def _CalibratedLinearRegressorWithQuantiles(self, feature_names,
feature_columns, **hparams_args):
"""Model that saves/retrieves quantiles."""
# Quantiles to be used for x2
quantiles_dir = self.get_temp_dir()
keypoints_initialization.save_quantiles_for_keypoints(
input_fn=self._test_data.threed_input_fn(True),
save_dir=quantiles_dir,
feature_columns=feature_columns,
num_steps=1)
# Keypoint initialization function for x0 and x1
def init_fn():
return keypoints_initialization.uniform_keypoints_for_signal(
_NUM_KEYPOINTS, -1., 1., -1., 1.)
hparams = tfl_hparams.CalibratedLinearHParams(
feature_names, num_keypoints=_NUM_KEYPOINTS, **hparams_args)
return calibrated_linear.calibrated_linear_regressor(
feature_columns=feature_columns,
hparams=hparams,
keypoints_initializers_fn={
'x0': init_fn,
'x1': init_fn
},
quantiles_dir=quantiles_dir # Used for 'x2'
)
def testLoadKeypointsFromQuantilesLoadingLabelQuantiles(self):
input_fn, unused_feature_names, unused_feature_columns = self._BuildInputs(
x0=np.random.uniform(0.0, 2.0, 100000),
x1=[0],
x2=[0],
label=np.random.uniform(0.0, 100.0, 100000))
save_dir = os.path.join(
self.get_temp_dir(),
'load_keypoints_from_quantiles_loading_label_quantiles')
keypoints_initialization.save_quantiles_for_keypoints(input_fn,
save_dir,
override=True)
with tf.Graph().as_default() as g, self.session(graph=g) as session:
result = keypoints_initialization.load_keypoints_from_quantiles(
feature_names=['x0'],
save_dir=save_dir,
num_keypoints=5,
use_label_quantiles_for_outputs=True)
result = session.run(result)
self.assertAllClose(
{
'x0': [
np.array([0.0, 0.5, 1.0, 1.5, 2.0]),
np.array([0.0, 25.0, 50.0, 75.0, 100.0])
]
},
result,
atol=0.2,
msg='load_keypoints_from_quantiles didn\'t produce expected labels'
)
def testQuantileInitWithMissingInputValuesDict(self):
num_examples = 10
x0 = np.linspace(-1.0, 1.0, num_examples)
x1 = np.linspace(0.0, 1.0, num_examples)
x2 = np.linspace(0.0, 1.0, num_examples)
input_fn, feature_names, feature_columns = self._BuildInputs(
x0, x1, x2)
save_dir = os.path.join(self.get_temp_dir(),
'exclude_input_values_dict')
keypoints_initialization.save_quantiles_for_keypoints(
input_fn,
save_dir,
feature_columns=feature_columns,
num_quantiles=num_examples,
override=True)
with ops.Graph().as_default() as g:
# Check by using load_keypoints_from_quantiles.
keypoints_init = keypoints_initialization.load_keypoints_from_quantiles(
feature_names,
save_dir,
num_keypoints=3,
output_min={
'x0': 0.,
'x1': 0.,
'x2': 0.
},
output_max={
'x0': 1.,
'x1': 1.,
'x2': 1.
},
missing_input_values_dict={
'x0': -1.0,
'x1': 0.0,
'x2': None
},
)
with self.test_session(graph=g) as sess:
keypoints_init = sess.run(keypoints_init)
self.assertAllClose(keypoints_init['x0'][0], [-0.778, 0.111, 1.0],
atol=0.1)
self.assertAllClose(keypoints_init['x0'][1], [0.0, 0.5, 1.0],
atol=0.01)
self.assertAllClose(keypoints_init['x1'][0], [0.111, 0.556, 1.0],
atol=0.1)
self.assertAllClose(keypoints_init['x1'][1], [0.0, 0.5, 1.0],
atol=0.01)
self.assertAllClose(keypoints_init['x2'][0], [0.0, 0.444, 1.0],
atol=0.01)
self.assertAllClose(keypoints_init['x2'][1], [0.0, 0.5, 1.0],
atol=0.01)
def testQuantileInitWithReversedDict(self):
num_examples = 100
x0 = np.linspace(0.0, 10.0, num_examples)
x1 = np.linspace(0.0, 10.0, num_examples)
x2 = np.linspace(0.0, 1.0, num_examples)
input_fn, feature_names, feature_columns = self._BuildInputs(
x0, x1, x2)
save_dir = os.path.join(self.get_temp_dir(), 'reversed_dict')
keypoints_initialization.save_quantiles_for_keypoints(
input_fn,
save_dir,
feature_columns=feature_columns,
num_quantiles=100,
override=True)
reversed_dict = {'x0': False, 'x1': True, 'x2': False}
with ops.Graph().as_default() as g:
# Check by using load_keypoints_from_quantiles.
keypoints_init = keypoints_initialization.load_keypoints_from_quantiles(
feature_names,
save_dir,
num_keypoints=3,
output_min={
'x0': 0.,
'x1': 0.,
'x2': 0.
},
output_max={
'x0': 1.,
'x1': 1.,
'x2': 1.
},
reversed_dict=reversed_dict)
with self.test_session(graph=g) as sess:
keypoints_init = sess.run(keypoints_init)
self.assertAllClose(keypoints_init['x0'][0], [0.0, 5.0, 10.0],
atol=0.1)
self.assertAllClose(keypoints_init['x0'][1], [0.0, 0.5, 1.0],
atol=0.01)
self.assertAllClose(keypoints_init['x1'][0], [0.0, 5.0, 10.0],
atol=0.1)
self.assertAllClose(keypoints_init['x1'][1], [1.0, 0.5, 0.0],
atol=0.01)
self.assertAllClose(keypoints_init['x2'][0], [0.0, 0.5, 1.0],
atol=0.01)
self.assertAllClose(keypoints_init['x2'][1], [0.0, 0.5, 1.0],
atol=0.01)
def testLoadKeypointsFromQuantilesRaises(self,
use_label_quantiles_for_outputs,
output_min, output_max, msg):
input_fn, unused_feature_names, unused_feature_columns = self._BuildInputs(
x0=np.random.uniform(0.0, 2.0, 100000),
x1=[0],
x2=[0],
label=np.random.uniform(0.0, 100.0, 100000))
save_dir = os.path.join(
self.get_temp_dir(),
'load_keypoints_from_quantiles_loading_label_quantiles')
keypoints_initialization.save_quantiles_for_keypoints(input_fn,
save_dir,
override=True)
with self.assertRaises(ValueError, msg=msg):
keypoints_initialization.load_keypoints_from_quantiles(
use_label_quantiles_for_outputs=use_label_quantiles_for_outputs,
output_min=output_min,
output_max=output_max,
feature_names=['x0'],
save_dir=save_dir,
num_keypoints=5)
def testSaveQuantilesForKeypointsSavingLabelQuantiles(self):
input_fn, unused_feature_names, unused_feature_columns = self._BuildInputs(
x0=[0],
x1=[0],
x2=[0],
label=np.random.uniform(0.0, 100.0, 100000))
save_dir = os.path.join(self.get_temp_dir(),
'save_quantiles_for_keypoints_saving_labels')
keypoints_initialization.save_quantiles_for_keypoints(input_fn,
save_dir,
override=True,
num_quantiles=5)
subdir = os.path.join(save_dir,
keypoints_initialization._QUANTILES_SUBDIRECTORY)
quantiles = keypoints_initialization._load_quantiles(
subdir, keypoints_initialization._LABEL_FEATURE_NAME)
self.assertAllClose(
np.linspace(0, 100.0, 5),
quantiles,
atol=0.2,
msg=(
'quantiles saved by save_quantiles_for_keypoints() do not much'
' expected quantiles'))
def _CheckSaveQuantilesForKeypoints(self, name, num_examples, num_steps,
x0, x1, x2, use_feature_columns,
override):
input_fn, feature_names, feature_columns = self._BuildInputs(
x0, x1, x2)
save_dir = os.path.join(self.get_temp_dir(), name)
keypoints_initialization.save_quantiles_for_keypoints(
input_fn,
save_dir,
feature_columns=(feature_columns if use_feature_columns else None),
num_quantiles=5,
override=override)
# Check by reading files directly.
subdir = os.path.join(save_dir,
keypoints_initialization._QUANTILES_SUBDIRECTORY)
quantiles_x0 = keypoints_initialization._load_quantiles(subdir, 'x0')
quantiles_x1 = keypoints_initialization._load_quantiles(subdir, 'x1')
quantiles_x2 = keypoints_initialization._load_quantiles(subdir, 'x2')
self.assertAllClose(quantiles_x0, [0, 2.5**2, 5.**2, 7.5**2, 100.],
atol=0.2)
self.assertAllClose(
quantiles_x1,
[1., math.pow(10., 0.5), 10.0,
math.pow(10., 1.5), 100.],
atol=0.2)
# x2 should start with [0,0,...] and end in [..., 1, 1], the middle value
# can be either 0 or 1.
self.assertAllClose(quantiles_x2[0:2], [0., 0.], atol=1e-3)
self.assertAllClose(quantiles_x2[-2:], [1., 1.], atol=1e-3)
# New graph is needed because default graph is changed by save
# keypoints, and self.test_session() will by default try to reuse a cached
# session, with a different graph.
with ops.Graph().as_default() as g:
# Check by using load_keypoints_from_quantiles.
keypoints_init = keypoints_initialization.load_keypoints_from_quantiles(
feature_names,
save_dir,
3,
output_min={
'x0': 0.,
'x1': 1.,
'x2': 7.
},
output_max={
'x0': 1.,
'x1': 10.,
'x2': 13.
})
with self.test_session(graph=g) as sess:
keypoints_init = sess.run(keypoints_init)
self.assertAllClose(keypoints_init['x0'][0], [0, 5.**2, 100.],
atol=0.2)
self.assertAllClose(keypoints_init['x0'][1], [0., 0.5, 1.])
self.assertAllClose(keypoints_init['x1'][0], [1., 10.0, 100.],
atol=0.2)
self.assertAllClose(keypoints_init['x1'][1], [1., 5.5, 10.])
# Notice x2 only has 2 unique values, so it should have lowered the
# num_keypoints to 2.
self.assertAllClose([0., 1.0], keypoints_init['x2'][0], atol=1e-3)
self.assertAllClose([7., 13.0], keypoints_init['x2'][1], atol=1e-3)
# Check that load_keypoints_from_quantiles don't generate anything
# if num_keypoints is 0 or unset.
with ops.Graph().as_default() as g:
# Check by using load_keypoints_from_quantiles.
keypoints_init = keypoints_initialization.load_keypoints_from_quantiles(
feature_names,
save_dir, {
'x0': 3,
'x2': 3,
'x1': 0
},
output_min={
'x0': 0.,
'x1': 1.,
'x2': 7.
},
output_max={
'x0': 1.,
'x1': 10.,
'x2': 13.
})
with self.test_session(graph=g) as sess:
keypoints_init = sess.run(keypoints_init)
self.assertTrue('x0' in keypoints_init)
self.assertTrue('x2' in keypoints_init)
self.assertTrue('x1' not in keypoints_init)
