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Update get_accuracy.py #2

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23058de
Update get_accuracy.py
KT-27 Jun 12, 2021
2bba77e
added more parameters to get the complete idea
KT-27 Jun 12, 2021
9d934a8
Update get_accuracy.py
KT-27 Jun 12, 2021
2383259
added some cleaning steps
KT-27 Jun 12, 2021
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8 changes: 7 additions & 1 deletion Data Transformation/codechef_problem/transform_description.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,17 @@
__author__ = 'Pranay'
import lemmatization, minimize_desc, remove_nondecodable_chars, removePunctuation

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def transform(desc):
print 'Description Transformation Started: '
desc = remove_nondecodable_chars.removeNondecodableChars(desc)
desc = minimize_desc.minimizeDescription(desc)
desc = removePunctuation.removePunctuation(desc)
desc = lemmatization.lemmatizeDescription(desc)
stop_words = set(stopwords.words('english'))
word_tokens = word_tokenize(desc)
filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]
desc=' '.join(filtered_sentence)
# print 'Description Transformation Ended '
return desc
9 changes: 6 additions & 3 deletions Model Training/get_accuracy.py
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Expand Up @@ -96,7 +96,7 @@ def get_recommendations(username, prev_sub, no_recomm, no_test):

with open('accuracy_val.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(['prev_sub', 'no_recomm', 'no_test', 'tp', 'tn', 'fp', 'fn', 'precision', 'recall', 'f1_score'])
writer.writerow(['prev_sub', 'no_recomm', 'no_test', 'tp', 'tn', 'fp', 'fn', 'precision', 'recall', 'f1_score','Specificity','False_Positive_Rate','True_Negative_Rate','False_Negative_Rate'])

prev_sub = 5
no_recomm = 10
Expand All @@ -117,10 +117,13 @@ def get_recommendations(username, prev_sub, no_recomm, no_test):
precision = tp/(tp + fp)
recall = tp/(tp + fn)
f1_score = 2 * precision * recall / (precision + recall)

Specificity = tn / tn + fp
False_Positive_Rate = fp / fp + tn
True_Negative_Rate = tn / tn + fp
False_Negative_Rate = fn / fn + tp
print "Precision - " + str(precision)
print "Recall - " + str(recall)
print "F1score - " + str(f1_score)

dat = [prev_sub, no_recomm, no_test, tp, tn, fp, fn, precision, recall, f1_score]
dat = [prev_sub, no_recomm, no_test, tp, tn, fp, fn, precision, recall, f1_score,Specificity,False_Positive_Rate,True_Negative_Rate,False_Negative_Rate]
writer.writerow(dat)