Create function in pure Python that calculates predictions from a Scikit-Learn RandomForestRegressor

pure_python_rfr.py

from sklearn.tree import _tree

tree_template = '''
def tree{i}(inputs):

    tree_undefined = {tree_undefined}
    
    features = {features}
    thresholds = {thresholds}
    children_left = {children_left}
    children_right = {children_right}
    values = {values}

    node = 0
    while features[node] != tree_undefined:
        feat = features[node]
        threshold = thresholds[node]
        if inputs[feat] <= threshold:
            node = children_left[node]
        else:
            node = children_right[node]

    output = values[node]

    return output    
'''

template_footer = '''

def forest(inputs):
    return ({combined_trees}) / {n}
'''

template_final = ''

n = len(rfr.estimators_)
for i, model in enumerate(rfr.estimators_):
    template_final += tree_template.format(
        i=i,
        tree_undefined=_tree.TREE_UNDEFINED,
        features=repr(model.tree_.feature.tolist()),
        thresholds=repr(model.tree_.threshold.tolist()),
        children_left=repr(model.tree_.children_left.tolist()),
        children_right=repr(model.tree_.children_right.tolist()),
        values=repr([val[0][0] for val in model.tree_.value]),
    )
    
template_final += template_footer.format(
    combined_trees=' + '.join(['tree{i}(inputs)'.format(i=i) for i in range(n)]),
    n=float(n)
)

# execute the constructed code to load the function `forest` in the environment
exec(template_final)