Merge pull request #226 from ds4dm/blacken-docs

Blacken docs

Author: AntoinePrv

.github/workflows/continuous-testing.yml

@@ -54,7 +54,8 @@ jobs:
         with: { environment-file: dev/conda.yaml }
       - name: "Configure, build, and test ecole-lib with sanitizer."
         shell: bash -l {0}
-        run: bash dev/run.sh --fix-pythonpath configure -D SANITIZE_ADDRESS=ON -- test-lib
+        # Using Ctest runner to avoid out of memory
+        run: bash dev/run.sh --fix-pythonpath configure -D SANITIZE_ADDRESS=ON -- ctest-lib
 
   check-code:
     runs-on: ubuntu-20.04

.pre-commit-config.yaml

@@ -20,9 +20,15 @@ repos:
       - id: clang-tidy
         args: [--fix, --fix-errors]
   - repo: https://github.com/psf/black
-    rev: 19.10b0
+    rev: 21.7b0
     hooks:
       - id: black
+  - repo: https://github.com/asottile/blacken-docs
+    rev: v1.10.0
+    hooks:
+    - id: blacken-docs
+      args: [ '--line-length', '100' ]
+      additional_dependencies: [black==21.7b0]
   - repo: https://github.com/Lucas-C/pre-commit-hooks
     rev: v1.1.9
     hooks:

README.rst

@@ -36,7 +36,7 @@ meant to mimic the `OpenAi Gym <https://gym.openai.com/>`_ API (as much as possi
    for _ in range(10):
        obs, action_set, reward_offset, done, info = env.reset(next(instances))
        while not done:
-          obs, action_set, reward, done, info = env.step(action_set[0])
+           obs, action_set, reward, done, info = env.step(action_set[0])
 
 
 Documentation

dev/conda.yaml

@@ -22,7 +22,7 @@ dependencies:
 
   # Documentation
   - doxygen
-  - sphinx>=3.0
+  - sphinx = 4.0
   - breathe>=4.15
   - sphinx_rtd_theme
 

dev/run.sh

@@ -184,6 +184,22 @@ function test_lib {
 }
 
 
+# CTest runner runs test individually, reducing mem consumption
+function ctest_lib {
+	if files_have_changed 'CMakeLists.txt' 'libecole';  then
+		if_rebuild_then build_lib_test
+		local extra_args=("$@")
+		if [ "${fail_fast}" = "true" ]; then
+			extra_args+=("--stop-on-failure ")
+		fi
+		# Possible option --parallel
+		cmake_build test -- ARGS="${extra_args[@]}"
+	else
+		log "Skipping ${FUNCNAME[0]} as unchanged since ${rev}."
+	fi
+}
+
+
 function test_py {
 	local -r relevant_files=('CMakeLists.txt' 'libecole/CMakeLists.txt' 'libecole/src' 'libecole/include' 'python')
 	if files_have_changed "${relevant_files[@]}";  then

docs/howto/create-environments.rst

@@ -59,7 +59,6 @@ example, so we do not override it.
 
 
    class SimpleBranchingDynamics(ecole.dynamics.BranchingDynamics):
-
        def reset_dynamics(self, model):
            # Share memory with Ecole model
            pyscipopt_model = model.as_pyscipopt()
@@ -95,7 +94,6 @@ To do so, we will take parameters in the constructor.
    :skipif: pyscipopt is None
 
    class SimpleBranchingDynamics(ecole.dynamics.BranchingDynamics):
-
        def __init__(self, disable_presolve=True, disable_cuts=True, *args, **kwargs):
            super().__init__(*args, **kwargs)
            self.disable_presolve = disable_presolve

docs/howto/create-functions.rst

@@ -31,7 +31,6 @@ maximum absolute value.
 
 
    class ScaledNodeBipartite(NodeBipartite):
-
        def extract(self, model, done):
            # Call parent method to get the original observation
            obs = super().extract(model, done)
@@ -55,7 +54,6 @@ This will illustrate how the class paradigm is useful to saving information betw
 .. testcode::
 
    class MovingScaledNodeBipartite(NodeBipartite):
-
        def __init__(self, alpha, *args, **kwargs):
            # Construct parent class with other parameters
            super().__init__(*args, **kwargs)
@@ -123,8 +121,7 @@ For instance, we can create a ``StochasticReward`` function that will wrap any g
 
 
    class StochasticReward:
-
-       def __init__(self, reward_function, probability = 0.05):
+       def __init__(self, reward_function, probability=0.05):
            self.reward_function = reward_function
            self.probability = probability
 
@@ -135,7 +132,7 @@ For instance, we can create a ``StochasticReward`` function that will wrap any g
            # Unconditionally getting reward as reward_funcition.extract may have side effects
            reward = self.reward_function.extract(model, done)
            if random.random() < probability:
-               return 0.
+               return 0.0
            else:
                return reward
 

docs/howto/observation-functions.rst

@@ -51,7 +51,7 @@ you can explicitly pass ``None`` to the environment constructor.
    >>> env = ecole.environment.Branching(observation_function=None)
    >>> env.observation_function  # doctest: +SKIP
    ecole.observation.nothing()
-   >>> obs, _, _, _, _= env.reset("path/to/problem")
+   >>> obs, _, _, _, _ = env.reset("path/to/problem")
    >>> obs is None
    True
 
@@ -64,8 +64,8 @@ To use multiple observation functions, wrap them in a ``list`` or ``dict``.
 .. doctest::
 
    >>> obs_func = {
-   ...    "some_name": ecole.observation.NodeBipartite(),
-   ...    "other_name": ecole.observation.Nothing(),
+   ...     "some_name": ecole.observation.NodeBipartite(),
+   ...     "other_name": ecole.observation.Nothing(),
    ... }
    >>> env = ecole.environment.Branching(observation_function=obs_func)
    >>> obs, _, _, _, _ = env.reset("path/to/problem")
@@ -78,9 +78,7 @@ Similarily with a tuple
 
 .. doctest::
 
-   >>> obs_func = (
-   ...    ecole.observation.NodeBipartite(), ecole.observation.Nothing()
-   ... )
+   >>> obs_func = (ecole.observation.NodeBipartite(), ecole.observation.Nothing())
    >>> env = ecole.environment.Branching(observation_function=obs_func)
    >>> obs, _, _, _, _ = env.reset("path/to/problem")
    >>> obs  # doctest: +SKIP

docs/howto/reward-functions.rst

@@ -86,7 +86,7 @@ Arithmetic operations are even allowed between different reward functions,
 
    from ecole.reward import LpIterations, IsDone
 
-   4.0 * LpIterations()**2 - 3 * IsDone()
+   4.0 * LpIterations() ** 2 - 3 * IsDone()
 
 which is especially powerful because in this normally it would *not* be possible to pass both
 :py:class:`~ecole.reward.LpIterations` and :py:class:`~ecole.reward.IsDone` to the
@@ -96,7 +96,7 @@ All operations that are valid between scalars are valid between reward functions
 
 .. testcode::
 
-   - IsDone() ** abs(LpIterations() // 4)
+   -IsDone() ** abs(LpIterations() // 4)
 
 In addition, not all commonly used mathematical operations have a dedicated Python operator: to
 accomodate this, Ecole implements a number of other operations as methods of reward functions.
@@ -110,7 +110,7 @@ This also works with rewards functions created from arithmetic expressions.
 
 .. testcode::
 
-   (3 - 2*LpIterations()).exp()
+   (3 - 2 * LpIterations()).exp()
 
 Finally, reward functions have an ``apply`` method to compose rewards with any
 function.

python/src/ecole/core/instance.cpp

@@ -158,11 +158,10 @@ void bind_submodule(py::module const& m) {
 	def_generate_instance(independent_set_gen, independent_set_params, R"(
 		Generate an independent set MILP problem instance.
 
-		Given an undireted graph, the problem is to find a maximum subset of nodes such that no pair of nodes are connected.
-		There are one variable per node in the underlying graph.
-		Instead of adding one constraint per edge, a greedy algorithm is run to replace these inequalities when clique is
-		found.
-		The maximization problem is unwheighted, that is all objective coefficients are equal to one.
+		Given an undireted graph, the problem is to find a maximum subset of nodes such that no pair of nodes are
+		connected. There are one variable per node in the underlying graph. Instead of adding one constraint per edge, a
+		greedy algorithm is run to replace these inequalities when clique is found. The maximization problem is
+		unwheighted, that is all objective coefficients are equal to one.
 
 		The problem are generated using the procedure from [Bergman2016]_, and the graphs are sampled following
 		[Erdos1959]_ and [Barabasi1999]_.
@@ -173,15 +172,15 @@ void bind_submodule(py::module const& m) {
 			The number of nodes in the graph, and therefore of variable.
 		graph_type:
 			The method used in which to generate graphs.
-			One of "barabasi_albert" or "erdos_renyi"
+			One of ``"barabasi_albert"`` or ``"erdos_renyi"``.
 		edge_probability:
 			The probability of generating each edge.
 			This parameter must be in the range [0, 1].
-			This parameter will only be used if `graph_type == "erdos_renyi"`.
+			This parameter will only be used if ``graph_type == "erdos_renyi"``.
 		affinity:
 			The number of nodes each new node will be attached to, in the sampling scheme.
 			This parameter must be an integer >= 1.
-			This parameter will only be used if `graph_type == "barabasi_albert"`.
+			This parameter will only be used if ``graph_type == "barabasi_albert"``.
 		random_engine:
 			The random number generator used to peform all sampling.