Merge branch 'dev' of github.com:saattrupdan/doubt into dev

CHANGELOG.md

@@ -8,6 +8,16 @@ and this project adheres to
 [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
 
+## [Unreleased]
+### Changed
+- Updated `README` to include generalised linear models, rather than only
+  mentioning linear regression.
+
+### Fixed
+- Removed mention of `PyTorch` model support, as that has not been implemented
+  yet
+
+
 ## [v4.1.0] - 2021-07-26
 ### Changed
 - The `verbose` argument to `QuantileRegressionForest` also displays a progress

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Dan Saattrup Nielsen
+Copyright (c) 2021
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -17,16 +17,16 @@ pip install doubt
 
 ## Features
 
-- Bootstrap wrapper for all Scikit-Learn and PyTorch models
+- Bootstrap wrapper for all Scikit-Learn models
     - Can also be used to calculate usual bootstrapped statistics of a dataset
-- (Linear) Quantile Regression
+- Quantile Regression for all generalised linear models
 - Quantile Regression Forests
 - A uniform dataset API, with 24 regression datasets and counting
 
 
 ## Quick Start
 
-If you already have a model in Scikit-Learn or PyTorch, then you can simply
+If you already have a model in Scikit-Learn, then you can simply
 wrap it in a `Boot` to enable predicting with prediction intervals:
 
 ```python

doubt/models/boot/boot.py

@@ -16,9 +16,9 @@ class Boot:
     Datasets can be any sequence of numeric input, from which bootstrapped
     statistics can be calculated, with confidence intervals included.
 
-    The models can be any model that is either callable or equipped with
-    a `predict` method, such as all the models in `scikit-learn`, `pytorch`
-    and `tensorflow`, and the bootstrapped model can then produce predictions
+    The models can be any model that has a `fit` method and is either callable
+    or equipped with a `predict` method, such as all the models in
+    `scikit-learn`, and the bootstrapped model can then produce predictions
     with prediction intervals.
 
     The bootstrapped prediction intervals are computed using the an extension
@@ -336,7 +336,7 @@ def fit(self, X: FloatArray, y: FloatArray, n_boots: Optional[int] = None):
 
     # Compute the m_i's and the validation residuals
     val_residuals = []
-    for boot_idx in range(n_boots):
+    for _ in range(n_boots):
         train_idxs = rng.choice(range(n), size=n, replace=True)
         val_idxs = [idx for idx in range(n) if idx not in train_idxs]