[FEA] Add support for computing feature_importances in RF

[teju85]

Is your feature request related to a problem? Please describe.
RF implementation should support computing feature_importances_ property, just like how it is exposed in sklearn.

Describe the solution you'd like

1. By default, we should compute normalized feature_importances_ (ie. all the importances across the features sum to 1.0)
2. Implementation that is done in sklearn is here. We have all of this information in our Node. We just need to, while building the tree, keep accumulating each feature's importance as we keep adding more nodes.

[JohnZed]

Definitely agreed. Not sure we'll have enough bandwidth to get this for 0.19 (given work going to new backend) but should be highly prioritized high after that.

[teju85]

Here's one use-case that requires this attribute to be present: https://github.com/willb/fraud-notebooks/blob/develop/03-model-random-forest.ipynb

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[sooryaa-thiruloga]

we are interested to use this feature in our use case too.

[beckernick]

This would also be useful for tools like Boruta, a popular feature selection library that's part of scikit-learn-contrib. There is a Boruta issue asking for support for cuML estimators

[teju85]

Tagging @vinaydes and @venkywonka to see if we can have Venkat start on this?

[hafarooki]

This is probably not the most efficient implementation, but in case anyone else needs it:

def calculate_importances(nodes, n_features):
    importances = np.zeros((len(nodes), n_features))
    feature_gains = np.zeros(n_features)


    def calculate_node_importances(node, i_root):
        if "gain" not in node:
            return

        samples = node["instance_count"]
        gain = node["gain"]
        feature = node["split_feature"]
        feature_gains[feature] += gain * samples

        for child in node["children"]:
            calculate_node_importances(child, i_root)


    for i, root in enumerate(nodes):
        calculate_node_importances(root, i)
        importances[i] = feature_gains / feature_gains.sum()

    return np.mean(importances, axis=0)

you can see the logic behind it here https://towardsdatascience.com/the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3

[beckernick]

Cross linking an issue that asks for this feature and OOB support #3361