set matplotlib.backend by export

deephyper · Sep 1, 2020 · b9c1bb1 · b9c1bb1
1 parent a30bddf
commit b9c1bb1
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Showing 4 changed files with 18 additions and 19 deletions.
diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh
@@ -174,6 +174,7 @@ conda create -n $CONDA_ENV_NAME --yes --quiet python="${PYTHON_VERSION:-*}" \
   scikit-image="${SCIKIT_IMAGE_VERSION:-*}" pandas="${PANDAS_VERSION:-*}" \
   joblib memory_profiler packaging
 
+export MPLBACKEND="agg"
 source activate testenv
 pip install sphinx-gallery
 pip install numpydoc

diff --git a/doc/conf.py b/doc/conf.py
@@ -15,20 +15,18 @@
 # import os
 # import sys
 # sys.path.insert(0, os.path.abspath('.'))
+import sys
+import os
+sys.path.insert(0, os.path.abspath('sphinxext'))
+import sphinx_gallery
 
 import warnings
-import os
 import re
 from packaging.version import parse
 # import pkg_resources
-import sys
 import skopt
-
-sys.path.insert(0, os.path.abspath('sphinxext'))
 from github_link import make_linkcode_resolve
-import matplotlib
-matplotlib.use('Agg')
-import sphinx_gallery
+
 
 #  __version__ = pkg_resources.get_distribution('skopt').version
 on_rtd = os.environ.get('READTHEDOCS', None) == 'True'

diff --git a/examples/exploration-vs-exploitation.py b/examples/exploration-vs-exploitation.py
@@ -59,12 +59,11 @@ def objective(x, noise_level=noise_level):
 
 def objective_wo_noise(x):
     return objective(x, noise_level=0)
-
 #############################################################################
 
+
 opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                 acq_optimizer="sampling")
-
 #############################################################################
 # Plotting parameters
 

diff --git a/examples/interruptible-optimization.py b/examples/interruptible-optimization.py
@@ -63,14 +63,15 @@ def obj_fun(x, noise_level=noise_level):
 
 checkpoint_saver = CheckpointSaver("./checkpoint.pkl", compress=9) # keyword arguments will be passed to `skopt.dump`
 
-gp_minimize(obj_fun,                      # the function to minimize
-            [(-20.0, 20.0)],              # the bounds on each dimension of x
-            x0=[-20.],                    # the starting point
-            acq_func="LCB",               # the acquisition function (optional)
-            n_calls=10,                   # the number of evaluations of f including at x0
-            n_random_starts=3,            # the number of random initial points
-            callback=[checkpoint_saver],  # a list of callbacks including the checkpoint saver
-            random_state=777);
+gp_minimize(obj_fun,            # the function to minimize
+            [(-20.0, 20.0)],    # the bounds on each dimension of x
+            x0=[-20.],          # the starting point
+            acq_func="LCB",     # the acquisition function (optional)
+            n_calls=10,         # number of evaluations of f including at x0
+            n_random_starts=3,  # the number of random initial points
+            callback=[checkpoint_saver],
+            # a list of callbacks including the checkpoint saver
+            random_state=777)
 
 #############################################################################
 # Now let's assume this did not finish at once but took some long time: you
@@ -111,10 +112,10 @@ def obj_fun(x, noise_level=noise_level):
             x0=x0,              # already examined values for x
             y0=y0,              # observed values for x0
             acq_func="LCB",     # the acquisition function (optional)
-            n_calls=10,         # the number of evaluations of f including at x0
+            n_calls=10,         # number of evaluations of f including at x0
             n_random_starts=3,  # the number of random initialization points
             callback=[checkpoint_saver],
-            random_state=777);
+            random_state=777)
 
 #############################################################################
 # Possible problems