Fixing bugs branch

JSSEnv/__init__.py

@@ -1,6 +1,6 @@
 from gym.envs.registration import register
 
 register(
-    id='jss-v1',
-    entry_point='JSSEnv.envs:JssEnv',
-)
+    id="JSSEnv-v1",
+    entry_point="JSSEnv.envs:JssEnv",
+)

JSSEnv/envs/__init__.py

@@ -1 +1 @@
-from JSSEnv.envs.JssEnv import JssEnv
+from JSSEnv.envs.JssEnv import JssEnv

JSSEnv/version.py

@@ -0,0 +1,2 @@
+VERSION = "1.0.2"
+ENV_LIST = ["JSSEnv-v1"]

README.md

@@ -31,7 +31,7 @@ Once installed, the environment will be available in your OpenAi's gym environme
 
 ```python
 import gym
-env = gym.make('JSSEnv:jss-v1', env_config={'instance_path': 'INSTANCE_PATH'})
+env = gym.make('JSSEnv:JSSEnv-v1', env_config={'instance_path': 'INSTANCE_PATH'})
 ```
 
 ### Important: Your instance must follow [Taillard's specification](http://jobshop.jjvh.nl/explanation.php#taillard_def). 

setup.py

@@ -1,19 +1,31 @@
 from setuptools import setup, find_packages
+from JSSEnv.version import VERSION
 
-setup(name='JSSEnv',
-      version='1.0.0',
-      author="Pierre Tassel",
-      author_email="[email protected]",
-      description="An optimized OpenAi gym's environment to simulate the Job-Shop Scheduling problem.",
-      url="https://github.com/prosysscience/JSSEnv",
-      packages=find_packages(),
-      classifiers=[
-          "Programming Language :: Python :: 3",
-          "License :: OSI Approved :: MIT License",
-          "Operating System :: OS Independent",
-      ],
-      python_requires='>=3.6',
-      install_requires=['gym', 'pandas', 'numpy', 'plotly', 'imageio', 'psutil', 'requests', 'kaleido', 'pytest',
-                        'codecov'],
-      include_package_data=True
-      )
+setup(
+    name="JSSEnv",
+    version=VERSION,
+    author="Pierre Tassel",
+    author_email="[email protected]",
+    description="An optimized OpenAi gym's environment to simulate the Job-Shop Scheduling problem.",
+    url="https://github.com/prosysscience/JSSEnv",
+    packages=find_packages(),
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.6",
+    install_requires=[
+        "gym",
+        "pandas",
+        "numpy",
+        "plotly",
+        "imageio",
+        "psutil",
+        "requests",
+        "kaleido",
+        "pytest",
+        "codecov",
+    ],
+    include_package_data=True,
+)

tests/test_rendering.py

@@ -4,10 +4,12 @@
 
 
 class TestRendering(unittest.TestCase):
-
     def test_optimum_ta01_gif(self):
         # http://optimizizer.com/solution.php?name=ta01&UB=1231&problemclass=ta
-        env = gym.make('JSSEnv:jss-v1', env_config={'instance_path': '../JSSEnv/envs/instances/ta01'})
+        env = gym.make(
+            "JSSEnv:JSSEnv-v1",
+            env_config={"instance_path": "../JSSEnv/envs/instances/ta01"},
+        )
         env.reset()
         self.assertEqual(env.current_time_step, 0)
         # for every machine give the jobs to process in order for every machine
@@ -26,7 +28,7 @@ def test_optimum_ta01_gif(self):
             [7, 9, 8, 5, 6, 0, 2, 3, 1, 13, 14, 12, 4, 10, 11],
             [6, 0, 7, 11, 5, 14, 10, 2, 4, 13, 8, 9, 3, 12, 1],
             [13, 10, 7, 9, 5, 3, 11, 1, 12, 14, 2, 4, 0, 6, 8],
-            [13, 11, 6, 8, 7, 4, 1, 5, 3, 10, 0, 14, 9, 2, 12]
+            [13, 11, 6, 8, 7, 4, 1, 5, 3, 10, 0, 14, 9, 2, 12],
         ]
         done = False
         job_nb = len(solution_sequence[0])
@@ -42,10 +44,18 @@ def test_optimum_ta01_gif(self):
                     break
                 if env.machine_legal[machine] and index_machine[machine] < job_nb:
                     action_to_do = solution_sequence[machine][index_machine[machine]]
-                    if env.needed_machine_jobs[action_to_do] == machine and env.legal_actions[action_to_do]:
+                    if (
+                        env.needed_machine_jobs[action_to_do] == machine
+                        and env.legal_actions[action_to_do]
+                    ):
                         no_op = False
-                        self.assertTrue(env.legal_actions[action_to_do], "We don't perform illegal actions")
-                        self.assertEqual(sum(env.legal_actions[:-1]), env.nb_legal_actions)
+                        self.assertTrue(
+                            env.legal_actions[action_to_do],
+                            "We don't perform illegal actions",
+                        )
+                        self.assertEqual(
+                            sum(env.legal_actions[:-1]), env.nb_legal_actions
+                        )
                         state, reward, done, _ = env.step(action_to_do)
                         index_machine[machine] += 1
                         step_nb += 1
@@ -54,10 +64,19 @@ def test_optimum_ta01_gif(self):
             if no_op and not done:
                 self.assertTrue(len(env.next_time_step) > 0, "step {}".format(step_nb))
                 previous_time_step = env.current_time_step
-                env._increase_time_step()
-                self.assertTrue(env.current_time_step > previous_time_step, "we increase the time step")
-        self.assertEqual(sum(index_machine), len(solution_sequence) * len(solution_sequence[0]))
+                env.increase_time_step()
+                self.assertTrue(
+                    env.current_time_step > previous_time_step,
+                    "we increase the time step",
+                )
+        self.assertEqual(
+            sum(index_machine), len(solution_sequence) * len(solution_sequence[0])
+        )
         self.assertEqual(env.current_time_step, 1231)
         imageio.mimsave("ta01.gif", images)
         env.reset()
         self.assertEqual(env.current_time_step, 0)
+
+
+if __name__ == "__main__":
+    unittest.main()