Merge branch 'main' of github.com:bambinos/bambi into governance

.github/workflows/publish-docs.yml

@@ -5,16 +5,18 @@ on:
     branches:
       - main
   pull_request:
+    types:
+      - opened
+      - reopened
+      - synchronize
+      - closed
 
 jobs:
   build:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
     steps:
-      - name: Set up Pandoc
-        uses: r-lib/actions/setup-pandoc@v2
-
       - name: Checkout source
         uses: actions/checkout@v2
         with:
@@ -25,37 +27,30 @@ jobs:
         with:
           python-version: "3.10"
 
-      - name: Install Bambi and all its dependencies
+      - name: Install quartodoc and griffe
         run: |
-          conda install -c conda-forge python-graphviz
-          conda install pip
-          pip install .
-          pip install .[dev]
-          pip install .[jax]
+          python -m pip install --upgrade pip
+          python -m pip install quartodoc==0.6.1
 
-      - name: Checkout gh-pages
-        uses: actions/checkout@v2
+      - name: Set up Quarto
+        uses: quarto-dev/quarto-actions/setup@v2   
         with:
-          ref: gh-pages
-          path: docs/_build/html
-
-      - name: Test build docs
-        if: github.ref != 'refs/heads/main' && ! startsWith(github.ref, 'refs/tags')
-        run: BUILDDIR=_build/html/main make -C docs/ local
+          version: 1.2.280 # since this is my local version, we could change it
 
       - name: Build docs
-        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags')
+        shell: bash
         run: |
-          sphinx-build docs docs/_build/html
-          touch docs/_build/html/.nojekyll
-
-      - name: Publish docs to gh-pages
-        # Only once from main or a tag
-        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags')
-        # We pin to the SHA, not the tag, for security reasons.
-        # https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/security-hardening-for-github-actions#using-third-party-actions
-        uses: peaceiris/actions-gh-pages@bbdfb200618d235585ad98e965f4aafc39b4c501 # v3.7.3
+          python -m quartodoc build --config docs/_quarto.yml
+          quarto render docs
+          touch docs/_site/.nojekyll
+
+      - name: Publish to GitHub 
+        if: github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags') # Only from main or a tag
+        uses: quarto-dev/quarto-actions/publish@v2
         with:
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: docs/_build/html
-          keep_files: true
+          target: gh-pages
+          render: false
+          path: docs/_site
+
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11"]
 
     name: Set up Python ${{ matrix.python-version }}
     steps:
@@ -50,7 +50,7 @@ jobs:
         shell: bash -l {0}
         run: |
           echo "Running black..."
-          black bambi --check
+          black bambi --check --diff --color
 
           echo "Checking code style with pylint..."
           pylint bambi

.gitignore

@@ -17,4 +17,5 @@ tags
 .cache
 docs/_build
 .vscode/
-pytest.ini
+pytest.ini
+/.quarto/

.pylintrc

@@ -61,9 +61,7 @@ disable=missing-docstring,
         too-many-locals,
         too-many-branches,
         too-many-statements,
-        no-self-use,
         too-few-public-methods,
-        bad-continuation,
         cyclic-import # There was a false positive when working on #607
 
 
@@ -132,12 +130,6 @@ max-line-length=100
 # Maximum number of lines in a module
 max-module-lines=1000
 
-# List of optional constructs for which whitespace checking is disabled. `dict-
-# separator` is used to allow tabulation in dicts, etc.: {1  : 1,\n222: 2}.
-# `trailing-comma` allows a space between comma and closing bracket: (a, ).
-# `empty-line` allows space-only lines.
-no-space-check=trailing-comma,
-               dict-separator
 
 # Allow the body of a class to be on the same line as the declaration if body
 # contains single statement.
@@ -259,7 +251,7 @@ good-names=b,
            ok,
            sd,
            tr,
-	       eta,
+           eta,
            Run,
            _log,
            _,

docs/CHANGELOG.md → CHANGELOG.md

@@ -10,10 +10,14 @@
 
 ### Maintenance and fixes
 
+* Bump `quartodoc` version to 0.6.1 (#720)
+
 ### Documentation
 
 ### Deprecation
 
+* Drop official suport for Python 3.8 (#720)
+
 
 ## 0.12.0
 

README.md

@@ -13,7 +13,7 @@ Bambi is a high-level Bayesian model-building interface written in Python. It's
 
 ## Installation
 
-Bambi requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.
+Bambi requires a working Python interpreter (3.9+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip:
 

bambi/interpret/effects.py

@@ -411,6 +411,7 @@ def predictions(
     use_hdi: bool = True,
     prob=None,
     transforms=None,
+    sample_new_groups=False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Predictions
 
@@ -443,6 +444,9 @@ def predictions(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -496,11 +500,15 @@ def predictions(
     response_transform = transforms.get(response_name, identity)
 
     if pps:
-        idata = model.predict(idata, data=cap_data, inplace=False, kind="pps")
+        idata = model.predict(
+            idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False, kind="pps"
+        )
         y_hat = response_transform(idata.posterior_predictive[response.name])
         y_hat_mean = y_hat.mean(("chain", "draw"))
     else:
-        idata = model.predict(idata, data=cap_data, inplace=False)
+        idata = model.predict(
+            idata, data=cap_data, sample_new_groups=sample_new_groups, inplace=False
+        )
         y_hat = response_transform(idata.posterior[response.name_target])
         y_hat_mean = y_hat.mean(("chain", "draw"))
 
@@ -534,6 +542,7 @@ def comparisons(
     use_hdi: bool = True,
     prob: Union[float, None] = None,
     transforms: Union[dict, None] = None,
+    sample_new_groups: bool = False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Comparisons
 
@@ -562,6 +571,9 @@ def comparisons(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -631,7 +643,9 @@ def comparisons(
     comparisons_data = create_differences_data(
         conditional_info, contrast_info, conditional_info.user_passed, kind="comparisons"
     )
-    idata = model.predict(idata, data=comparisons_data, inplace=False)
+    idata = model.predict(
+        idata, data=comparisons_data, sample_new_groups=sample_new_groups, inplace=False
+    )
 
     predictive_difference = PredictiveDifferences(
         model,
@@ -663,6 +677,7 @@ def slopes(
     use_hdi: bool = True,
     prob: Union[float, None] = None,
     transforms: Union[dict, None] = None,
+    sample_new_groups: bool = False,
 ) -> pd.DataFrame:
     """Compute Conditional Adjusted Slopes
 
@@ -702,6 +717,9 @@ def slopes(
     transforms : dict, optional
         Transformations that are applied to each of the variables being plotted. The keys are the
         name of the variables, and the values are functions to be applied. Defaults to ``None``.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
 
     Returns
     -------
@@ -776,7 +794,9 @@ def slopes(
     slopes_data = create_differences_data(
         conditional_info, wrt_info, conditional_info.user_passed, effect_type
     )
-    idata = model.predict(idata, data=slopes_data, inplace=False)
+    idata = model.predict(
+        idata, data=slopes_data, sample_new_groups=sample_new_groups, inplace=False
+    )
 
     predictive_difference = PredictiveDifferences(
         model, slopes_data, wrt_info, conditional_info, response, use_hdi, effect_type

bambi/interpret/plotting.py

@@ -86,6 +86,7 @@ def plot_predictions(
     idata: az.InferenceData,
     covariates: Union[str, list],
     target: str = "mean",
+    sample_new_groups: bool = False,
     pps: bool = False,
     use_hdi: bool = True,
     prob=None,
@@ -110,6 +111,9 @@ def plot_predictions(
         Which model parameter to plot. Defaults to 'mean'. Passing a parameter into target only
         works when pps is False as the target may not be available in the posterior predictive
         distribution.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     pps: bool, optional
         Whether to plot the posterior predictive samples. Defaults to ``False``.
     use_hdi : bool, optional
@@ -174,6 +178,7 @@ def plot_predictions(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     response_name = get_aliased_name(model.response_component.response_term)
@@ -219,6 +224,7 @@ def plot_comparisons(
     conditional: Union[str, dict, list, None] = None,
     average_by: Union[str, list] = None,
     comparison_type: str = "diff",
+    sample_new_groups: bool = False,
     use_hdi: bool = True,
     prob=None,
     legend: bool = True,
@@ -245,6 +251,9 @@ def plot_comparisons(
         over the other covariates in the model. Defaults to ``None``.
     comparison_type : str, optional
         The type of comparison to plot. Defaults to 'diff'.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     use_hdi : bool, optional
         Whether to compute the highest density interval (defaults to True) or the quantiles.
     prob : float, optional
@@ -332,6 +341,7 @@ def plot_comparisons(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     return _plot_differences(
@@ -355,6 +365,7 @@ def plot_slopes(
     average_by: Union[str, list] = None,
     eps: float = 1e-4,
     slope: str = "dydx",
+    sample_new_groups: bool = False,
     use_hdi: bool = True,
     prob=None,
     transforms=None,
@@ -395,6 +406,9 @@ def plot_slopes(
         change in the response.
         'dyex' represents a percent change in 'wrt' is associated with a unit increase
         in the response.
+    sample_new_groups : bool, optional
+        If the model contains group-level effects, and data is passed for unseen groups, whether
+        to sample from the new groups. Defaults to ``False``.
     use_hdi : bool, optional
         Whether to compute the highest density interval (defaults to True) or the quantiles.
     prob : float, optional
@@ -486,6 +500,7 @@ def plot_slopes(
         use_hdi=use_hdi,
         prob=prob,
         transforms=transforms,
+        sample_new_groups=sample_new_groups,
     )
 
     return _plot_differences(