Quick start¶

If you are already using tf.estimator.Estimator, the fastest way to get up and running with AdaNet is to use the adanet.AutoEnsembleEstimator. This estimator will automatically convert a list of estimators into subnetworks, and learn to ensemble them for you.

The first step is to import the adanet package:

import adanet


AutoEnsembleEstimator¶

Next you will want to define which estimators you want to ensemble. For example, if you don’t know if the best model a linear model, or a neural network, or some combination, then you can try using tf.estimator.LinearEstimator and tf.estimator.DNNEstimator as subnetworks:

import adanet
import tensorflow as tf

# Define the model head for computing loss and evaluation metrics.

# Feature columns define how to process examples.
feature_columns = ...

# Learn to ensemble linear and neural network models.
candidate_pool=lambda config: {
"linear":
tf.estimator.LinearEstimator(
feature_columns=feature_columns,
config=config,
optimizer=...),
"dnn":
tf.estimator.DNNEstimator(
feature_columns=feature_columns,
config=config,
optimizer=...,
hidden_units=[1000, 500, 100])},
max_iteration_steps=50)

estimator.train(input_fn=train_input_fn, steps=100)
metrics = estimator.evaluate(input_fn=eval_input_fn)
predictions = estimator.predict(input_fn=predict_input_fn)


The above code will train both the linear and dnn subnetworks in parallel, and will average their predictions. After max_iteration_steps=100 steps, the best subnetwork will compose the ensemble according to its performance on the training set.

Ensemble strategies¶

The way AdaNet chooses which subnetworks to include in a candidate ensemble is via ensemble strategies.

Grow strategy¶

The default ensemble strategy is adanet.ensemble.GrowStrategy which will only select the subnetwork that most improved the ensemble’s performance. The remaining subnetworks will be pruned from the graph.

All strategy¶

Suppose instead of only selecting the single best subnetwork, you want to ensemble all of the subnetworks, regardless of their individual performance. You can pass an instance of the adanet.ensemble.AllStrategy to the adanet.AutoEnsembleEstimator constructor:

estimator = adanet.AutoEnsembleEstimator(
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