add dr tests

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))

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)

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)