Module forust_ml::utils

Functions

• constrained_weight
  Calculate the constraint weight given bounds and a constraint.
• cull_gain
  Cull gain, if it does not conform to constraints.
• fast_f64_sum
  Fast summation, but using f64 as the internal representation so that we don’t have issues with the precision. This way, we can still work with f32 values, but get the correct sum value.
• fast_sum
  Fast summation, ends up being roughly 8 to 10 times faster than values.iter().copied().sum(). Shamelessly stolen from https://stackoverflow.com/a/67191480
• gain
  Calculate the gain given the gradient and hessian of the node.
• gain_given_weight
  Calculate the gain of a split given a specific weight value. This is for if the weight has to be constrained, for example for monotonicity constraints.
• map_bin
  Return the index of the first value in a sorted vector that is greater than a provided value.
• missing_compare
  Function to compare a value to our split value. Our split value will never be missing (0), thus we don’t have to worry about that.
• naive_sum
• percentiles
  Naive weighted percentiles calculation.
• pivot_on_split
  Provided a list of index values, pivot those values around a specific split value so all of the values less than the split value are on one side, and then all of the values greater than or equal to the split value are above.
• pivot_on_split_exclude_missing
  Provided a list of index values, pivot those values around a specific split value so all of the values less than the split value are on one side, and then all of the values greater than or equal to the split value are above. Missing values, will be pushed to the bottom, a value of zero is missing in this case.
• precision_round
• weight
  Calculate the weight of a given node, given the sum of the gradients, and the hessians in a node.