def main():
"""Run experiment with multiple classifiers."""
# Get classifiers
classifiers = [
('k nn', KNeighborsClassifier(3)),
]
print("Start getting data.")
data = get_data('hasy')
print("Got data. Start.")
# Fit them all
classifier_data = {}
with open('classifier-comp.md', 'w') as f:
for clf_name, clf in classifiers:
print(clf_name)
classifier_data[clf_name] = []
f.write("#" * 80)
f.write("\n")
f.write("Start fitting '%s' classifier.\n" % clf_name)
for fold in data:
tmp = max_k_samples(fold['train']['X'], fold['train']['y'], 50)
fold['train']['X'], fold['train']['y'] = tmp
print("Got %i training samples and %i test samples." %
(len(fold['train']['X']), len(fold['test']['X'])))
t0 = time.time()
examples = 10**9
clf.fit(fold['train']['X'][:examples],
fold['train']['y'][:examples])
t1 = time.time()
an_data = analyze(clf,
fold,
t1 - t0,
clf_name=clf_name,
handle=f)
classifier_data[clf_name].append({
'training_time':
t1 - t0,
'testing_time':
an_data['testing_time'],
'accuracy':
an_data['accuracy']
})
pretty_print(classifier_data)
pretty_print(classifier_data)
if loops * batch_size < len(hasy.test.images):
loops += 1
for i in range(loops):
data = hasy.test.images[i * batch_size:(i + 1) * batch_size]
data = data.reshape((-1, 32 * 32))
predicted = tf.argmax(y_conv, 1).eval(feed_dict={x: data})
actual = np.argmax(
hasy.test.labels[i * batch_size:(i + 1) * batch_size], 1)
for pred, act in zip(predicted, actual):
cm[act][pred] += 1
t1 = time.time()
results['testing_time'] = t1 - t0
results['accuracy'] = (float(sum([cm[i][i] for i in range(369)])) /
len(hasy.test.images))
classifier_data[MODEL_NAME].append({
'training_time':
results['fit_time'],
'testing_time':
results['testing_time'],
'accuracy':
results['accuracy']
})
with open("cnn-comp.md", "a") as handle:
write_analyzation_results(handle, 'CNN %s' % MODEL_NAME, results,
cm)
pretty_print(classifier_data)
# Save the variables to disk.
# save_path = saver.save(sess, model_checkpoint_path)
# print("Model saved in file: %s" % save_path)
