replace function name tin tests

Author: rflamary

ot/datasets.py

@@ -12,7 +12,7 @@
 from .utils import check_random_state, deprecated
 
 
-def get_1D_gauss(n, m, s):
+def make_1D_gauss(n, m, s):
     """return a 1D histogram for a gaussian distribution (n bins, mean m and std s)
 
     Parameters
@@ -37,6 +37,12 @@ def get_1D_gauss(n, m, s):
     return h / h.sum()
 
 
+@deprecated()
+def get_1D_gauss(n, m, sigma, random_state=None):
+    """ Deprecated see  make_1D_gauss   """
+    return make_1D_gauss(n, m, sigma, random_state=None)
+
+
 def make_2D_samples_gauss(n, m, sigma, random_state=None):
     """return n samples drawn from 2D gaussian N(m,sigma)
 

test/test_bregman.py

@@ -83,8 +83,8 @@ def test_bary():
     n_bins = 100  # nb bins
 
     # Gaussian distributions
-    a1 = ot.datasets.get_1D_gauss(n_bins, m=30, s=10)  # m= mean, s= std
-    a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10)
+    a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10)  # m= mean, s= std
+    a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10)
 
     # creating matrix A containing all distributions
     A = np.vstack((a1, a2)).T
@@ -110,10 +110,10 @@ def test_unmix():
     n_bins = 50  # nb bins
 
     # Gaussian distributions
-    a1 = ot.datasets.get_1D_gauss(n_bins, m=20, s=10)  # m= mean, s= std
-    a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10)
+    a1 = ot.datasets.make_1D_gauss(n_bins, m=20, s=10)  # m= mean, s= std
+    a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10)
 
-    a = ot.datasets.get_1D_gauss(n_bins, m=30, s=10)
+    a = ot.datasets.make_1D_gauss(n_bins, m=30, s=10)
 
     # creating matrix A containing all distributions
     D = np.vstack((a1, a2)).T

test/test_da.py

@@ -8,7 +8,7 @@
 from numpy.testing.utils import assert_allclose, assert_equal
 
 import ot
-from ot.datasets import get_data_classif
+from ot.datasets import make_data_classif
 from ot.utils import unif
 
 
@@ -19,8 +19,8 @@ def test_sinkhorn_lpl1_transport_class():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     otda = ot.da.SinkhornLpl1Transport()
 
@@ -45,7 +45,7 @@ def test_sinkhorn_lpl1_transport_class():
     transp_Xs = otda.transform(Xs=Xs)
     assert_equal(transp_Xs.shape, Xs.shape)
 
-    Xs_new, _ = get_data_classif('3gauss', ns + 1)
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
     transp_Xs_new = otda.transform(Xs_new)
 
     # check that the oos method is working
@@ -55,7 +55,7 @@ def test_sinkhorn_lpl1_transport_class():
     transp_Xt = otda.inverse_transform(Xt=Xt)
     assert_equal(transp_Xt.shape, Xt.shape)
 
-    Xt_new, _ = get_data_classif('3gauss2', nt + 1)
+    Xt_new, _ = make_data_classif('3gauss2', nt + 1)
     transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
 
     # check that the oos method is working
@@ -92,8 +92,8 @@ def test_sinkhorn_l1l2_transport_class():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     otda = ot.da.SinkhornL1l2Transport()
 
@@ -119,7 +119,7 @@ def test_sinkhorn_l1l2_transport_class():
     transp_Xs = otda.transform(Xs=Xs)
     assert_equal(transp_Xs.shape, Xs.shape)
 
-    Xs_new, _ = get_data_classif('3gauss', ns + 1)
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
     transp_Xs_new = otda.transform(Xs_new)
 
     # check that the oos method is working
@@ -129,7 +129,7 @@ def test_sinkhorn_l1l2_transport_class():
     transp_Xt = otda.inverse_transform(Xt=Xt)
     assert_equal(transp_Xt.shape, Xt.shape)
 
-    Xt_new, _ = get_data_classif('3gauss2', nt + 1)
+    Xt_new, _ = make_data_classif('3gauss2', nt + 1)
     transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
 
     # check that the oos method is working
@@ -173,8 +173,8 @@ def test_sinkhorn_transport_class():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     otda = ot.da.SinkhornTransport()
 
@@ -200,7 +200,7 @@ def test_sinkhorn_transport_class():
     transp_Xs = otda.transform(Xs=Xs)
     assert_equal(transp_Xs.shape, Xs.shape)
 
-    Xs_new, _ = get_data_classif('3gauss', ns + 1)
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
     transp_Xs_new = otda.transform(Xs_new)
 
     # check that the oos method is working
@@ -210,7 +210,7 @@ def test_sinkhorn_transport_class():
     transp_Xt = otda.inverse_transform(Xt=Xt)
     assert_equal(transp_Xt.shape, Xt.shape)
 
-    Xt_new, _ = get_data_classif('3gauss2', nt + 1)
+    Xt_new, _ = make_data_classif('3gauss2', nt + 1)
     transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
 
     # check that the oos method is working
@@ -252,8 +252,8 @@ def test_emd_transport_class():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     otda = ot.da.EMDTransport()
 
@@ -278,7 +278,7 @@ def test_emd_transport_class():
     transp_Xs = otda.transform(Xs=Xs)
     assert_equal(transp_Xs.shape, Xs.shape)
 
-    Xs_new, _ = get_data_classif('3gauss', ns + 1)
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
     transp_Xs_new = otda.transform(Xs_new)
 
     # check that the oos method is working
@@ -288,7 +288,7 @@ def test_emd_transport_class():
     transp_Xt = otda.inverse_transform(Xt=Xt)
     assert_equal(transp_Xt.shape, Xt.shape)
 
-    Xt_new, _ = get_data_classif('3gauss2', nt + 1)
+    Xt_new, _ = make_data_classif('3gauss2', nt + 1)
     transp_Xt_new = otda.inverse_transform(Xt=Xt_new)
 
     # check that the oos method is working
@@ -329,9 +329,9 @@ def test_mapping_transport_class():
     ns = 60
     nt = 120
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
-    Xs_new, _ = get_data_classif('3gauss', ns + 1)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
+    Xs_new, _ = make_data_classif('3gauss', ns + 1)
 
     ##########################################################################
     # kernel == linear mapping tests
@@ -449,8 +449,8 @@ def test_linear_mapping():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     A, b = ot.da.OT_mapping_linear(Xs, Xt)
 
@@ -467,8 +467,8 @@ def test_linear_mapping_class():
     ns = 150
     nt = 200
 
-    Xs, ys = get_data_classif('3gauss', ns)
-    Xt, yt = get_data_classif('3gauss2', nt)
+    Xs, ys = make_data_classif('3gauss', ns)
+    Xt, yt = make_data_classif('3gauss2', nt)
 
     otmap = ot.da.LinearTransport()
 
@@ -491,8 +491,8 @@ def test_otda():
     n_samples = 150  # nb samples
     np.random.seed(0)
 
-    xs, ys = ot.datasets.get_data_classif('3gauss', n_samples)
-    xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples)
+    xs, ys = ot.datasets.make_data_classif('3gauss', n_samples)
+    xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples)
 
     a, b = ot.unif(n_samples), ot.unif(n_samples)
 

test/test_dr.py

@@ -22,7 +22,7 @@ def test_fda():
     np.random.seed(0)
 
     # generate gaussian dataset
-    xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples)
+    xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
 
     n_features_noise = 8
 
@@ -44,7 +44,7 @@ def test_wda():
     np.random.seed(0)
 
     # generate gaussian dataset
-    xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples)
+    xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples)
 
     n_features_noise = 8
 

test/test_gromov.py

@@ -15,7 +15,7 @@ def test_gromov():
     mu_s = np.array([0, 0])
     cov_s = np.array([[1, 0], [0, 1]])
 
-    xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
 
     xt = xs[::-1].copy()
 
@@ -55,7 +55,7 @@ def test_entropic_gromov():
     mu_s = np.array([0, 0])
     cov_s = np.array([[1, 0], [0, 1]])
 
-    xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
 
     xt = xs[::-1].copy()
 
@@ -96,8 +96,8 @@ def test_gromov_barycenter():
     ns = 50
     nt = 60
 
-    Xs, ys = ot.datasets.get_data_classif('3gauss', ns)
-    Xt, yt = ot.datasets.get_data_classif('3gauss2', nt)
+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns)
+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)
 
     C1 = ot.dist(Xs)
     C2 = ot.dist(Xt)
@@ -123,8 +123,8 @@ def test_gromov_entropic_barycenter():
     ns = 50
     nt = 60
 
-    Xs, ys = ot.datasets.get_data_classif('3gauss', ns)
-    Xt, yt = ot.datasets.get_data_classif('3gauss2', nt)
+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns)
+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)
 
     C1 = ot.dist(Xs)
     C2 = ot.dist(Xt)
@@ -133,13 +133,13 @@ def test_gromov_entropic_barycenter():
     Cb = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],
                                                [ot.unif(ns), ot.unif(nt)
                                                 ], ot.unif(n_samples), [.5, .5],
-                                               'square_loss', 1e-3,
+                                               'square_loss', 2e-3,
                                                max_iter=100, tol=1e-3)
     np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
 
     Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],
                                                 [ot.unif(ns), ot.unif(nt)
                                                  ], ot.unif(n_samples), [.5, .5],
-                                                'kl_loss', 1e-3,
+                                                'kl_loss', 2e-3,
                                                 max_iter=100, tol=1e-3)
     np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples))

test/test_optim.py

@@ -16,8 +16,8 @@ def test_conditional_gradient():
     x = np.arange(n_bins, dtype=np.float64)
 
     # Gaussian distributions
-    a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
-    b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10)
+    a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10)
 
     # loss matrix
     M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1)))
@@ -45,8 +45,8 @@ def test_generalized_conditional_gradient():
     x = np.arange(n_bins, dtype=np.float64)
 
     # Gaussian distributions
-    a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
-    b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10)
+    a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10)
 
     # loss matrix
     M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1)))

test/test_ot.py

@@ -9,7 +9,7 @@
 import numpy as np
 
 import ot
-from ot.datasets import get_1D_gauss as gauss
+from ot.datasets import make_1D_gauss as gauss
 import pytest
 
 

test/test_plot.py

@@ -20,8 +20,8 @@ def test_plot1D_mat():
     x = np.arange(n_bins, dtype=np.float64)
 
     # Gaussian distributions
-    a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
-    b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10)
+    a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10)
 
     # loss matrix
     M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1)))
@@ -43,8 +43,8 @@ def test_plot2D_samples_mat():
     mu_t = np.array([4, 4])
     cov_t = np.array([[1, -.8], [-.8, 1]])
 
-    xs = ot.datasets.get_2D_samples_gauss(n_bins, mu_s, cov_s)
-    xt = ot.datasets.get_2D_samples_gauss(n_bins, mu_t, cov_t)
+    xs = ot.datasets.make_2D_samples_gauss(n_bins, mu_s, cov_s)
+    xt = ot.datasets.make_2D_samples_gauss(n_bins, mu_t, cov_t)
 
     G = 1.0 * (np.random.rand(n_bins, n_bins) < 0.01)