def knn_experiment():
K = len(topics)
dm = data.create_data_manager()
ordered_topics = dm.order_topics(topics)
print 'loading train data . . .'
X_train, Y_train = dm.load_data('train')
Y_train_slice = dm.slice_Y(Y_train, ordered_topics)
print 'loading test data . . .'
X_test, Y_test = dm.load_data('test')
Y_gold = dm.slice_Y(Y_test, ordered_topics)
final_results = {}
print 'interating over models . . .'
for k in [1,15,31,45,61]:
model = '%d-nearest neighbours' % k
print 'now using model %s . . .' % model
learner = knn.MultikNN(K, k)
# K being the number of topics, and k being the number of neighbours
learner.train(X_train, Y_train_slice)
Y_pred = learner.batch_predict_classes(X_test)
results = evaluate.per_topic_results(Y_pred, Y_gold)
results_dict = {topic: result for (topic, result) in zip(ordered_topics, results)}
pprint(results_dict)
final_results[model] = results_dict
print 'saving final results . . .'
with open('results/knn_final_results.txt', 'w') as f:
pprint(final_results, stream=f)
def svm_experiment(C, balance=''):
K = len(topics)
dm = data.create_data_manager()
ordered_topics = dm.order_topics(topics)
print 'loading train data . . .'
X_train, Y_train = dm.load_data('train')
Y_train_slice = dm.slice_Y(Y_train, ordered_topics)
print 'loading test data . . .'
X_test, Y_test = dm.load_data('test')
Y_gold = dm.slice_Y(Y_test, ordered_topics)
final_results = {}
#print (Y_train_slice.sum(axis=0) + Y_train_slice.shape[0]).tolist()
#raise Exception
print 'interating over models . . .'
for model, kernel in svm_kernels:
print 'now using model %s . . .' % model
learner = svm.MultiSVM(K, C, kernel)
learner.train(X_train, Y_train_slice,
ordered_topics,
balance=balance, # use balance = '' if don't want to balance
max_per_class=3000)
Y_pred = learner.batch_predict_classes(X_test)
results = evaluate.per_topic_results(Y_pred, Y_gold)
results_dict = {topic: result for (topic, result) in zip(ordered_topics, results)}
pprint(results_dict)
final_results[model] = results_dict
print 'saving final results . . .'
with open('results/svm_final_results_C_%.1f_%s.txt' % (C,balance), 'w') as f:
pprint(final_results, stream=f)
