scikit-learn_cookbook1: 高性能机器学习-NumPy

源码下载

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

可爱的python测试开发库 谢谢在github上点赞。 python中文库文档汇总 接口自动化性能测试线上培训大纲 python测试开发自动化测试数据分析人工智能自学每周一练 python3标准库-中文版 更多内容请关注 雪峰磁针石：简书

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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
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    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)
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    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]])

Alt Text

  • 初始化NumPy数组和dtypes
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    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
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    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
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    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
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    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
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    # 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

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In [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
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import numpy as np    #Load the numpy library for fast array computations
import pandas as pd   #Load the pandas data-analysis library
import matplotlib.pyplot as plt   #Load the pyplot visualization library

%matplotlib inline

from 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)

Alt Text

```

!python

for 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)

![Alt Text]({filename}/images/scikit-learn-cookbook1-pandas2.png)   

 ```
#!python

np.where(iris.target == class_number)[0]

执行结果

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array([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

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import numpy as np
import 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 viewing
plt.figure(figsize=(12,5))

# First subplot
plt.subplot(121)

# Visualize the first two columns of data:
plt.scatter(data[:,0], data[:,1], c=target)

# Second subplot
plt.subplot(122)

# Visualize the last two columns of data:
plt.scatter(data[:,2], data[:,3], c=target)

执行结果参见

最小机器学习快速入门 - 向量机分类

为了做出预测，我们将： * 说明要解决的问题 * 选择一个模型来解决问题 * 训练模型 * 作出预测 * 衡量模型的表现如何

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