Replace load_boston due to Ethical Issues

README.md

@@ -150,11 +150,11 @@ But our implementations work on multiple GPUs, TPUs and scale dramatically...
 ```python
 from pl_bolts.models.regression import LinearRegression
 from pl_bolts.datamodules import SklearnDataModule
-from sklearn.datasets import load_boston
+from sklearn.datasets import load_diabetes
 import pytorch_lightning as pl
 
 # sklearn dataset
-X, y = load_boston(return_X_y=True)
+X, y = load_diabetes(return_X_y=True)
 loaders = SklearnDataModule(X, y)
 
 model = LinearRegression(input_dim=13)

docs/source/classic_ml.rst

@@ -22,9 +22,9 @@ Add either L1 or L2 regularization, or both, by specifying the regularization st
     from pl_bolts.models.regression import LinearRegression
     import pytorch_lightning as pl
     from pl_bolts.datamodules import SklearnDataModule
-    from sklearn.datasets import load_boston
+    from sklearn.datasets import load_diabetes
 
-    X, y = load_boston(return_X_y=True)
+    X, y = load_diabetes(return_X_y=True)
     loaders = SklearnDataModule(X, y)
 
     model = LinearRegression(input_dim=13)

docs/source/datamodules_sklearn.rst

@@ -7,10 +7,10 @@ Utilities to map sklearn or numpy datasets to PyTorch Dataloaders with automatic
 
 .. code-block:: python
 
-    from sklearn.datasets import load_boston
+    from sklearn.datasets import load_diabetes
     from pl_bolts.datamodules import SklearnDataModule
 
-    X, y = load_boston(return_X_y=True)
+    X, y = load_diabetes(return_X_y=True)
     loaders = SklearnDataModule(X, y)
 
     train_loader = loaders.train_dataloader(batch_size=32)
@@ -21,10 +21,10 @@ Or build your own torch datasets
 
 .. code-block:: python
 
-    from sklearn.datasets import load_boston
+    from sklearn.datasets import load_diabetes
     from pl_bolts.datamodules import SklearnDataset
 
-    X, y = load_boston(return_X_y=True)
+    X, y = load_diabetes(return_X_y=True)
     dataset = SklearnDataset(X, y)
     loader = DataLoader(dataset)
 

docs/source/introduction_guide.rst

@@ -338,10 +338,10 @@ We even have prebuilt modules to bridge the gap between Numpy, Sklearn and PyTor
 
 .. code-block:: python
 
-    from sklearn.datasets import load_boston
+    from sklearn.datasets import load_diabetes
     from pl_bolts.datamodules import SklearnDataModule
 
-    X, y = load_boston(return_X_y=True)
+    X, y = load_diabetes(return_X_y=True)
     datamodule = SklearnDataModule(X, y)
 
     model = LitModel(datamodule)
@@ -382,10 +382,10 @@ Here's an example for Linear regression
 
     import pytorch_lightning as pl
     from pl_bolts.datamodules import SklearnDataModule
-    from sklearn.datasets import load_boston
+    from sklearn.datasets import load_diabetes
 
     # link the numpy dataset to PyTorch
-    X, y = load_boston(return_X_y=True)
+    X, y = load_diabetes(return_X_y=True)
     loaders = SklearnDataModule(X, y)
 
     # training runs training batches while validating against a validation set

pl_bolts/datamodules/sklearn_datamodule.py

@@ -20,10 +20,10 @@ class SklearnDataset(Dataset):
     Mapping between numpy (or sklearn) datasets to PyTorch datasets.
 
     Example:
-        >>> from sklearn.datasets import load_boston
+        >>> from sklearn.datasets import load_diabetes
         >>> from pl_bolts.datamodules import SklearnDataset
         ...
-        >>> X, y = load_boston(return_X_y=True)
+        >>> X, y = load_diabetes(return_X_y=True)
         >>> dataset = SklearnDataset(X, y)
         >>> len(dataset)
         506
@@ -114,10 +114,10 @@ class SklearnDataModule(LightningDataModule):
 
     Example:
 
-        >>> from sklearn.datasets import load_boston
+        >>> from sklearn.datasets import load_diabetes
         >>> from pl_bolts.datamodules import SklearnDataModule
         ...
-        >>> X, y = load_boston(return_X_y=True)
+        >>> X, y = load_diabetes(return_X_y=True)
         >>> loaders = SklearnDataModule(X, y, batch_size=32)
         ...
         >>> # train set

pl_bolts/models/regression/linear_regression.py

@@ -116,7 +116,7 @@ def cli_main():
 
     # create dataset
     if _SKLEARN_AVAILABLE:
-        from sklearn.datasets import load_boston
+        from sklearn.datasets import load_diabetes
     else:  # pragma: no cover
         raise ModuleNotFoundError(
             'You want to use `sklearn` which is not installed yet, install it with `pip install sklearn`.'
@@ -133,7 +133,7 @@ def cli_main():
     # model = LinearRegression(**vars(args))
 
     # data
-    X, y = load_boston(return_X_y=True)  # these are numpy arrays
+    X, y = load_diabetes(return_X_y=True)  # these are numpy arrays
     loaders = SklearnDataModule(X, y, batch_size=args.batch_size)
 
     # train