RITs Pseudocode

• The following is pseudocode to use for the RITs algorithm
• It is based on the original Meinshausen/ Shah paper

RITs inputs

• M Number of trees to build
• D Max Tree Depth
• p Children sample node probability threshold (= 0 for no split, i.e. based on uniform (0, 1) RNG with respect the the threshold)
• n Min number of children to sample at each node (if p != 0 then at each node if the split node prob <= p, then sample n children at that node, else sample n + 1 children at that node each node)

i.e. if we want just a binary RIT i.e. always 2 children sampled at each node then set p = 0 and n = 2.

RITs outputs

Our version of the RITs should output the following:

• Node class and The RIT class
• The random number list of nodes that we generated i.e. as a generator function (for reproducibility and testing)
• The entire RITs (for all M trees)

RIT Node class

• We need to return the rich RIT object
  • The authors mention calculating prevalence and sparsity, how should we best calculate these metrics?
  • Needs to return clean attributes:
    • IsNode
    • HasChildren
    • NumChildren
    • Is leaf node
    • getIntersectedPath

Summary

• At it's core, the RIT is comprised of 3 main modules
• FILTERING: Subsetting to either the 1's or the 0's
• RANDOM SAMPLING: The path-nodes in a weighted manner, with/ without replacement, within tree/ outside tree
• INTERSECTION: Intersecting the selected node paths in a systematic manner

Pseudocode for iRFs and RITs

• Question for SVW: How to specify random seeds for all K iterations?

def iterative_random_forest(#RF params **rf_params, rf_B, #number of decision trees to fit for each random forest K=4,

                         #RIT params
                         M_trees=20, 
                         max_depth=5, 
                         n_splits=2,
                         noisy_splits=False):

every_irf_output = {}

for k in range(K):
    if k == 0:
        #set weights uniformly here for the first iteration
        #get the number of features to set this uniform parameter
         rf = RandomForestClassifier(**rf_params, 
                            n_estimators=B, 
                            rf_weights=None)
    else:
        rf = RandomForestClassifier(**rf_params, 
                            n_estimators=B, 
                            rf_weights=rf_weights)

    all_rf_tree_data = irf_utils.get_rf_tree_data(rf=rf,
                                                  X_train=X_train, y_train=y_train, 
                                                  X_test=X_test, y_test=y_test)

    #Run the RIT using the decision tree outputs
    #should be a dictionary structure similar to 
    all_rit_tree_data = irf_utils.get_rit_tree_data(
        all_rf_tree_data=all_rf_tree_data,
        bin_class_type=1,
        random_state=12,
        M=10,
        max_depth=3,
        noisy_split=False,
        num_splits=2)

    #should be able to access the rit_output
    stability_score = ...

    #Append the stability score to the RIT
    all_rit_tree_data['stability_score'] = stability_score

    every_irf_output["irf{}".format(k)] = (all_rf_tree_data, all_rf_tree_data)

#return the dictionar
return every_irf_output