我是这方面的新手,所以我很感激你的帮助.我正在玩mnist数据集.我从http://g.sweyla.com/blog/2012/mnist-numpy/获取了代码,但将"images"更改为2维,以便每个图像都是特征向量.然后我在数据上运行PCA,然后运行SVM并检查分数.一切似乎工作正常,但我得到以下警告,我不知道为什么.
"DataConversionWarning: A column-vector y was passed when a 1d array was expected.\ Please change the shape of y to (n_samples, ), for example using ravel()."
我尝试了几件事,但似乎无法摆脱这个警告.有什么建议?这是完整的代码(忽略缺少的缩进,似乎他们有点混乱在这里复制代码):
import os, struct from array import array as pyarray from numpy import append, array, int8, uint8, zeros, arange from sklearn import svm, decomposition #from pylab import * #from matplotlib import pyplot as plt def load_mnist(dataset="training", digits=arange(10), path="."): """ Loads MNIST files into 3D numpy arrays Adapted from: http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py """ if dataset == "training": fname_img = os.path.join(path, 'train-images.idx3-ubyte') fname_lbl = os.path.join(path, 'train-labels.idx1-ubyte') elif dataset == "testing": fname_img = os.path.join(path, 't10k-images.idx3-ubyte') fname_lbl = os.path.join(path, 't10k-labels.idx1-ubyte') else: raise ValueError("dataset must be 'testing' or 'training'") flbl = open(fname_lbl, 'rb') magic_nr, size = struct.unpack(">II", flbl.read(8)) lbl = pyarray("b", flbl.read()) flbl.close() fimg = open(fname_img, 'rb') magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16)) img = pyarray("B", fimg.read()) fimg.close() ind = [ k for k in range(size) if lbl[k] in digits ] N = len(ind) images = zeros((N, rows*cols), dtype=uint8) labels = zeros((N, 1), dtype=int8) for i in range(len(ind)): images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]) labels[i] = lbl[ind[i]] return images, labels if __name__ == "__main__": images, labels = load_mnist('training', arange(10),"path...") pca = decomposition.PCA() pca.fit(images) pca.n_components = 200 images_reduced = pca.fit_transform(images) lin_classifier = svm.LinearSVC() lin_classifier.fit(images_reduced, labels) images2, labels2 = load_mnist('testing', arange(10),"path...") images2_reduced = pca.transform(images2) score = lin_classifier.score(images2_reduced,labels2) print score
谢谢您的帮助!
我认为scikit-learn希望y成为一维阵列.你的labels
变量是2-D - labels.shape
是(N,1).警告告诉您使用labels.ravel()
,它将labels
变成一维阵列,形状为(N,).
重塑也会起作用:labels=labels.reshape((N,))
来想一想,所以会调用挤压:labels=labels.squeeze()
我想这里的问题是,在numpy中,1-D数组与2-D数组不同,其中一个维度等于1.