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, 6)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertEquals(5, len(predictions))
self.assertAllClose([0.703549], predictions[0]['predictions'])
self.assertAllClose([0.266539], predictions[1]['predictions'])
self.assertAllClose([0.256479], predictions[2]['predictions'])
self.assertAllClose([1.088732], predictions[3]['predictions'])
self.assertAllClose([1.901732], predictions[4]['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, 6)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertEquals(5, len(predictions))
self.assertAllClose([0.703549], predictions[0]['predictions'])
self.assertAllClose([0.266539], predictions[1]['predictions'])
self.assertAllClose([0.256479], predictions[2]['predictions'])
self.assertAllClose([1.088732], predictions[3]['predictions'])
self.assertAllClose([1.901732], predictions[4]['predictions'])
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 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, 11)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 0.913176)
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, 11)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 0.913176)
def testTrainAndEvaluateEstimatorWithCenterBias(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,
center_bias=True)
# It will stop after 11 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)
# 10 steps for training and 2 step for bias centering.
self._assert_checkpoint(
est.model_dir, global_step=12, finalized_trees=2, attempted_layers=10)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 0.614642)
def testTrainAndEvaluateEstimatorWithPostPruning(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='post')
# 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'], 2.37652)
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 testTrainAndEvaluateEstimatorWithCenterBias(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,
center_bias=True)
# It will stop after 11 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)
# 10 steps for training and 2 step for bias centering.
self._assert_checkpoint(est.model_dir,
global_step=12,
finalized_trees=2,
attempted_layers=10)
eval_res = est.evaluate(input_fn=input_fn, steps=1)
self.assertAllClose(eval_res['average_loss'], 0.614642)
def testTrainAndEvaluateEstimatorWithPostPruning(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='post')
# 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'], 2.37652)
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 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 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)
self.assertAllClose(({
0: -0.070499420166015625,
1: -0.095000028610229492,
2: 0.0
}, {
0: -0.53763031959533691,
1: 0.063333392143249512,
2: 0.0
}, {
0: -0.51756942272186279,
1: -0.095000028610229492,
2: 0.0
}, {
0: 0.1563495397567749,
1: 0.063333392143249512,
2: 0.0
}, {
0: 0.96934974193572998,
1: 0.063333392143249512,
2: 0.0
}), dfcs)
# 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)
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)
self.assertAllClose(({
0: -0.070499420166015625,
1: -0.095000028610229492,
2: 0.0
}, {
0: -0.53763031959533691,
1: 0.063333392143249512,
2: 0.0
}, {
0: -0.51756942272186279,
1: -0.095000028610229492,
2: 0.0
}, {
0: 0.1563495397567749,
1: 0.063333392143249512,
2: 0.0
}, {
0: 0.96934974193572998,
1: 0.063333392143249512,
2: 0.0
}), dfcs)
# 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)
