def testInferEstimatorWithCenterBias(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._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=1,
max_depth=5,
center_bias=True,
head=self._head)
# It will stop after 6 steps because of the max depth and num trees (5 for
# training and 2 for bias centering).
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(train_input_fn, steps=num_steps)
self._assert_checkpoint(est.model_dir,
global_step=7,
finalized_trees=1,
attempted_layers=5)
# Validate predictions.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose(
[[1.634501], [1.325703], [1.187431], [2.019683], [2.832683]],
[pred['predictions'] for pred in predictions])
def testInferEstimator(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._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=1,
max_depth=5,
head=self._head)
# It will stop after 5 steps because of the max depth and num trees.
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(train_input_fn, steps=num_steps)
self._assert_checkpoint(est.model_dir,
global_step=5,
finalized_trees=1,
attempted_layers=5)
# Validate predictions.
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 testTrainAndEvaluateEstimator(self):
input_fn = _make_train_input_fn(is_classification=False)
est = boosted_trees._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=2,
head=self._head,
max_depth=5)
# It will stop after 10 steps because of the max depth and num trees.
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(input_fn, steps=num_steps)
self._assert_checkpoint(
est.model_dir, global_step=10, finalized_trees=2, attempted_layers=10)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 1.008551)
def testTrainAndEvaluateEstimatorWithPrePruning(self):
input_fn = _make_train_input_fn(is_classification=False)
est = boosted_trees._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
n_trees=2,
head=self._head,
max_depth=5,
tree_complexity=0.001,
pruning_mode='pre')
num_steps = 100
# Train for a few steps, and validate final checkpoint.
est.train(input_fn, steps=num_steps)
# We stop actually after 2*depth*n_trees steps (via a hook) because we still
# could not grow 2 trees of depth 5 (due to pre-pruning).
self._assert_checkpoint(
est.model_dir, global_step=21, finalized_trees=0, attempted_layers=21)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 3.83943)
def testContribEstimatorThatDFCIsInPredictions(self):
# pylint:disable=protected-access
head = canned_boosted_trees._create_regression_head(label_dimension=1)
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._BoostedTreesEstimator(
feature_columns=self._feature_columns,
n_batches_per_layer=1,
head=head,
n_trees=1,
max_depth=5,
center_bias=True)
# pylint:enable=protected-access
num_steps = 100
# Train for a few steps. Validate debug outputs in prediction dicts.
est.train(train_input_fn, steps=num_steps)
debug_predictions = est.experimental_predict_with_explanations(
predict_input_fn)
biases, dfcs = zip(*[(pred['bias'], pred['dfc'])
for pred in debug_predictions])
self.assertAllClose([1.8] * 5, biases)
expected_dfcs = (collections.OrderedDict(
(('f_1_bucketized', -0.09500002861022949),
('f_0_bucketized', -0.07049942016601562), ('f_2_bucketized',
0.0))),
collections.OrderedDict(
(('f_0_bucketized', -0.5376303195953369),
('f_1_bucketized',
0.06333339214324951), ('f_2_bucketized', 0.0))),
collections.OrderedDict(
(('f_0_bucketized', -0.5175694227218628),
('f_1_bucketized', -0.09500002861022949),
('f_2_bucketized', 0.0))),
collections.OrderedDict(
(('f_0_bucketized', 0.1563495397567749),
('f_1_bucketized',
0.06333339214324951), ('f_2_bucketized', 0.0))),
collections.OrderedDict(
(('f_0_bucketized', 0.96934974193573),
('f_1_bucketized',
0.06333339214324951), ('f_2_bucketized', 0.0))))
for expected, dfc in zip(expected_dfcs, dfcs):
self.assertAllEqual(expected.keys(), dfc.keys())
self.assertAllClose(expected.values(), dfc.values())
# Assert sum(dfcs) + bias == predictions.
expected_predictions = [[1.6345005], [1.32570302], [1.1874305],
[2.01968288], [2.83268309]]
predictions = [[sum(dfc.values()) + bias]
for (dfc, bias) in zip(dfcs, biases)]
self.assertAllClose(expected_predictions, predictions)
# Test when user doesn't include bias or dfc in predict_keys.
debug_predictions = est.experimental_predict_with_explanations(
predict_input_fn, predict_keys=['predictions'])
for prediction_dict in debug_predictions:
self.assertTrue('bias' in prediction_dict)
self.assertTrue('dfc' in prediction_dict)
self.assertTrue('predictions' in prediction_dict)
self.assertEqual(len(prediction_dict), 3)
