Modified to speed up UpliftTreeClassifier.growDecisionTreeFrom.

[peterloleungyau]

Mainly reduce allocation, in a synthetic data of 40000 rows, about 400 features, 4 treatments, there is a speed up of about 65x over the original version.

The main modifications are in the comments, reproduced below for convenience.

Proposed changes

Modified to speed up UpliftTreeClassifier.growDecisionTreeFrom and
consequently can also speed up UpliftTreeClassifier.fit, and
UpliftRandomForestClassifier.

Modification version 1, on 2023-10-04 by Peter Lo peterloleungyau@gmail.com

• created divideSet_len from divideSet, which does not split the whole
  X into X_l and X_r, but returns len(X_l) and len(X_r) instead,
  because in finding the best split, the whole X_l and X_r are not
  used. This saves some allocations.

• in finding the best split, we use mostly scalar values
  (e.g. best_col and best_value in favor of one single tuple
  bestAttribute) to keep the current best, and further avoids
  allocating many objects. After finding the best split, we use the
  original divideSet to get the X_l and X_r and subsequently
  best_set_left and best_set_right which are used in subsequent
  construction of subtrees of the left and right branches; and
  calculate bestAttribute as a tuple.

• also, from the annotation of Cython, it seems some constant values
  such as the list of percentile values to test for threshold will be
  constructed in each iteration of the loop, because the Cython
  compiler cannot be sure that the list will not be modified by
  np.percentile, but since we can be sure, we create the list once
  outside the loop.

Modification verison 2, on 2023-10-05 by Peter Lo peterloleungyau@gmail.com

• modified group_uniqueCounts to group_uniqueCounts_to_arr, to use
  pre-allocated flat numpy array to fill in the counts, instead of
  using list of list.

• modified tree_node_summary to tree_node_summary_to_arr to use
  pre-allocated flat numpy arrays, and to fill in the node summary in
  new format, which consist of two parallel arrays: out_summary_p for
  [P(Y=1|T=i)...] and out_summary_n for [N(T=i)...], instead of the
  previous list of list.

• for all the evaluation functions, created the arr_evaluate_*
  versions that uses the new node summary format. Also created
  arr_normI as the counter-part of normI using the new node summary
  format.

• added some cdef to give type declarations to help speed up some
  calculations.

Modifcation version 3, on 2023-10-06 by Peter Lo peterloleungyau@gmail.com

• combine divideSet_len and group_uniqueCounts_to_arr to be
  group_counts_by_divide, so as to reduce creating intermediate
  objects. This calculates the group counts for the left branch.

• created tree_node_summary_from_counts from tree_node_summary_to_arr,
  to use pre-calculated group counts (e.g. by group_counts_by_divide).

• for right branch, calculate the group counts by subtracting the
  group counts of left branch from the total count (pre-calculated
  outside the looping of choosing split points), which should be more
  efficient than looping through the data again.

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[jeongyoonlee]

LGTM. Thanks for your contribution, @peterloleungyau!