Code snippet to print various ML related metrics given the y_labels and probabilities of each label (output of softmax)

print_ml_metrics.py

# Code snippet to print various ML related metrics given the y_labels and probabilities of each label (output of softmax)
# Aaditya Prakash

from sklearn.metrics import f1_score, roc_auc_score, precision_score, recall_score, accuracy_score, average_precision_score, precision_recall_curve, hamming_loss

def print(y_labels, probs):

    threshold = 0.5
    macro_auc                  = roc_auc_score(y_labels, probs, average            = 'macro')
    micro_auc                  = roc_auc_score(y_labels, probs, average            = 'micro')
    zero_values_indices        = probs < threshold
    one_values_indices         = probs >= threshold
    probs[zero_values_indices] = 0
    probs[one_values_indices]  = 1
    macro_f1                   = f1_score(y_labels, probs, average                 = 'macro')
    micro_f1                   = f1_score(y_labels, probs, average                 = 'micro')
    precision                  = precision_score(y_labels, probs, average          = 'micro')
    recall                     = recall_score(y_labels, probs, average             = 'micro')
    average_precision          = average_precision_score(y_labels, probs, average  = 'weighted')
    precision_recall           = precision_recall_curve(y_labels, probs)
    hamming_loss_v             = hamming_loss(y_labels, probs)
    accuracy_1                 = accuracy_score(y_labels, probs)
    accuracy_5                 = accuracy_5 / len(y_labels)
    accuracy_10                = accuracy_10 / len(y_labels)
    auc                        = roc_auc_score(y_labels, probs_f)
    time_str                   = datetime.datetime.now().isoformat()
    print("{}: step {}, macro_f1_score {:g}, micro_f1_score {:g}, micro_auc {:g}, macro_auc {:g}, precision {:g}, recall {:g}, accuracy_1 {:g}, accuracy_5 {:g}, accuracy_10 {:g}, average_precision {:g}, hamming_loss_v {:g}".format(
        time_str, step, macro_f1, micro_f1, micro_auc, macro_auc, precision, recall, accuracy_1, accuracy_5, accuracy_10, average_precision, hamming_loss_v))