[554]sklearn提供的自带的数据集(make |
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sklearn 的数据集有好多个种
自带的小数据集(packaged dataset):sklearn.datasets.load_可在线下载的数据集(Downloaded Dataset):sklearn.datasets.fetch_计算机生成的数据集(Generated Dataset):sklearn.datasets.make_svmlight/libsvm格式的数据集:sklearn.datasets.load_svmlight_file(…)从买了data.org在线下载获取的数据集:sklearn.datasets.fetch_mldata(…)
①自带的数据集
其中的自带的小的数据集为:sklearn.datasets.load_ sklearn包含一些不许要下载的toy数据集,见下表: 导入toy数据的方法介绍任务数据规模load_boston()加载和返回一个boston房屋价格的数据集回归506*13load_iris([return_X_y])加载和返回一个鸢尾花数据集分类150*4load_diabetes()加载和返回一个糖尿病数据集回归442*10load_digits([n_class])加载和返回一个手写字数据集分类1797*64load_linnerud()加载和返回健身数据集多分类20这些数据集都可以在官网上查到,以鸢尾花为例,可以在官网上找到demo,http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html
手写数字数据集load_digits():用于多分类任务的数据集 from sklearn.datasets import load_digits digits=load_digits() print(digits.data.shape) import matplotlib.pyplot as plt plt.gray() plt.matshow(digits.images[0]) plt.show() from sklearn.datasets import load_digits digits=load_digits() digits.keys() n_samples,n_features=digits.data.shape print((n_samples,n_features)) print(digits.data.shape) print(digits.images.shape) import numpy as np print(np.all(digits.images.reshape((1797,64))==digits.data)) fig=plt.figure(figsize=(6,6)) fig.subplots_adjust(left=0,right=1,bottom=0,top=1,hspace=0.05,wspace=0.05) #绘制数字:每张图像8*8像素点 for i in range(64): ax=fig.add_subplot(8,8,i+1,xticks=[],yticks=[]) ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest') #用目标值标记图像 ax.text(0,7,str(digits.target[i])) plt.show()
乳腺癌数据集load-barest-cancer():简单经典的用于二分类任务的数据集 糖尿病数据集:load-diabetes():经典的用于回归认为的数据集,值得注意的是,这10个特征中的每个特征都已经被处理成0均值,方差归一化的特征值, 波士顿房价数据集:load-boston():经典的用于回归任务的数据集 体能训练数据集:load-linnerud():经典的用于多变量回归任务的数据集,其内部包含两个小数据集:Excise是对3个训练变量的20次观测(体重,腰围,脉搏),physiological是对3个生理学变量的20次观测(引体向上,仰卧起坐,立定跳远) svmlight/libsvm的每一行样本的存放格式: 这种格式比较适合用来存放稀疏数据,在sklearn中,用scipy sparse CSR矩阵来存放X,用numpy数组来存放Y from sklearn.datasets import load_svmlight_file x_train,y_train=load_svmlight_file("/path/to/train_dataset.txt","")#如果要加在多个数据的时候,可以用逗号隔开 Sample imagessklearn 带有一组JPEG格式的图片,可用与测试需要2D数据的算法和流程 导入图片数据的方法介绍load_sample_images()导入样本图片,用于加载自带的2个图片load_sample_image(image_name)导入单个图片,返回numpy数组,用于加载外部图片 ②生成数据集生成数据集:可以用来分类任务,可以用来回归任务,可以用来聚类任务,用于流形学习的,用于因子分解任务的 用于分类任务和聚类任务的:这些函数产生样本特征向量矩阵以及对应的类别标签集合 make_blobs:多类单标签数据集,为每个类分配一个或多个正太分布的点集 make_classification:多类单标签数据集,为每个类分配一个或多个正太分布的点集,提供了为数据添加噪声的方式,包括维度相关性,无效特征以及冗余特征等 make_gaussian-quantiles:将一个单高斯分布的点集划分为两个数量均等的点集,作为两类 make_hastie-10-2:产生一个相似的二元分类数据集,有10个维度 make_circle和make_moom产生二维二元分类数据集来测试某些算法的性能,可以为数据集添加噪声,可以为二元分类器产生一些球形判决界面的数据 #生成多类单标签数据集 import numpy as np import matplotlib.pyplot as plt from sklearn.datasets.samples_generator import make_blobs center=[[1,1],[-1,-1],[1,-1]] cluster_std=0.3 X,labels=make_blobs(n_samples=200,centers=center,n_features=2, cluster_std=cluster_std,random_state=0) print('X.shape',X.shape) print("labels",set(labels)) unique_lables=set(labels) colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables))) for k,col in zip(unique_lables,colors): x_k=X[labels==k] plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k", markersize=14) plt.title('data by make_blob()') plt.show() #生成用于分类的数据集 from sklearn.datasets.samples_generator import make_classification X,labels=make_classification(n_samples=200,n_features=2,n_redundant=0,n_informative=2, random_state=1,n_clusters_per_class=2) rng=np.random.RandomState(2) X+=2*rng.uniform(size=X.shape) unique_lables=set(labels) colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables))) for k,col in zip(unique_lables,colors): x_k=X[labels==k] plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k", markersize=14) plt.title('data by make_classification()') plt.show() #生成球形判决界面的数据 from sklearn.datasets.samples_generator import make_circles X,labels=make_circles(n_samples=200,noise=0.2,factor=0.2,random_state=1) print("X.shape:",X.shape) print("labels:",set(labels)) unique_lables=set(labels) colors=plt.cm.Spectral(np.linspace(0,1,len(unique_lables))) for k,col in zip(unique_lables,colors): x_k=X[labels==k] plt.plot(x_k[:,0],x_k[:,1],'o',markerfacecolor=col,markeredgecolor="k", markersize=14) plt.title('data by make_moons()') plt.show()make_blobs 产生多类数据集,对每个类的中心和标准差有很好的控制 输入参数: sklearn.datasets.samples_generator.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None) 参数类型默认说明n_samplesint类型可选参数 (default=100)总的点数,平均的分到每个clusters中。n_featuresint类型可选参数 (default=2)每个样本的特征维度。centersint类型 or 聚类中心坐标元组构成的数组类型可选参数(default=3)产生的中心点的数量, or 固定中心点位置。cluster_stdfloat or floats序列可选参数 (default=1.0)clusters的标准差。center_box一对floats (min, max)可选参数 (default=(-10.0, 10.0))随机产生数据的时候,每个cluster中心的边界。shuffleboolean可选参数 (default=True)打乱样本。random_stateint, RandomState对象 or None可选参数 (default=None)如果是int,random_state作为随机数产生器的seed; 如果是RandomState对象, random_state是随机数产生器; 如果是None, RandomState 对象是随机数产生器通过np.random.返回的是: X:[n_samples,n_features]大小的特征矩阵 y: [n_samples]大小的标签数据矩阵,对应特征矩阵的每一行 例子: 例子:产生两类样本点,两个聚类中心,坐标是(-3, -3)和(3, 3); 方差是0.5和0.7; 样本点有1000个,每个点维度是2维 from sklearn.datasets.samples_generator import make_blobs centers = [(-3, -3),(3, 3)] cluster_std = [0.5,0.7] X,y = make_blobs(n_samples=1000, centers=centers,n_features=2, random_state=0, cluster_std=cluster_std) %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.style.use('ggplot') plt.figure(figsize=(20,5)); plt.subplot(1, 2, 1 ); plt.scatter(X[:,0] , X[:,1], c = y, alpha = 0.7); plt.subplot(1, 2, 2); plt.hist(y) plt.show()产生3类样本点,3个距离中心,方差分别是0.5,0.7,0.5,样本点2000个 from sklearn.datasets.samples_generator import make_blobs centers = [(-3, -3),(0,0),(3, 3)] cluster_std = [0.5,0.7,0.5] X,y = make_blobs(n_samples=2000, centers=centers,n_features=2, random_state=0, cluster_std=cluster_std) %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.style.use('ggplot') plt.figure(figsize=(20,5)); plt.subplot(1, 2, 1 ); plt.scatter(X[:,0] , X[:,1], c = y, alpha = 0.7); plt.subplot(1, 2, 2); plt.hist(y) plt.show()输入参数: sklearn.datasets.samples_generator.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None) 参数类型默认说明n_samplesint类型可选 (default=100)样本数量.n_featuresint可选 (default=20)总的特征数量,是从有信息的数据点,冗余数据点,重复数据点,和特征点-有信息的点-冗余的点-重复点中随机选择的。n_informativeintoptional (default=2)informative features数量n_redundantintoptional (default=2)redundant features数量n_repeatedintoptional (default=0)duplicated features数量n_classesintoptional (default=2)类别或者标签数量n_clusters_per_classintoptional (default=2)每个class中cluster数量weightsfloats列表 or None(default=None)每个类的权重,用于分配样本点flip_yfloatoptional (default=0.01)随机交换样本的一段class_sepfloatoptional (default=1.0)The factor multiplying the hypercube dimension.hypercubebooleanoptional (default=True)If True the clusters are put on the vertices of a hypercube. If False,the clusters are put on the vertices of a random polytope.shiftfloat,array of shape [n_features] or Noneoptional (default=0.0)Shift features by the specified value. If None,then features are shifted by a random value drawn in [-class_sep,class_sep].scalefloat array of shape [n_features] or Noneoptional (default=1.0)Multiply features by the specified value. If None,then features are scaled by a random value drawn in [1,100]. Note that scaling happens after shifting.shufflebooleanoptional (default=True)Shuffle the samples and the features.random_stateint,RandomState instance or Noneoptional (default=None)If int,random_state is the seed used by the random number generator; If RandomState instance,random_state is the random number generator; If None,the random number generator is the RandomState instance used by np.random.返回的是: X : array of shape [n_samples, n_features]; 特征矩阵 y : array of shape [n_samples]:矩阵每一行的整数类型标签 例子: from sklearn.datasets.samples_generator import make_classification X,y = make_classification(n_samples=2000, n_features=10, n_informative=4, n_classes=4, random_state=0) %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.style.use('ggplot') plt.figure(figsize=(20,5)); plt.subplot(1, 2, 1 ); plt.scatter(X[:,0] , X[:,1], c = y, alpha = 0.7); plt.subplot(1, 2, 2); plt.hist(y) plt.show()输入参数: sklearn.datasets.samples_generator.make_gaussian_quantiles(mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3, shuffle=True, random_state=None) 参数类型默认说明meanarray of shape [n_features]optional (default=None)The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, …).covfloatoptional (default=1.)The covariance matrix will be this value times the unit matrix. This dataset only produces symmetric normal distributions.n_samplesintoptional (default=100)The total number of points equally divided among classes.n_featuresintoptional (default=2)The number of features for each sample.n_classesintoptional (default=3)The number of classesshufflebooleanoptional (default=True)Shuffle the samples.random_stateint, RandomState instance or Noneoptional (default=None)If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. from sklearn.datasets.samples_generator import make_gaussian_quantiles X,y = make_gaussian_quantiles(mean=(1,1), cov=1.0, n_samples=1000, n_features=2, n_classes=2, shuffle=True, random_state=None) %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.style.use('ggplot') plt.figure(figsize=(20,5)); plt.subplot(1, 2, 1 ); plt.scatter(X[:,0] , X[:,1], c = y, alpha = 0.7); plt.subplot(1, 2, 2); plt.hist(y) plt.show()产生用于二分类的数据。Hastie et al. 2009 输入参数: 参数类型默认说明n_samplesintoptional (default=12000)The number of samples.random_stateint, RandomState instance or Noneoptional (default=None)If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.输出: X : array of shape [n_samples, 10] 特征矩阵。 y : array of shape [n_samples],对应特征矩阵每一个行的真实值。 from sklearn.datasets.samples_generator import make_hastie_10_2 X,y = make_hastie_10_2(n_samples=1000, random_state=None) %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.style.use('ggplot') plt.figure(figsize=(20,5)); plt.subplot(1, 2, 1 ); plt.scatter(X[:,0] , X[:,1], c = y, alpha = 0.7); plt.subplot(1, 2, 2); plt.hist(y) plt.show()参考:https://www.cnblogs.com/nolonely/p/6980160.html https://blog.csdn.net/kevinelstri/article/details/52622960 https://scikit-learn.org/dev/modules/generated/sklearn.datasets.make_blobs.html https://blog.csdn.net/sa14023053/article/details/52086695 |
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