def build_estimator(model_dir):
params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(
num_classes=15,
num_features=3,
num_trees=FLAGS.num_trees,
max_nodes=FLAGS.max_nodes)
return random_forest.TensorForestEstimator(params, model_dir=model_dir)
def build_estimator(model_dir):
"""Build an estimator."""
params = tensor_forest.ForestHParams(
num_classes=1,
regression=True,
num_features=392,
num_trees=FLAGS.num_trees,
max_nodes=FLAGS.max_nodes,
).fill()
return random_forest.TensorForestEstimator(params, model_dir=model_dir)
def build_estimator(model_dir):
"""Build an estimator."""
params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(
num_classes=10, num_features=784,
num_trees=FLAGS.num_trees, max_nodes=FLAGS.max_nodes)
graph_builder_class = tensor_forest.RandomForestGraphs
if FLAGS.use_training_loss:
graph_builder_class = tensor_forest.TrainingLossForest
return random_forest.TensorForestEstimator(
params, graph_builder_class=graph_builder_class,
model_dir=model_dir)
def testClassification(self):
"""Tests multi-class classification using matrix data as input."""
hparams = tensor_forest.ForestHParams(num_trees=3,
max_nodes=1000,
num_classes=3,
num_features=4,
split_after_samples=20)
classifier = random_forest.TensorForestEstimator(hparams.fill())
iris = base.load_iris()
data = iris.data.astype(np.float32)
labels = iris.target.astype(np.float32)
classifier.fit(x=data, y=labels, steps=100, batch_size=50)
classifier.evaluate(x=data, y=labels, steps=10)
def testClassificationTrainingLoss(self):
"""Tests multi-class classification using matrix data as input."""
hparams = tensor_forest.ForestHParams(num_trees=3,
max_nodes=1000,
num_classes=3,
num_features=4)
classifier = random_forest.TensorForestEstimator(
hparams, graph_builder_class=(tensor_forest.TrainingLossForest))
iris = base.load_iris()
data = iris.data.astype(np.float32)
labels = iris.target.astype(np.float32)
monitors = [random_forest.TensorForestLossHook(10)]
classifier.fit(x=data, y=labels, steps=100, monitors=monitors)
classifier.evaluate(x=data, y=labels, steps=10)
def testRegression(self):
"""Tests multi-class classification using matrix data as input."""
hparams = tensor_forest.ForestHParams(num_trees=3,
max_nodes=1000,
num_classes=1,
num_features=13,
regression=True,
split_after_samples=20)
regressor = random_forest.TensorForestEstimator(hparams.fill())
boston = base.load_boston()
data = boston.data.astype(np.float32)
labels = boston.target.astype(np.float32)
regressor.fit(x=data, y=labels, steps=100, batch_size=50)
regressor.evaluate(x=data, y=labels, steps=10)
