chore(opytimizer): Runs the pre-commit hooks for the first time.

gugarosa · May 3, 2022 · 75b21ca · 75b21ca
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diff --git a/docs/conf.py b/docs/conf.py
@@ -19,14 +19,14 @@
 
 import opytimizer
 
-sys.path.insert(0, os.path.abspath('.'))
+sys.path.insert(0, os.path.abspath("."))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'opytimizer'
-copyright = '2020, Gustavo de Rosa'
-author = 'Gustavo de Rosa'
+project = "opytimizer"
+copyright = "2020, Gustavo de Rosa"
+author = "Gustavo de Rosa"
 
 # The short X.Y version
 version = opytimizer.__version__
@@ -45,33 +45,33 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = ".rst"
 
 # The master toctree document.
-master_doc = 'index'
+master_doc = "index"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = 'en'
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = None
@@ -82,7 +82,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -100,9 +100,9 @@
 # documentation.
 #
 html_theme_options = {
-    'collapse_navigation': False,
-    'display_version': True,
-    'logo_only': True,
+    "collapse_navigation": False,
+    "display_version": True,
+    "logo_only": True,
 }
 
 # Custom sidebar templates, must be a dictionary that maps document names
@@ -119,7 +119,7 @@
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'opytimizer_doc'
+htmlhelp_basename = "opytimizer_doc"
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -128,15 +128,12 @@
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',
-
     # The font size ('10pt', '11pt' or '12pt').
     #
     # 'pointsize': '10pt',
-
     # Additional stuff for the LaTeX preamble.
     #
     # 'preamble': '',
-
     # Latex figure (float) alignment
     #
     # 'figure_align': 'htbp',
@@ -146,19 +143,21 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'opytimizer.tex', 'Opytimizer Documentation',
-     'Gustavo de Rosa', 'manual'),
+    (
+        master_doc,
+        "opytimizer.tex",
+        "Opytimizer Documentation",
+        "Gustavo de Rosa",
+        "manual",
+    ),
 ]
 
 
 # -- Options for manual page output ------------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, 'opytimizer', 'Opytimizer Documentation',
-     [author], 1)
-]
+man_pages = [(master_doc, "opytimizer", "Opytimizer Documentation", [author], 1)]
 
 
 # -- Options for Texinfo output ----------------------------------------------
@@ -167,9 +166,15 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'opytimizer', 'Opytimizer Documentation',
-     author, 'opytimizer', 'One line description of project.',
-     'Miscellaneous'),
+    (
+        master_doc,
+        "opytimizer",
+        "Opytimizer Documentation",
+        author,
+        "opytimizer",
+        "One line description of project.",
+        "Miscellaneous",
+    ),
 ]
 
 
@@ -188,12 +193,10 @@
 # epub_uid = ''
 
 # A list of files that should not be packed into the epub file.
-epub_exclude_files = ['search.html']
+epub_exclude_files = ["search.html"]
 
 
 # -- Extension configuration -------------------------------------------------
-autodoc_default_options = {
-    'exclude-members': '__weakref__'
-}
+autodoc_default_options = {"exclude-members": "__weakref__"}
 
-autodoc_member_order = 'bysource'
+autodoc_member_order = "bysource"
diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -1,2 +1,2 @@
 sphinx>=3.1.0
-sphinx-rtd-theme>=0.4.3
+sphinx-rtd-theme>=0.4.3
diff --git a/examples/applications/additional_features/resume_optimization_from_file.py b/examples/applications/additional_features/resume_optimization_from_file.py
@@ -34,5 +34,5 @@
 
 # Loads the task from file and resumes it
 # Note that the following lines achieves the same results as a 35-iteration running
-opt = Opytimizer.load('iter_10_checkpoint.pkl')
+opt = Opytimizer.load("iter_10_checkpoint.pkl")
 opt.start(n_iterations=25)
diff --git a/examples/applications/single_objective/boolean_optimization.py b/examples/applications/single_objective/boolean_optimization.py
@@ -16,14 +16,16 @@
 
 # Parameters for the optimizer
 params = {
-    'c1': r.generate_binary_random_number(size=(n_variables, 1)),
-    'c2': r.generate_binary_random_number(size=(n_variables, 1))
+    "c1": r.generate_binary_random_number(size=(n_variables, 1)),
+    "c2": r.generate_binary_random_number(size=(n_variables, 1)),
 }
 
 # Creates the space, optimizer and function
 space = BooleanSpace(n_agents, n_variables)
 optimizer = BPSO(params)
-function = Function(Knapsack(values=(55, 10, 47, 5, 4), weights=(95, 4, 60, 32, 23), max_capacity=100))
+function = Function(
+    Knapsack(values=(55, 10, 47, 5, 4), weights=(95, 4, 60, 32, 23), max_capacity=100)
+)
 
 # Bundles every piece into Opytimizer class
 opt = Opytimizer(space, optimizer, function, save_agents=False)

diff --git a/examples/applications/single_objective/discrete_optimization.py b/examples/applications/single_objective/discrete_optimization.py
@@ -30,4 +30,6 @@
 opt = Opytimizer(space, optimizer, function, save_agents=False)
 
 # Runs the optimization task
-opt.start(n_iterations=5, callbacks=[DiscreteSearchCallback(allowed_values=allowed_values)])
+opt.start(
+    n_iterations=5, callbacks=[DiscreteSearchCallback(allowed_values=allowed_values)]
+)
diff --git a/examples/applications/single_objective/genetic_programming_optimization.py b/examples/applications/single_objective/genetic_programming_optimization.py
@@ -19,13 +19,21 @@
 max_depth = 5
 
 # Functions nodes, lower and upper bounds
-functions = ['SUM', 'MUL', 'DIV']
+functions = ["SUM", "MUL", "DIV"]
 lower_bound = [-10, -10]
 upper_bound = [10, 10]
 
 # Creates the space, optimizer and function
-space = TreeSpace(n_agents, n_variables, lower_bound, upper_bound,
-                  n_terminals, min_depth, max_depth, functions)
+space = TreeSpace(
+    n_agents,
+    n_variables,
+    lower_bound,
+    upper_bound,
+    n_terminals,
+    min_depth,
+    max_depth,
+    functions,
+)
 optimizer = GP()
 function = Function(Sphere())
 

diff --git a/examples/core/create_function.py b/examples/core/create_function.py
@@ -14,5 +14,5 @@ def test_function(z):
 f = Function(test_function)
 
 # Prints out some properties
-print(f'x: {x}')
-print(f'f(x): {f(x)}')
+print(f"x: {x}")
+print(f"f(x): {f(x)}")
diff --git a/examples/core/create_node.py b/examples/core/create_node.py
@@ -3,20 +3,22 @@
 from opytimizer.core.node import Node
 
 # Creates two new Nodes
-n1 = Node(name='0', category='TERMINAL', value=np.array(1))
-n2 = Node(name='1', category='TERMINAL', value=np.array(2))
+n1 = Node(name="0", category="TERMINAL", value=np.array(1))
+n2 = Node(name="1", category="TERMINAL", value=np.array(2))
 
 # Outputting information about one of the nodes
 print(n1)
-print(f'Post Order: {n1.post_order} | Size: {n1.n_nodes}.')
+print(f"Post Order: {n1.post_order} | Size: {n1.n_nodes}.")
 
 # Additionally, one can stack nodes to create a tree
-t = Node(name='SUM', category='FUNCTION', left=n1, right=n2)
+t = Node(name="SUM", category="FUNCTION", left=n1, right=n2)
 
 # Defines `n1` and `n2` parent as `t`
 n1.parent = t
 n2.parent = t
 
 # Outputting information about the tree
 print(t)
-print(f'Post Order: {t.post_order} | Size: {t.n_nodes} | Minimum Depth: {t.min_depth} | Maximum Depth: {t.max_depth}.')
+print(
+    f"Post Order: {t.post_order} | Size: {t.n_nodes} | Minimum Depth: {t.min_depth} | Maximum Depth: {t.max_depth}."
+)
diff --git a/examples/functions/create_constrained_function.py b/examples/functions/create_constrained_function.py
@@ -18,5 +18,5 @@ def c_1(z):
 f = ConstrainedFunction(test_function, [c_1], 10000.0)
 
 # Prints out some properties
-print(f'x: {x}')
-print(f'f(x): {f(x)}')
+print(f"x: {x}")
+print(f"f(x): {f(x)}")
diff --git a/examples/functions/multi_objective/create_multi_objective_function.py b/examples/functions/multi_objective/create_multi_objective_function.py
@@ -18,7 +18,7 @@ def test_function2(z):
 h = MultiObjectiveFunction([test_function1, test_function2])
 
 # Testing out your new Function class
-print(f'x: {x}')
-print(f'f(x): {h.functions[0](x)}')
-print(f'g(x): {h.functions[1](x)}')
-print(f'h(x) = [f(x), g(x)]: {h(x)}')
+print(f"x: {x}")
+print(f"f(x): {h.functions[0](x)}")
+print(f"g(x): {h.functions[1](x)}")
+print(f"h(x) = [f(x), g(x)]: {h(x)}")
diff --git a/examples/functions/multi_objective/create_multi_objective_weighted_function.py b/examples/functions/multi_objective/create_multi_objective_weighted_function.py
@@ -18,7 +18,7 @@ def test_function2(z):
 h = MultiObjectiveWeightedFunction([test_function1, test_function2], [0.5, 0.5])
 
 # Testing out your new Function class
-print(f'x: {x}')
-print(f'f(x): {h.functions[0](x)}')
-print(f'g(x): {h.functions[1](x)}')
-print(f'h(x) = 0.5f(x) + 0.5g(x): {h(x)}')
+print(f"x: {x}")
+print(f"f(x): {h.functions[0](x)}")
+print(f"g(x): {h.functions[1](x)}")
+print(f"h(x) = 0.5f(x) + 0.5g(x): {h(x)}")
diff --git a/examples/integrations/learnergy/dropout_rbm.py b/examples/integrations/learnergy/dropout_rbm.py
@@ -8,16 +8,29 @@
 
 # Creates training and testing dataset
 train = torchvision.datasets.MNIST(
-    root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor())
+    root="./data",
+    train=True,
+    download=True,
+    transform=torchvision.transforms.ToTensor(),
+)
 
 
 def dropout_rbm(opytimizer):
     # Gathers params
     dropout = opytimizer[0][0]
 
     # Creates an RBM
-    model = DropoutRBM(n_visible=784, n_hidden=128, steps=1, learning_rate=0.1,
-                       momentum=0, decay=0, temperature=1, dropout=dropout, use_gpu=False)
+    model = DropoutRBM(
+        n_visible=784,
+        n_hidden=128,
+        steps=1,
+        learning_rate=0.1,
+        momentum=0,
+        decay=0,
+        temperature=1,
+        dropout=dropout,
+        use_gpu=False,
+    )
 
     # Training an RBM
     error, _ = model.fit(train, batch_size=128, epochs=5)

diff --git a/examples/integrations/learnergy/rbm.py b/examples/integrations/learnergy/rbm.py
@@ -8,7 +8,11 @@
 
 # Creates training and testing dataset
 train = torchvision.datasets.MNIST(
-    root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor())
+    root="./data",
+    train=True,
+    download=True,
+    transform=torchvision.transforms.ToTensor(),
+)
 
 
 def rbm(opytimizer):
@@ -18,8 +22,16 @@ def rbm(opytimizer):
     decay = opytimizer[2][0]
 
     # Creates an RBM
-    model = RBM(n_visible=784, n_hidden=128, steps=1, learning_rate=lr,
-                momentum=momentum, decay=decay, temperature=1, use_gpu=False)
+    model = RBM(
+        n_visible=784,
+        n_hidden=128,
+        steps=1,
+        learning_rate=lr,
+        momentum=momentum,
+        decay=decay,
+        temperature=1,
+        use_gpu=False,
+    )
 
     # Training an RBM
     error, _ = model.fit(train, batch_size=128, epochs=5)