Merge pull request #8 from wearepal/hz

add hydra

pyproject.toml

@@ -15,6 +15,7 @@ dependencies = [
     "polars>=1.2.0",
     "loguru>=0.7.2",
     "ranzen>=2.5.1",
+    "hydra-zen>=0.13.0",
 ]
 classifiers = [
     "Programming Language :: Python :: 3.10",

requirements-dev.lock

@@ -10,6 +10,9 @@
 #   universal: false
 
 -e file:.
+antlr4-python3-runtime==4.9.3
+    # via hydra-core
+    # via omegaconf
 cloudpickle==3.0.0
     # via gymnasium
 contourpy==1.2.1
@@ -22,6 +25,10 @@ fonttools==4.53.1
     # via matplotlib
 gymnasium==0.29.1
     # via fescher
+hydra-core==1.3.2
+    # via hydra-zen
+hydra-zen==0.13.0
+    # via fescher
 iniconfig==2.0.0
     # via pytest
 joblib==1.4.2
@@ -41,7 +48,11 @@ numpy==1.26.4
     # via scikit-learn
     # via scipy
     # via seaborn
+omegaconf==2.3.0
+    # via hydra-core
+    # via hydra-zen
 packaging==24.1
+    # via hydra-core
     # via matplotlib
     # via pytest
 pandas==2.2.2
@@ -58,9 +69,11 @@ pytest==8.2.2
 python-dateutil==2.9.0.post0
     # via matplotlib
     # via pandas
-python-type-stubs @ git+https://github.com/wearepal/python-type-stubs@95a26e597ca5e3cc45ff76b435b537202aedd9c7
+python-type-stubs @ git+https://github.com/wearepal/python-type-stubs@95a26e5
 pytz==2024.1
     # via pandas
+pyyaml==6.0.1
+    # via omegaconf
 ranzen==2.5.1
     # via fescher
 scikit-learn==1.5.1
@@ -77,6 +90,7 @@ tqdm==4.66.4
     # via fescher
 typing-extensions==4.12.2
     # via gymnasium
+    # via hydra-zen
     # via ranzen
 tzdata==2024.1
     # via pandas

requirements.lock

@@ -10,6 +10,9 @@
 #   universal: false
 
 -e file:.
+antlr4-python3-runtime==4.9.3
+    # via hydra-core
+    # via omegaconf
 cloudpickle==3.0.0
     # via gymnasium
 contourpy==1.2.1
@@ -22,6 +25,10 @@ fonttools==4.53.1
     # via matplotlib
 gymnasium==0.29.1
     # via fescher
+hydra-core==1.3.2
+    # via hydra-zen
+hydra-zen==0.13.0
+    # via fescher
 joblib==1.4.2
     # via scikit-learn
 kiwisolver==1.4.5
@@ -39,7 +46,11 @@ numpy==1.26.4
     # via scikit-learn
     # via scipy
     # via seaborn
+omegaconf==2.3.0
+    # via hydra-core
+    # via hydra-zen
 packaging==24.1
+    # via hydra-core
     # via matplotlib
 pandas==2.2.2
     # via seaborn
@@ -54,6 +65,8 @@ python-dateutil==2.9.0.post0
     # via pandas
 pytz==2024.1
     # via pandas
+pyyaml==6.0.1
+    # via omegaconf
 ranzen==2.5.1
     # via fescher
 scikit-learn==1.5.1
@@ -70,6 +83,7 @@ tqdm==4.66.4
     # via fescher
 typing-extensions==4.12.2
     # via gymnasium
+    # via hydra-zen
     # via ranzen
 tzdata==2024.1
     # via pandas

run/rrm_credit_lr.py

@@ -11,10 +11,12 @@
 sys.path.append(str(Path(__file__).parent / ".."))
 
 
+from hydra_zen import ZenStore, zen
+
 from src.dynamics.env import DynamicEnv
 from src.dynamics.response import LinearResponse
 from src.dynamics.state import State
-from src.loader.credit import CreditData
+from src.loader.credit import CreditData, Data
 from src.models.lr import Lr, logistic_loss
 
 
@@ -29,10 +31,7 @@ class EpisodeRecord:
 
 
 def repeated_risk_minimization(
-    *,
-    env: DynamicEnv,
-    num_steps: int,
-    lr: Lr,
+    *, env: DynamicEnv, num_steps: int, lr: Lr, l2_penalty: float
 ) -> EpisodeRecord:
     """Run repeated risk minimization for num_iters steps"""
     # Track loss and accuracy before/after updating model on new distribution
@@ -84,10 +83,16 @@ def make_env(*, initial_state: State, epsilon: float) -> DynamicEnv:
     return env
 
 
-if __name__ == "__main__":
+store = ZenStore()
+data_store = store(group="dataset")
+data_store(CreditData, seed=0, name="credit")
+
+
+@store(name="fescher", hydra_defaults=["_self_", {"dataset": "credit"}])
+def main(dataset: Data):
     # We use the credit simulator, which is a strategic classification
     # simulator based on the 'Kaggle Give Me Some Credit' (GMSC) dataset.
-    initial_state = CreditData.as_state(seed=0)
+    initial_state = dataset.as_state()
     # The state of the environment is a dataset consisting of (1) financial
     # features of individuals, e.g. DebtRatio, and (2) a binary label indicating
     # whether an individual experienced financial distress in the subsequent two
@@ -128,9 +133,7 @@ def make_env(*, initial_state: State, epsilon: float) -> DynamicEnv:
         env = make_env(initial_state=initial_state, epsilon=epsilon)
         logger.info(f"Running retraining for epsilon {epsilon:.2f}")
         record = repeated_risk_minimization(
-            env=env,
-            lr=lr,
-            num_steps=num_steps,
+            env=env, lr=lr, num_steps=num_steps, l2_penalty=l2_penalty
         )
         loss_starts.append(record.loss_start)
         loss_ends.append(record.loss_end)
@@ -245,3 +248,12 @@ def make_env(*, initial_state: State, epsilon: float) -> DynamicEnv:
         # ax.set_ylim([0.5, 0.75])
     plt.subplots_adjust(hspace=0.25)
     plt.show()
+
+
+if __name__ == "__main__":
+    store.add_to_hydra_store()
+    zen(main).hydra_main(
+        config_name="fescher",
+        version_base="1.1",
+        config_path=None,
+    )

src/dynamics/registration.py

@@ -91,7 +91,7 @@ def __call__(
         **kwargs: Any,
     ) -> DynamicEnv:
         del kwargs
-        initial_state = unwrap_or(initial_state, default=CreditData.as_state())
+        initial_state = unwrap_or(initial_state, default=CreditData().as_state())
         reward_fn = unwrap_or(reward_fn, default=LogisticReward(l2_penalty=0.0))
         response_fn = unwrap_or(response_fn, default=LinearResponse(epsilon=1.0))
         simulator = Simulator(

src/loader/credit.py

@@ -1,6 +1,7 @@
 """Load and preprocess Kaggle credit dataset."""
 
 from pathlib import Path
+from typing import Protocol
 
 import numpy as np
 import polars as pl
@@ -12,6 +13,23 @@
 __all__ = ["CreditData"]
 
 
+class Data(Protocol):
+    def __init__(self, seed: int | None = None) -> None: ...
+
+    def as_state(self) -> State: ...
+
+    @property
+    def features(self) -> FloatArray: ...
+
+    @property
+    def labels(self) -> IntArray: ...
+
+    @property
+    def num_agents(self) -> int: ...
+
+    def load(self) -> tuple[FloatArray, FloatArray]: ...
+
+
 class CreditData:
     """Class to lazily load the credit dataset."""
 
@@ -21,10 +39,8 @@ def __init__(self, seed: int | None = None) -> None:
         self._labels = None
         self.seed = seed
 
-    @classmethod
-    def as_state(cls, seed: int | None = None) -> State:
-        data = cls(seed=seed)
-        return State(features=data.features, labels=data.labels)
+    def as_state(self) -> State:
+        return State(features=self.features, labels=self.labels)
 
     @property
     def features(self) -> FloatArray: