Format code

CODE_OF_CONDUCT.md

@@ -0,0 +1,111 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for
+everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity
+and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion,
+or sexual identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take
+appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits,
+issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for
+moderation decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing
+the community in public spaces. Examples of representing our community include using an official e-mail address, posting
+via an official social media account, or acting as an appointed representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible
+for enforcement at
+[INSERT CONTACT METHOD]. All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem
+in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the
+community.
+
+**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation
+and an explanation of why the behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including
+unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding
+interactions in community spaces as well as external channels like social media. Violating these terms may lead to a
+temporary or permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified
+period of time. No public or private interaction with the people involved, including unsolicited interaction with those
+enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate
+behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.0, available at
+[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available
+at [https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+
+[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html
+
+[Mozilla CoC]: https://github.com/mozilla/diversity
+
+[FAQ]: https://www.contributor-covenant.org/faq
+
+[translations]: https://www.contributor-covenant.org/translations

LICENSE

@@ -1,6 +1,6 @@
 BSD 2-Clause License
 
-Copyright (c) 2023, IHME Math Sciences
+Copyright (c) 2025, IHME Math Sciences
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.md

@@ -0,0 +1,13 @@
+[![build](https://github.com/ihmeuw-msca/modrover/workflows/build/badge.svg)](https://github.com/ihmeuw-msca/modrover/actions)
+
+# ModRover: Model Space Explorer
+
+## Installation
+
+```shell
+pip install git+https://github.com/ihmeuw-msca/modrover.git@main
+```
+
+## Documentation
+
+https://ihmeuw-msca.github.io/modrover/

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "modrover"
 version = "0.1.3"
 description = "Model space explorer for inference and prediction"
-readme = "README.rst"
+readme = "README.md"
 requires-python = ">=3.10"
 license = { file = "LICENSE" }
 authors = [

ruff.toml

@@ -0,0 +1,8 @@
+line-length = 80
+src = ["src"]
+
+[format]
+docstring-code-format = true
+
+[lint.pydocstyle]
+convention = "numpy"

src/modrover/__init__.py

@@ -1 +1,3 @@
 from .rover import Rover
+
+__all__ = ["Rover"]

src/modrover/exceptions.py

@@ -3,4 +3,4 @@ class NotFittedError(Exception):
 
 
 class InvalidConfigurationError(Exception):
-    """Raised if a provided configuration parameter violates a rule."""
+    """Raised if a provided configuration parameter violates a rule."""

src/modrover/globals.py

@@ -1,9 +1,6 @@
-from typing import Optional
-
 import numpy as np
 from numpy.typing import ArrayLike
-from regmod.models import (BinomialModel, GaussianModel, PoissonModel,
-                           TobitModel)
+from regmod.models import BinomialModel, GaussianModel, PoissonModel, TobitModel
 
 model_type_dict = {
     "gaussian": GaussianModel,
@@ -13,17 +10,18 @@
 }
 
 
-def get_r2(obs: ArrayLike, pred: ArrayLike,
-           obs_mean: Optional[float] = None) -> float:
-    ss_residual = np.sum((obs - pred)**2)
+def get_r2(
+    obs: ArrayLike, pred: ArrayLike, obs_mean: float | None = None
+) -> float:
+    ss_residual = np.sum((obs - pred) ** 2)
     if obs_mean is None:
         obs_mean = obs.mean()
-    ss_total = np.sum((obs - obs_mean)**2)
+    ss_total = np.sum((obs - obs_mean) ** 2)
     return float(min(max(0.0, 1 - ss_residual / ss_total), 1.0))
 
 
 def get_rmse(obs: ArrayLike, pred: ArrayLike) -> float:
-    metric = np.sqrt(np.mean((obs - pred)**2))
+    metric = np.sqrt(np.mean((obs - pred) ** 2))
     metric = np.exp(-metric)
     return float(metric)
 

src/modrover/learner.py

@@ -2,7 +2,7 @@
 
 from collections import defaultdict
 from enum import Enum
-from typing import Callable, Optional
+from typing import Callable
 
 import numpy as np
 from numpy.typing import NDArray
@@ -50,7 +50,7 @@ def __init__(
         main_param: str,
         param_specs: dict[str, dict],
         weights: str = "weights",
-        get_score: Optional[Callable] = None,
+        get_score: Callable | None = None,
     ) -> None:
         self.model_class = model_class
         self.obs = obs
@@ -60,7 +60,9 @@ def __init__(
 
         # convert str to Variable
         for param_spec in param_specs.values():
-            param_spec["variables"] = list(map(Variable, param_spec["variables"]))
+            param_spec["variables"] = list(
+                map(Variable, param_spec["variables"])
+            )
         self.param_specs = param_specs
 
         # initialize null model
@@ -74,7 +76,7 @@ def __init__(
         self._cv_status = defaultdict(lambda: ModelStatus.NOT_FITTED)
 
     @property
-    def coef(self) -> Optional[NDArray]:
+    def coef(self) -> NDArray | None:
         """Coefficients in the regmod model."""
         return self.model.opt_coefs
 
@@ -85,7 +87,7 @@ def coef(self, coef: NDArray):
         self.model.opt_coefs = coef
 
     @property
-    def vcov(self) -> Optional[NDArray]:
+    def vcov(self) -> NDArray | None:
         """Variance-covarianace matrix for the coefficients in the regmod model."""
         return self.model.opt_vcov
 
@@ -98,7 +100,7 @@ def vcov(self, vcov: NDArray):
     def fit(
         self,
         data: DataFrame,
-        holdouts: Optional[list[str]] = None,
+        holdouts: list[str] | None = None,
         **optimizer_options,
     ) -> None:
         """
@@ -160,7 +162,7 @@ def fit(
     def predict(
         self,
         data: DataFrame,
-        model: Optional[RegmodModel] = None,
+        model: RegmodModel | None = None,
         return_ui: bool = False,
         alpha: float = 0.05,
     ) -> NDArray:
@@ -204,7 +206,9 @@ def predict(
                 raise ValueError("`alpha` has to be between 0 and 0.5")
             vcov = model.opt_vcov[coef_index, coef_index]
             lin_param_sd = np.sqrt((mat.dot(vcov) * mat).sum(axis=1))
-            lin_param_lower = norm.ppf(0.5 * alpha, loc=lin_param, scale=lin_param_sd)
+            lin_param_lower = norm.ppf(
+                0.5 * alpha, loc=lin_param, scale=lin_param_sd
+            )
             lin_param_upper = norm.ppf(
                 1 - 0.5 * alpha, loc=lin_param, scale=lin_param_sd
             )
@@ -219,7 +223,9 @@ def predict(
         model.data.detach_df()
         return pred
 
-    def evaluate(self, data: DataFrame, model: Optional[RegmodModel] = None) -> float:
+    def evaluate(
+        self, data: DataFrame, model: RegmodModel | None = None
+    ) -> float:
         """Given a model and a test set, generate a performance score.
 
         Score is based on the provided evaluation metric, comparing the
@@ -237,7 +243,9 @@ def evaluate(self, data: DataFrame, model: Optional[RegmodModel] = None) -> floa
         model = model or self.model
         if self.get_score is None:
             model.attach_df(data)
-            score = np.exp(-model.objective(model.opt_coefs) / model.data.weights.sum())
+            score = np.exp(
+                -model.objective(model.opt_coefs) / model.data.weights.sum()
+            )
             model = _detach_df(model)
         else:
             score = self.get_score(
@@ -265,7 +273,7 @@ def _get_model(self) -> RegmodModel:
     def _fit(
         self,
         data: DataFrame,
-        model: Optional[RegmodModel] = None,
+        model: RegmodModel | None = None,
         **optimizer_options,
     ) -> ModelStatus:
         model = model or self.model
@@ -277,7 +285,7 @@ def _fit(
             try:
                 model.fit(**optimizer_options)
                 status = ModelStatus.SUCCESS
-            except:
+            except Exception:
                 status = ModelStatus.SOLVER_FAILED
         model = _detach_df(model)
         return status