Merge branch 'master' into prepare_v0.7

rflamary · web-flow · commit 321d4540db55 · 2020-05-04T11:31:50.000+02:00
diff --git a/.github/requirements_strict.txt b/.github/requirements_strict.txt
@@ -0,0 +1,7 @@
+numpy==1.16.*
+scipy==1.0.*
+cython==0.23.*
+matplotlib
+cvxopt
+scikit-learn
+pytest
diff --git a/.github/workflows/build_tests.yml b/.github/workflows/build_tests.yml
@@ -2,9 +2,9 @@ name: build
 
 on:
   push:
-    branches:
-      - '**'
+
   pull_request:
+
   create:
     branches:
       - 'master'
@@ -49,6 +49,34 @@ jobs:
         codecov
 
 
+  linux-minimal-deps:
+
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 4
+      matrix:
+        python-version: [3.6]
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r .github/requirements_strict.txt
+        pip install pytest
+        pip install -U "sklearn"
+    - name: Install POT
+      run: |
+        pip install -e .
+    - name: Run tests
+      run: |
+        python -m pytest -v test/ ot/ --ignore ot/gpu/
+
+
   macos:
      runs-on: macOS-latest
      strategy:
diff --git a/README.md b/README.md
@@ -69,7 +69,7 @@ year={2017}
 
 The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:
 
-- Numpy (>=1.11)
+- Numpy (>=1.16)
 - Scipy (>=1.0)
 - Cython (>=0.23)
 - Matplotlib (>=1.5)
diff --git a/examples/barycenters/plot_free_support_barycenter.py b/examples/barycenters/plot_free_support_barycenter.py
@@ -4,7 +4,7 @@
 2D free support Wasserstein barycenters of distributions
 ====================================================
 
-Illustration of 2D Wasserstein barycenters if discributions that are weighted
+Illustration of 2D Wasserstein barycenters if distributions are weighted
 sum of diracs.
 
 """
@@ -21,7 +21,7 @@
 ##############################################################################
 # Generate data
 # -------------
-#%% parameters and data generation
+
 N = 3
 d = 2
 measures_locations = []
@@ -46,7 +46,7 @@
 
 ##############################################################################
 # Compute free support barycenter
-# -------------
+# -------------------------------
 
 k = 10  # number of Diracs of the barycenter
 X_init = np.random.normal(0., 1., (k, d))  # initial Dirac locations
diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py
@@ -25,7 +25,7 @@
 import ot.plot
 
 ##############################################################################
-# generate data
+# Generate data
 # -------------
 
 n_samples_source = 150
diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py
@@ -9,8 +9,8 @@
 a linear or a kernelized mapping as introduced in [8].
 
 [8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
-    "Mapping estimation for discrete optimal transport",
-    Neural Information Processing Systems (NIPS), 2016.
+"Mapping estimation for discrete optimal transport",
+Neural Information Processing Systems (NIPS), 2016.
 """
 
 # Authors: Remi Flamary <remi.flamary@unice.fr>
diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py
@@ -9,6 +9,7 @@
 
 [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
 discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+
 [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
 discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
 
diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py
@@ -4,14 +4,15 @@
 Plot graphs' barycenter using FGW
 =================================
 
-This example illustrates the computation barycenter of labeled graphs using FGW
+This example illustrates the computation barycenter of labeled graphs using
+FGW [18].
 
 Requires networkx >=2
 
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
-      and Courty Nicolas
-    "Optimal Transport for structured data with application on graphs"
-    International Conference on Machine Learning (ICML). 2019.
+[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
+and Courty Nicolas
+"Optimal Transport for structured data with application on graphs"
+International Conference on Machine Learning (ICML). 2019.
 
 """
 
diff --git a/examples/gromov/plot_fgw.py b/examples/gromov/plot_fgw.py
@@ -4,12 +4,12 @@
 Plot Fused-gromov-Wasserstein
 ==============================
 
-This example illustrates the computation of FGW for 1D measures[18].
+This example illustrates the computation of FGW for 1D measures [18].
 
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain
-      and Courty Nicolas
-    "Optimal Transport for structured data with application on graphs"
-    International Conference on Machine Learning (ICML). 2019.
+[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
+and Courty Nicolas
+"Optimal Transport for structured data with application on graphs"
+International Conference on Machine Learning (ICML). 2019.
 
 """
 
diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py
@@ -4,8 +4,8 @@
 Plot multiple EMD
 =================
 
-Shows how to compute multiple EMD and Sinkhorn with two differnt
-ground metrics and plot their values for diffeent distributions.
+Shows how to compute multiple EMD and Sinkhorn with two different
+ground metrics and plot their values for different distributions.
 
 
 """
diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py
@@ -6,16 +6,16 @@
 
 Illustrates the use of the generic solver for regularized OT with
 user-designed regularization term. It uses Conditional gradient as in [6] and
-generalized Conditional Gradient as proposed in [5][7].
+generalized Conditional Gradient as proposed in [5,7].
 
 
 [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for
 Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine
 Intelligence , vol.PP, no.99, pp.1-1.
 
 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).
-Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,
-7(3), 1853-1882.
+Regularized discrete optimal transport. SIAM Journal on Imaging
+Sciences, 7(3), 1853-1882.
 
 [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized
 conditional gradient: analysis of convergence and applications.
diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py
@@ -4,8 +4,11 @@
 1D Screened optimal transport
 ===============================
 
-This example illustrates the computation of Screenkhorn:
-Screening Sinkhorn Algorithm for Optimal transport.
+This example illustrates the computation of Screenkhorn [26].
+
+[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019).
+Screening Sinkhorn Algorithm for Regularized Optimal Transport,
+Advances in Neural Information Processing Systems 33 (NeurIPS).
 """
 
 # Author: Mokhtar Z. Alaya <mokhtarzahdi.alaya@gmail.com>
diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py
diff --git a/requirements.txt b/requirements.txt
diff --git a/setup.py b/setup.py
