Merge pull request #29 from marimeireles/feat/fix-versioning

Feat/fix versioning

setup.py

@@ -11,15 +11,15 @@
     install_requires=[
         "autograd",
         "dataclasses; python_version<'3.7'",
-        "gym",
-        "mesa",
+        "gym==0.23.0",
+        "mesa<=0.8.7",
         "numpy",
         "networkx",
         "pandas",
-        "pyomo",
         "py_mini_racer",
+        "pyomo",
         "scipy",
-        "sklearn",
+        "scikit-learn",
         "statsmodels",
         "tqdm",
     ],
@@ -29,5 +29,5 @@
         "Operating System :: MacOS",
         "Operating System :: POSIX",
     ],
-    python_requires=">=3.6",
+    python_requires=">=3.8.10, <=3.10.11",
 )

tests/test_dynamics.py

@@ -40,6 +40,7 @@ class State(wn.dynamics.BaseState):
         state1: float = 0
         state2: float = 1
         state3: float = 3
+        num_features: np.ndarray = dataclasses.field(default_factory=lambda: np.array([]))
 
     assert State.num_variables() == 3
     assert State.variable_names() == ["state1", "state2", "state3"]

whynot/causal_graphs.py

@@ -14,7 +14,7 @@
 
 # Allow tracing to use ints and bools (since we only care about the forward
 # pass and not derivatives).
-for type_ in [bool, np.bool, np.bool_, int, np.int32, np.int64]:
+for type_ in [bool, np.bool_, np.bool_, int, np.int32, np.int64]:
     ArrayBox.register(type_)
 
 

whynot/simulators/credit/simulator.py

@@ -25,10 +25,11 @@ class State(BaseState):
     """State of the Credit model."""
 
     #: Matrix of agent features (e.g. https://www.kaggle.com/c/GiveMeSomeCredit/data)
-    features: np.ndarray = CreditData.features
+    features: np.ndarray = dataclasses.field(default_factory=lambda: np.array([]))
+    # features: np.ndarray = CreditData.features
 
     #: Vector indicating whether or not the agent experiences financial distress
-    labels: np.ndarray = CreditData.labels
+    labels: np.ndarray = dataclasses.field(default_factory=lambda: np.array([]))
 
     def values(self):
         """Return the state as a dictionary of numpy arrays."""
@@ -49,13 +50,13 @@ class Config(BaseConfig):
 
     # Dynamics parameters
     #: Subset of the features that can be manipulated by the agent
-    changeable_features: np.ndarray = np.array([0, 5, 7])
+    changeable_features: np.ndarray = dataclasses.field(default_factory=lambda: np.array([0, 5, 7]))
 
     #: Model how much the agent adapt her features in response to a classifier
     epsilon: float = 0.1
 
     #: Parameters for logistic regression classifier used by the institution
-    theta: np.ndarray = np.ones((11, 1))
+    theta: np.ndarray = dataclasses.field(default_factory=lambda: np.ones((11, 1)))
 
     #: L2 penalty on the logistic regression loss
     l2_penalty: float = 0.0
@@ -64,7 +65,7 @@ class Config(BaseConfig):
     memory: bool = False
 
     #: State systems resets to if no memory.
-    base_state: Any = State()
+    base_state: Any = dataclasses.field(default_factory=lambda: State())
 
     # Simulator book-keeping
     #: Start time of the simulator