Migrate to mkdocs

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,3 +1,3 @@
 # Checklist
 
-- [ ] Added a `CHANGELOG.rst` entry
+-   [ ] Added a `CHANGELOG.rst` entry

.github/workflows/ci.yml

@@ -35,8 +35,8 @@ jobs:
       - name: pre-commit
         run: pixi run pre-commit-run --color=always --show-diff-on-failure
 
-  mypy-example-nbs:
-    name: Check notebooks mypy
+  check-example-nbs:
+    name: Check notebooks mypy and output exists
     timeout-minutes: 5
     runs-on: ubuntu-latest
     steps:
@@ -48,6 +48,9 @@ jobs:
         run: |
           pixi run nb-convert
           pixi run nb-mypy
+      - name: Check output exists
+        run: |
+          pixi run nb-assert-output
 
   unit-tests:
     name: Unit Tests

.prettierrc

@@ -3,5 +3,13 @@
   "bracketSpacing": true,
   "printWidth": 200,
   "endOfLine": "auto",
-  "tabWidth": 2
+  "tabWidth": 2,
+  "overrides": [
+    {
+      "files": "*.md",
+      "options": {
+        "tabWidth": 4
+      }
+    }
+  ]
 }

.readthedocs.yml

@@ -6,9 +6,7 @@ build:
   commands:
     - mamba install -c conda-forge -c nodefaults pixi
     - pixi run -e docs postinstall
-    - pixi run -e docs docs
+    - pixi run -e docs docs-build
     - pixi run -e docs readthedocs
-sphinx:
-  configuration: docs/conf.py
 formats:
   - pdf

CHANGELOG.md

@@ -0,0 +1,116 @@
+# Changelog
+
+## 0.11.0 (2024-09-05)
+
+### New features
+
+-   Add support for using `scipy.sparse.csr_matrix` as datastructure for covariates `X`.
+
+## 0.10.0 (2024-08-13)
+
+### New features
+
+-   Add abstract method [`MetaLearner.predict_conditional_average_outcomes`][metalearners.metalearner.MetaLearner.predict_conditional_average_outcomes] to [`metalearners.metalearner.MetaLearner`][metalearners.metalearner.MetaLearner].
+-   Implement [`RLearner.predict_conditional_average_outcomes`][metalearners.rlearner.RLearner.predict_conditional_average_outcomes] for [`metalearners.rlearner.RLearner`][metalearners.rlearner.RLearner].
+
+### Bug fixes
+
+-   Fix bug in which the [`metalearners.slearner.SLearner`][metalearners.slearner.SLearner]'s inference step would have some leakage in the in-sample scenario.
+
+## 0.9.0 (2024-08-02)
+
+### New features
+
+-   Add [`MetaLearner.init_args`][metalearners.metalearner.MetaLearner.init_args].
+-   Add [`FixedBinaryPropensity`][metalearners.utils.FixedBinaryPropensity].
+-   Add `MetaLearner._build_onnx` to [`metalearners.MetaLearner`][metalearners.metalearner.MetaLearner] abstract class and implement it for [`TLearner`][metalearners.tlearner.TLearner], [`XLearner`][metalearners.xlearner.XLearner], [`RLearner`][metalearners.rlearner.RLearner], and [`DRLearner`][metalearners.drlearner.DRLearner].
+-   Add `MetaLearner._necessary_onnx_models`.
+-   Add [`DRLearner.average_treatment_effect`][metalearners.drlearner.DRLearner.average_treatment_effect] to compute the AIPW point estimate and standard error for average treatment effects (ATE) without requiring a full model fit.
+
+## 0.8.0 (2024-07-22)
+
+### New features
+
+-   Add [`MetaLearner.fit_all_nuisance`][metalearners.metalearner.MetaLearner.fit_all_nuisance] and [`MetaLearner.fit_all_treatment`][metalearners.metalearner.MetaLearner.fit_all_treatment].
+-   Add optional `store_raw_results` and `store_results` parameters to [`MetaLearnerGridSearch`][metalearners.grid_search.MetaLearnerGridSearch].
+-   Renamed `_GSResult` to [`GSResult`][metalearners.grid_search.GSResult].
+-   Added `grid_size_` attribute to [`MetaLearnerGridSearch`][metalearners.grid_search.MetaLearnerGridSearch].
+-   Implement [`CrossFitEstimator.score`][metalearners.cross_fit_estimator.CrossFitEstimator.score].
+
+### Bug fixes
+
+-   Fixed a bug in [`MetaLearner.evaluate`][metalearners.metalearner.MetaLearner.evaluate] where it failed in the case of `feature_set` being different from `None`.
+
+## 0.7.0 (2024-07-12)
+
+### New features
+
+-   Add optional `adaptive_clipping` parameter to [`DRLearner`][metalearners.drlearner.DRLearner].
+
+### Other changes
+
+-   Change the index columns order in `MetaLearnerGridSearch.results_`.
+-   Raise a custom error if only one class is present in a classification outcome.
+-   Raise a custom error if there are some treatment variants which have seen classification outcomes that have not appeared for some other treatment variant.
+
+## 0.6.0 (2024-07-08)
+
+### New features
+
+-   Implement [`MetaLearnerGridSearch`][metalearners.grid_search.MetaLearnerGridSearch].
+-   Add a `scoring` parameter to [`MetaLearner.evaluate`][metalearners.metalearner.MetaLearner.evaluate] and implement the abstract method for [`XLearner`][metalearners.xlearner.XLearner] and [`DRLearner`][metalearners.drlearner.DRLearner].
+
+### Other changes
+
+-   Increase the lower bound on `scikit-learn` from 1.3 to 1.4.
+-   Drop the run dependency on `git_root`.
+
+## 0.5.0 (2024-06-18)
+
+-   No longer raise an error if `feature_set` is provided to [`SLearner`][metalearners.slearner.SLearner].
+-   Fix a bug where base model dictionaries -- e.g., `n_folds` or `feature-set` -- were improperly initialized if the provided dictionary's keys were a strict superset of the expected keys.
+
+## 0.4.2 (2024-06-18)
+
+-   Ship license file.
+
+## 0.4.1 (2024-06-18)
+
+-   Fix dependencies for pip.
+
+## 0.4.0 (2024-06-18)
+
+-   Implemented [`CrossFitEstimator.clone`][metalearners.cross_fit_estimator.CrossFitEstimator.clone].
+-   Added `n_jobs_base_learners` to [`MetaLearner.fit`][metalearners.metalearner.MetaLearner.fit].
+-   Renamed [`Explainer.feature_importances`][metalearners.explainer.Explainer.feature_importances]. Note this is a breaking change.
+-   Renamed [`MetaLearner.feature_importances`][metalearners.metalearner.MetaLearner.feature_importances]. Note this is a breaking change.
+-   Renamed [`Explainer.shap_values`][metalearners.explainer.Explainer.shap_values]. Note this is a breaking change.
+-   Renamed [`MetaLearner.shap_values`][metalearners.metalearner.MetaLearner.shap_values]. Note this is a breaking change.
+-   Renamed [`MetaLearner.explainer`][metalearners.metalearner.MetaLearner.explainer]. Note this is a breaking change.
+-   Implemented `synchronize_cross_fitting` parameter for [`MetaLearner.fit`][metalearners.metalearner.MetaLearner.fit].
+-   Implemented `cv` parameter for [`CrossFitEstimator.fit`][metalearners.cross_fit_estimator.CrossFitEstimator.fit].
+
+## 0.3.0 (2024-06-03)
+
+-   Implemented [`Explainer`][metalearners.explainer.Explainer] with support for binary classification and regression outcomes and discrete treatment variants.
+-   Integration of [`Explainer`][metalearners.explainer.Explainer] with [`MetaLearner`][metalearners.metalearner.MetaLearner] for feature importance and SHAP values calculations.
+-   Implemented model reuse through the `fitted_nuisance_models` and `fitted_propensity_model` parameters of [`MetaLearner`][metalearners.metalearner.MetaLearner].
+-   Allow for `fit_params` in [`MetaLearner.fit`][metalearners.metalearner.MetaLearner.fit].
+
+## 0.2.0 (2024-05-28)
+
+Beta release with:
+
+-   [`DRLearner`][metalearners.drlearner.DRLearner] with support for binary classification and regression outcomes and discrete treatment variants.
+-   Generalization of [`TLearner`][metalearners.tlearner.TLearner], [`XLearner`][metalearners.xlearner.XLearner], and [`RLearner`][metalearners.rlearner.RLearner] to allow for more than two discrete treatment variants.
+-   Unification of shapes returned by `predict` methods.
+-   [`simplify_output`][metalearners.utils.simplify_output] and [`metalearner_factory`][metalearners.utils.metalearner_factory].
+
+## 0.1.0 (2024-05-16)
+
+Alpha release with:
+
+-   [`TLearner`][metalearners.tlearner.TLearner] with support for binary classification and regression outcomes and binary treatment variants.
+-   [`SLearner`][metalearners.slearner.SLearner] with support for binary classification and regression outcomes and discrete treatment variants.
+-   [`XLearner`][metalearners.xlearner.XLearner] with support for binary classification and regression outcomes and binary treatment variants.
+-   [`RLearner`][metalearners.rlearner.RLearner] with support for binary classification and regression outcomes and binary treatment variants.

README.md

@@ -10,11 +10,11 @@ MetaLearners for Conditional Average Treatment Effect (CATE) estimation
 
 The library focuses on providing
 
-- Methodologically sound cross-fitting
-- Convenient access to and reuse of base models
-- Consistent APIs across Metalearners
-- Support for more than binary treatment variants
-- Integrations with `pandas`, `shap`, `lime`, `optuna` and soon `onnx`
+-   Methodologically sound cross-fitting
+-   Convenient access to and reuse of base models
+-   Consistent APIs across Metalearners
+-   Support for more than binary treatment variants
+-   Integrations with `pandas`, `shap`, `lime`, `optuna` and soon `onnx`
 
 ## Example
 

docs/.gitignore

@@ -0,0 +1,2 @@
+# Ignore artifacts created during notebook execution
+***.onnx

docs/api_documentation.md

@@ -0,0 +1,3 @@
+# API Documentation
+
+::: metalearners