Confusion Metrics written in tensorflow format

tensorflow_confusion_metrics.py

# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
def tf_confusion_metrics(model, actual_classes, session, feed_dict):
  predictions = tf.argmax(model, 1)
  actuals = tf.argmax(actual_classes, 1)

  ones_like_actuals = tf.ones_like(actuals)
  zeros_like_actuals = tf.zeros_like(actuals)
  ones_like_predictions = tf.ones_like(predictions)
  zeros_like_predictions = tf.zeros_like(predictions)

  tp_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, ones_like_actuals), 
        tf.equal(predictions, ones_like_predictions)
      ), 
      "float"
    )
  )

  tn_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, zeros_like_actuals), 
        tf.equal(predictions, zeros_like_predictions)
      ), 
      "float"
    )
  )

  fp_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, zeros_like_actuals), 
        tf.equal(predictions, ones_like_predictions)
      ), 
      "float"
    )
  )

  fn_op = tf.reduce_sum(
    tf.cast(
      tf.logical_and(
        tf.equal(actuals, ones_like_actuals), 
        tf.equal(predictions, zeros_like_predictions)
      ), 
      "float"
    )
  )

  tp, tn, fp, fn = \
    session.run(
      [tp_op, tn_op, fp_op, fn_op], 
      feed_dict
    )

  tpr = float(tp)/(float(tp) + float(fn))
  fpr = float(fp)/(float(tp) + float(fn))

  accuracy = (float(tp) + float(tn))/(float(tp) + float(fp) + float(fn) + float(tn))

  recall = tpr
  precision = float(tp)/(float(tp) + float(fp))
  
  f1_score = (2 * (precision * recall)) / (precision + recall)
  
  print 'Precision = ', precision
  print 'Recall = ', recall
  print 'F1 Score = ', f1_score
  print 'Accuracy = ', accuracy

Correct me if I'm wrong, but building on the reply by @carlthome above, it looks like you could further simplify some of your performance measures with things like:

accuracy = tf.reduce_mean(tf.cast(tf.equal(actual, predicted), tf.float32))
error = tf.reduce_mean(tf.cast(tf.not_equal(actual, predicted), tf.float32))

The accuracy operation above gives you the sum of all cases where actual == predicted (equivalent to tp + tn), divided by the total number of samples (equivalent to tp + fp + fn + tn). The error measure gives you the inverse of this, so (fp + fn) / total_samples.

I am not sure which method ends up being faster, but if all you need is accuracy or error (as defined here), this saves you having to find all of tp, tn, fp, and fn.

I think for multi-labels, there is no real true negative, so in the replay from @carlthome, the calculation of tn is no need and actually wrong

You're not supposed to use the sigmoid for a one-hot encoding because you get numerical instability. You need to use softmax EVERY TIME for one-hot else you will get erroneous results form the floating-point rounding errors.

The fpr is wrong. It should be fpr = float(fp)/(float(fp) + float(tn))
https://en.wikipedia.org/wiki/False_positive_rate