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    {
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      "source": [
        "\n# OT with Laplacian regularization for domain adaptation\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Example added in release: 0.7.0.</p></div>\n\nThis example introduces a domain adaptation in a 2D setting and OTDA\napproach with Laplacian regularization.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "source": [
        "# Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>\n\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif(\"3gauss\", n_source_samples)\nXt, yt = ot.datasets.make_data_classif(\"3gauss2\", n_target_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Instantiate the different transport algorithms and fit them\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=0.01)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# EMD Transport with Laplacian regularization\not_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)\not_emd_laplace.fit(Xs=Xs, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fig 1 : plots source and target samples\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker=\"+\", label=\"Source samples\")\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title(\"Source  samples\")\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker=\"o\", label=\"Target samples\")\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title(\"Target samples\")\npl.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fig 2 : plot optimal couplings and transported samples\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "param_img = {\"interpolation\": \"nearest\", \"cmap\": \"gray_r\"}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title(\"Optimal coupling\\nEMDTransport\")\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title(\"Optimal coupling\\nSinkhornTransport\")\n\npl.subplot(2, 3, 3)\npl.imshow(ot_emd_laplace.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title(\"Optimal coupling\\nEMDLaplaceTransport\")\n\npl.subplot(2, 3, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker=\"o\", label=\"Target samples\", alpha=0.3)\npl.scatter(\n    transp_Xs_emd[:, 0],\n    transp_Xs_emd[:, 1],\n    c=ys,\n    marker=\"+\",\n    label=\"Transp samples\",\n    s=30,\n)\npl.xticks([])\npl.yticks([])\npl.title(\"Transported samples\\nEmdTransport\")\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 3, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker=\"o\", label=\"Target samples\", alpha=0.3)\npl.scatter(\n    transp_Xs_sinkhorn[:, 0],\n    transp_Xs_sinkhorn[:, 1],\n    c=ys,\n    marker=\"+\",\n    label=\"Transp samples\",\n    s=30,\n)\npl.xticks([])\npl.yticks([])\npl.title(\"Transported samples\\nSinkhornTransport\")\n\npl.subplot(2, 3, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker=\"o\", label=\"Target samples\", alpha=0.3)\npl.scatter(\n    transp_Xs_emd_laplace[:, 0],\n    transp_Xs_emd_laplace[:, 1],\n    c=ys,\n    marker=\"+\",\n    label=\"Transp samples\",\n    s=30,\n)\npl.xticks([])\npl.yticks([])\npl.title(\"Transported samples\\nEMDLaplaceTransport\")\npl.tight_layout()\n\npl.show()"
      ]
    }
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