scikit-learn_cookbook1: 高性能機器學習-NumPy
源碼下載python
在本章主要內容：git
- NumPy基礎知識
- 加載iris數據集
- 查看iris數據集
- 用pandas查看iris數據集
- 用NumPy和matplotlib繪圖
- 最小機器學習配方 - SVM分類
- 介紹交叉驗證
- 以上彙總
- 機器學習概述 - 分類與迴歸
簡介
本章咱們將學習如何使用scikit-learn進行預測。 機器學習強調衡量預測能力，並用scikit-learn進行準確和快速的預測。咱們將檢查iris數據集，該數據集由三種iris的測量結果組成：Iris Setosa，Iris Versicolor和Iris Virginica。github
爲了衡量預測，咱們將：api
- 保存一些數據以進行測試
- 僅使用訓練數據構建模型
- 測量測試集的預測能力
解決問題的方法數組
- 類別(Classification)：
- 非文本，好比Iris
- 迴歸
- 聚類
- 降維
-
技術支持 (能夠加qq羣：887934385）機器學習
NumPy基礎
數據科學常常處理結構化的數據表。scikit-learn庫須要二維NumPy數組。 在本節中，您將學習學習
- NumPy的shape和dimension
1 In [1]: import numpy as np 2 3 In [2]: np.arange(10) 4 Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) 5 6 In [3]: array_1 = np.arange(10) 7 8 In [4]: array_1.shape 9 Out[4]: (10,) 10 11 In [5]: array_1.ndim 12 Out[5]: 1 13 14 In [6]: array_1.reshape((5,2)) 15 Out[6]: 16 array([[0, 1], 17 [2, 3], 18 [4, 5], 19 [6, 7], 20 [8, 9]]) 21 22 In [7]: array_1 = array_1.reshape((5,2)) 23 24 In [8]: array_1.ndim 25 Out[8]: 2
- NumPy廣播(broadcasting)
1 In [9]: array_1 + 1 2 Out[9]: 3 array([[ 1, 2], 4 [ 3, 4], 5 [ 5, 6], 6 [ 7, 8], 7 [ 9, 10]]) 8 9 In [10]: array_2 = np.arange(10) 10 11 In [11]: array_2 * array_2 12 Out[11]: array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81]) 13 14 In [12]: array_2 = array_2 ** 2 #Note that this is equivalent to array_2 * 15 16 In [13]: array_2 17 Out[13]: array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81]) 18 19 In [14]: array_2 = array_2.reshape((5,2)) 20 21 In [15]: array_2 22 Out[15]: 23 array([[ 0, 1], 24 [ 4, 9], 25 [16, 25], 26 [36, 49], 27 [64, 81]]) 28 29 In [16]: array_1 = array_1 + 1 30 31 In [17]: array_1 32 Out[17]: 33 array([[ 1, 2], 34 [ 3, 4], 35 [ 5, 6], 36 [ 7, 8], 37 [ 9, 10]]) 38 39 In [18]: array_1 + array_2 40 Out[18]: 41 array([[ 1, 3], 42 [ 7, 13], 43 [21, 31], 44 [43, 57], 45 [73, 91]])
- 初始化NumPy數組和dtypes
1 In [19]: np.zeros((5,2)) 2 Out[19]: 3 array([[0., 0.], 4 [0., 0.], 5 [0., 0.], 6 [0., 0.], 7 [0., 0.]]) 8 9 In [20]: np.ones((5,2), dtype = np.int) 10 Out[20]: 11 array([[1, 1], 12 [1, 1], 13 [1, 1], 14 [1, 1], 15 [1, 1]]) 16 17 In [21]: np.empty((5,2), dtype = np.float) 18 Out[21]: 19 array([[0.00000000e+000, 0.00000000e+000], 20 21 22 [6.90082649e-310, 6.90082647e-310], 23 [6.90072710e-310, 6.90072711e-310], 24 [6.90083466e-310, 0.00000000e+000], 25 [6.90083921e-310, 1.90979621e-310]])
- 索引
1 In [22]: array_1[0,0] #Finds value in first row and first column. 2 Out[22]: 1 3 4 In [23]: array_1[0,:] # View the first row 5 Out[23]: array([1, 2]) 6 7 In [24]: array_1[:,0] # view the first column 8 Out[24]: array([1, 3, 5, 7, 9]) 9 10 In [25]: array_1[2:5, :] 11 Out[25]: 12 array([[ 5, 6], 13 [ 7, 8], 14 [ 9, 10]]) 15 16 In [26]: array_1 17 Out[26]: 18 array([[ 1, 2], 19 [ 3, 4], 20 [ 5, 6], 21 [ 7, 8], 22 [ 9, 10]]) 23 24 In [27]: array_1[2:5,0] 25 Out[27]: array([5, 7, 9])
- 布爾數組
1 In [28]: array_1 > 5 2 Out[28]: 3 array([[False, False], 4 [False, False], 5 [False, True], 6 [ True, True], 7 [ True, True]]) 8 9 In [29]: array_1[array_1 > 5] 10 Out[29]: array([ 6, 7, 8, 9, 10])
- 算術運算
1 In [30]: array_1.sum() 2 Out[30]: 55 3 4 In [31]: array_1.sum(axis = 1) # Find all the sums by row: 5 Out[31]: array([ 3, 7, 11, 15, 19]) 6 7 In [32]: array_1.sum(axis = 0) # Find all the sums by column 8 Out[32]: array([25, 30]) 9 10 In [33]: array_1.mean(axis = 0) 11 Out[33]: array([5., 6.])
- NaN值
1 # Scikit-learn不接受np.nan 2 In [34]: array_3 = np.array([np.nan, 0, 1, 2, np.nan]) 3 4 In [35]: np.isnan(array_3) 5 Out[35]: array([ True, False, False, False, True]) 6 7 In [36]: array_3[~np.isnan(array_3)] 8 Out[36]: array([0., 1., 2.]) 9 10 In [37]: array_3[np.isnan(array_3)] = 0 11 12 In [38]: array_3 13 Out[38]: array([0., 0., 1., 2., 0.])
Scikit-learn只接受實數的二維NumPy數組，沒有缺失的np.nan值。從經驗來看，最好將np.nan改成某個值丟棄。 就我我的而言，我喜歡跟蹤布爾模板並保持數據的形狀大體相同，由於這會致使更少的編碼錯誤和更多的編碼靈活性。測試
加載數據
1 In [1]: import numpy as np 2 3 In [2]: import pandas as pd 4 5 In [3]: import matplotlib.pyplot as plt 6 7 In [4]: from sklearn import datasets 8 9 In [5]: iris = datasets.load_iris() 10 11 In [6]: iris.data 12 Out[6]: 13 array([[5.1, 3.5, 1.4, 0.2], 14 [4.9, 3. , 1.4, 0.2], 15 [4.7, 3.2, 1.3, 0.2], 16 [4.6, 3.1, 1.5, 0.2], 17 [5. , 3.6, 1.4, 0.2], 18 [5.4, 3.9, 1.7, 0.4], 19 [4.6, 3.4, 1.4, 0.3], 20 [5. , 3.4, 1.5, 0.2], 21 [4.4, 2.9, 1.4, 0.2], 22 [4.9, 3.1, 1.5, 0.1], 23 [5.4, 3.7, 1.5, 0.2], 24 [4.8, 3.4, 1.6, 0.2], 25 [4.8, 3. , 1.4, 0.1], 26 [4.3, 3. , 1.1, 0.1], 27 [5.8, 4. , 1.2, 0.2], 28 [5.7, 4.4, 1.5, 0.4], 29 [5.4, 3.9, 1.3, 0.4], 30 [5.1, 3.5, 1.4, 0.3], 31 [5.7, 3.8, 1.7, 0.3], 32 [5.1, 3.8, 1.5, 0.3], 33 [5.4, 3.4, 1.7, 0.2], 34 [5.1, 3.7, 1.5, 0.4], 35 [4.6, 3.6, 1. , 0.2], 36 [5.1, 3.3, 1.7, 0.5], 37 [4.8, 3.4, 1.9, 0.2], 38 [5. , 3. , 1.6, 0.2], 39 [5. , 3.4, 1.6, 0.4], 40 [5.2, 3.5, 1.5, 0.2], 41 [5.2, 3.4, 1.4, 0.2], 42 [4.7, 3.2, 1.6, 0.2], 43 [4.8, 3.1, 1.6, 0.2], 44 [5.4, 3.4, 1.5, 0.4], 45 [5.2, 4.1, 1.5, 0.1], 46 [5.5, 4.2, 1.4, 0.2], 47 [4.9, 3.1, 1.5, 0.1], 48 [5. , 3.2, 1.2, 0.2], 49 [5.5, 3.5, 1.3, 0.2], 50 [4.9, 3.1, 1.5, 0.1], 51 [4.4, 3. , 1.3, 0.2], 52 [5.1, 3.4, 1.5, 0.2], 53 [5. , 3.5, 1.3, 0.3], 54 [4.5, 2.3, 1.3, 0.3], 55 [4.4, 3.2, 1.3, 0.2], 56 [5. , 3.5, 1.6, 0.6], 57 [5.1, 3.8, 1.9, 0.4], 58 [4.8, 3. , 1.4, 0.3], 59 [5.1, 3.8, 1.6, 0.2], 60 [4.6, 3.2, 1.4, 0.2], 61 [5.3, 3.7, 1.5, 0.2], 62 [5. , 3.3, 1.4, 0.2], 63 [7. , 3.2, 4.7, 1.4], 64 [6.4, 3.2, 4.5, 1.5], 65 [6.9, 3.1, 4.9, 1.5], 66 [5.5, 2.3, 4. , 1.3], 67 [6.5, 2.8, 4.6, 1.5], 68 [5.7, 2.8, 4.5, 1.3], 69 [6.3, 3.3, 4.7, 1.6], 70 [4.9, 2.4, 3.3, 1. ], 71 [6.6, 2.9, 4.6, 1.3], 72 [5.2, 2.7, 3.9, 1.4], 73 [5. , 2. , 3.5, 1. ], 74 [5.9, 3. , 4.2, 1.5], 75 [6. , 2.2, 4. , 1. ], 76 [6.1, 2.9, 4.7, 1.4], 77 [5.6, 2.9, 3.6, 1.3], 78 [6.7, 3.1, 4.4, 1.4], 79 [5.6, 3. , 4.5, 1.5], 80 [5.8, 2.7, 4.1, 1. ], 81 [6.2, 2.2, 4.5, 1.5], 82 [5.6, 2.5, 3.9, 1.1], 83 [5.9, 3.2, 4.8, 1.8], 84 [6.1, 2.8, 4. , 1.3], 85 [6.3, 2.5, 4.9, 1.5], 86 [6.1, 2.8, 4.7, 1.2], 87 [6.4, 2.9, 4.3, 1.3], 88 [6.6, 3. , 4.4, 1.4], 89 [6.8, 2.8, 4.8, 1.4], 90 [6.7, 3. , 5. , 1.7], 91 [6. , 2.9, 4.5, 1.5], 92 [5.7, 2.6, 3.5, 1. ], 93 [5.5, 2.4, 3.8, 1.1], 94 [5.5, 2.4, 3.7, 1. ], 95 [5.8, 2.7, 3.9, 1.2], 96 [6. , 2.7, 5.1, 1.6], 97 [5.4, 3. , 4.5, 1.5], 98 [6. , 3.4, 4.5, 1.6], 99 [6.7, 3.1, 4.7, 1.5], 100 [6.3, 2.3, 4.4, 1.3], 101 [5.6, 3. , 4.1, 1.3], 102 [5.5, 2.5, 4. , 1.3], 103 [5.5, 2.6, 4.4, 1.2], 104 [6.1, 3. , 4.6, 1.4], 105 [5.8, 2.6, 4. , 1.2], 106 [5. , 2.3, 3.3, 1. ], 107 [5.6, 2.7, 4.2, 1.3], 108 [5.7, 3. , 4.2, 1.2], 109 [5.7, 2.9, 4.2, 1.3], 110 [6.2, 2.9, 4.3, 1.3], 111 [5.1, 2.5, 3. , 1.1], 112 [5.7, 2.8, 4.1, 1.3], 113 [6.3, 3.3, 6. , 2.5], 114 [5.8, 2.7, 5.1, 1.9], 115 [7.1, 3. , 5.9, 2.1], 116 [6.3, 2.9, 5.6, 1.8], 117 [6.5, 3. , 5.8, 2.2], 118 [7.6, 3. , 6.6, 2.1], 119 [4.9, 2.5, 4.5, 1.7], 120 [7.3, 2.9, 6.3, 1.8], 121 [6.7, 2.5, 5.8, 1.8], 122 [7.2, 3.6, 6.1, 2.5], 123 [6.5, 3.2, 5.1, 2. ], 124 [6.4, 2.7, 5.3, 1.9], 125 [6.8, 3. , 5.5, 2.1], 126 [5.7, 2.5, 5. , 2. ], 127 [5.8, 2.8, 5.1, 2.4], 128 [6.4, 3.2, 5.3, 2.3], 129 [6.5, 3. , 5.5, 1.8], 130 [7.7, 3.8, 6.7, 2.2], 131 [7.7, 2.6, 6.9, 2.3], 132 [6. , 2.2, 5. , 1.5], 133 [6.9, 3.2, 5.7, 2.3], 134 [5.6, 2.8, 4.9, 2. ], 135 [7.7, 2.8, 6.7, 2. ], 136 [6.3, 2.7, 4.9, 1.8], 137 [6.7, 3.3, 5.7, 2.1], 138 [7.2, 3.2, 6. , 1.8], 139 [6.2, 2.8, 4.8, 1.8], 140 [6.1, 3. , 4.9, 1.8], 141 [6.4, 2.8, 5.6, 2.1], 142 [7.2, 3. , 5.8, 1.6], 143 [7.4, 2.8, 6.1, 1.9], 144 [7.9, 3.8, 6.4, 2. ], 145 [6.4, 2.8, 5.6, 2.2], 146 [6.3, 2.8, 5.1, 1.5], 147 [6.1, 2.6, 5.6, 1.4], 148 [7.7, 3. , 6.1, 2.3], 149 [6.3, 3.4, 5.6, 2.4], 150 [6.4, 3.1, 5.5, 1.8], 151 [6. , 3. , 4.8, 1.8], 152 [6.9, 3.1, 5.4, 2.1], 153 [6.7, 3.1, 5.6, 2.4], 154 [6.9, 3.1, 5.1, 2.3], 155 [5.8, 2.7, 5.1, 1.9], 156 [6.8, 3.2, 5.9, 2.3], 157 [6.7, 3.3, 5.7, 2.5], 158 [6.7, 3. , 5.2, 2.3], 159 [6.3, 2.5, 5. , 1.9], 160 [6.5, 3. , 5.2, 2. ], 161 [6.2, 3.4, 5.4, 2.3], 162 [5.9, 3. , 5.1, 1.8]]) 163 164 In [7]: iris.data.shape 165 Out[7]: (150, 4) 166 167 In [8]: iris.data[0] 168 Out[8]: array([5.1, 3.5, 1.4, 0.2]) 169 170 In [9]: iris.feature_names 171 Out[9]: 172 ['sepal length (cm)', 173 'sepal width (cm)', 174 'petal length (cm)', 175 'petal width (cm)'] 176 177 In [10]: iris.target 178 Out[10]: 179 array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 180 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 181 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 182 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 183 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 184 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 185 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) 186 187 In [11]: iris.target.shape 188 Out[11]: (150,) 189 190 In [12]: iris.target_names 191 Out[12]: array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
- 用pandas查看數據
1 import numpy as np #Load the numpy library for fast array computations 2 import pandas as pd #Load the pandas data-analysis library 3 import matplotlib.pyplot as plt #Load the pyplot visualization library 4 5 %matplotlib inline 6 7 from sklearn import datasets 8 iris = datasets.load_iris() 9 10 iris_df = pd.DataFrame(iris.data, columns = iris.feature_names) 11 12 iris_df['sepal length (cm)'].hist(bins=30)
```ui
!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)this
```
#!python
np.where(iris.target == class_number)[0]
執行結果
1 array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 2 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 3 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 4 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], dtype=int64)
matplotlib和NumPy做圖
1 import numpy as np 2 import matplotlib.pyplot as plt 3 %matplotlib inline 4 5 plt.plot(np.arange(10), np.arange(10)) 6 7 plt.plot(np.arange(10), np.exp(np.arange(10))) 8 9 10 # 兩張圖片放在一塊兒 11 plt.figure() 12 plt.subplot(121) 13 plt.plot(np.arange(10), np.exp(np.arange(10))) 14 plt.subplot(122) 15 plt.scatter(np.arange(10), np.exp(np.arange(10))) 16 17 18 19 plt.figure() 20 plt.subplot(211) 21 plt.plot(np.arange(10), np.exp(np.arange(10))) 22 plt.subplot(212) 23 plt.scatter(np.arange(10), np.exp(np.arange(10))) 24 25 plt.figure() 26 plt.subplot(221) 27 plt.plot(np.arange(10), np.exp(np.arange(10))) 28 plt.subplot(222) 29 plt.scatter(np.arange(10), np.exp(np.arange(10))) 30 plt.subplot(223) 31 plt.scatter(np.arange(10), np.exp(np.arange(10))) 32 plt.subplot(224) 33 plt.scatter(np.arange(10), np.exp(np.arange(10))) 34 35 from sklearn.datasets import load_iris 36 37 iris = load_iris() 38 data = iris.data 39 target = iris.target 40 41 # Resize the figure for better viewing 42 plt.figure(figsize=(12,5)) 43 44 # First subplot 45 plt.subplot(121) 46 47 # Visualize the first two columns of data: 48 plt.scatter(data[:,0], data[:,1], c=target) 49 50 # Second subplot 51 plt.subplot(122) 52 53 # Visualize the last two columns of data: 54 plt.scatter(data[:,2], data[:,3], c=target)
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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))) # 兩張圖片放在一塊兒 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 |