import config
tee = config.Tee(
'../Results/%s/GNB_GroupAC%s%s_model.txt' %
(config.args.dataset, config.args.tf_idf, config.args.remove_non), 'w')
from header_model_data import *
from sklearn.naive_bayes import GaussianNB
print("Regrouping the labels")
for i in range(len(yDF)):
if (yDF[i] == pr.y_conversion('challenge')):
yDF[i] = pr.y_conversion('agree')
print("The Class Distribution is:")
classDist = Counter(yDF)
for k in classDist.keys():
print("\t" + str(pr.conversion_y(k)) + ":" + str(classDist[k]))
print("Defining and doing a Gaussian Naive Bayes classifier")
NB = GaussianNB()
scores = cross_validate(NB, xDF, yDF, cv=logo, scoring=scorer)
#print(scores)
#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
compute_stats(originalclass, predictedclass, True)
print("Confusion Matrix")
labelList = [x for x in classDist.keys()]
print_cm(confusion_matrix(originalclass, predictedclass, labels=labelList),
[pr.conversion_y(x) for x in labelList])
import config
tee = config.Tee(
'../Results/%s/Three_Label_GNB%s%s_model.txt' %
(config.args.dataset, config.args.tf_idf, config.args.remove_non), 'w')
from header_model_data import *
from sklearn.naive_bayes import GaussianNB
print("Regrouping the labels")
labelCheck = [pr.y_conversion(x) for x in ['agree', 'challenge', 'extension']]
labelList = set([])
for i in range(len(yDF)):
if (yDF[i] in labelCheck):
yDF[i] = labelCheck[0]
print("Post Regroup Stats")
print("The Class Distribution is:")
classDist = Counter(yDF)
for k in classDist.keys():
labelList.add(pr.conversion_y(k))
print("\t" + str(pr.conversion_y(k)) + ":" + str(classDist[k]))
alpha = 1
print(
"Defining and doing a Gaussian Naive Bayes classifier Trying to three class labels"
)
print("Non is non, new-idea is initiation, agree is the other labels.")
print("this is smoothed to an alpha of %s" % (alpha))
NB = GaussianNB()
scores = cross_validate(NB, xDF, yDF, cv=logo, scoring=scorer)
#print(scores)
#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
import config
tee = config.Tee(
'../Results/%s/Dummy_DetectThree%s%s_model.txt' %
(config.args.dataset, config.args.tf_idf, config.args.remove_non), 'w')
from header_model_data import *
from sklearn.dummy import DummyClassifier
print("Regrouping the labels")
labelCheck = [pr.y_conversion(x) for x in ['agree', 'challenge', 'extension']]
labelList = set([])
for i in range(len(yDF)):
if (yDF[i] in labelCheck):
yDF[i] = labelCheck[0]
print("Post Regroup Stats")
print("The Class Distribution is:")
classDist = Counter(yDF)
for k in classDist.keys():
labelList.add(pr.conversion_y(k))
print("\t" + str(pr.conversion_y(k)) + ":" + str(classDist[k]))
print(
"Defining and doing a dummy classifier again set to predict based on the class distribution"
)
print("Non is non, new-idea is initiation, agree is the other labels.")
dumDum = DummyClassifier(strategy='stratified',
random_state=None,
constant=None)
scores = cross_validate(dumDum, xDF, yDF, cv=logo, scoring=scorer)
#print(scores)
import config
tee = config.Tee(
'../Results/%s/Non_Dummy%s%s_model.txt' %
(config.args.dataset, config.args.tf_idf, config.args.remove_non), 'w')
from header_model_data import *
from sklearn.dummy import DummyClassifier
print("Regrouping the labels")
for i in range(len(yDF)):
if (yDF[i] != pr.y_conversion('Non')):
yDF[i] = pr.y_conversion('new-idea')
print(
"Defining and doing a dummy classifier set to predict based on the class distribution"
)
dumDum = DummyClassifier(strategy='stratified',
random_state=None,
constant=None)
scores = cross_validate(dumDum, xDF, yDF, cv=logo, scoring=scorer)
#print(scores)
#print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
compute_stats(originalclass, predictedclass, True)
print("Confusion Matrix")
print_cm(
confusion_matrix(originalclass, predictedclass, labels=list(range(2))),
[pr.conversion_y(x) for x in range(2)])
tee.close()