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import corpus
from random import shuffle
import sent_rating_feature
import ngram_feature
import numpy as np
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
"""
Calls all feature extraction programms and combines
resulting arrays to a single input vector (for a
single corpus instance)
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, bigram_pos_vocab)
f4 = sent_rating_feature.extract(corpus_instance)
return np.concatenate((f1,f4))
if __name__ == '__main__':
corpus = corpus.read_corpus("corpus.csv")
# shuffle & split data set 80:20
shuffle(corpus)
train_set = corpus[:1000]
test_set = corpus[1000:]
# vocabularies
unigram_vocab = ngram_feature.get_vocabulary(train_set, 1)
bigram_vocab = ngram_feature.get_vocabulary(train_set, 2)
bigram_pos_vocab = postagger.get_pos_vocabulary(train_set)
#print(bigram_pos_vocab) #already lookin' good
# inputs:
train_inputs = [create_vector(el, unigram_vocab)
for el in train_set] # 1000 vectors
test_inputs = [create_vector(el, unigram_vocab)
for el in test_set] # 254 vectors
# labels
train_labels = np.array([int(el['LABEL']) for el in train_set]) # 1000 labels
test_labels = np.array([int(el['LABEL']) for el in test_set]) # 254 labels
print("Number of train samples: {}".format(len(train_inputs)))
print("Number of features per train sample: {}".format(len(train_inputs[0])))
print("Unigram vocab size: {}".format(len(unigram_vocab)))
print("Bigram vocab size: {}".format(len(bigram_vocab)))
print("POS-Bigram vocab size: {}".format(len(bigram_pos_vocab)))
# training
# SVM
svm_classifier = svm.SVC(C=200.0) # large C: smaller-margin hyperplane
svm_classifier.fit(train_inputs, train_labels)
print("\nSVM: Score on train Data:")
print(svm_classifier.score(train_inputs, train_labels))
# predictions = svm_classifier.predict(train_inputs)
# print("Predictions: \n {}".format(predictions))
# print("Targets: \n {}".format(train_labels))
print("\nSVM: Score on test Data:")
print(svm_classifier.score(test_inputs, test_labels))
# predictions = svm_classifier.predict(test_inputs)
# print("Predictions: \n {}".format(predictions))
# print("Targets: \n {}".format(test_labels))
# Trees
tree_clf = DecisionTreeClassifier()
tree_clf.fit(train_inputs, train_labels)
print("\nDTree: Score on train Data:")
print(tree_clf.score(train_inputs, train_labels))
# predictions = tree_clf.predict(test_inputs)
# print("Predictions: \n {}".format(predictions))
# print("Targets: \n {}".format(test_labels))
print("\nDTree: Score on test Data:")
print(tree_clf.score(test_inputs, test_labels))