coding utf8 convert onehot representation into one column def wrapperactual predicted if lenactual shape 1 and actual shape1 1 actual actual argmaxaxis1 if lenpredicted shape 1 and predicted shape1 1 predicted predicted argmaxaxis1 return functionactual predicted return wrapper def absoluteerroractual predicted return np absactual predicted unhot def classificationerroractual predicted return actual predicted sum floatactual shape0 unhot def accuracyactual predicted return 1 0 classificationerroractual predicted def meanabsoluteerroractual predicted return np meanabsoluteerroractual predicted def squarederroractual predicted return actual predicted 2 def squaredlogerroractual predicted return np lognp arrayactual 1 np lognp arraypredicted 1 2 def meansquaredlogerroractual predicted return np meansquaredlogerroractual predicted def meansquarederroractual predicted return np meansquarederroractual predicted def rootmeansquarederroractual predicted return np sqrtmeansquarederroractual predicted def rootmeansquaredlogerroractual predicted return np sqrtmeansquaredlogerroractual predicted def loglossactual predicted predicted np clippredicted eps 1 eps loss np sumactual np logpredicted return loss floatactual shape0 def hingeactual predicted return np meannp max1 0 actual predicted 0 0 def binarycrossentropyactual predicted predicted np clippredicted eps 1 eps return np meannp sumactual np logpredicted 1 actual np log1 predicted aliases mse meansquarederror rmse rootmeansquarederror mae meanabsoluteerror def getmetricname coding utf 8 convert one hot representation into one column aliases return metric function by name
import autograd.numpy as np

EPS = 1e-15


def unhot(function):
    

    def wrapper(actual, predicted):
        if len(actual.shape) > 1 and actual.shape[1] > 1:
            actual = actual.argmax(axis=1)
        if len(predicted.shape) > 1 and predicted.shape[1] > 1:
            predicted = predicted.argmax(axis=1)
        return function(actual, predicted)

    return wrapper


def absolute_error(actual, predicted):
    return np.abs(actual - predicted)


@unhot
def classification_error(actual, predicted):
    return (actual != predicted).sum() / float(actual.shape[0])


@unhot
def accuracy(actual, predicted):
    return 1.0 - classification_error(actual, predicted)


def mean_absolute_error(actual, predicted):
    return np.mean(absolute_error(actual, predicted))


def squared_error(actual, predicted):
    return (actual - predicted) ** 2


def squared_log_error(actual, predicted):
    return (np.log(np.array(actual) + 1) - np.log(np.array(predicted) + 1)) ** 2


def mean_squared_log_error(actual, predicted):
    return np.mean(squared_log_error(actual, predicted))


def mean_squared_error(actual, predicted):
    return np.mean(squared_error(actual, predicted))


def root_mean_squared_error(actual, predicted):
    return np.sqrt(mean_squared_error(actual, predicted))


def root_mean_squared_log_error(actual, predicted):
    return np.sqrt(mean_squared_log_error(actual, predicted))


def logloss(actual, predicted):
    predicted = np.clip(predicted, EPS, 1 - EPS)
    loss = -np.sum(actual * np.log(predicted))
    return loss / float(actual.shape[0])


def hinge(actual, predicted):
    return np.mean(np.max(1.0 - actual * predicted, 0.0))


def binary_crossentropy(actual, predicted):
    predicted = np.clip(predicted, EPS, 1 - EPS)
    return np.mean(-np.sum(actual * np.log(predicted) + (1 - actual) * np.log(1 - predicted)))



mse = mean_squared_error
rmse = root_mean_squared_error
mae = mean_absolute_error


def get_metric(name):
    
    try:
        return globals()[name]
    except Exception:
        raise ValueError("Invalid metric function.")