def fillTree()

in causalml/inference/tree/uplift.pyx [0:0]
22 lines of code
10 McCabe index (conditional complexity)

    def fillTree(self, X, treatment_idx, y, tree):
        """ Fill the data into an existing tree.
        This is a lower-level function to execute on the tree filling task.

        Args
        ----
        X : ndarray, shape = [num_samples, num_features]
            An ndarray of the covariates used to train the uplift model.
        treatment_idx : array-like, shape = [num_samples]
            An array containing the treatment group index for each unit.
        y : array-like, shape = [num_samples]
            An array containing the outcome of interest for each unit.
        tree : object
            object of DecisionTree class

        Returns
        -------
        self : object
        """
        # Current Node Summary for Validation Data Set
        currentNodeSummary = self.tree_node_summary(treatment_idx, y,
                                                    min_samples_treatment=0,
                                                    n_reg=0,
                                                    parentNodeSummary=None)
        tree.nodeSummary = currentNodeSummary

        # Divide sets for child nodes
        if tree.trueBranch or tree.falseBranch:
            X_l, X_r, w_l, w_r, y_l, y_r = self.divideSet(X, treatment_idx, y, tree.col, tree.value)

            # recursive call for each branch
            if tree.trueBranch is not None:
                self.fillTree(X_l, w_l, y_l, tree.trueBranch)
            if tree.falseBranch is not None:
                self.fillTree(X_r, w_r, y_r, tree.falseBranch)

        # Update Information

        # matchScore
        matchScore = (currentNodeSummary[tree.bestTreatment][0] - currentNodeSummary[0][0])
        tree.matchScore = round(matchScore, 4)
        tree.summary['matchScore'] = round(matchScore, 4)

        # Samples, Group_size
        tree.summary['samples'] = len(y)
        tree.summary['group_size'] = ''
        for treatment_group, summary in zip(self.classes_, currentNodeSummary):
            tree.summary['group_size'] += ' ' + treatment_group + ': ' + str(summary[1])
        # classProb
        if tree.results is not None:
            tree.results = self.uplift_classification_results(treatment_idx, y)
        return self