0.15.6

tkagg handling

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -4860,7 +4860,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.9"
+      "version": "3.11.7"
     }
   },
   "nbformat": 4,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.15.5"
+version = "0.15.6"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/spot/spot.py

@@ -44,7 +44,8 @@
 from spotpython.utils.numpy2json import NumpyEncoder
 
 # Setting up the backend to use QtAgg
-matplotlib.use("TkAgg")
+# matplotlib.use("TkAgg")
+# matplotlib.use("Agg")
 
 
 logger = logging.getLogger(__name__)
@@ -259,6 +260,9 @@ def __init__(
         self.n_points = self.fun_control["n_points"]
         self.max_surrogate_points = self.fun_control["max_surrogate_points"]
         self.progress_file = self.fun_control["progress_file"]
+        self.tkagg = self.fun_control["tkagg"]
+        if self.tkagg:
+            matplotlib.use("TkAgg")
 
         # Tensorboard:
         self.init_spot_writer()
@@ -1447,7 +1451,7 @@ def infill(self, x) -> float:
             return self.surrogate.predict(X)
 
     def plot_progress(
-        self, show=True, log_x=False, log_y=False, filename="plot.png", style=["ko", "k", "ro-"], dpi=300
+        self, show=True, log_x=False, log_y=False, filename="plot.png", style=["ko", "k", "ro-"], dpi=300, tkagg=False
     ) -> None:
         """Plot the progress of the hyperparameter tuning (optimization).
 
@@ -1495,6 +1499,8 @@ def plot_progress(
                 S.plot_progress(log_y=True)
 
         """
+        if tkagg:
+            matplotlib.use("TkAgg")
         fig = pylab.figure(figsize=(9, 6))
         s_y = pd.Series(self.y)
         s_c = s_y.cummin()
@@ -1904,6 +1910,7 @@ def plot_contour(
         figsize=(12, 6),
         use_min=False,
         use_max=True,
+        tkagg=False,
     ) -> None:
         """Plot the contour of any dimension."""
 
@@ -1942,6 +1949,8 @@ def plot_contour_subplots(X, Y, Z, ax, min_z, max_z, contour_levels):
             contour = ax.contourf(X, Y, Z, contour_levels, zorder=1, cmap="jet", vmin=min_z, vmax=max_z)
             pylab.colorbar(contour, ax=ax)
 
+        if tkagg:
+            matplotlib.use("TkAgg")
         fig = setup_plot()
 
         (X, Y), x, y = generate_mesh_grid(self.lower, self.upper, n_grid)
@@ -2017,6 +2026,7 @@ def plot_important_hyperparameter_contour(
         dpi=200,
         use_min=False,
         use_max=True,
+        tkagg=False,
     ) -> None:
         """
         Plot the contour of important hyperparameters.
@@ -2123,6 +2133,7 @@ def plot_important_hyperparameter_contour(
                         dpi=dpi,
                         use_max=use_max,
                         use_min=use_min,
+                        tkagg=tkagg,
                     )
 
     def get_importance(self) -> list:
@@ -2181,7 +2192,7 @@ def print_importance(self, threshold=0.1, print_screen=True) -> list:
             print("Importance requires more than one theta values (n_theta>1).")
         return output
 
-    def plot_importance(self, threshold=0.1, filename=None, dpi=300, show=True) -> None:
+    def plot_importance(self, threshold=0.1, filename=None, dpi=300, show=True, tkagg=False) -> None:
         """Plot the importance of each variable.
 
         Args:
@@ -2198,6 +2209,8 @@ def plot_importance(self, threshold=0.1, filename=None, dpi=300, show=True) -> N
             None
         """
         if self.surrogate.n_theta > 1:
+            if tkagg:
+                matplotlib.use("TkAgg")
             theta = np.power(10, self.surrogate.theta)
             imp = 100 * theta / np.max(theta)
             idx = np.where(imp > threshold)[0]

src/spotpython/torch/traintest.py

@@ -129,7 +129,7 @@ def evaluate_cv(
             best_val_loss = float("inf")
             counter = 0
             for epoch in range(epochs_instance):
-                print(f"Epoch: {epoch +1}", end=" | ")
+                print(f"Epoch: {epoch+1}", end=" | ")
                 # training loss from one epoch:
                 training_loss = train_one_epoch(
                     net=net,

src/spotpython/utils/init.py

@@ -79,6 +79,7 @@ def fun_control_init(
     test=None,
     test_seed=1234,
     test_size=0.4,
+    tkagg=False,
     train=None,
     tolerance_x=0,
     upper=None,
@@ -240,6 +241,8 @@ def fun_control_init(
         test_size (float):
             The size of the test set. Default is 0.4, i.e.,
             60% of the data is used for training and 40% for testing.
+        tkagg (bool):
+            Whether to use matplotlib TkAgg or not. Default is False.
         tolerance_x (float):
             tolerance for new x solutions. Minimum distance of new solutions,
             generated by `suggest_new_X`, to already existing solutions.
@@ -415,6 +418,7 @@ def fun_control_init(
         "test": test,
         "test_seed": test_seed,
         "test_size": test_size,
+        "tkagg": tkagg,
         "tolerance_x": tolerance_x,
         "train": train,
         "upper": upper,