def get_negatives(funcs, numNegatives):
funcmat = np.any(vectorized_getlabelmat(funcs.astype(int)), axis=1)
negatives = np.zeros((funcs.shape[0], numNegatives))
for row in range(funcmat.shape[0]):
negatives[row, :] = np.random.choice(np.nonzero(~funcmat[row, :])[0], size=numNegatives)
return negatives
def evaluate(predictions, labels, action=None):
#ipdb.set_trace()
labelmat = np.any(vectorized_getlabelmat(labels), axis=1)
predmat = np.any(vectorized_getlabelmat(predictions), axis=1)
return numpy_calc_performance_metrics(labelmat, predmat, threshold=0.2)
def evaluate(predictions, labels):
labelmat = np.any(vectorized_getlabelmat(labels), axis=1)
predmat = np.any(vectorized_getlabelmat(predictions), axis=1)
return numpy_calc_performance_metrics(labelmat, predmat, threshold=0.2)
