def eval_model(model,outfilename, word_to_ix, all_tags=None,trainfile=TRAIN_FILE,testfile=DEV_FILE):
"""Calculate confusion_matrix for a given model
Parameters:
tagger -- Model mapping (words) to an optimal
sequence of tags for the words
outfilename -- Filename to write tagger predictions to
testfile -- (optional) Filename containing true labels
Returns:
confusion_matrix -- dict of occurences of (true_label, pred_label)
"""
apply_model(model,outfilename,word_to_ix, all_tags,trainfile,testfile)
return scorer.get_confusion(testfile,outfilename) #run the scorer on the prediction file
def test_nr_hmm_test_accuracy():
confusion = scorer.get_confusion(NR_TEST_FILE, 'hmm-te-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .853)
def test_nr_hmm_dev_accuracy():
confusion = scorer.get_confusion(NR_DEV_FILE, 'hmm-dev-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .861)
def test_hmm_test_accuracy():
confusion = scorer.get_confusion(TEST_FILE, 'hmm-te-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .840)
def test_hmm_dev_accuracy():
confusion = scorer.get_confusion(DEV_FILE, 'hmm-dev-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .840)
def test_nr_test_accuracy3():
confusion = scorer.get_confusion(NR_TEST_FILE, 'bakeoff-te-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .86)
def test_en_dev_accuracy1():
confusion = scorer.get_confusion(DEV_FILE, 'bakeoff-dev-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .85)
def test_nr_dev_accuracy3():
confusion = scorer.get_confusion(NR_DEV_FILE, 'bakeoff-dev-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .87)
def test_en_test_accuracy3():
confusion = scorer.get_confusion(TEST_FILE, 'bakeoff-te-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .86)
def test_bilstm_dev_accuracy():
confusion = scorer.get_confusion(DEV_FILE, 'bilstm-dev-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .85)
