.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_species_distribution_modeling.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py: ============================= 物种分布建模 ============================= 建模物种的地理分布是保护生物学中的一个重要问题。在这个例子中，我们根据过去的观察和14个环境变量来建模两种南美洲哺乳动物的地理分布。由于我们只有正样本（没有不成功的观察），我们将这个问题视为一个密度估计问题，并使用 :class:`~sklearn.svm.OneClassSVM` 作为我们的建模工具。数据集由Phillips等人（2006）提供。如果可用，示例使用 `basemap `_ 来绘制南美洲的海岸线和国家边界。 这两种物种是： - `"Bradypus variegatus" `_ ， 棕喉树懒。 - `"Microryzomys minutus" `_ ， 也被称为森林小稻鼠，一种生活在秘鲁、哥伦比亚、厄瓜多尔、秘鲁和委内瑞拉的啮齿动物。 参考文献 ---------- * `"物种地理分布的最大熵建模" `_ S. J. Phillips, R. P. Anderson, R. E. Schapire - 生态建模, 190:231-259, 2006. .. GENERATED FROM PYTHON SOURCE LINES 27-234 .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_species_distribution_modeling_001.png :alt: bradypus variegatus, microryzomys minutus :srcset: /auto_examples/applications/images/sphx_glr_plot_species_distribution_modeling_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none ________________________________________________________________________________ Modeling distribution of species 'bradypus variegatus' - fit OneClassSVM ... done. - plot coastlines from coverage - predict species distribution Area under the ROC curve : 0.868443 ________________________________________________________________________________ Modeling distribution of species 'microryzomys minutus' - fit OneClassSVM ... done. - plot coastlines from coverage - predict species distribution Area under the ROC curve : 0.993919 time elapsed: 3.00s | .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier: BSD-3-Clause from time import time import matplotlib.pyplot as plt import numpy as np from sklearn import metrics, svm from sklearn.datasets import fetch_species_distributions from sklearn.utils import Bunch # 如果 basemap 可用，我们将使用它。 # 否则，我们稍后将即兴处理…… try: from mpl_toolkits.basemap import Basemap basemap = True except ImportError: basemap = False def construct_grids(batch): """从批处理对象构建地图网格 Parameters ---------- batch : 批处理对象 由 :func:`fetch_species_distributions` 返回的对象 返回 ------- (xgrid, ygrid) : 一维数组 与 batch.coverages 中的值对应的网格 """ # 角落单元的 x,y 坐标 xmin = batch.x_left_lower_corner + batch.grid_size xmax = xmin + (batch.Nx * batch.grid_size) ymin = batch.y_left_lower_corner + batch.grid_size ymax = ymin + (batch.Ny * batch.grid_size) # 网格单元的 x 坐标 xgrid = np.arange(xmin, xmax, batch.grid_size) # 网格单元的 y 坐标 ygrid = np.arange(ymin, ymax, batch.grid_size) return (xgrid, ygrid) def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid): """创建一个包含特定生物信息的集合 这将使用测试/训练记录数组来提取特定物种名称的数据。 """ bunch = Bunch(name=" ".join(species_name.split("_")[:2])) species_name = species_name.encode("ascii") points = dict(test=test, train=train) for label, pts in points.items(): # 选择与所需物种相关的点 pts = pts[pts["species"] == species_name] bunch["pts_%s" % label] = pts # 确定每个训练和测试点的覆盖值 ix = np.searchsorted(xgrid, pts["dd long"]) iy = np.searchsorted(ygrid, pts["dd lat"]) bunch["cov_%s" % label] = coverages[:, -iy, ix].T return bunch def plot_species_distribution( species=("bradypus_variegatus_0", "microryzomys_minutus_0") ): """ 绘制物种分布图。 """ if len(species) > 2: print( "Note: when more than two species are provided," " only the first two will be used" ) t0 = time() # 加载压缩数据 data = fetch_species_distributions() # 设置数据网格 xgrid, ygrid = construct_grids(data) # x,y坐标系中的网格 X, Y = np.meshgrid(xgrid, ygrid[::-1]) # 为每个物种创建一组 BV_bunch = create_species_bunch( species[0], data.train, data.test, data.coverages, xgrid, ygrid ) MM_bunch = create_species_bunch( species[1], data.train, data.test, data.coverages, xgrid, ygrid ) # 评估用背景点（网格坐标） np.random.seed(13) background_points = np.c_[ np.random.randint(low=0, high=data.Ny, size=10000), np.random.randint(low=0, high=data.Nx, size=10000), ].T # 我们将利用coverages[6]在所有陆地区域都有测量值这一事实。这将帮助我们在陆地和水域之间做出判断。 land_reference = data.coverages[6] # 拟合、预测和绘制每个物种的图表。 for i, species in enumerate([BV_bunch, MM_bunch]): print("_" * 80) print("Modeling distribution of species '%s'" % species.name) # 标准化特征 mean = species.cov_train.mean(axis=0) std = species.cov_train.std(axis=0) train_cover_std = (species.cov_train - mean) / std # Fit OneClassSVM print(" - fit OneClassSVM ... ", end="") clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.5) clf.fit(train_cover_std) print("done.") # 绘制南美洲地图 plt.subplot(1, 2, i + 1) if basemap: print(" - plot coastlines using basemap") m = Basemap( projection="cyl", llcrnrlat=Y.min(), urcrnrlat=Y.max(), llcrnrlon=X.min(), urcrnrlon=X.max(), resolution="c", ) m.drawcoastlines() m.drawcountries() else: print(" - plot coastlines from coverage") plt.contour( X, Y, land_reference, levels=[-9998], colors="k", linestyles="solid" ) plt.xticks([]) plt.yticks([]) print(" - predict species distribution") # 使用训练数据预测物种分布 Z = np.ones((data.Ny, data.Nx), dtype=np.float64) # 我们只会预测陆地上的点。 idx = np.where(land_reference > -9999) coverages_land = data.coverages[:, idx[0], idx[1]].T pred = clf.decision_function((coverages_land - mean) / std) Z *= pred.min() Z[idx[0], idx[1]] = pred levels = np.linspace(Z.min(), Z.max(), 25) Z[land_reference == -9999] = -9999 # 绘制预测的等高线图 plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds) plt.colorbar(format="%.2f") # 散点训练/测试点 plt.scatter( species.pts_train["dd long"], species.pts_train["dd lat"], s=2**2, c="black", marker="^", label="train", ) plt.scatter( species.pts_test["dd long"], species.pts_test["dd lat"], s=2**2, c="black", marker="x", label="test", ) plt.legend() plt.title(species.name) plt.axis("equal") # 计算相对于背景点的AUC pred_background = Z[background_points[0], background_points[1]] pred_test = clf.decision_function((species.cov_test - mean) / std) scores = np.r_[pred_test, pred_background] y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)] fpr, tpr, thresholds = metrics.roc_curve(y, scores) roc_auc = metrics.auc(fpr, tpr) plt.text(-35, -70, "AUC: %.3f" % roc_auc, ha="right") print("\n Area under the ROC curve : %f" % roc_auc) print("\ntime elapsed: %.2fs" % (time() - t0)) plot_species_distribution() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.081 seconds) .. _sphx_glr_download_auto_examples_applications_plot_species_distribution_modeling.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/applications/plot_species_distribution_modeling.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_species_distribution_modeling.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_species_distribution_modeling.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_species_distribution_modeling.zip ` .. include:: plot_species_distribution_modeling.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_