[pre-commit.ci] pre-commit autoupdate (#463)

.pre-commit-config.yaml

@@ -52,7 +52,7 @@ repos:
       - id: check-docstring-first
         exclude: src/estimagic/optimization/algo_options.py
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.30.0
+    rev: v1.31.0
     hooks:
       - id: yamllint
         exclude: tests/optimization/fixtures
@@ -67,7 +67,7 @@ repos:
       - id: blacken-docs
         exclude: docs/source/how_to_guides/optimization/how_to_specify_constraints.md
   - repo: https://github.com/PyCQA/docformatter
-    rev: v1.5.1
+    rev: v1.6.4
     hooks:
       - id: docformatter
         args:
@@ -77,19 +77,11 @@ repos:
           - --wrap-descriptions
           - '88'
           - --blank
+        exclude: src/estimagic/optimization/algo_options.py
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.261
+    rev: v0.0.263
     hooks:
       - id: ruff
-      #   args:
-      #     - --verbose
-  # - repo: https://github.com/kynan/nbstripout
-  #   rev: 0.6.1
-  #   hooks:
-  #     - id: nbstripout
-  #       args:
-  #         - --extra-keys
-  #         - metadata.kernelspec metadata.language_info.version metadata.vscode
   - repo: https://github.com/nbQA-dev/nbQA
     rev: 1.7.0
     hooks:

src/estimagic/differentiation/derivatives.py

@@ -723,7 +723,7 @@ def _convert_richardson_candidates_to_frame(jac, err):
 def _convert_evals_to_numpy(
     raw_evals, key, registry, is_scalar_out=False, is_vector_out=False
 ):
-    """harmonize the output of the function evaluations.
+    """Harmonize the output of the function evaluations.
 
     The raw_evals might contain dictionaries of which we only need one entry, scalar
     np.nan where we need arrays filled with np.nan or pandas objects. The processed

src/estimagic/examples/numdiff_functions.py

@@ -4,7 +4,6 @@
 
 Example inputs for the binary choice functions are in binary_choice_inputs.pickle. They
 come from the statsmodels documentation:
-
 https://tinyurl.com/y4x67vwl
 We pickled them so we don't need statsmodels as a dependency.
 

src/estimagic/inference/bootstrap_samples.py

@@ -75,7 +75,7 @@ def get_bootstrap_samples(data, rng, cluster_by=None, n_draws=1000):
 
 
 def _get_bootstrap_samples_from_indices(data, bootstrap_indices):
-    """convert bootstrap indices into actual bootstrap samples.
+    """Convert bootstrap indices into actual bootstrap samples.
 
     Args:
         data (pandas.DataFrame): original dataset.

src/estimagic/optimization/algo_options.py

@@ -187,7 +187,6 @@
 
 """
 
-
 """
 -------------------------
 Trust Region Parameters

src/estimagic/optimization/tiktak.py

@@ -3,16 +3,10 @@
 TikTak (`Arnoud, Guvenen, and Kleineberg
 <https://www.nber.org/system/files/working_papers/w26340/w26340.pdf>`_)
 
-
-
-
-
-
-is an algorithm for solving global optimization problems. It performs local searches
+ is an algorithm for solving global optimization problems. It performs local searches
 from a set of carefully-selected points in the parameter space.
 
 First implemented in Python by Alisdair McKay (
-
 `GitHub Repository <https://github.com/amckay/TikTak>`_)
 
 """

src/estimagic/parameters/consolidate_constraints.py

@@ -215,7 +215,7 @@ def _consolidate_fixes_with_equality_constraints(
 def _consolidate_bounds_with_equality_constraints(
     equality_constraints, lower_bounds, upper_bounds
 ):
-    """consolidate bounds with equality constraints.
+    """Consolidate bounds with equality constraints.
 
     Check that there are no incompatible bounds on equality constrained parameters and
     set the bounds for equal parameters to the strictest bound encountered on any of

src/estimagic/sensitivity/msm_sensitivity.py

@@ -16,7 +16,7 @@
 
 
 def calculate_sensitivity_to_bias(jac, weights):
-    """calculate the sensitivity to bias.
+    """Calculate the sensitivity to bias.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 
@@ -47,7 +47,7 @@ def calculate_sensitivity_to_bias(jac, weights):
 def calculate_fundamental_sensitivity_to_noise(
     jac, weights, moments_cov, params_cov_opt
 ):
-    """calculate the fundamental sensitivity to noise.
+    """Calculate the fundamental sensitivity to noise.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 
@@ -104,7 +104,7 @@ def calculate_fundamental_sensitivity_to_noise(
 def calculate_actual_sensitivity_to_noise(
     sensitivity_to_bias, weights, moments_cov, params_cov
 ):
-    """calculate the actual sensitivity to noise.
+    """Calculate the actual sensitivity to noise.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 
@@ -159,7 +159,7 @@ def calculate_actual_sensitivity_to_noise(
 
 
 def calculate_actual_sensitivity_to_removal(jac, weights, moments_cov, params_cov):
-    """calculate the actual sensitivity to removal.
+    """Calculate the actual sensitivity to removal.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 
@@ -211,7 +211,7 @@ def calculate_actual_sensitivity_to_removal(jac, weights, moments_cov, params_co
 
 
 def calculate_fundamental_sensitivity_to_removal(jac, moments_cov, params_cov_opt):
-    """calculate the fundamental sensitivity to removal.
+    """Calculate the fundamental sensitivity to removal.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 
@@ -269,7 +269,7 @@ def calculate_fundamental_sensitivity_to_removal(jac, moments_cov, params_cov_op
 
 
 def calculate_sensitivity_to_weighting(jac, weights, moments_cov, params_cov):
-    """calculate the sensitivity to weighting.
+    """Calculate the sensitivity to weighting.
 
     The sensitivity measure is calculated for each parameter wrt each moment.
 

tests/optimization/test_optimize.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import pytest
 from estimagic.examples.criterion_functions import sos_scalar_criterion
-from estimagic.exceptions import InvalidKwargsError
+from estimagic.exceptions import InvalidKwargsError, InvalidFunctionError
 from estimagic.optimization.optimize import maximize, minimize
 
 
@@ -23,9 +23,9 @@ def test_scipy_lbfgsb_actually_calls_criterion_and_derivative():
     params = pd.DataFrame(data=np.ones((10, 1)), columns=["value"])
 
     def raising_crit_and_deriv(params):  # noqa: ARG001
-        raise Exception()
+        raise NotImplementedError("This should not be called.")
 
-    with pytest.raises(Exception):
+    with pytest.raises(InvalidFunctionError, match="Error while evaluating"):
         minimize(
             criterion=sos_scalar_criterion,
             params=params,