Model_ecologico.ipynb

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      "authorship_tag": "ABX9TyOfl3UkJr7NiOeDGo2upIVF",
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    "language_info": {
      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Polux79/Ingenieria-ambiental/blob/main/Model_ecologico.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "pZMu-U1Tl5GY",
        "outputId": "3dd76d87-a5f1-4460-a905-136b62df1bf2"
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      "outputs": [
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          "text": [
            "\n",
            "=============================\n",
            "Species distribution modeling\n",
            "=============================\n",
            "\n",
            "Modeling species' geographic distributions is an important\n",
            "problem in conservation biology. In this example we\n",
            "model the geographic distribution of two south american\n",
            "mammals given past observations and 14 environmental\n",
            "variables. Since we have only positive examples (there are\n",
            "no unsuccessful observations), we cast this problem as a\n",
            "density estimation problem and use the :class:`sklearn.svm.OneClassSVM`\n",
            "as our modeling tool. The dataset is provided by Phillips et. al. (2006).\n",
            "If available, the example uses\n",
            "`basemap <https://matplotlib.org/basemap/>`_\n",
            "to plot the coast lines and national boundaries of South America.\n",
            "\n",
            "The two species are:\n",
            "\n",
            " - `\"Bradypus variegatus\"\n",
            "   <http://www.iucnredlist.org/details/3038/0>`_ ,\n",
            "   the Brown-throated Sloth.\n",
            "\n",
            " - `\"Microryzomys minutus\"\n",
            "   <http://www.iucnredlist.org/details/13408/0>`_ ,\n",
            "   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n",
            "   Colombia, Ecuador, Peru, and Venezuela.\n",
            "\n",
            "References\n",
            "----------\n",
            "\n",
            " * `\"Maximum entropy modeling of species geographic distributions\"\n",
            "   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n",
            "   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n",
            "   190:231-259, 2006.\n",
            "\n",
            "________________________________________________________________________________\n",
            "Modeling distribution of species 'bradypus variegatus'\n",
            " - fit OneClassSVM ... done.\n",
            " - plot coastlines from coverage\n",
            " - predict species distribution\n",
            "\n",
            " Area under the ROC curve : 0.868443\n",
            "________________________________________________________________________________\n",
            "Modeling distribution of species 'microryzomys minutus'\n",
            " - fit OneClassSVM ... done.\n",
            " - plot coastlines from coverage\n",
            " - predict species distribution\n",
            "\n",
            " Area under the ROC curve : 0.993919\n",
            "\n",
            "time elapsed: 25.31s\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\"\"\"\n",
        "=============================\n",
        "Species distribution modeling\n",
        "=============================\n",
        "\n",
        "Modeling species' geographic distributions is an important\n",
        "problem in conservation biology. In this example we\n",
        "model the geographic distribution of two south american\n",
        "mammals given past observations and 14 environmental\n",
        "variables. Since we have only positive examples (there are\n",
        "no unsuccessful observations), we cast this problem as a\n",
        "density estimation problem and use the :class:`sklearn.svm.OneClassSVM`\n",
        "as our modeling tool. The dataset is provided by Phillips et. al. (2006).\n",
        "If available, the example uses\n",
        "`basemap <https://matplotlib.org/basemap/>`_\n",
        "to plot the coast lines and national boundaries of South America.\n",
        "\n",
        "The two species are:\n",
        "\n",
        " - `\"Bradypus variegatus\"\n",
        "   <http://www.iucnredlist.org/details/3038/0>`_ ,\n",
        "   the Brown-throated Sloth.\n",
        "\n",
        " - `\"Microryzomys minutus\"\n",
        "   <http://www.iucnredlist.org/details/13408/0>`_ ,\n",
        "   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n",
        "   Colombia, Ecuador, Peru, and Venezuela.\n",
        "\n",
        "References\n",
        "----------\n",
        "\n",
        " * `\"Maximum entropy modeling of species geographic distributions\"\n",
        "   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n",
        "   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n",
        "   190:231-259, 2006.\n",
        "\"\"\"\n",
        "\n",
        "# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n",
        "#          Jake Vanderplas <vanderplas@astro.washington.edu>\n",
        "#\n",
        "# License: BSD 3 clause\n",
        "\n",
        "from time import time\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.utils import Bunch\n",
        "from sklearn.datasets import fetch_species_distributions\n",
        "from sklearn import svm, metrics\n",
        "\n",
        "# if basemap is available, we'll use it.\n",
        "# otherwise, we'll improvise later...\n",
        "try:\n",
        "    from mpl_toolkits.basemap import Basemap\n",
        "    basemap = True\n",
        "except ImportError:\n",
        "    basemap = False\n",
        "\n",
        "print(__doc__)\n",
        "\n",
        "\n",
        "def construct_grids(batch):\n",
        "    \"\"\"Construct the map grid from the batch object\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    batch : Batch object\n",
        "        The object returned by :func:`fetch_species_distributions`\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    (xgrid, ygrid) : 1-D arrays\n",
        "        The grid corresponding to the values in batch.coverages\n",
        "    \"\"\"\n",
        "    # x,y coordinates for corner cells\n",
        "    xmin = batch.x_left_lower_corner + batch.grid_size\n",
        "    xmax = xmin + (batch.Nx * batch.grid_size)\n",
        "    ymin = batch.y_left_lower_corner + batch.grid_size\n",
        "    ymax = ymin + (batch.Ny * batch.grid_size)\n",
        "\n",
        "    # x coordinates of the grid cells\n",
        "    xgrid = np.arange(xmin, xmax, batch.grid_size)\n",
        "    # y coordinates of the grid cells\n",
        "    ygrid = np.arange(ymin, ymax, batch.grid_size)\n",
        "\n",
        "    return (xgrid, ygrid)\n",
        "\n",
        "\n",
        "def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):\n",
        "    \"\"\"Create a bunch with information about a particular organism\n",
        "\n",
        "    This will use the test/train record arrays to extract the\n",
        "    data specific to the given species name.\n",
        "    \"\"\"\n",
        "    bunch = Bunch(name=' '.join(species_name.split(\"_\")[:2]))\n",
        "    species_name = species_name.encode('ascii')\n",
        "    points = dict(test=test, train=train)\n",
        "\n",
        "    for label, pts in points.items():\n",
        "        # choose points associated with the desired species\n",
        "        pts = pts[pts['species'] == species_name]\n",
        "        bunch['pts_%s' % label] = pts\n",
        "\n",
        "        # determine coverage values for each of the training & testing points\n",
        "        ix = np.searchsorted(xgrid, pts['dd long'])\n",
        "        iy = np.searchsorted(ygrid, pts['dd lat'])\n",
        "        bunch['cov_%s' % label] = coverages[:, -iy, ix].T\n",
        "\n",
        "    return bunch\n",
        "\n",
        "\n",
        "def plot_species_distribution(species=(\"bradypus_variegatus_0\",\n",
        "                                       \"microryzomys_minutus_0\")):\n",
        "    \"\"\"\n",
        "    Plot the species distribution.\n",
        "    \"\"\"\n",
        "    if len(species) > 2:\n",
        "        print(\"Note: when more than two species are provided,\"\n",
        "              \" only the first two will be used\")\n",
        "\n",
        "    t0 = time()\n",
        "\n",
        "    # Load the compressed data\n",
        "    data = fetch_species_distributions()\n",
        "\n",
        "    # Set up the data grid\n",
        "    xgrid, ygrid = construct_grids(data)\n",
        "\n",
        "    # The grid in x,y coordinates\n",
        "    X, Y = np.meshgrid(xgrid, ygrid[::-1])\n",
        "\n",
        "    # create a bunch for each species\n",
        "    BV_bunch = create_species_bunch(species[0],\n",
        "                                    data.train, data.test,\n",
        "                                    data.coverages, xgrid, ygrid)\n",
        "    MM_bunch = create_species_bunch(species[1],\n",
        "                                    data.train, data.test,\n",
        "                                    data.coverages, xgrid, ygrid)\n",
        "\n",
        "    # background points (grid coordinates) for evaluation\n",
        "    np.random.seed(13)\n",
        "    background_points = np.c_[np.random.randint(low=0, high=data.Ny,\n",
        "                                                size=10000),\n",
        "                              np.random.randint(low=0, high=data.Nx,\n",
        "                                                size=10000)].T\n",
        "\n",
        "    # We'll make use of the fact that coverages[6] has measurements at all\n",
        "    # land points.  This will help us decide between land and water.\n",
        "    land_reference = data.coverages[6]\n",
        "\n",
        "    # Fit, predict, and plot for each species.\n",
        "    for i, species in enumerate([BV_bunch, MM_bunch]):\n",
        "        print(\"_\" * 80)\n",
        "        print(\"Modeling distribution of species '%s'\" % species.name)\n",
        "\n",
        "        # Standardize features\n",
        "        mean = species.cov_train.mean(axis=0)\n",
        "        std = species.cov_train.std(axis=0)\n",
        "        train_cover_std = (species.cov_train - mean) / std\n",
        "\n",
        "        # Fit OneClassSVM\n",
        "        print(\" - fit OneClassSVM ... \", end='')\n",
        "        clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.5)\n",
        "        clf.fit(train_cover_std)\n",
        "        print(\"done.\")\n",
        "\n",
        "        # Plot map of South America\n",
        "        plt.subplot(1, 2, i + 1)\n",
        "        if basemap:\n",
        "            print(\" - plot coastlines using basemap\")\n",
        "            m = Basemap(projection='cyl', llcrnrlat=Y.min(),\n",
        "                        urcrnrlat=Y.max(), llcrnrlon=X.min(),\n",
        "                        urcrnrlon=X.max(), resolution='c')\n",
        "            m.drawcoastlines()\n",
        "            m.drawcountries()\n",
        "        else:\n",
        "            print(\" - plot coastlines from coverage\")\n",
        "            plt.contour(X, Y, land_reference,\n",
        "                        levels=[-9998], colors=\"k\",\n",
        "                        linestyles=\"solid\")\n",
        "            plt.xticks([])\n",
        "            plt.yticks([])\n",
        "\n",
        "        print(\" - predict species distribution\")\n",
        "\n",
        "        # Predict species distribution using the training data\n",
        "        Z = np.ones((data.Ny, data.Nx), dtype=np.float64)\n",
        "\n",
        "        # We'll predict only for the land points.\n",
        "        idx = np.where(land_reference > -9999)\n",
        "        coverages_land = data.coverages[:, idx[0], idx[1]].T\n",
        "\n",
        "        pred = clf.decision_function((coverages_land - mean) / std)\n",
        "        Z *= pred.min()\n",
        "        Z[idx[0], idx[1]] = pred\n",
        "\n",
        "        levels = np.linspace(Z.min(), Z.max(), 25)\n",
        "        Z[land_reference == -9999] = -9999\n",
        "\n",
        "        # plot contours of the prediction\n",
        "        plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)\n",
        "        plt.colorbar(format='%.2f')\n",
        "\n",
        "        # scatter training/testing points\n",
        "        plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],\n",
        "                    s=2 ** 2, c='black',\n",
        "                    marker='^', label='train')\n",
        "        plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],\n",
        "                    s=2 ** 2, c='black',\n",
        "                    marker='x', label='test')\n",
        "        plt.legend()\n",
        "        plt.title(species.name)\n",
        "        plt.axis('equal')\n",
        "\n",
        "        # Compute AUC with regards to background points\n",
        "        pred_background = Z[background_points[0], background_points[1]]\n",
        "        pred_test = clf.decision_function((species.cov_test - mean) / std)\n",
        "        scores = np.r_[pred_test, pred_background]\n",
        "        y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]\n",
        "        fpr, tpr, thresholds = metrics.roc_curve(y, scores)\n",
        "        roc_auc = metrics.auc(fpr, tpr)\n",
        "        plt.text(-35, -70, \"AUC: %.3f\" % roc_auc, ha=\"right\")\n",
        "        print(\"\\n Area under the ROC curve : %f\" % roc_auc)\n",
        "\n",
        "    print(\"\\ntime elapsed: %.2fs\" % (time() - t0))\n",
        "\n",
        "\n",
        "plot_species_distribution()\n",
        "plt.show()\n"
      ]
    }
  ]
}