{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Pottok for image color adaptation with labels - OptimalTransportGridSearch\n\nUsing sinkhorn L1l2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport pottok"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load pottoks\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Loading X and y\nXs,ys,Xt,yt = pottok.datasets.load_pottoks()\n\nXs = Xs/255\nXt = Xt/255\n\n# Loading images array\nbrown_pottok,black_pottok = pottok.datasets.load_pottoks(return_X_y=False)\nbrown_pottok = brown_pottok/255\nblack_pottok = black_pottok/255"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Optimal transport with SinkhornL1l2 with circular gridsearch\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "gridsearch_transport_circular = pottok.OptimalTransportGridSearch(transport_function=ot.da.SinkhornL1l2Transport,\n                                        params=dict(reg_e=[1e-1,1e-0], reg_cl=[1e-1,1e-0]))\ngridsearch_transport_circular.preprocessing(Xs=Xs,ys=ys,Xt=Xt,yt=yt,scaler=False)\ngridsearch_transport_circular.fit_circular()\n\n\n# Best grid is {'reg_e': 1.0, 'reg_cl': 0.1}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot images\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "brown_pottok_transp_circular = gridsearch_transport_circular.predict_transfer(brown_pottok.reshape(-1,3))\npl.figure(1, figsize=(10,8))\n\npl.subplot(2, 2, 1)\npl.imshow(brown_pottok)\npl.axis('off')\npl.title('Brown pottok (Source)')\n\npl.subplot(2, 2, 3)\npl.imshow(black_pottok)\npl.axis('off')\npl.title('Black pottok (Target)')\n\npl.subplot(2, 2, 4)\npl.imshow(brown_pottok_transp_circular.reshape(*brown_pottok.shape))\npl.axis('off')\npl.title('SinkhornL1l2 (Source to Target with labels)')\n\npl.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Optimal transport with SinkhornL1l2 with crossed gridsearch\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "gridsearch_transport_crossed = pottok.OptimalTransportGridSearch(transport_function=ot.da.SinkhornL1l2Transport,\n                                        params=dict(reg_e=[1e-1,1e-0], reg_cl=[1e-1,1e-0]))\ngridsearch_transport_crossed.preprocessing(Xs=Xs,ys=ys,Xt=Xt,yt=yt,scaler=False)\ngridsearch_transport_crossed.fit_crossed()\n\n# Best grid is {'reg_e': 0.1, 'reg_cl': 0.1}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot images\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "brown_pottok_transp_crossed = gridsearch_transport_crossed.predict_transfer(brown_pottok.reshape(-1,3))\npl.figure(2, figsize=(10,8))\n\npl.subplot(2, 2, 1)\npl.imshow(brown_pottok)\npl.axis('off')\npl.title('Brown pottok (Source)')\n\npl.subplot(2, 2, 3)\npl.imshow(black_pottok)\npl.axis('off')\npl.title('Black pottok (Target)')\n\npl.subplot(2, 2, 4)\npl.imshow(brown_pottok_transp_crossed.reshape(*brown_pottok.shape))\npl.axis('off')\npl.title('SinkhornL1l2 (Source to Target with labels)')\n\npl.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Comparison with pot\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ot_mapping_linear_circular = ot.da.SinkhornL1l2Transport(\n    reg_e=1.0, reg_cl=0.1,verbose=True)\not_mapping_linear_circular.fit(Xs=Xs, ys = ys, Xt=Xt, yt = yt)\nbrown_pottok_transp_pot_circular = ot_mapping_linear_circular.transform(brown_pottok.reshape(-1,3))\n\nif (brown_pottok_transp_pot_circular == brown_pottok_transp_circular).all() : \n    print (\"POT and Pottok give same transformation - circular\")\nelse : \n    print (\"ERROR : POT and Pottok do not give same transformation\")\n   \not_mapping_linear_crossed = ot.da.SinkhornL1l2Transport(\n    reg_e=0.1, reg_cl=0.1,verbose=True)\not_mapping_linear_crossed.fit(Xs=Xs, ys = ys, Xt=Xt, yt = yt)\nbrown_pottok_transp_pot_crossed = ot_mapping_linear_crossed.transform(brown_pottok.reshape(-1,3))\n   \n   \nif (brown_pottok_transp_pot_crossed == brown_pottok_transp_crossed).all() : \n    print (\"POT and Pottok give same transformation - crossed\")\nelse : \n    print (\"ERROR : POT and Pottok do not give same transformation\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}