def testRegressorTrainInMemoryWithDataset(self):
train_input_fn = _make_train_input_fn_dataset(is_classification=False)
predict_input_fn = numpy_io.numpy_input_fn(x=FEATURES_DICT,
y=None,
batch_size=1,
num_epochs=1,
shuffle=False)
est = boosted_trees.boosted_trees_regressor_train_in_memory(
train_input_fn=train_input_fn,
feature_columns=self._feature_columns,
n_trees=1,
max_depth=5)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(est.model_dir,
global_step=5,
finalized_trees=1,
attempted_layers=5)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 2.478283)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[0.571619], [0.262821], [0.124549], [0.956801], [1.769801]],
[pred['predictions'] for pred in predictions])
def DISABLED_testRegressorTrainInMemoryWithFloatColumns(self):
train_input_fn = _make_train_input_fn(is_classification=False)
predict_input_fn = numpy_io.numpy_input_fn(x=FEATURES_DICT,
y=None,
batch_size=1,
num_epochs=1,
shuffle=False)
est = boosted_trees.boosted_trees_regressor_train_in_memory(
train_input_fn=train_input_fn,
feature_columns=self._numeric_feature_columns,
n_trees=1,
max_depth=5,
quantile_sketch_epsilon=0.33)
# It will stop after 5 steps because of the max depth and num trees.
self._assert_checkpoint(est.model_dir,
global_step=5,
finalized_trees=1,
attempted_layers=5,
bucket_boundaries=[[-2.001, -1.999, 12.5],
[-3., 0.4995, 2.],
[-100., 20., 102.75]])
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 2.4182191)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[0.663432], [0.31798199], [0.081902], [0.75843203], [1.86384201]],
[pred['predictions'] for pred in predictions])
