很抱歉，如果我在这里使用的术语不正确。我有一个带有自己数据的csv文件。我首先需要将其转换为另一个format，以便可以将其加载到另一个Python code中。我在下面显示了一个格式示例，它是虹膜数据集的子集，示例通过该数据集进行加载：
from sklearn import datasets
data = datasets.load_iris()
print(data)
哪位给我（我为了保持可读性而删节了部分内容）：
{'data': array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
...
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, ... 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': 'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\'s paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}
我可以产生第一个“数据”数组和第二个“目标”数组。但是我在文件的最后部分苦苦挣扎，我相信其中包含一些字典标记，例如“ target_names”，“ feature_names”，“ mean”等等。
在其余的代码中，我需要这些标签，可以在这里找到： https://github.com/gaurav-kaushik/Data-Visualizations-Medium/blob/master/pca_feature_correlation.py
数据集信息在这里： http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
理想情况下，正在寻找一段代码来从我的csv文件生成这种格式。
到目前为止，我的代码：
from numpy import genfromtxt
data = genfromtxt('myfile.csv', delimiter=',')
features = data[:, :3]
targets = data[:, 3]
myfile.csv只是4列的随机数，其中包含标题和几行，仅供测试。
答案 0 :(得分：1)
好。我在这篇文章的帮助下找到了一种方法： How to create my own datasets using in scikit-learn?
我的iris.csv文件如下：
f1,f2,f3,f4,t
5.1,3.5,1.4,0.2,0
4.9,3,1.4,0.2,0
....(150 rows)
以及将代码转换成我在OP中描述的格式的.csv的代码：
import numpy as np
import csv
from sklearn.datasets.base import Bunch
def load_my_dataset():
with open('iris.csv') as csv_file:
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = 150 #number of data rows, don't count header
n_features = 4 #number of columns for features, don't count target column
feature_names = ['f1','f2','f3','f4'] #adjust accordingly
target_names = ['t1','t2','t3'] #adjust accordingly
data = np.empty((n_samples, n_features))
target = np.empty((n_samples,), dtype=np.int)
for i, sample in enumerate(data_file):
data[i] = np.asarray(sample[:-1], dtype=np.float64)
target[i] = np.asarray(sample[-1], dtype=np.int)
return Bunch(data=data, target=target, feature_names = feature_names, target_names = target_names)
data = load_my_dataset()
我同意可以使代码更加智能，但是它可以工作，您只需要进行以下调整：