在本章主要内容：
NumPy基础知识
加载iris数据集
查看iris数据集
用pandas查看iris数据集
用NumPy和matplotlib绘图
最小机器学习配方 - SVM分类
介绍交叉验证
以上汇总
机器学习概述 - 分类与回归
简介
本章我们将学习如何使用scikit-learn进行预测。 机器学习强调衡量预测能力，并用scikit-learn进行准确和快速的预测。我们将检查iris数据集，该数据集由三种iris的测量结果组成：Iris Setosa，Iris Versicolor和Iris Virginica。
为了衡量预测，我们将：
保存一些数据以进行测试
仅使用训练数据构建模型
测量测试集的预测能力
解决问题的方法
类别(Classification)：
非文本，比如Iris
回归
聚类
降维
NumPy基础
数据科学经常处理结构化的数据表。scikit-learn库需要二维NumPy数组。 在本节中，您将学习
NumPy的shape和dimension
#!python In [1]: import numpy as np In [2]: np.arange(10) Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [3]: array_1 = np.arange(10) In [4]: array_1.shape Out[4]: (10,) In [5]: array_1.ndim Out[5]: 1 In [6]: array_1.reshape((5,2)) Out[6]: array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) In [7]: array_1 = array_1.reshape((5,2)) In [8]: array_1.ndim Out[8]: 2
NumPy广播(broadcasting)
#!python In [9]: array_1 + 1 Out[9]: array([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10]]) In [10]: array_2 = np.arange(10) In [11]: array_2 * array_2 Out[11]: array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81]) In [12]: array_2 = array_2 ** 2 #Note that this is equivalent to array_2 * In [13]: array_2 Out[13]: array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81]) In [14]: array_2 = array_2.reshape((5,2)) In [15]: array_2 Out[15]: array([[ 0, 1], [ 4, 9], [16, 25], [36, 49], [64, 81]]) In [16]: array_1 = array_1 + 1 In [17]: array_1 Out[17]: array([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10]]) In [18]: array_1 + array_2 Out[18]: array([[ 1, 3], [ 7, 13], [21, 31], [43, 57], [73, 91]])
scikit-learn-cookbook-numpy-compare-rule.png
初始化NumPy数组和dtypes
#!python In [19]: np.zeros((5,2)) Out[19]: array([[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]]) In [20]: np.ones((5,2), dtype = np.int) Out[20]: array([[1, 1], [1, 1], [1, 1], [1, 1], [1, 1]]) In [21]: np.empty((5,2), dtype = np.float) Out[21]: array([[0.00000000e+000, 0.00000000e+000], [6.90082649e-310, 6.90082647e-310], [6.90072710e-310, 6.90072711e-310], [6.90083466e-310, 0.00000000e+000], [6.90083921e-310, 1.90979621e-310]])
索引
#!python In [22]: array_1[0,0] #Finds value in first row and first column. Out[22]: 1 In [23]: array_1[0,:] # View the first row Out[23]: array([1, 2]) In [24]: array_1[:,0] # view the first column Out[24]: array([1, 3, 5, 7, 9]) In [25]: array_1[2:5, :] Out[25]: array([[ 5, 6], [ 7, 8], [ 9, 10]]) In [26]: array_1 Out[26]: array([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10]]) In [27]: array_1[2:5,0] Out[27]: array([5, 7, 9])
布尔数组
#!python In [28]: array_1 > 5 Out[28]: array([[False, False], [False, False], [False, True], [ True, True], [ True, True]]) In [29]: array_1[array_1 > 5] Out[29]: array([ 6, 7, 8, 9, 10])
算术运算
#!python In [30]: array_1.sum() Out[30]: 55 In [31]: array_1.sum(axis = 1) # Find all the sums by row: Out[31]: array([ 3, 7, 11, 15, 19]) In [32]: array_1.sum(axis = 0) # Find all the sums by column Out[32]: array([25, 30]) In [33]: array_1.mean(axis = 0) Out[33]: array([5., 6.])
NaN值
#!python # Scikit-learn不接受np.nan In [34]: array_3 = np.array([np.nan, 0, 1, 2, np.nan]) In [35]: np.isnan(array_3) Out[35]: array([ True, False, False, False, True]) In [36]: array_3[~np.isnan(array_3)] Out[36]: array([0., 1., 2.]) In [37]: array_3[np.isnan(array_3)] = 0 In [38]: array_3 Out[38]: array([0., 0., 1., 2., 0.])
Scikit-learn只接受实数的二维NumPy数组，没有缺失的np.nan值。从经验来看，最好将np.nan改为某个值丢弃。 就我个人而言，我喜欢跟踪布尔模板并保持数据的形状大致相同，因为这会导致更少的编码错误和更多的编码灵活性。
加载数据
#!pythonIn [1]: import numpy as np In [2]: import pandas as pd In [3]: import matplotlib.pyplot as plt In [4]: from sklearn import datasets In [5]: iris = datasets.load_iris() In [6]: iris.data Out[6]: 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], [5. , 3.6, 1.4, 0.2], [5.4, 3.9, 1.7, 0.4], [4.6, 3.4, 1.4, 0.3], [5. , 3.4, 1.5, 0.2], [4.4, 2.9, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [4.8, 3. , 1.4, 0.1], [4.3, 3. , 1.1, 0.1], [5.8, 4. , 1.2, 0.2], [5.7, 4.4, 1.5, 0.4], [5.4, 3.9, 1.3, 0.4], [5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1. , 0.2], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5. , 3. , 1.6, 0.2], [5. , 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [5.2, 3.4, 1.4, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [5.2, 4.1, 1.5, 0.1], [5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5. , 3.2, 1.2, 0.2], [5.5, 3.5, 1.3, 0.2], [4.9, 3.1, 1.5, 0.1], [4.4, 3. , 1.3, 0.2], [5.1, 3.4, 1.5, 0.2], [5. , 3.5, 1.3, 0.3], [4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2], [5. , 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3. , 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2], [5.3, 3.7, 1.5, 0.2], [5. , 3.3, 1.4, 0.2], [7. , 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4. , 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1. ], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5. , 2. , 3.5, 1. ], [5.9, 3. , 4.2, 1.5], [6. , 2.2, 4. , 1. ], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3. , 4.5, 1.5], [5.8, 2.7, 4.1, 1. ], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4. , 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.4, 2.9, 4.3, 1.3], [6.6, 3. , 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3. , 5. , 1.7], [6. , 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1. ], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1. ], [5.8, 2.7, 3.9, 1.2], [6. , 2.7, 5.1, 1.6], [5.4, 3. , 4.5, 1.5], [6. , 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3. , 4.1, 1.3], [5.5, 2.5, 4. , 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3. , 4.6, 1.4], [5.8, 2.6, 4. , 1.2], [5. , 2.3, 3.3, 1. ], [5.6, 2.7, 4.2, 1.3], [5.7, 3. , 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3. , 1.1], [5.7, 2.8, 4.1, 1.3], [6.3, 3.3, 6. , 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3. , 5.9, 2.1], [6.3, 2.9, 5.6, 1.8], [6.5, 3. , 5.8, 2.2], [7.6, 3. , 6.6, 2.1], [4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8], [6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1, 2.5], [6.5, 3.2, 5.1, 2. ], [6.4, 2.7, 5.3, 1.9], [6.8, 3. , 5.5, 2.1], [5.7, 2.5, 5. , 2. ], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3. , 5.5, 1.8], [7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6. , 2.2, 5. , 1.5], [6.9, 3.2, 5.7, 2.3], [5.6, 2.8, 4.9, 2. ], [7.7, 2.8, 6.7, 2. ], [6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7, 2.1], [7.2, 3.2, 6. , 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3. , 4.9, 1.8], [6.4, 2.8, 5.6, 2.1], [7.2, 3. , 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2. ], [6.4, 2.8, 5.6, 2.2], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [7.7, 3. , 6.1, 2.3], [6.3, 3.4, 5.6, 2.4], [6.4, 3.1, 5.5, 1.8], [6. , 3. , 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.7, 3.1, 5.6, 2.4], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3], [6.7, 3.3, 5.7, 2.5], [6.7, 3. , 5.2, 2.3], [6.3, 2.5, 5. , 1.9], [6.5, 3. , 5.2, 2. ], [6.2, 3.4, 5.4, 2.3], [5.9, 3. , 5.1, 1.8]]) In [7]: iris.data.shape Out[7]: (150, 4) In [8]: iris.data[0] Out[8]: array([5.1, 3.5, 1.4, 0.2]) In [9]: iris.feature_names Out[9]: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] In [10]: iris.target Out[10]: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) In [11]: iris.target.shape Out[11]: (150,) In [12]: iris.target_names Out[12]: array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
用pandas查看数据
#!pythonimport numpy as np #Load the numpy library for fast array computationsimport pandas as pd #Load the pandas data-analysis libraryimport matplotlib.pyplot as plt #Load the pyplot visualization library%matplotlib inlinefrom sklearn import datasets iris = datasets.load_iris() iris_df = pd.DataFrame(iris.data, columns = iris.feature_names) iris_df['sepal length (cm)'].hist(bins=30)
scikit-learn-cookbook1-pandas1.png
#!pythonfor class_number in np.unique(iris.target): plt.figure(1) iris_df['sepal length (cm)'].iloc[np.where(iris.target == class_number)[0]].hist(bins=30)
#!pythonnp.where(iris.target == class_number)[0]
执行结果
#!pythonarray([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], dtype=int64)
matplotlib和NumPy作图
#!pythonimport numpy as npimport matplotlib.pyplot as plt %matplotlib inline plt.plot(np.arange(10), np.arange(10)) plt.plot(np.arange(10), np.exp(np.arange(10)))# 两张图片放在一起plt.figure() plt.subplot(121) plt.plot(np.arange(10), np.exp(np.arange(10))) plt.subplot(122) plt.scatter(np.arange(10), np.exp(np.arange(10))) plt.figure() plt.subplot(211) plt.plot(np.arange(10), np.exp(np.arange(10))) plt.subplot(212) plt.scatter(np.arange(10), np.exp(np.arange(10))) plt.figure() plt.subplot(221) plt.plot(np.arange(10), np.exp(np.arange(10))) plt.subplot(222) plt.scatter(np.arange(10), np.exp(np.arange(10))) plt.subplot(223) plt.scatter(np.arange(10), np.exp(np.arange(10))) plt.subplot(224) plt.scatter(np.arange(10), np.exp(np.arange(10)))from sklearn.datasets import load_iris iris = load_iris() data = iris.data target = iris.target# Resize the figure for better viewingplt.figure(figsize=(12,5))# First subplotplt.subplot(121)# Visualize the first two columns of data:plt.scatter(data[:,0], data[:,1], c=target)# Second subplotplt.subplot(122)# Visualize the last two columns of data:plt.scatter(data[:,2], data[:,3], c=target)
作者：python作业AI毕业设计
链接：https://www.jianshu.com/p/5ec36c415df7
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