[MRG] Update tests and documentation (#484)

* remove old macos and windows tets update requirements

* speedup ssw and continuaous ot exmaples

* speedup regpath and variane

* speedup conv 2d example + continuous stick

* speedup regpath

Author: rflamary

.github/workflows/build_tests.yml

@@ -83,7 +83,7 @@ jobs:
         pip install -e .
     - name: Run tests
       run: |
-        python -m pytest --durations=20 -v test/ ot/ --ignore ot/gpu/ --color=yes
+        python -m pytest --durations=20 -v test/ ot/ --color=yes
 
 
   macos:
@@ -92,7 +92,7 @@ jobs:
      strategy:
        max-parallel: 4
        matrix:
-         python-version: ["3.7", "3.8", "3.9", "3.10"]
+         python-version: ["3.10"]
 
      steps:
      - uses: actions/checkout@v1
@@ -107,10 +107,10 @@ jobs:
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
-         pip install pytest "pytest-cov<2.6"
+         pip install pytest 
      - name: Run tests
        run: |
-         python -m pytest --durations=20  -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot --color=yes
+         python -m pytest --durations=20  -v test/ ot/ --color=yes
 
 
   windows:
@@ -119,7 +119,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.10"]
 
     steps:
     - uses: actions/checkout@v1
@@ -151,8 +151,8 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install -r .github/requirements_test_windows.txt
-        python -m pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
-        python -m pip install pytest "pytest-cov<2.6"
+        python -m pip3 install torch torchvision torchaudio
+        python -m pip install pytest
     - name: Run tests
       run: |
-        python -m pytest --durations=20  -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot --color=yes
+        python -m pytest --durations=20  -v test/ ot/ --color=yes

RELEASES.md

@@ -7,6 +7,7 @@
 - Added the sparsity-constrained OT solver to `ot.smooth` and added ` projection_sparse_simplex` to `ot.utils` (PR #459)
 - Add tests on GPU for master branch and approved PR (PR #473)
 - Add `median` method to all inherited classes of `backend.Backend` (PR #472)
+- Update tests for macOS and Windows, speedup documentation (PR #484)
 
 #### Closed issues
 

docs/Makefile

@@ -50,6 +50,8 @@ help:
 .PHONY: clean
 clean:
 	rm -rf $(BUILDDIR)/*
+	rm -rf source/gen_modules/*
+	rm -rf source/auto_examples/*
 
 .PHONY: html
 html:

examples/backends/plot_sliced_wass_grad_flow_pytorch.py

@@ -40,8 +40,8 @@
 import ot
 import matplotlib.animation as animation
 
-I1 = pl.imread('../../data/redcross.png').astype(np.float64)[::4, ::4, 2]
-I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::4, ::4, 2]
+I1 = pl.imread('../../data/redcross.png').astype(np.float64)[::5, ::5, 2]
+I2 = pl.imread('../../data/tooth.png').astype(np.float64)[::5, ::5, 2]
 
 sz = I2.shape[0]
 XX, YY = np.meshgrid(np.arange(sz), np.arange(sz))
@@ -67,7 +67,7 @@
 
 
 lr = 1e3
-nb_iter_max = 100
+nb_iter_max = 50
 
 x_all = np.zeros((nb_iter_max, x1.shape[0], 2))
 
@@ -129,7 +129,7 @@ def _update_plot(i):
 xbary_torch = torch.tensor(xbinit).to(device=device).requires_grad_(True)
 
 lr = 1e3
-nb_iter_max = 100
+nb_iter_max = 50
 
 x_all = np.zeros((nb_iter_max, xbary_torch.shape[0], 2))
 

examples/backends/plot_ssw_unif_torch.py

@@ -35,7 +35,7 @@
 
 torch.manual_seed(1)
 
-N = 1000
+N = 500
 x0 = torch.rand(N, 3)
 x0 = F.normalize(x0, dim=-1)
 
@@ -72,8 +72,8 @@ def plot_sphere(ax):
 x = x0.clone()
 x.requires_grad_(True)
 
-n_iter = 500
-lr = 100
+n_iter = 100
+lr = 150
 
 losses = []
 xvisu = torch.zeros(n_iter, N, 3)
@@ -82,7 +82,7 @@ def plot_sphere(ax):
     sw = ot.sliced_wasserstein_sphere_unif(x, n_projections=500)
     grad_x = torch.autograd.grad(sw, x)[0]
 
-    x = x - lr * grad_x
+    x = x - lr * grad_x / np.sqrt(i / 10 + 1)
     x = F.normalize(x, p=2, dim=1)
 
     losses.append(sw.item())
@@ -102,7 +102,7 @@ def plot_sphere(ax):
 # Plot trajectories of generated samples along iterations
 # -------------------------------------------------------
 
-ivisu = [0, 25, 50, 75, 100, 150, 200, 350, 499]
+ivisu = [0, 10, 20, 30, 40, 50, 60, 70, 80]
 
 fig = pl.figure(3, (10, 10))
 for i in range(9):
@@ -149,5 +149,5 @@ def _update_plot(i):
 ax.set_title('Iter. {}'.format(ivisu[i]))
 
 
-ani = animation.FuncAnimation(pl.gcf(), _update_plot, n_iter // 5, interval=100, repeat_delay=2000)
+ani = animation.FuncAnimation(pl.gcf(), _update_plot, n_iter // 5, interval=200, repeat_delay=2000)
 # %%

examples/backends/plot_stoch_continuous_ot_pytorch.py

@@ -27,8 +27,8 @@
 torch.manual_seed(42)
 np.random.seed(42)
 
-n_source_samples = 10000
-n_target_samples = 10000
+n_source_samples = 1000
+n_target_samples = 1000
 theta = 2 * np.pi / 20
 noise_level = 0.1
 
@@ -89,7 +89,7 @@ def forward(self, x):
 optimizer = torch.optim.Adam(list(u.parameters()) + list(v.parameters()), lr=.005)
 
 # number of iteration
-n_iter = 1000
+n_iter = 500
 n_batch = 500
 
 

examples/barycenters/plot_convolutional_barycenter.py

@@ -29,10 +29,10 @@
 this_file = os.path.realpath('__file__')
 data_path = os.path.join(Path(this_file).parent.parent.parent, 'data')
 
-f1 = 1 - plt.imread(os.path.join(data_path, 'redcross.png'))[:, :, 2]
-f2 = 1 - plt.imread(os.path.join(data_path, 'tooth.png'))[:, :, 2]
-f3 = 1 - plt.imread(os.path.join(data_path, 'heart.png'))[:, :, 2]
-f4 = 1 - plt.imread(os.path.join(data_path, 'duck.png'))[:, :, 2]
+f1 = 1 - plt.imread(os.path.join(data_path, 'redcross.png'))[::2, ::2, 2]
+f2 = 1 - plt.imread(os.path.join(data_path, 'tooth.png'))[::2, ::2, 2]
+f3 = 1 - plt.imread(os.path.join(data_path, 'heart.png'))[::2, ::2, 2]
+f4 = 1 - plt.imread(os.path.join(data_path, 'duck.png'))[::2, ::2, 2]
 
 f1 = f1 / np.sum(f1)
 f2 = f2 / np.sum(f2)

examples/plot_OT_1D_smooth.py

@@ -14,7 +14,7 @@
 #
 # License: MIT License
 
-# sphinx_gallery_thumbnail_number = 6
+# sphinx_gallery_thumbnail_number = 5
 
 import numpy as np
 import matplotlib.pylab as pl

examples/sliced-wasserstein/plot_variance.py

@@ -29,7 +29,7 @@
 
 # %% parameters and data generation
 
-n = 500  # nb samples
+n = 200  # nb samples
 
 mu_s = np.array([0, 0])
 cov_s = np.array([[1, 0], [0, 1]])
@@ -58,9 +58,9 @@
 # Sliced Wasserstein distance for different seeds and number of projections
 # -------------------------------------------------------------------------
 
-n_seed = 50
-n_projections_arr = np.logspace(0, 3, 25, dtype=int)
-res = np.empty((n_seed, 25))
+n_seed = 20
+n_projections_arr = np.logspace(0, 3, 10, dtype=int)
+res = np.empty((n_seed, 10))
 
 # %% Compute statistics
 for seed in range(n_seed):

examples/sliced-wasserstein/plot_variance_ssw.py

@@ -28,7 +28,7 @@
 
 # %% parameters and data generation
 
-n = 500  # nb samples
+n = 200  # nb samples
 
 xs = np.random.randn(n, 3)
 xt = np.random.randn(n, 3)
@@ -81,9 +81,9 @@
 # Spherical Sliced Wasserstein for different seeds and number of projections
 # --------------------------------------------------------------------------
 
-n_seed = 50
-n_projections_arr = np.logspace(0, 3, 25, dtype=int)
-res = np.empty((n_seed, 25))
+n_seed = 20
+n_projections_arr = np.logspace(0, 3, 10, dtype=int)
+res = np.empty((n_seed, 10))
 
 # %% Compute statistics
 for seed in range(n_seed):