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fix lasso tests on GPU #463

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merged 4 commits into from
Jan 24, 2020
Merged

fix lasso tests on GPU #463

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090f01d
fix lasso tests
mtar Jan 22, 2020
6d8bee8
Update CHANGELOG.md
mtar Jan 23, 2020
6f0ebb0
changelog formatting
Jan 23, 2020
61c163e
Merge branch 'master' into bug/462-test_lasso
mtar Jan 24, 2020
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10 changes: 5 additions & 5 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
# v0.2.2

This version adds support for PyTorch 1.4.0. There are also several minor feature improvements and bug fixes listed below.

- added option for neutral elements to be used in the place of empty tensors in reduction operations (`operations.__reduce_op`) (cf. [#369](https://github.com/helmholtz-analytics/heat/issues/369) and [#444](https://github.com/helmholtz-analytics/heat/issues/444))
- `var` and `std` both now support iterable axis arguments
- updated pull request template
- bug fix: `x.unique()` returns a DNDarray both in distributed and non-distributed mode (cf. [#464])
- [#443](https://github.com/helmholtz-analytics/heat/pull/443) added option for neutral elements to be used in the place of empty tensors in reduction operations (`operations.__reduce_op`) (cf. [#369](https://github.com/helmholtz-analytics/heat/issues/369) and [#444](https://github.com/helmholtz-analytics/heat/issues/444))
- [#445](https://github.com/helmholtz-analytics/heat/pull/445) `var` and `std` both now support iterable axis arguments
- [#452](https://github.com/helmholtz-analytics/heat/pull/452) updated pull request template
- [#465](https://github.com/helmholtz-analytics/heat/pull/465) bug fix: `x.unique()` returns a DNDarray both in distributed and non-distributed mode (cf. [#464])
- [#463](https://github.com/helmholtz-analytics/heat/pull/463) Bugfix: Lasso tests now run with both GPUs and CPUs

# v0.2.1

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32 changes: 22 additions & 10 deletions heat/core/regression/lasso/tests/test_lasso.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,10 +24,14 @@ def test_lasso(self):
# ToDo: add additional tests
# get some test data
X = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="x",
device=ht_device,
)
y = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="y",
device=ht_device,
)

# normalize dataset
Expand Down Expand Up @@ -60,15 +64,19 @@ def test_lasso(self):
self.assertEqual(yest.shape, (m,))

X = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="x",
device=ht_device,
)
y = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="y",
device=ht_device,
)

# Now the same stuff again in PyTorch
X = torch.tensor(X._DNDarray__array)
y = torch.tensor(y._DNDarray__array)
X = torch.tensor(X._DNDarray__array, device=device)
y = torch.tensor(y._DNDarray__array, device=device)

# normalize dataset
X = X / torch.sqrt((torch.mean(X ** 2, 0)))
Expand Down Expand Up @@ -100,15 +108,19 @@ def test_lasso(self):
self.assertEqual(yest.shape, (m,))

X = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="x"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="x",
device=ht_device,
)
y = ht.load_hdf5(
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"), dataset="y"
os.path.join(os.getcwd(), "heat/datasets/data/diabetes.h5"),
dataset="y",
device=ht_device,
)

# Now the same stuff again in PyTorch
X = X._DNDarray__array.numpy()
y = y._DNDarray__array.numpy()
X = X._DNDarray__array.cpu().numpy()
y = y._DNDarray__array.cpu().numpy()

# normalize dataset
X = X / np.sqrt((np.mean(X ** 2, axis=0, keepdims=True)))
Expand Down