[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

profit/al/active_learning.py

@@ -4,6 +4,7 @@
 In order to get the most out of the least number of training points, the next point is inferred by calculating an
 acquisition function like the minimization of local variance or expected improvement.
 """
+
 import numpy as np
 from abc import abstractmethod
 from warnings import warn

profit/al/mcmc_al.py

@@ -73,9 +73,11 @@ def __init__(
             var.name for var in variables.list if var.kind.lower() == "activelearning"
         ]
         Xpred = [
-            np.linspace(*var.constraints, nsearch)
-            if var.name in al_keys
-            else np.unique(var.value)
+            (
+                np.linspace(*var.constraints, nsearch)
+                if var.name in al_keys
+                else np.unique(var.value)
+            )
             for var in variables.input_list
         ]
         self.Xpred = np.hstack(

profit/config.py

@@ -536,11 +536,11 @@ def process_entries(self, base_config):
                         {
                             "class": name,
                             "columns": select,
-                            "parameters": {
-                                k: float(v) for k, v in config.get("parameters", {})
-                            }
-                            if not isinstance(config, str)
-                            else {},
+                            "parameters": (
+                                {k: float(v) for k, v in config.get("parameters", {})}
+                                if not isinstance(config, str)
+                                else {}
+                            ),
                         }
                     )
 

profit/defaults.py

@@ -1,4 +1,5 @@
 """Global default configuration values."""
+
 from os import path, getcwd
 
 # Base Config

profit/run/local.py

@@ -35,9 +35,11 @@ def __repr__(self):
         return (
             f"<{self.__class__.__name__} (" + ", debug"
             if self.debug
-            else "" + f", {self.command}"
-            if self.command != "profit-worker"
-            else "" + ")>"
+            else (
+                "" + f", {self.command}"
+                if self.command != "profit-worker"
+                else "" + ")>"
+            )
         )
 
     @property

profit/run/slurm.py

@@ -68,11 +68,11 @@ def __repr__(self):
         return (
             f"<{self.__class__.__name__} (" + f", {self.cpus} cpus" + ", OpenMP"
             if self.openmp
-            else "" + ", debug"
-            if self.debug
-            else "" + ", custom script"
-            if self.custom
-            else "" + ")>"
+            else (
+                "" + ", debug"
+                if self.debug
+                else "" + ", custom script" if self.custom else "" + ")>"
+            )
         )
 
     @property

profit/sur/encoders.py

@@ -16,6 +16,7 @@ class Encoder(CustomABC):
     Attributes:
         label (str): Label of the encoder class.
     """
+
     labels = {}
 
     def __init__(self, columns, parameters=None):

profit/sur/gp/backend/init_kernels.py

@@ -4,6 +4,7 @@
 that is written to `kernels_base.f90`. This code has to
 be compiled via `make` subsequently.
 """
+
 # %%
 from sympy import symbols, sqrt, exp, diff
 from sympy.utilities.codegen import codegen

profit/sur/gp/gpy_surrogate.py

@@ -155,13 +155,15 @@ def select_kernel(self, kernel):
                 kern = []
                 for key in full_str:
                     kern += [
-                        key
-                        if key in ("+", "*")
-                        else "self.GPy.kern.{}({}, lengthscale={}, variance={})".format(
-                            key,
-                            self.ndim,
-                            self.hyperparameters.get("length_scale", [1]),
-                            self.hyperparameters.get("sigma_f", 1) ** 2,
+                        (
+                            key
+                            if key in ("+", "*")
+                            else "self.GPy.kern.{}({}, lengthscale={}, variance={})".format(
+                                key,
+                                self.ndim,
+                                self.hyperparameters.get("length_scale", [1]),
+                                self.hyperparameters.get("sigma_f", 1) ** 2,
+                            )
                         )
                     ]
                 return eval("".join(kern))

profit/sur/gp/sklearn_surrogate.py

@@ -144,10 +144,12 @@ def select_kernel(self, kernel):
             kernel = []
             for key in full_str:
                 kernel += [
-                    key
-                    if key in ("+", "*")
-                    else getattr(sklearn_kernels, key)(
-                        length_scale=self.hyperparameters["length_scale"]
+                    (
+                        key
+                        if key in ("+", "*")
+                        else getattr(sklearn_kernels, key)(
+                            length_scale=self.hyperparameters["length_scale"]
+                        )
                     )
                 ]
         except AttributeError: