Bump pylint from 3.2.7 to 3.3.0 (#98)

sigopt · Oct 14, 2024 · 0c2147f · 0c2147f
1 parent 13782f9
commit 0c2147f
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Showing 19 changed files with 74 additions and 44 deletions.
diff --git a/libsigopt/compute/expected_improvement.py b/libsigopt/compute/expected_improvement.py
@@ -164,6 +164,7 @@ def __init__(
     self,
     predictor,
     num_points_to_sample,
+    *,
     points_being_sampled=None,
     num_mc_iterations=DEFAULT_MC_ITERATIONS_TOTAL_QEI,
     num_mc_iterations_per_loop=DEFAULT_MC_ITERATIONS_PER_LOOP_QEI,
@@ -332,14 +333,15 @@ def __init__(
     predictor,
     num_points_to_sample,
     failure_model,
+    *,
     points_being_sampled=None,
     num_mc_iterations=DEFAULT_MC_ITERATIONS_TOTAL_QEIWF,
     num_mc_iterations_per_loop=DEFAULT_MC_ITERATIONS_PER_LOOP_QEIWF,
   ):
     super().__init__(
       predictor,
       num_points_to_sample,
-      points_being_sampled,
+      points_being_sampled=points_being_sampled,
       num_mc_iterations=num_mc_iterations,
       num_mc_iterations_per_loop=num_mc_iterations_per_loop,
     )

diff --git a/libsigopt/compute/log_likelihood.py b/libsigopt/compute/log_likelihood.py
@@ -30,6 +30,7 @@ def __init__(
     covariance,
     historical_data,
     mean_poly_indices=None,
+    *,
     use_auto_noise=False,
     log_domain=False,
     scaling_factor=DEFAULT_LOG_LIKELIHOOD_SCALING_FACTOR,

diff --git a/libsigopt/compute/misc/multimetric.py b/libsigopt/compute/misc/multimetric.py
@@ -329,6 +329,7 @@ def force_minimum_successful_points(optimizing_metric, points_sampled_values, po
   return modified_points_sampled_failures
 
 
+# pylint: disable=too-many-positional-arguments
 def filter_convex_combination(
   multimetric_info,
   points_sampled_points,
@@ -567,3 +568,6 @@ def filter_multimetric_points_sampled_spe(
   if multimetric_info.method != EPSILON_CONSTRAINT:
     modified_points_sampled_values[points_sampled_failures] = modified_lie_value
   return modified_points_sampled_points, modified_points_sampled_values
+
+
+# pylint: enable=too-many-positional-arguments
diff --git a/libsigopt/compute/sigopt_parzen_estimator.py b/libsigopt/compute/sigopt_parzen_estimator.py
@@ -34,6 +34,7 @@ class SigOptParzenEstimator(ScipyOptimizable):
 
   def __init__(
     self,
+    *,
     lower_covariance,
     greater_covariance,
     points_sampled_points,

diff --git a/libsigopt/compute/vectorized_optimizers.py b/libsigopt/compute/vectorized_optimizers.py
@@ -19,7 +19,7 @@ class VectorizedOptimizer(Optimizer):
   optimizer_name: str
   optimizer_parameters_type: type
 
-  def __init__(self, domain, acquisition_function, num_multistarts, optimizer_parameters, maxiter):
+  def __init__(self, domain, acquisition_function, num_multistarts, *, optimizer_parameters, maxiter):
     """
         This is the base class for vectorized _maximization_.
         """
@@ -144,10 +144,17 @@ def __init__(
     domain,
     acquisition_function,
     num_multistarts,
+    *,
     optimizer_parameters=None,
     maxiter=None,
   ):
-    super().__init__(domain, acquisition_function, num_multistarts, optimizer_parameters, maxiter)
+    super().__init__(
+      domain,
+      acquisition_function,
+      num_multistarts=num_multistarts,
+      optimizer_parameters=optimizer_parameters,
+      maxiter=maxiter,
+    )
     self.strategy = self.optimizer_parameters.strategy
     self.mutation = self.optimizer_parameters.mutation
     self.crossover_probability = self.optimizer_parameters.crossover_probability
@@ -219,10 +226,13 @@ def __init__(
     domain,
     acquisition_function,
     num_multistarts,
+    *,
     optimizer_parameters=None,
     maxiter=None,
   ):
-    super().__init__(domain, acquisition_function, num_multistarts, optimizer_parameters, maxiter)
+    super().__init__(
+      domain, acquisition_function, num_multistarts, optimizer_parameters=optimizer_parameters, maxiter=maxiter
+    )
     self.learning_rate = self.optimizer_parameters.learning_rate
     assert self.learning_rate >= 0
     self.beta_1 = self.optimizer_parameters.beta_1

diff --git a/libsigopt/views/rest/gp_hyper_opt_multimetric.py b/libsigopt/views/rest/gp_hyper_opt_multimetric.py
@@ -41,6 +41,7 @@ def form_one_hot_hyperparameter_domain(
   use_auto_noise,
   discrete_lower_limit,
   task_cost_populated,
+  *,
   select_hyper_opt_in_log_domain=SELECT_HYPER_OPT_IN_LOG_DOMAIN,
 ):
   ALPHA_LOWER_FACTOR = 0.001

diff --git a/libsigopt/views/rest/spe_next_points.py b/libsigopt/views/rest/spe_next_points.py
@@ -193,6 +193,7 @@ def draw_samples(
     sigopt_parzen_estimator,
     num_to_sample,
     domain,
+    *,
     num_multistarts=SPE_NUM_MULTISTARTS,
     batch_size=SPE_BATCH_SIZE,
     rejection_samples_limit=SPE_REJECTION_SAMPLES_LIMIT,

diff --git a/libsigopt/views/view.py b/libsigopt/views/view.py
@@ -338,6 +338,7 @@ def form_single_gaussian_process(
     filtered_points_sampled_values,
     filtered_points_sampled_value_vars,
     filtered_scaled_lie_value,
+    *,
     hyperparameter_dict,
   ):
     one_hot_historical_data = HistoricalData(self.dim_with_task)
@@ -390,7 +391,7 @@ def form_gaussian_process_for_acquisition_function(self):
           filtered_points_sampled_values[:, i],
           filtered_points_sampled_value_vars[:, i],
           filtered_scaled_lie_value[i],
-          self.params["model_info"].hyperparameters[metric_index],
+          hyperparameter_dict=self.params["model_info"].hyperparameters[metric_index],
         )
         gaussian_process_list.append(gp)
       assert isinstance(self.multimetric_info.params, ConvexCombinationParams)
@@ -408,7 +409,7 @@ def form_gaussian_process_for_acquisition_function(self):
         filtered_points_sampled_values,
         filtered_points_sampled_value_vars,
         filtered_scaled_lie_value,
-        self.params["model_info"].hyperparameters[metric_index],
+        hyperparameter_dict=self.params["model_info"].hyperparameters[metric_index],
       )
     return main_gaussian_process
 
@@ -436,7 +437,7 @@ def _form_gp_for_probabilistic_failures(self, relative_index, for_af_values=True
       points_sampled_values[:, relative_index],
       points_sampled_value_vars[:, relative_index],
       scaled_lie_values[relative_index],
-      self.params["model_info"].hyperparameters[metric_indexes[relative_index]],
+      hyperparameter_dict=self.params["model_info"].hyperparameters[metric_indexes[relative_index]],
     )
 
   # TODO(RTL-85): I think a lot of this workflow can be cleanup up with find_epsilon_constraint_value better now

diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -2,5 +2,5 @@ build==1.2.2.post1
 flaky==3.8.1
 mock==5.1.0
 pre-commit==3.8.0
-pylint==3.2.7
+pylint==3.3.0
 pytest==8.3.3
diff --git a/test/testaux/numerical_test_case.py b/test/testaux/numerical_test_case.py
@@ -58,7 +58,7 @@ def assert_vector_within_relative_norm(value, truth, tol, norm=2):
     assert err / mag < tol, f"error = {err} / magnitude = {mag} > tol = {tol}"
 
   @staticmethod
-  def check_gradient_with_finite_difference(x, func, grad, fd_step, tol, use_complex=False):
+  def check_gradient_with_finite_difference(x, func, grad, fd_step, tol, *, use_complex=False):
     """
         Approximate gradient using finite difference using either the centered method or complex step.
         """

diff --git a/test/testcompute/spe_test.py b/test/testcompute/spe_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import random
 
 import numpy
@@ -94,20 +95,20 @@ def test_default(self, form_multimetric_info, phase):
       assert phase == EPSILON_CONSTRAINT
       with pytest.raises(SPEInsufficientDataError):
         SigOptParzenEstimator(
-          C0RadialMatern(hparams),
-          greater_covariance,
-          points_sampled.points,
-          points_sampled.values,
-          gamma,
+          lower_covariance=C0RadialMatern(hparams),
+          greater_covariance=greater_covariance,
+          points_sampled_points=points_sampled.points,
+          points_sampled_values=points_sampled.values,
+          gamma=gamma,
         )
       return
 
     spe = SigOptParzenEstimator(
-      C0RadialMatern(hparams),
-      greater_covariance,
-      points_sampled.points,
-      points_sampled.values,
-      gamma,
+      lower_covariance=C0RadialMatern(hparams),
+      greater_covariance=greater_covariance,
+      points_sampled_points=points_sampled.points,
+      points_sampled_values=points_sampled.values,
+      gamma=gamma,
     )
     lpdf, gpdf, ei_vals = spe.evaluate_expected_improvement(points_to_sample)
     assert all(lpdf) > 0 and all(gpdf) > 0 and all(ei_vals) > 0 and len(ei_vals) == len(points_to_sample)
@@ -116,11 +117,11 @@ def test_default(self, form_multimetric_info, phase):
       spe.evaluate_grad_expected_improvement(points_to_sample)
 
     spe = SigOptParzenEstimator(
-      C4RadialMatern(hparams),
-      greater_covariance,
-      points_sampled.points,
-      points_sampled.values,
-      gamma,
+      lower_covariance=C4RadialMatern(hparams),
+      greater_covariance=greater_covariance,
+      points_sampled_points=points_sampled.points,
+      points_sampled_values=points_sampled.values,
+      gamma=gamma,
     )
     assert spe.differentiable
     ei_grad = spe.evaluate_grad_expected_improvement(points_to_sample)
@@ -152,11 +153,11 @@ def test_greater_lower_split(self, form_multimetric_info, phase):
     # NOTE: max is used here since we don't apply MMI to values when creating SigOptParzenEstimator
     numpy.place(values, failures, numpy.max(values))
     spe = SigOptParzenEstimator(
-      C0RadialMatern(hparams),
-      greater_covariance,
-      points,
-      values,
-      gamma,
+      lower_covariance=C0RadialMatern(hparams),
+      greater_covariance=greater_covariance,
+      points_sampled_points=points,
+      points_sampled_values=values,
+      gamma=gamma,
     )
     sorted_indexed = numpy.argsort(values)
     points_sorted = points[sorted_indexed, :]
@@ -193,11 +194,11 @@ def test_insufficient_data(self, form_multimetric_info, phase):
     )
     with pytest.raises(SPEInsufficientDataError):
       SigOptParzenEstimator(
-        C0RadialMatern(hparams),
-        greater_covariance,
-        points_sampled.points,
-        points_sampled.values,
-        gamma,
+        lower_covariance=C0RadialMatern(hparams),
+        greater_covariance=greater_covariance,
+        points_sampled_points=points_sampled.points,
+        points_sampled_values=points_sampled.values,
+        gamma=gamma,
       )
 
   @pytest.mark.parametrize(
@@ -224,11 +225,11 @@ def test_insufficient_data_with_forget_factor(self, form_multimetric_info, phase
     )
     with pytest.raises(SPEInsufficientDataError):
       SigOptParzenEstimator(
-        C0RadialMatern(hparams),
-        greater_covariance,
-        points_sampled.points,
-        points_sampled.values,
-        gamma,
+        lower_covariance=C0RadialMatern(hparams),
+        greater_covariance=greater_covariance,
+        points_sampled_points=points_sampled.points,
+        points_sampled_values=points_sampled.values,
+        gamma=gamma,
         forget_factor=0.5,
       )
 
@@ -237,11 +238,11 @@ def test_append_and_clear_lies(self):
     points_sampled_points = domain.generate_quasi_random_points_in_domain(num_sampled)
     points_sampled_values = numpy.random.rand(num_sampled)
     spe = SigOptParzenEstimator(
-      C0RadialMatern(hparams),
-      greater_covariance,
-      points_sampled_points,
-      points_sampled_values,
-      gamma,
+      lower_covariance=C0RadialMatern(hparams),
+      greater_covariance=greater_covariance,
+      points_sampled_points=points_sampled_points,
+      points_sampled_values=points_sampled_values,
+      gamma=gamma,
     )
     assert spe.lower_points is not None
     assert spe.greater_points is not None

diff --git a/test/testviews/rest/gp_ei_test.py b/test/testviews/rest/gp_ei_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import pytest
 from testviews.zigopt_input_utils import ZigoptSimulator
 

diff --git a/test/testviews/rest/gp_hyper_opt_test.py b/test/testviews/rest/gp_hyper_opt_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 from copy import deepcopy
 
 import numpy

diff --git a/test/testviews/rest/gp_next_points_test.py b/test/testviews/rest/gp_next_points_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 import pytest
 from flaky import flaky

diff --git a/test/testviews/rest/search_next_points_test.py b/test/testviews/rest/search_next_points_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 import pytest
 from mock import patch

diff --git a/test/testviews/rest/spe_next_points_test.py b/test/testviews/rest/spe_next_points_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 import pytest
 from flaky import flaky

diff --git a/test/testviews/rest/spe_search_next_points_test.py b/test/testviews/rest/spe_search_next_points_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 import pytest
 from testviews.zigopt_input_utils import ZigoptSimulator

diff --git a/test/testviews/view_test.py b/test/testviews/view_test.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 import pytest
 from testviews.zigopt_input_utils import ZigoptSimulator, form_points_sampled

diff --git a/test/testviews/zigopt_input_utils.py b/test/testviews/zigopt_input_utils.py
@@ -1,6 +1,7 @@
 # Copyright © 2022 Intel Corporation
 #
 # SPDX-License-Identifier: Apache License 2.0
+# pylint: disable=too-many-positional-arguments
 import numpy
 
 from libsigopt.aux.adapter_info_containers import DomainInfo, GPModelInfo, MetricsInfo, PointsContainer