From 3fd007818efc150f43917f0dd24fbc7f525dd743 Mon Sep 17 00:00:00 2001
From: Maximilian Blunck <blunck@cl.uni-heidelberg.de>
Date: Sun, 7 Jan 2018 18:33:34 +0100
Subject: [PATCH] F1 score output & adaptation to renamed pos_feature
---
training_testing.py | 21 ++++++++++-----------
1 file changed, 10 insertions(+), 11 deletions(-)
diff --git a/training_testing.py b/training_testing.py
index b6efc5c..58e2f81 100644
--- a/training_testing.py
+++ b/training_testing.py
@@ -6,7 +6,7 @@ import numpy as np
from sklearn import svm
from sklearn import tree
from sklearn.model_selection import cross_val_score
-import postagger
+import pos_feature
def create_vector(corpus_instance, vocabulary=None, pos_vocabulary=None):
@@ -17,12 +17,9 @@ def create_vector(corpus_instance, vocabulary=None, pos_vocabulary=None):
Example for corpus instance: OrderedDict([('LABEL', '0'), ('FILENAME', '36_19_RPRRQDRSHDV6J.txt'), ('STARS', '5.0'), ('TITLE', etc.
"""
f1 = ngram_feature.extract(corpus_instance, vocabulary)
- f2 = postagger.extract(corpus_instance, pos_vocabulary)
+ f2 = pos_feature.extract(corpus_instance, pos_vocabulary)
f4 = sent_rating_feature.extract(corpus_instance)
- print(f2)
- print(len(f2))
-
return np.concatenate((f1, f2, f4))
@@ -44,8 +41,7 @@ if __name__ == '__main__':
bigram_vocab = ngram_feature.get_vocabulary(train_set, 2)
# pos_bags
- pos_bigram_vocab = postagger.get_pos_vocabulary(train_set)
- #print(pos_bigram_vocab) #already lookin' good
+ pos_bigram_vocab = pos_feature.get_pos_vocabulary(train_set)
# inputs:
train_inputs = [create_vector(el, unigram_vocab, pos_bigram_vocab)
@@ -75,12 +71,15 @@ if __name__ == '__main__':
train_multiple([svm_clf, tree_clf], train_inputs, train_labels)
# validation
- svm_score = cross_val_score(svm_clf, train_inputs, train_labels, cv=5).mean()#, scoring='f1')
- tree_score = cross_val_score(tree_clf, train_inputs, train_labels, cv=5).mean()#, scoring='f1')
+ svm_acc = cross_val_score(svm_clf, train_inputs, train_labels, cv=5, scoring='accuracy').mean()
+ tree_acc = cross_val_score(tree_clf, train_inputs, train_labels, cv=5, scoring='accuracy').mean()
+
+ svm_f1 = cross_val_score(svm_clf, train_inputs, train_labels, cv=5, scoring='f1').mean()
+ tree_f1 = cross_val_score(tree_clf, train_inputs, train_labels, cv=5, scoring='f1').mean()
print("\n--Cross Validation Scores-- ")
- print("\nSVM: {}".format(svm_score))
- print("\nTree: {}".format(tree_score))
+ print("\nSVM: Accuracy: {}, F1-Score: {}".format(svm_acc, svm_f1))
+ print("\nTree: Accuracy: {}, F1-Score: {}".format(tree_acc, tree_f1))
# testing
# print("\nSVM: Score on test Data:")
--
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