def execute(language):
language = language
data = Dataset(language)
print("{}: {} training - {} dev".format(language, len(data.trainset),
len(data.devset)))
baseline = Baseline(language)
estimator = SVC(gamma=300)
title = 'Spanish Learning Curves (SVM, γ=300)'
X, y = baseline.train(data.trainset)
plot_learning_curve(estimator,
title,
X,
y,
ylim=None,
n_jobs=1,
train_sizes=np.linspace(.1, 1.0, 5))
predictions = baseline.test(data.devset)
gold_labels = [sent['gold_label'] for sent in data.devset]
target_words = [sent['target_word'] for sent in data.devset]
prediction = []
for i in predictions:
prediction.append(i)
df = pd.DataFrame(columns=['target_word', 'prediction'])
df["target_word"] = target_words
df['gold_label'] = gold_labels
df['prediction'] = prediction
df.to_csv('out_s2.csv')
report_score(gold_labels, predictions)
