result = 0
conf = confidence[i][y[i]]
f.write("%s,%d,%f\n" % (item_id, result, conf))
ip = ImagesProcessor()
images, y = ip.getImages('../imgs/test/dataset/', size=None, training=False)
# Esto es lo que hay que usar para predecir el resultado final
if True:
ensemble = Ensemble()
ensemble.load()
X_predictions = ensemble.predict_small(images)
y_hat = ensemble.predict_big(X_predictions)
confidence = ensemble.ensemble_logistic_regression.predict_proba(
X_predictions)
printResult(y, y_hat, confidence)
#score(y_hat, y)
# Esto es lo que hay que usar para calcular al regression lineal y gurdarla
if False:
ensemble = Ensemble()
ensemble.load()
X_validation_predictions = ensemble.predict_small(images)
ensemble.fit_big(X_validation_predictions, y)
f = file("./ensemble_logistic_regression", 'wb')
cPickle.dump(ensemble.ensemble_logistic_regression,
f,
protocol=cPickle.HIGHEST_PROTOCOL)
f.close()
