["gl"]) + (
es_f1 * tweetCount["es"]) + (en_f1 * tweetCount["en"]) + (pt_f1 * tweetCount["pt"])) / sum(tweetCount.values())), 4)
f.write(str(accuracy) + "\n")
f.write(str(eu_precision) + " " + str(ca_precision) + " " + str(gl_precision) + " " + str(
es_precision) + " " + str(en_precision) + " " + str(pt_precision) + "\n")
f.write(str(eu_recall) + " " + str(ca_recall) + " " + str(gl_recall) + " " + str(
es_recall) + " " + str(en_recall) + " " + str(pt_recall) + "\n")
f.write(str(eu_f1) + " " + str(ca_f1) + " " + str(gl_f1) + " " + str(
es_f1) + " " + str(en_f1) + " " + str(pt_f1) + "\n")
f.write(str(macro_f1) + " " + str(weighted_avg))
f.close()
def generateAllEvalFiles():
for v in range(3):
for n in range(1, 4):
for d in range(0, 11):
delta = d * 0.1
trace_file_name = "./trace_files/trace_" + str(v) + "_" + str(n) + "_" + str(round(delta, 1)) + ".txt"
eval_file_name = "./eval_files/eval_" + str(v) + "_" + str(n) + "_" + str(round(delta, 1)) + ".txt"
f = open(eval_file_name, "w")
print("Doing this trace file: " + trace_file_name)
with open(trace_file_name, "r") as file:
eu_P = {"correct": 0, "wrong": 0, "actual": 0}
ca_P = {"correct": 0, "wrong": 0, "actual": 0}
gl_P = {"correct": 0, "wrong": 0, "actual": 0}
es_P = {"correct": 0, "wrong": 0, "actual": 0}
en_P = {"correct": 0, "wrong": 0, "actual": 0}
pt_P = {"correct": 0, "wrong": 0, "actual": 0}
num_correct = 0
num_wrong = 0
for line in file:
trace_array = line.split(" ")
tweet_id = trace_array[0]
most_likely_class = trace_array[1]
score = trace_array[2]
actual_class = trace_array[3]
result = trace_array[4].rstrip() # correct or wrong
if result == "correct":
num_correct += 1
if most_likely_class == "eu":
eu_P["correct"] += 1
elif most_likely_class == "ca":
ca_P["correct"] += 1
elif most_likely_class == "gl":
gl_P["correct"] += 1
elif most_likely_class == "es":
es_P["correct"] += 1
elif most_likely_class == "en":
en_P["correct"] += 1
elif most_likely_class == "pt":
pt_P["correct"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
else:
num_wrong += 1
if most_likely_class == "eu":
eu_P["wrong"] += 1
elif most_likely_class == "ca":
ca_P["wrong"] += 1
elif most_likely_class == "gl":
gl_P["wrong"] += 1
elif most_likely_class == "es":
es_P["wrong"] += 1
elif most_likely_class == "en":
en_P["wrong"] += 1
elif most_likely_class == "pt":
pt_P["wrong"] += 1
else:
pass
if actual_class == "eu":
eu_P["actual"] += 1
elif actual_class == "ca":
ca_P["actual"] += 1
elif actual_class == "gl":
gl_P["actual"] += 1
elif actual_class == "es":
es_P["actual"] += 1
elif actual_class == "en":
en_P["actual"] += 1
elif actual_class == "pt":
pt_P["actual"] += 1
else:
pass
tweetCount = getTweetCount("./OriginalDataSet/test-tweets-given.txt")
accuracy = round((num_correct / (num_correct + num_wrong)), 4)
eu_precision = round((eu_P["correct"] / (eu_P["correct"] + eu_P["wrong"])), 4)
ca_precision = round((ca_P["correct"] / (ca_P["correct"] + ca_P["wrong"])), 4)
if gl_P["correct"] == 0 and gl_P["wrong"] == 0:
gl_precision = .000
else:
gl_precision = round((gl_P["correct"] / (gl_P["correct"] + gl_P["wrong"])), 4)
es_precision = round((es_P["correct"] / (es_P["correct"] + es_P["wrong"])), 4)
en_precision = round((en_P["correct"] / (en_P["correct"] + en_P["wrong"])), 4)
pt_precision = round((pt_P["correct"] / (pt_P["correct"] + pt_P["wrong"])), 4)
eu_recall = round((eu_P["correct"] / (eu_P["actual"])), 4)
ca_recall = round((ca_P["correct"] / (ca_P["actual"])), 4)
gl_recall = round((gl_P["correct"] / (gl_P["actual"])), 4)
es_recall = round((es_P["correct"] / (es_P["actual"])), 4)
en_recall = round((en_P["correct"] / (en_P["actual"])), 4)
pt_recall = round((pt_P["correct"] / (pt_P["actual"])), 4)
eu_f1 = round((2 * ((eu_precision * eu_recall) / (eu_precision + eu_recall))), 4)
ca_f1 = round((2 * ((ca_precision * ca_recall) / (ca_precision + ca_recall))), 4)
if (gl_precision + gl_recall) == 0:
gl_f1 = 0
else:
gl_f1 = round((2 * ((gl_precision * gl_recall) / (gl_precision + gl_recall))), 4)
es_f1 = round((2 * ((es_precision * es_recall) / (es_precision + es_recall))), 4)
en_f1 = round((2 * ((en_precision * en_recall) / (en_precision + en_recall))), 4)
pt_f1 = round((2 * ((pt_precision * pt_recall) / (pt_precision + pt_recall))), 4)
macro_f1 = round(((eu_f1 + ca_f1 + gl_f1 + es_f1 + en_f1 + pt_f1) / 6), 4)
print("MACRO_F1\n")
print(macro_f1)
print("tweet count\n")
print(tweetCount)
weighted_avg = round(
(((eu_f1 * tweetCount["eu"]) + (ca_f1 * tweetCount["ca"]) + (gl_f1 * tweetCount["gl"]) + (
es_f1 * tweetCount["es"]) + (en_f1 * tweetCount["en"]) + (pt_f1 * tweetCount["pt"])) / sum(
tweetCount.values())), 4)
print("weighted avg\n")
print(weighted_avg)
f.write(str(accuracy) + "\n")
f.write(str(eu_precision) + " " + str(ca_precision) + " " + str(gl_precision) + " " + str(
es_precision) + " " + str(en_precision) + " " + str(pt_precision) + "\n")
f.write(str(eu_recall) + " " + str(ca_recall) + " " + str(gl_recall) + " " + str(
es_recall) + " " + str(en_recall) + " " + str(pt_recall) + "\n")
f.write(str(eu_f1) + " " + str(ca_f1) + " " + str(gl_f1) + " " + str(
es_f1) + " " + str(en_f1) + " " + str(pt_f1) + "\n")
f.write(str(macro_f1) + " " + str(weighted_avg))
f.close()
def generateStatsFile():
acc = 0.0
accv = 0
accn = 0
accd = 0.0
eu_precision = 0
eu_precisionv = 0
eu_precisionn = 0
eu_precisiond = 0.0
ca_precision = 0
ca_precisionv = 0
ca_precisionn = 0
ca_precisiond = 0.0
gl_precision = 0
gl_precisionv = 0
gl_precisionn = 0
gl_precisiond = 0.0
es_precision = 0
es_precisionv = 0
es_precisionn = 0
es_precisiond = 0.0
en_precision = 0
en_precisionv = 0
en_precisionn = 0
en_precisiond = 0.0
pt_precision = 0
pt_precisionv = 0
pt_precisionn = 0
pt_precisiond = 0.0
eu_recall = 0
eu_recallv = 0
eu_recalln = 0
eu_recalld = 0.0
ca_recall = 0
ca_recallv = 0
ca_recalln = 0
ca_recalld = 0.0
gl_recall = 0
gl_recallv = 0
gl_recalln = 0
gl_recalld = 0.0
es_recall = 0
es_recallv = 0
es_recalln = 0
es_recalld = 0.0
en_recall = 0
en_recallv = 0
en_recalln = 0
en_recalld = 0.0
pt_recall = 0
pt_recallv = 0
pt_recalln = 0
pt_recalld = 0.0
for v in range(3):
for n in range(1, 4):
for d in range(1, 10):
delta = d * 0.1
eval_file_name = "./eval_files/eval_" + str(v) + "_" + str(n) + "_" + str(round(delta, 1)) + ".txt"
lines = []
with open(eval_file_name, "r") as file:
for line in file:
lines.append(line.rstrip())
if acc < float(lines[0]):
acc = float(lines[0])
accv = v
accn = n
accd = round(delta, 4)
precisions = lines[1].split(" ")
if eu_precision < float(precisions[0]):
eu_precision = float(precisions[0])
eu_precisionv = v
eu_precisionn = n
eu_precisiond = round(delta, 4)
if ca_precision < float(precisions[1]):
ca_precision = float(precisions[1])
ca_precisionv = v
ca_precisionn = n
ca_precisiond = round(delta, 4)
if gl_precision < float(precisions[2]):
gl_precision = float(precisions[2])
gl_precisionv = v
gl_precisionn = n
gl_precisiond = round(delta, 4)
if es_precision < float(precisions[3]):
es_precision = float(precisions[3])
es_precisionv = v
es_precisionn = n
es_precisiond = round(delta, 4)
if en_precision < float(precisions[4]):
en_precision = float(precisions[4])
en_precisionv = v
en_precisionn = n
en_precisiond = round(delta, 4)
if pt_precision < float(precisions[5]):
pt_precision = float(precisions[5])
pt_precisionv = v
pt_precisionn = n
pt_precisiond = round(delta, 4)
recalls = lines[2].split(" ")
if eu_recall < float(recalls[0]):
eu_recall = float(recalls[0])
eu_recallv = v
eu_recalln = n
eu_recalld = round(delta, 4)
if ca_recall < float(recalls[1]):
ca_recall = float(recalls[1])
ca_recallv = v
ca_recalln = n
ca_recalld = round(delta, 4)
if gl_recall < float(recalls[2]):
gl_recall = float(recalls[2])
gl_recallv = v
gl_recalln = n
gl_recalld = round(delta, 4)
if es_recall < float(recalls[3]):
es_recall = float(recalls[3])
es_recallv = v
es_recalln = n
es_recalld = round(delta, 4)
if en_recall < float(recalls[4]):
en_recall = float(recalls[4])
en_recallv = v
en_recalln = n
en_recalld = round(delta, 4)
if pt_recall < float(recalls[5]):
pt_recall = float(recalls[5])
pt_recallv = v
pt_recalln = n
pt_recalld = round(delta, 4)
f = open("stats.txt", "w")
f.write("Some stats regarding the classifier. \n\n")
f.write("Model with best accuracy: " + str(acc) + " is V = " + str(accv) + ", N = " + str(accn) + " and D = " + str(
accd) + "\n")
f.write("\n\n")
f.write("eu with best precision: " + str(eu_precision) + " is V = " + str(eu_precisionv) + ", N = " + str(
eu_precisionn) + " and D = " + str(eu_precisiond) + "\n")
f.write("eu with best recall: " + str(eu_recall) + " is V = " + str(eu_recallv) + ", N = " + str(
eu_recalln) + " and D = " + str(eu_recalld) + "\n")
f.write("\n\n")
f.write("ca with best precision: " + str(ca_precision) + " is V = " + str(ca_precisionv) + ", N = " + str(
ca_precisionn) + " and D = " + str(ca_precisiond) + "\n")
f.write("ca with best recall: " + str(ca_recall) + " is V = " + str(ca_recallv) + ", N = " + str(
ca_recalln) + " and D = " + str(ca_recalld) + "\n")
f.write("\n\n")
f.write("gl with best precision: " + str(gl_precision) + " is V = " + str(gl_precisionv) + ", N = " + str(
gl_precisionn) + " and D = " + str(gl_precisiond) + "\n")
f.write("gl with best recall: " + str(gl_recall) + " is V = " + str(gl_recallv) + ", N = " + str(
gl_recalln) + " and D = " + str(gl_recalld) + "\n")
f.write("\n\n")
f.write("es with best precision: " + str(es_precision) + " is V = " + str(es_precisionv) + ", N = " + str(
es_precisionn) + " and D = " + str(es_precisiond) + "\n")
f.write("es with best recall: " + str(es_recall) + " is V = " + str(es_recallv) + ", N = " + str(
es_recalln) + " and D = " + str(es_recalld) + "\n")
f.write("\n\n")
f.write("en with best precision: " + str(en_precision) + " is V = " + str(en_precisionv) + ", N = " + str(
en_precisionn) + " and D = " + str(en_precisiond) + "\n")
f.write("en with best recall: " + str(en_recall) + " is V = " + str(en_recallv) + ", N = " + str(
en_recalln) + " and D = " + str(en_recalld) + "\n")
f.write("\n\n")
f.write("pt with best precision: " + str(pt_precision) + " is V = " + str(pt_precisionv) + ", N = " + str(
pt_precisionn) + " and D = " + str(pt_precisiond) + "\n")
f.write("pt with best recall: " + str(pt_recall) + " is V = " + str(pt_recallv) + ", N = " + str(
pt_recalln) + " and D = " + str(pt_recalld) + "\n")
print("Finished generating stats file :)")
f.close()