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Fix some import related style guide violations in ES #371

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Sep 17, 2024
Merged

Fix some import related style guide violations in ES #371

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8 changes: 6 additions & 2 deletions compiler_opt/es/blackbox_learner_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,13 @@

from compiler_opt.distributed import worker
from compiler_opt.distributed.local import local_worker_manager
from compiler_opt.es import blackbox_learner, policy_utils
from compiler_opt.es import blackbox_learner
from compiler_opt.es import policy_utils
from compiler_opt.es import blackbox_optimizers
from compiler_opt.rl import corpus, inlining, policy_saver, registry
from compiler_opt.rl import corpus
from compiler_opt.rl import inlining
from compiler_opt.rl import policy_saver
from compiler_opt.rl import registry
from compiler_opt.rl.inlining import config as inlining_config
from compiler_opt.es import blackbox_evaluator

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73 changes: 41 additions & 32 deletions compiler_opt/es/blackbox_optimizers_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,6 @@
import numpy as np

from compiler_opt.es import blackbox_optimizers
from compiler_opt.es.blackbox_optimizers import EstimatorType, UpdateMethod, RegressionType
from compiler_opt.es import gradient_ascent_optimization_algorithms

perturbation_array = np.array([[0, 1], [2, -1], [4, 2],
Expand All @@ -65,10 +64,12 @@
class BlackboxOptimizationAlgorithmsTest(parameterized.TestCase):

@parameterized.parameters(
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 3,
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 3,
np.array([[4, 2], [2, 6], [-1, 5], [-2, -2], [8, -6], [1, -5]
]), np.array([10, -8, 4, -10, 8, -4])),
(perturbation_array, function_value_array, EstimatorType.FORWARD_FD, 5,
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 5,
np.array([[4, 2], [8, -6], [-1, 5], [0, -3], [2, -1]
]), np.array([10, 8, 4, 2, 1])))
def test_filtering(self, perturbations, function_values, est_type,
Expand All @@ -79,21 +80,23 @@ def test_filtering(self, perturbations, function_values, est_type,
np.testing.assert_array_equal(expected_fs, top_fs)

@parameterized.parameters(
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 3,
np.array([100, -16])), (perturbation_array, function_value_array,
EstimatorType.FORWARD_FD, 5, np.array([76, -9])),
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 0,
np.array([102, -34])),
(perturbation_array, function_value_array, EstimatorType.FORWARD_FD, 0,
np.array([74, -34])))
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 3, np.array([100, -16])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 5, np.array([76, -9])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 0, np.array([102, -34])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 0, np.array([74, -34])))
def test_monte_carlo_gradient(self, perturbations, function_values, est_type,
num_top_directions, expected_gradient):
precision_parameter = 0.1
step_size = 0.01
current_value = 2
blackbox_object = blackbox_optimizers.MonteCarloBlackboxOptimizer(
precision_parameter, est_type, False, UpdateMethod.NO_METHOD, None,
step_size, num_top_directions)
precision_parameter, est_type, False,
blackbox_optimizers.UpdateMethod.NO_METHOD, None, step_size,
num_top_directions)
current_input = np.zeros(2)
step = blackbox_object.run_step(perturbations, function_values,
current_input, current_value)
Expand All @@ -106,13 +109,14 @@ def test_monte_carlo_gradient(self, perturbations, function_values, est_type,
np.testing.assert_array_almost_equal(expected_gradient, gradient)

@parameterized.parameters(
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 3,
np.array([100, -16])), (perturbation_array, function_value_array,
EstimatorType.FORWARD_FD, 5, np.array([76, -9])),
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 0,
np.array([102, -34])),
(perturbation_array, function_value_array, EstimatorType.FORWARD_FD, 0,
np.array([74, -34])))
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 3, np.array([100, -16])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 5, np.array([76, -9])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 0, np.array([102, -34])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 0, np.array([74, -34])))
def test_monte_carlo_gradient_with_gradient_ascent_optimizer(
self, perturbations, function_values, est_type, num_top_directions,
expected_gradient):
Expand All @@ -124,8 +128,9 @@ def test_monte_carlo_gradient_with_gradient_ascent_optimizer(
step_size, 0.0))
blackbox_object = (
blackbox_optimizers.MonteCarloBlackboxOptimizer(
precision_parameter, est_type, False, UpdateMethod.NO_METHOD, None,
None, num_top_directions, gradient_ascent_optimizer))
precision_parameter, est_type, False,
blackbox_optimizers.UpdateMethod.NO_METHOD, None, None,
num_top_directions, gradient_ascent_optimizer))
current_input = np.zeros(2)
step = blackbox_object.run_step(perturbations, function_values,
current_input, current_value)
Expand All @@ -137,15 +142,18 @@ def test_monte_carlo_gradient_with_gradient_ascent_optimizer(

np.testing.assert_array_almost_equal(expected_gradient, gradient)

@parameterized.parameters(
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 3,
np.array([0.00483, -0.007534])),
(perturbation_array, function_value_array, EstimatorType.FORWARD_FD, 5,
np.array([0.012585, 0.000748])),
(perturbation_array, function_value_array, EstimatorType.ANTITHETIC, 0,
np.array([0.019319, -0.030134])),
(perturbation_array, function_value_array, EstimatorType.FORWARD_FD, 0,
np.array([0.030203, 0.001796])))
@parameterized.parameters((perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 3,
np.array([0.00483, -0.007534])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 5,
np.array([0.012585, 0.000748])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.ANTITHETIC, 0,
np.array([0.019319, -0.030134])),
(perturbation_array, function_value_array,
blackbox_optimizers.EstimatorType.FORWARD_FD, 0,
np.array([0.030203, 0.001796])))
def test_sklearn_gradient(self, perturbations, function_values, est_type,
num_top_directions, expected_gradient):
precision_parameter = 0.1
Expand All @@ -156,8 +164,9 @@ def test_sklearn_gradient(self, perturbations, function_values, est_type,
gradient_ascent_optimization_algorithms.MomentumOptimizer(
step_size, 0.0))
blackbox_object = blackbox_optimizers.SklearnRegressionBlackboxOptimizer(
RegressionType.RIDGE, regularizer, est_type, True,
UpdateMethod.NO_METHOD, [], None, gradient_ascent_optimizer)
blackbox_optimizers.RegressionType.RIDGE, regularizer, est_type, True,
blackbox_optimizers.UpdateMethod.NO_METHOD, [], None,
gradient_ascent_optimizer)
current_input = np.zeros(2)
step = blackbox_object.run_step(perturbations, function_values,
current_input, current_value)
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4 changes: 3 additions & 1 deletion compiler_opt/es/es_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,9 @@
# limitations under the License.
"""Local ES trainer."""

from absl import app, flags, logging
from absl import app
from absl import flags
from absl import logging
import gin

from compiler_opt.es import es_trainer_lib
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3 changes: 2 additions & 1 deletion compiler_opt/es/es_trainer_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,8 @@
from compiler_opt.es import gradient_ascent_optimization_algorithms
from compiler_opt.es import blackbox_learner
from compiler_opt.es import policy_utils
from compiler_opt.rl import policy_saver, corpus
from compiler_opt.rl import policy_saver
from compiler_opt.rl import corpus

POLICY_NAME = "policy"

Expand Down