Covert Tree Models to Tensorflow Tree.

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The Goal is to have one unified tree runtime

* Convert a xgboost Tree/Forest into Tensorflow Graph.

* Convert a sciki-learn Tree/Forest into Tensorflow Graph.

Example

Convert fitted

• sklearn.DecisionTreeClassifier
• sklearn.DecisionTreeRegressor
• sklearn.RandomForestRegressor
• sklearn.RandomForestClassifier
• xgboost.XGBClassifier
• xgboost.XGBRegressor

to tensorflow.saved_model

All you need to do is pass your desired model_dir, './tmp' in this example and a fitted classifier.

    
    from ttt import export_decision_tree

    clf = sklearn.ensemble.RandomForestClassifier()
    clf.fit(X, y)
    features = {'features': tf.placeholder(tf.float64, [None, X.shape[1]])}
    export_decision_tree(clf, features, 'sklearn_saved_model')

    xgb_model = xgboost.XGBClassifier().fit(X, y)
    export_decision_tree(xgb_model, features, 'xgb_saved_model')
    

And then you can server this model with tf/serving using 'saved_model'