def testBinaryClassifierTrainInMemoryAndEvalAndInfer(self):
train_input_fn = _make_train_input_fn(is_classification=True)
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_classifier_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, 6)
# Check eval.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
# Check predict that all labels are correct.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertEquals(5, len(predictions))
self.assertAllClose([0], predictions[0]['class_ids'])
self.assertAllClose([1], predictions[1]['class_ids'])
self.assertAllClose([1], predictions[2]['class_ids'])
self.assertAllClose([0], predictions[3]['class_ids'])
self.assertAllClose([0], predictions[4]['class_ids'])
def testBinaryClassifierTrainInMemoryAndEvalAndInfer(self):
train_input_fn = _make_train_input_fn(is_classification=True)
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_classifier_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, 6)
# Check eval.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
# Check predict that all labels are correct.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertEquals(5, len(predictions))
self.assertAllClose([0], predictions[0]['class_ids'])
self.assertAllClose([1], predictions[1]['class_ids'])
self.assertAllClose([1], predictions[2]['class_ids'])
self.assertAllClose([0], predictions[3]['class_ids'])
self.assertAllClose([0], predictions[4]['class_ids'])
def testBinaryClassifierTrainInMemoryWithDataset(self):
train_input_fn = _make_train_input_fn_dataset(is_classification=True)
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_classifier_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['accuracy'], 1.0)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
def testBinaryClassifierTrainInMemoryAndEvalAndInferWithPrePruning(self):
train_input_fn = _make_train_input_fn(is_classification=True)
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_classifier_train_in_memory(
train_input_fn=train_input_fn,
feature_columns=self._feature_columns,
n_trees=1,
max_depth=5,
pruning_mode='pre',
tree_complexity=0.01)
# We stop actually after 2*depth*n_trees steps (via a hook) because we still
# could not grow 1 trees of depth 5 (due to pre-pruning).
self._assert_checkpoint(est.model_dir,
global_step=11,
finalized_trees=0,
attempted_layers=11)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
# Validate predictions.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
def testBinaryClassifierTrainInMemoryWithDataset(self):
train_input_fn = _make_train_input_fn_dataset(is_classification=True)
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_classifier_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['accuracy'], 1.0)
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
def testBinaryClassifierTrainInMemoryAndEvalAndInferWithPrePruning(self):
train_input_fn = _make_train_input_fn(is_classification=True)
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_classifier_train_in_memory(
train_input_fn=train_input_fn,
feature_columns=self._feature_columns,
n_trees=1,
max_depth=5,
pruning_mode='pre',
tree_complexity=0.01)
# We stop actually after 2*depth*n_trees steps (via a hook) because we still
# could not grow 1 trees of depth 5 (due to pre-pruning).
self._assert_checkpoint(
est.model_dir, global_step=11, finalized_trees=0, attempted_layers=11)
# Check evaluate and predict.
eval_res = est.evaluate(input_fn=train_input_fn, steps=1)
self.assertAllClose(eval_res['accuracy'], 1.0)
# Validate predictions.
predictions = list(est.predict(input_fn=predict_input_fn))
self.assertAllClose([[0], [1], [1], [0], [0]],
[pred['class_ids'] for pred in predictions])
