CircleCI update of dev docs (3367).

Author: Circle CI

master/_downloads/06f3dd7c3258690ab730be0a658b5e36/plot_ssw_unif_torch.zip

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "import ot\nimport numpy as np\n\nn_problems = 4  # nb problems/batch size\nn_samples = 8  # nb samples\ndim = 2  # nb dimensions\n\nnp.random.seed(0)\nsamples_source = np.random.randn(n_problems, n_samples, dim)\nsamples_target = samples_source + 0.1 * np.random.randn(n_problems, n_samples, dim)\n\n# Naive approach\nM_list = []\nfor i in range(n_problems):\n    M_list.append(\n        ot.dist(samples_source[i], samples_target[i])\n    )  # List of cost matrices n_samples x n_samples\n# Batched approach\nM_batch = ot.batch.dist_batch(\n    samples_source, samples_target\n)  # Array of cost matrices n_problems x n_samples x n_samples\n\nfor i in range(n_problems):\n    assert np.allclose(M_list[i], M_batch[i])"
+        "import ot\nimport numpy as np\n\nn_problems = 4  # nb problems/batch size\nn_samples = 8  # nb samples\ndim = 2  # nb dimensions\n\nnp.random.seed(0)\nsamples_source = np.random.randn(n_problems, n_samples, dim)\nsamples_target = samples_source + 0.1 * np.random.randn(n_problems, n_samples, dim)\n\n# Naive approach\nM_list = []\nfor i in range(n_problems):\n    M_list.append(\n        ot.dist(samples_source[i], samples_target[i])\n    )  # List of cost matrices n_samples x n_samples\n# Batched approach\nM_batch = ot.dist_batch(\n    samples_source, samples_target\n)  # Array of cost matrices n_problems x n_samples x n_samples\n\nfor i in range(n_problems):\n    assert np.allclose(M_list[i], M_batch[i])"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "reg = 1.0\nmax_iter = 100\ntol = 1e-3\n\n# Naive approach\nresults_values_list = []\nfor i in range(n_problems):\n    res = ot.solve(M_list[i], reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\")\n    results_values_list.append(res.value_linear)\n\n# Batched approach\nresults_batch = ot.batch.solve_batch(\n    M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\"\n)\nresults_values_batch = results_batch.value_linear\n\nassert np.allclose(np.array(results_values_list), results_values_batch, atol=tol * 10)"
+        "reg = 1.0\nmax_iter = 100\ntol = 1e-3\n\n# Naive approach\nresults_values_list = []\nfor i in range(n_problems):\n    res = ot.solve(M_list[i], reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\")\n    results_values_list.append(res.value_linear)\n\n# Batched approach\nresults_batch = ot.solve_batch(\n    M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\"\n)\nresults_values_batch = results_batch.value_linear\n\nassert np.allclose(np.array(results_values_list), results_values_batch, atol=tol * 10)"
       ]
     },
     {
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import perf_counter\n\nn_problems = 128\nn_samples = 8\ndim = 2\nreg = 10.0\nmax_iter = 1000\ntol = 1e-3\n\nsamples_source = np.random.randn(n_problems, n_samples, dim)\nsamples_target = samples_source + 0.1 * np.random.randn(n_problems, n_samples, dim)\n\n\ndef benchmark_naive(samples_source, samples_target):\n    start = perf_counter()\n    for i in range(n_problems):\n        M = ot.dist(samples_source[i], samples_target[i])\n        res = ot.solve(M, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\")\n    end = perf_counter()\n    return end - start\n\n\ndef benchmark_batch(samples_source, samples_target):\n    start = perf_counter()\n    M_batch = ot.batch.dist_batch(samples_source, samples_target)\n    res_batch = ot.batch.solve_batch(\n        M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\"\n    )\n    end = perf_counter()\n    return end - start\n\n\ntime_naive = benchmark_naive(samples_source, samples_target)\ntime_batch = benchmark_batch(samples_source, samples_target)\n\nprint(f\"Naive approach time: {time_naive:.4f} seconds\")\nprint(f\"Batched approach time: {time_batch:.4f} seconds\")"
+        "from time import perf_counter\n\nn_problems = 128\nn_samples = 8\ndim = 2\nreg = 10.0\nmax_iter = 1000\ntol = 1e-3\n\nsamples_source = np.random.randn(n_problems, n_samples, dim)\nsamples_target = samples_source + 0.1 * np.random.randn(n_problems, n_samples, dim)\n\n\ndef benchmark_naive(samples_source, samples_target):\n    start = perf_counter()\n    for i in range(n_problems):\n        M = ot.dist(samples_source[i], samples_target[i])\n        res = ot.solve(M, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\")\n    end = perf_counter()\n    return end - start\n\n\ndef benchmark_batch(samples_source, samples_target):\n    start = perf_counter()\n    M_batch = ot.dist_batch(samples_source, samples_target)\n    res_batch = ot.solve_batch(\n        M=M_batch, reg=reg, max_iter=max_iter, tol=tol, reg_type=\"entropy\"\n    )\n    end = perf_counter()\n    return end - start\n\n\ntime_naive = benchmark_naive(samples_source, samples_target)\ntime_batch = benchmark_batch(samples_source, samples_target)\n\nprint(f\"Naive approach time: {time_naive:.4f} seconds\")\nprint(f\"Batched approach time: {time_batch:.4f} seconds\")"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from ot import solve_gromov\nfrom ot.batch import solve_gromov_batch\n\n\ndef benchmark_naive_gw(samples_source, samples_target):\n    start = perf_counter()\n    avg_value = 0\n    for i in range(n_problems):\n        C1 = ot.dist(samples_source[i], samples_source[i])\n        C2 = ot.dist(samples_target[i], samples_target[i])\n        res = solve_gromov(C1, C2, max_iter=1000, tol=tol)\n        avg_value += res.value\n    avg_value /= n_problems\n    end = perf_counter()\n    return end - start, avg_value\n\n\ndef benchmark_batch_gw(samples_source, samples_target):\n    start = perf_counter()\n    C1_batch = ot.batch.dist_batch(samples_source, samples_source)\n    C2_batch = ot.batch.dist_batch(samples_target, samples_target)\n    res_batch = solve_gromov_batch(\n        C1_batch, C2_batch, reg=1, max_iter=100, max_iter_inner=50, tol=tol\n    )\n    avg_value = np.mean(res_batch.value)\n    end = perf_counter()\n    return end - start, avg_value\n\n\ntime_naive_gw, avg_value_naive_gw = benchmark_naive_gw(samples_source, samples_target)\ntime_batch_gw, avg_value_batch_gw = benchmark_batch_gw(samples_source, samples_target)\n\nprint(f\"{'Method':<20}{'Time (s)':<15}{'Avg Value':<15}\")\nprint(f\"{'Naive GW':<20}{time_naive_gw:<15.4f}{avg_value_naive_gw:<15.4f}\")\nprint(f\"{'Batched GW':<20}{time_batch_gw:<15.4f}{avg_value_batch_gw:<15.4f}\")"
+        "from ot import solve_gromov, solve_gromov_batch\n\n\ndef benchmark_naive_gw(samples_source, samples_target):\n    start = perf_counter()\n    avg_value = 0\n    for i in range(n_problems):\n        C1 = ot.dist(samples_source[i], samples_source[i])\n        C2 = ot.dist(samples_target[i], samples_target[i])\n        res = solve_gromov(C1, C2, max_iter=1000, tol=tol)\n        avg_value += res.value\n    avg_value /= n_problems\n    end = perf_counter()\n    return end - start, avg_value\n\n\ndef benchmark_batch_gw(samples_source, samples_target):\n    start = perf_counter()\n    C1_batch = ot.dist_batch(samples_source, samples_source)\n    C2_batch = ot.dist_batch(samples_target, samples_target)\n    res_batch = solve_gromov_batch(\n        C1_batch, C2_batch, reg=1, max_iter=100, max_iter_inner=50, tol=tol\n    )\n    avg_value = np.mean(res_batch.value)\n    end = perf_counter()\n    return end - start, avg_value\n\n\ntime_naive_gw, avg_value_naive_gw = benchmark_naive_gw(samples_source, samples_target)\ntime_batch_gw, avg_value_batch_gw = benchmark_batch_gw(samples_source, samples_target)\n\nprint(f\"{'Method':<20}{'Time (s)':<15}{'Avg Value':<15}\")\nprint(f\"{'Naive GW':<20}{time_naive_gw:<15.4f}{avg_value_naive_gw:<15.4f}\")\nprint(f\"{'Batched GW':<20}{time_batch_gw:<15.4f}{avg_value_batch_gw:<15.4f}\")"
       ]
     },
     {