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authorRémi Flamary <remi.flamary@gmail.com>2018-02-20 16:11:56 +0100
committerRémi Flamary <remi.flamary@gmail.com>2018-02-20 16:11:56 +0100
commit6d9b281271167d3676538f2ef8518abea82ef9c8 (patch)
tree7d1fae1d15a0ec70e229819a68b9f3a1ceea8f02 /notebooks/plot_gromov.ipynb
parent806a406e1ca2e9ca0bfdfe0516c75865e8098205 (diff)
parent5ff8030ce300f3d066e1edba2b36e60709b023b8 (diff)
Merge branch 'master' of github.com:rflamary/POT
Diffstat (limited to 'notebooks/plot_gromov.ipynb')
-rw-r--r--notebooks/plot_gromov.ipynb179
1 files changed, 108 insertions, 71 deletions
diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb
index 11c19d3..6a237e6 100644
--- a/notebooks/plot_gromov.ipynb
+++ b/notebooks/plot_gromov.ipynb
@@ -32,15 +32,15 @@
},
"outputs": [],
"source": [
- "# Author: Erwan Vautier <erwan.vautier@gmail.com>\r\n",
- "# Nicolas Courty <ncourty@irisa.fr>\r\n",
- "#\r\n",
- "# License: MIT License\r\n",
- "\r\n",
- "import scipy as sp\r\n",
- "import numpy as np\r\n",
- "import matplotlib.pylab as pl\r\n",
- "from mpl_toolkits.mplot3d import Axes3D # noqa\r\n",
+ "# Author: Erwan Vautier <erwan.vautier@gmail.com>\n",
+ "# Nicolas Courty <ncourty@irisa.fr>\n",
+ "#\n",
+ "# License: MIT License\n",
+ "\n",
+ "import scipy as sp\n",
+ "import numpy as np\n",
+ "import matplotlib.pylab as pl\n",
+ "from mpl_toolkits.mplot3d import Axes3D # noqa\n",
"import ot"
]
},
@@ -48,12 +48,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Sample two Gaussian distributions (2D and 3D)\r\n",
- " ---------------------------------------------\r\n",
- "\r\n",
- " The Gromov-Wasserstein distance allows to compute distances with samples that\r\n",
- " do not belong to the same metric space. For demonstration purpose, we sample\r\n",
- " two Gaussian distributions in 2- and 3-dimensional spaces.\r\n",
+ "Sample two Gaussian distributions (2D and 3D)\n",
+ "---------------------------------------------\n",
+ "\n",
+ "The Gromov-Wasserstein distance allows to compute distances with samples that\n",
+ "do not belong to the same metric space. For demonstration purpose, we sample\n",
+ "two Gaussian distributions in 2- and 3-dimensional spaces.\n",
"\n"
]
},
@@ -65,17 +65,17 @@
},
"outputs": [],
"source": [
- "n_samples = 30 # nb samples\r\n",
- "\r\n",
- "mu_s = np.array([0, 0])\r\n",
- "cov_s = np.array([[1, 0], [0, 1]])\r\n",
- "\r\n",
- "mu_t = np.array([4, 4, 4])\r\n",
- "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n",
- "\r\n",
- "\r\n",
- "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\n",
- "P = sp.linalg.sqrtm(cov_t)\r\n",
+ "n_samples = 30 # nb samples\n",
+ "\n",
+ "mu_s = np.array([0, 0])\n",
+ "cov_s = np.array([[1, 0], [0, 1]])\n",
+ "\n",
+ "mu_t = np.array([4, 4, 4])\n",
+ "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n",
+ "\n",
+ "\n",
+ "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n",
+ "P = sp.linalg.sqrtm(cov_t)\n",
"xt = np.random.randn(n_samples, 3).dot(P) + mu_t"
]
},
@@ -83,8 +83,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Plotting the distributions\r\n",
- "--------------------------\r\n",
+ "Plotting the distributions\n",
+ "--------------------------\n",
"\n"
]
},
@@ -97,9 +97,9 @@
"outputs": [
{
"data": {
- "image/png": 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Sya6vrwdTOkexmzR0U5DL5WJycpLGxkZSUlJwOp2kpaVta51wHPH5fFE/GB0OB0VFRXR1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\nwlBiJ4QQC0OJnRBCLAwldkIIsTD/D1lnmNmlAPxdAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fe3102491d0>"
+ "<matplotlib.figure.Figure at 0x7f5c871e15f8>"
]
},
"metadata": {},
@@ -107,11 +107,11 @@
}
],
"source": [
- "fig = pl.figure()\r\n",
- "ax1 = fig.add_subplot(121)\r\n",
- "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\n",
- "ax2 = fig.add_subplot(122, projection='3d')\r\n",
- "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\n",
+ "fig = pl.figure()\n",
+ "ax1 = fig.add_subplot(121)\n",
+ "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n",
+ "ax2 = fig.add_subplot(122, projection='3d')\n",
+ "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n",
"pl.show()"
]
},
@@ -119,8 +119,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Compute distance kernels, normalize them and then display\r\n",
- "---------------------------------------------------------\r\n",
+ "Compute distance kernels, normalize them and then display\n",
+ "---------------------------------------------------------\n",
"\n"
]
},
@@ -133,9 +133,9 @@
"outputs": [
{
"data": {
- "image/png": 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hGD7BFnTDMAyfYAu6YRiGTzjggi4i1SLyrIhsE5GtIvJ3E/YiEXlKROon/tZe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\n8dMc2rnMPuyVi2EYhk+wBd0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+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fe30e043a90>"
+ "<matplotlib.figure.Figure at 0x7f5c9220df60>"
]
},
"metadata": {},
@@ -143,17 +143,17 @@
}
],
"source": [
- "C1 = sp.spatial.distance.cdist(xs, xs)\r\n",
- "C2 = sp.spatial.distance.cdist(xt, xt)\r\n",
- "\r\n",
- "C1 /= C1.max()\r\n",
- "C2 /= C2.max()\r\n",
- "\r\n",
- "pl.figure()\r\n",
- "pl.subplot(121)\r\n",
- "pl.imshow(C1)\r\n",
- "pl.subplot(122)\r\n",
- "pl.imshow(C2)\r\n",
+ "C1 = sp.spatial.distance.cdist(xs, xs)\n",
+ "C2 = sp.spatial.distance.cdist(xt, xt)\n",
+ "\n",
+ "C1 /= C1.max()\n",
+ "C2 /= C2.max()\n",
+ "\n",
+ "pl.figure()\n",
+ "pl.subplot(121)\n",
+ "pl.imshow(C1)\n",
+ "pl.subplot(122)\n",
+ "pl.imshow(C2)\n",
"pl.show()"
]
},
@@ -161,8 +161,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Compute Gromov-Wasserstein plans and distance\r\n",
- "---------------------------------------------\r\n",
+ "Compute Gromov-Wasserstein plans and distance\n",
+ "---------------------------------------------\n",
"\n"
]
},
@@ -177,14 +177,38 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Gromov-Wasserstein distances between the distribution: 0.201997813845\n"
+ "It. |Loss |Delta loss\n",
+ "--------------------------------\n",
+ " 0|3.731009e-02|0.000000e+00\n",
+ " 1|1.846414e-02|-1.020678e+00\n",
+ " 2|1.752056e-02|-5.385587e-02\n",
+ " 3|1.470479e-02|-1.914863e-01\n",
+ " 4|1.371582e-02|-7.210441e-02\n",
+ " 5|1.263823e-02|-8.526431e-02\n",
+ " 6|1.190590e-02|-6.151022e-02\n",
+ " 7|1.014664e-02|-1.733837e-01\n",
+ " 8|1.011396e-02|-3.230516e-03\n",
+ " 9|1.011363e-02|-3.245532e-05\n",
+ " 10|1.011363e-02|-3.245682e-07\n",
+ " 11|1.011363e-02|-3.245683e-09\n",
+ " 12|1.011363e-02|-3.245598e-11\n",
+ "It. |Err \n",
+ "-------------------\n",
+ " 0|7.847531e-02|\n",
+ " 10|2.424218e-03|\n",
+ " 20|4.250670e-02|\n",
+ " 30|5.679949e-05|\n",
+ " 40|1.219762e-08|\n",
+ " 50|1.121975e-11|\n",
+ "Gromov-Wasserstein distances: 0.01011363084946804\n",
+ "Entropic Gromov-Wasserstein distances: 0.0063386519916289385\n"
]
},
{
"data": {
- "image/png": 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+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fe31024a390>"
+ "<matplotlib.figure.Figure at 0x7f5c850d1550>"
]
},
"metadata": {},
@@ -192,38 +216,51 @@
}
],
"source": [
- "p = ot.unif(n_samples)\r\n",
- "q = ot.unif(n_samples)\r\n",
- "\r\n",
- "gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n",
- "gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n",
- "\r\n",
- "print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n",
- "\r\n",
- "pl.figure()\r\n",
- "pl.imshow(gw, cmap='jet')\r\n",
- "pl.colorbar()\r\n",
+ "p = ot.unif(n_samples)\n",
+ "q = ot.unif(n_samples)\n",
+ "\n",
+ "gw0, log0 = ot.gromov.gromov_wasserstein(\n",
+ " C1, C2, p, q, 'square_loss', verbose=True, log=True)\n",
+ "\n",
+ "gw, log = ot.gromov.entropic_gromov_wasserstein(\n",
+ " C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n",
+ "\n",
+ "\n",
+ "print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\n",
+ "print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n",
+ "\n",
+ "\n",
+ "pl.figure(1, (10, 5))\n",
+ "\n",
+ "pl.subplot(1, 2, 1)\n",
+ "pl.imshow(gw0, cmap='jet')\n",
+ "pl.title('Gromov Wasserstein')\n",
+ "\n",
+ "pl.subplot(1, 2, 2)\n",
+ "pl.imshow(gw, cmap='jet')\n",
+ "pl.title('Entropic Gromov Wasserstein')\n",
+ "\n",
"pl.show()"
]
}
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