Migrate to uv

.github/workflows/build-doc.yml

@@ -17,19 +17,19 @@ jobs:
       with:
         python-version: '3.11'
 
-    - name: Install pipenv
-      run: |
-        python -m pip install --upgrade pip
-        pip install pipenv
-    
+    - name: Install uv
+      uses: astral-sh/setup-uv@v4
+      with:
+        version: "latest"
+
     - name: Install dependencies
       run: |
-        pipenv install --dev --system
+        uv sync --dev
 
     - name: Build documentation
       run: |
         cd docs
-        make html
+        uv run make html
 
     - name: Deploy to GitHub Pages
       uses: peaceiris/actions-gh-pages@v4

.github/workflows/build.yml

@@ -22,16 +22,19 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
 
+    - name: Install uv
+      uses: astral-sh/setup-uv@v4
+      with:
+        version: "latest"
+
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip
-        pip install .
-        pip install -r requirements-dev.txt
+        uv sync --dev
 
     - name: Lint with ruff
       run: |
-        ruff check .
+        uv run ruff check .
 
     - name: Run unit tests
       run: |
-        python -m unittest
+        uv run python -m unittest

.github/workflows/publish.yml

@@ -17,14 +17,19 @@ jobs:
         with:
           python-version: '3.11'
 
-      - name: Install dependencies
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
+      - name: Install build dependencies
         run: |
-          python -m pip install --upgrade pip
-          pip install build twine
+          uv tool install build
+          uv tool install twine
 
       - name: Build package
         run: |
-          python -m build
+          uv run python -m build
 
       - name: Publish package to PyPI
         env:

.gitignore

@@ -168,4 +168,4 @@ cython_debug/
 
 # Used for experiments
 *.csv
-experiments
+experiments

CONTRIBUTING.md

@@ -28,29 +28,38 @@ Pull requests are welcome! If you plan to make significant changes, please open
 - Open a pull request in the original repository.
 
 ## Development
-Important commands and major dependencies are managed in Pipenv. Here are the basic commands you can use to develop this package.
+Important commands and major dependencies are managed with uv. Here are the basic commands you can use to develop this package.
 
-### Install Pipenv
-If you don't have `pipenv` installed, you can install it using `pip`:
+### Install Dependencies
+Install all development dependencies using uv:
 
 ```sh
-pip install pipenv
+uv sync --dev
 ```
 
-### Linting
-We use `ruff` for linting the code. To run the linter, use the following command:
+### Pre-commit Hooks
+We use pre-commit hooks to automatically format code and check for issues. Install them with:
+
+```sh
+uv run pre-commit install
+```
+
+### Manual Commands
+
+#### Linting
+We use `ruff` for linting the code. To run the linter manually:
 ```sh
-pipenv run lint
+uv run ruff check
 ```
 
-### Auto format
-We use `ruff` for formatting the code. To run the formatter, use the following command:
+#### Auto format
+We use `ruff` for formatting the code. To run the formatter manually:
 ```sh
-pipenv run format
+uv run ruff format
 ```
 
-### Unit test
-We use `unittest` for testing the code. To run the unit tests, use the following command:
+#### Unit test
+We use `unittest` for testing the code. To run the unit tests:
 ```sh
-pipenv run unittest
-```
+uv run python -m unittest
+```

README.md

@@ -28,4 +28,4 @@ We welcome contributions to the project! Please review our [Contribution Guide](
 This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
 
 ## Maintainers
-- [Tomu Hirata](https://github.com/TomeHirata)
+- [Tomu Hirata](https://github.com/TomeHirata)

docs/source/api/local.rst

@@ -19,4 +19,4 @@ AdjustedLocalDistributionEstimator
    :members:
    :undoc-members:
    :show-inheritance:
-   :no-index:
+   :no-index:

docs/source/api/plot.rst

@@ -7,4 +7,4 @@ This page documents plotting utilities for visualizing distribution treatment ef
    :members:
    :undoc-members:
    :show-inheritance:
-   :no-index:
+   :no-index:

docs/source/api/simple.rst

@@ -27,4 +27,4 @@ AdjustedDistributionEstimator
    :inherited-members:
    :undoc-members:
    :show-inheritance:
-   :no-index:
+   :no-index:

docs/source/api/stratified.rst

@@ -27,4 +27,4 @@ AdjustedStratifiedDistributionEstimator
    :inherited-members:
    :undoc-members:
    :show-inheritance:
-   :no-index:
+   :no-index:

docs/source/api_reference.rst

@@ -11,7 +11,7 @@ The dte_adj package provides several types of estimators for computing distribut
 * **Simple Randomization Estimators**: For estimating distributional effects in simple randomized experiments where treatment assignment is independent of all covariates
 * **Covariate Adaptive Randomization Estimators**: For estimating distributional effects under covariate-adaptive randomization (CAR) designs, including stratified block randomization and other adaptive schemes
 * **Local Distribution Estimators**: For estimating local distribution treatment effects weighted by treatment propensity within strata
-* **Utility Functions**: Helper functions for confidence intervals and statistical computations  
+* **Utility Functions**: Helper functions for confidence intervals and statistical computations
 * **Plotting Utilities**: Visualization tools for treatment effects and distributions
 
 For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization and Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization.
@@ -33,4 +33,4 @@ Detailed Documentation
    api/simple
    api/stratified
    api/local
-   api/plot
+   api/plot

docs/source/contributing.rst

@@ -1,4 +1,4 @@
 Contribution Guide
 ==================
 
-Regarding how to contribute to this package, please refer to https://github.com/CyberAgentAILab/python-dte-adjustment/blob/main/CONTRIBUTING.md for more details.
+Regarding how to contribute to this package, please refer to https://github.com/CyberAgentAILab/python-dte-adjustment/blob/main/CONTRIBUTING.md for more details.

docs/source/get_started.rst

@@ -25,26 +25,26 @@ Generate data for training cumulative distribution function:
       """
       # Generate covariates X from a uniform distribution on (0, 1)
       X = np.random.uniform(0, 1, (n, d_x))
-      
+
       # Generate treatment variable D from a Bernoulli distribution with success probability rho
       D = np.random.binomial(1, rho, n)
-      
+
       # Define beta_j and gamma_j according to the problem statement
       beta = np.zeros(d_x)
       gamma = np.zeros(d_x)
-      
+
       # Set the first 50 values of beta and gamma to 1
       beta[:50] = 1
       gamma[:50] = 1
-      
+
       # Compute the outcome Y
       U = np.random.normal(0, 1, n)  # Error term
       linear_term = np.dot(X, beta)
       quadratic_term = np.dot(X**2, gamma)
-      
+
       # Outcome equation
       Y = 5 * D + linear_term + quadratic_term + U
-      
+
       return X, D, Y
 
   n = 1000  # Sample size
@@ -56,7 +56,7 @@ Then, let's build an empirical cumulative distribution function (CDF).
 
   import dte_adj
   from dte_adj.plot import plot
-  
+
   estimator = dte_adj.SimpleDistributionEstimator()
   estimator.fit(X, D, Y)
   locations = np.linspace(Y.min(), Y.max(), 20)
@@ -159,7 +159,7 @@ To compute QTE, you can use ``predict_qte`` method. The confidence band is compu
    :width: 450px
    :align: center
 
-You can use any model with ``predict_proba`` or ``predict`` method to adjust the distribution function estimation. 
+You can use any model with ``predict_proba`` or ``predict`` method to adjust the distribution function estimation.
 For example, the following code use XGBoost classifier to estimate the conditional distribution.
 
 .. code-block:: python
@@ -178,4 +178,4 @@ To enable the feature, pass ``is_multi_task=True``.
   model = LinearRegression()
   estimator = dte_adj.AdjustedDistributionEstimator(model, folds=3)
   estimator.fit(X, D, Y)
-  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, is_multi_task=True, locations=locations, variance_type="moment")
+  dte, lower_bound, upper_bound = estimator.predict_dte(target_treatment_arm=1, control_treatment_arm=0, is_multi_task=True, locations=locations, variance_type="moment")

docs/source/index.rst

@@ -4,7 +4,7 @@ dte_adj
 A Python Package for Estimating Distribution Treatment Effects
 --------------------------------------------------------------
 
-`dte_adj` is a Python package for estimating distribution treatment effects in randomized experiments. 
+`dte_adj` is a Python package for estimating distribution treatment effects in randomized experiments.
 It provides APIs for conducting regression adjustment to estimate precise distribution functions, enabling deeper insights beyond average treatment effects through machine learning-enhanced estimation methods.
 
 .. image:: _static/distributional_effect.png
@@ -27,7 +27,7 @@ Theoretical Foundations
 
 For theoretical foundations, see:
 
-* **Simple randomization**: Byambadalai et al. (2024) [#simple2024]_ 
+* **Simple randomization**: Byambadalai et al. (2024) [#simple2024]_
 * **Covariate-adaptive randomization**: Byambadalai et al. (2025) [#car2025]_
 * **Multi-task learning**: Byambadalai et al. (2024) [#multitask2024]_
 
@@ -55,4 +55,4 @@ Indices and tables
 
 License
 ~~~~~~~
-MIT License
+MIT License