Stateless dist map offshoot

pyproject.toml

@@ -6,7 +6,6 @@ authors = [
     { name = "Predictive Analytics Lab (PAL)", email = "info@predictive-analytics-lab.com" },
 ]
 license = "Apache-2.0"
-packages = [{ include = "src" }]
 dependencies = [
     "tqdm>=4.66.4",
     "scikit-learn>=1.5.1",
@@ -16,6 +15,7 @@ dependencies = [
     "loguru>=0.7.2",
     "ranzen>=2.5.1",
     "hydra-zen>=0.13.0",
+    "beartype>=0.18.5",
 ]
 classifiers = [
     "Programming Language :: Python :: 3.10",
@@ -24,7 +24,7 @@ classifiers = [
     "Operating System :: OS Independent",
     "Typing :: Typed",
 ]
-repository = "https://github.com/wearepal/fescher"
+urls = {github="https://github.com/wearepal/fescher"}
 readme = "README.md"
 requires-python = ">= 3.10, <3.12"
 
@@ -40,7 +40,7 @@ dev-dependencies = [
 ]
 
 [tool.rye.scripts]
-rrm = {cmd="python run/rrm_credit_lr.py"}
+rrm = {cmd="python -m src.rrm_credit_lr"}
 
 [tool.hatch.metadata]
 allow-direct-references = true
@@ -150,4 +150,4 @@ addopts = ["--import-mode=importlib"]
 filterwarnings = [
    "ignore::DeprecationWarning:_pytest.*:",
    "ignore::DeprecationWarning:pkg_resources.*:"
-]
+]

requirements-dev.lock

@@ -13,6 +13,8 @@
 antlr4-python3-runtime==4.9.3
     # via hydra-core
     # via omegaconf
+beartype==0.18.5
+    # via fescher
 cloudpickle==3.0.0
     # via gymnasium
 contourpy==1.2.1

requirements.lock

@@ -13,6 +13,8 @@
 antlr4-python3-runtime==4.9.3
     # via hydra-core
     # via omegaconf
+beartype==0.18.5
+    # via fescher
 cloudpickle==3.0.0
     # via gymnasium
 contourpy==1.2.1

src/conftest.py

@@ -1,7 +1,73 @@
 from __future__ import annotations
-import sys
-from typing import Final
+from typing_extensions import Self
 
-__all__ = ["TESTING"]
+import numpy as np
+import pytest
 
-TESTING: Final[bool] = "pytest" in sys.modules
+from src.dynamics.registration import make_env
+from src.dynamics.state import State
+from src.models.lr import Model
+from src.types import FloatArray, IntArray
+
+
+@pytest.fixture
+def mock_state():
+    features = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float64)
+    labels = np.array([1, 0], dtype=np.uint8)
+    return State(features=features, labels=labels)
+
+
+@pytest.fixture
+def mock_env(mock_state: State):
+    return make_env(initial_state=mock_state, epsilon=0.1, memory=False, changeable_features=[0])
+
+
+@pytest.fixture
+def mock_model():
+    class MockModel(Model):
+        def __init__(self):
+            self._weights = np.array([0.1, 0.2])
+
+        @property
+        def weights(self) -> FloatArray:
+            return self._weights
+
+        @weights.setter
+        def weights(self, value: FloatArray) -> None:
+            self._weights = value
+
+        def fit(
+            self,
+            *,
+            x: FloatArray,
+            y: IntArray,
+            tol: float = 1e-7,
+        ) -> Self:
+            self.weights = np.array([0.3, 0.4])
+            return self
+
+        def acc(self, *, features: FloatArray, labels: IntArray) -> float:
+            return 0.5
+
+        def loss(
+            self,
+            *,
+            x: FloatArray,
+            y: IntArray,
+        ) -> float:
+            return 0.6
+
+        @property
+        def l2_penalty(self) -> float:
+            raise NotImplementedError
+
+        def logits(self, features: FloatArray) -> FloatArray:
+            raise NotImplementedError
+
+        def preds(self, features: FloatArray) -> IntArray:
+            raise NotImplementedError
+
+        def probs(self, features: FloatArray) -> FloatArray:
+            raise NotImplementedError
+
+    return MockModel()