add dr tests

PythonOT · rflamary · Jul 26, 2017 · Jul 24, 2017 · Jul 24, 2017 · Jul 24, 2017
commit 709d8cbc9f9961a5175eb64ae497b854e0b9b184
diff --git a/test/test_dr.py b/test/test_dr.py
@@ -0,0 +1,63 @@
+import ot
+import numpy as np
+import pytest
+
+try:  # test if cudamat installed
+    import ot.dr
+    nogo = False
+except ImportError:
+    nogo = True
+
+
+@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
+def test_fda():
+
+    n = 100  # nb samples in source and target datasets
+    nz = 0.2
+    np.random.seed(0)
+
+    # generate circle dataset
+    t = np.random.rand(n) * 2 * np.pi
+    ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
+    xs = np.concatenate(
+        (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
+    xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
+
+    nbnoise = 8
+
+    xs = np.hstack((xs, np.random.randn(n, nbnoise)))
+
+    p = 2
+
+    Pfda, projfda = ot.dr.fda(xs, ys, p)
+
+    projfda(xs)
+
+    assert np.allclose(np.sum(Pfda**2, 0), np.ones(p))
+
+
+@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)")
+def test_wda():
+
+    n = 100  # nb samples in source and target datasets
+    nz = 0.2
+    np.random.seed(0)
+
+    # generate circle dataset
+    t = np.random.rand(n) * 2 * np.pi
+    ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1
+    xs = np.concatenate(
+        (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)
+    xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)
+
+    nbnoise = 8
+
+    xs = np.hstack((xs, np.random.randn(n, nbnoise)))
+
+    p = 2
+
+    Pwda, projwda = ot.dr.wda(xs, ys, p, maxiter=10)
+
+    projwda(xs)
+
+    assert np.allclose(np.sum(Pwda**2, 0), np.ones(p))
diff --git a/test/test_gpu.py b/test/test_gpu.py
@@ -12,7 +12,8 @@
 
 @pytest.mark.skipif(nogpu, reason="No GPU available")
 def test_gpu_sinkhorn():
-    import ot.gpu
+
+    np.random.seed(0)
 
     def describeRes(r):
         print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format(
@@ -41,29 +42,31 @@ def describeRes(r):
 
 @pytest.mark.skipif(nogpu, reason="No GPU available")
 def test_gpu_sinkhorn_lpl1():
-        def describeRes(r):
-            print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}"
-                  .format(np.min(r), np.max(r), np.mean(r), np.std(r)))
+    np.random.seed(0)
+
+    def describeRes(r):
+        print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}"
+              .format(np.min(r), np.max(r), np.mean(r), np.std(r)))
 
-        for n in [50, 100, 500, 1000]:
-            print(n)
-            a = np.random.rand(n // 4, 100)
-            labels_a = np.random.randint(10, size=(n // 4))
-            b = np.random.rand(n, 100)
-            time1 = time.time()
-            transport = ot.da.OTDA_lpl1()
-            transport.fit(a, labels_a, b)
-            G1 = transport.G
-            time2 = time.time()
-            transport = ot.gpu.da.OTDA_lpl1()
-            transport.fit(a, labels_a, b)
-            G2 = transport.G
-            time3 = time.time()
-            print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(
-                time2 - time1))
-            describeRes(G1)
-            print("   GPU sinkhorn lpl1, time: {:6.2f} sec ".format(
-                time3 - time2))
-            describeRes(G2)
+    for n in [50, 100, 500, 1000]:
+        print(n)
+        a = np.random.rand(n // 4, 100)
+        labels_a = np.random.randint(10, size=(n // 4))
+        b = np.random.rand(n, 100)
+        time1 = time.time()
+        transport = ot.da.OTDA_lpl1()
+        transport.fit(a, labels_a, b)
+        G1 = transport.G
+        time2 = time.time()
+        transport = ot.gpu.da.OTDA_lpl1()
+        transport.fit(a, labels_a, b)
+        G2 = transport.G
+        time3 = time.time()
+        print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(
+            time2 - time1))
+        describeRes(G1)
+        print("   GPU sinkhorn lpl1, time: {:6.2f} sec ".format(
+            time3 - time2))
+        describeRes(G2)
 
-            assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5)
+        assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5)
diff --git a/test/test_optim.py b/test/test_optim.py
@@ -9,7 +9,7 @@
 def test_conditional_gradient():
 
     n = 100  # nb bins
-
+    np.random.seed(0)
     # bin positions
     x = np.arange(n, dtype=np.float64)
 
@@ -38,7 +38,7 @@ def df(G):
 def test_generalized_conditional_gradient():
 
     n = 100  # nb bins
-
+    np.random.seed(0)
     # bin positions
     x = np.arange(n, dtype=np.float64)