replace sphinx, cover pep8

doc/conf.py

@@ -19,16 +19,16 @@
 import warnings
 import os
 import re
-import matplotlib
-matplotlib.use('Agg')
-import sphinx_gallery
 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'

examples/exploration-vs-exploitation.py

@@ -49,6 +49,7 @@
 
 noise_level = 0.1
 
+
 # Our 1D toy problem, this is the function we are trying to
 # minimize
 def objective(x, noise_level=noise_level):
@@ -61,7 +62,7 @@ def objective_wo_noise(x):
 
 #############################################################################
 
-opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points = 3,
+opt = Optimizer([(-2.0, 2.0)], "GP", n_initial_points=3,
                 acq_optimizer="sampling")
 
 #############################################################################

examples/interruptible-optimization.py

@@ -63,14 +63,14 @@ 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 initialization 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,                   # 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);
 
 #############################################################################
 # Now let's assume this did not finish at once but took some long time: you
@@ -107,14 +107,14 @@ def obj_fun(x, noise_level=noise_level):
 y0 = res.func_vals
 
 gp_minimize(obj_fun,            # the function to minimize
-              [(-20.0, 20.0)],    # the bounds on each dimension of x
-              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_random_starts=3,  # the number of random initialization points
-              callback=[checkpoint_saver],
-              random_state=777);
+            [(-20.0, 20.0)],    # the bounds on each dimension of x
+            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_random_starts=3,  # the number of random initialization points
+            callback=[checkpoint_saver],
+            random_state=777);
 
 #############################################################################
 # Possible problems

examples/store-and-load-results.py

@@ -60,7 +60,7 @@ def obj_fun(x, noise_level=noise_level):
                   x0=[0.],            # the starting point
                   acq_func="LCB",     # the acquisition function (optional)
                   n_calls=15,         # the number of evaluations of f including at x0
-                  n_random_starts=3,  # the number of random initialization points
+                  n_random_starts=3,  # the number of random initial points
                   random_state=777)
 
 #############################################################################