04机器学习实战之朴素贝叶斯scikit-learn实现

In [8]:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsClassifier


def iris_type(s):
    it = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
    return it[s]

In [15]:

data = np.loadtxt('D:\\mlInAction\\8.iris.data', encoding='utf-8', dtype=float, delimiter=',',
                  converters={4: iris_type})
data

Out[15]:

array([[5.1, 3.5, 1.4, 0.2, 0. ],
       [4.9, 3. , 1.4, 0.2, 0. ],
       [4.7, 3.2, 1.3, 0.2, 0. ],
       [4.6, 3.1, 1.5, 0.2, 0. ],
       [5. , 3.6, 1.4, 0.2, 0. ],
       [5.4, 3.9, 1.7, 0.4, 0. ],
       [4.6, 3.4, 1.4, 0.3, 0. ],
       [5. , 3.4, 1.5, 0.2, 0. ],
       [4.4, 2.9, 1.4, 0.2, 0. ],
       [4.9, 3.1, 1.5, 0.1, 0. ],
       [5.4, 3.7, 1.5, 0.2, 0. ],
       [4.8, 3.4, 1.6, 0.2, 0. ],
       [4.8, 3. , 1.4, 0.1, 0. ],
       [4.3, 3. , 1.1, 0.1, 0. ],
       [5.8, 4. , 1.2, 0.2, 0. ],
       [5.7, 4.4, 1.5, 0.4, 0. ],
       [5.4, 3.9, 1.3, 0.4, 0. ],
       [5.1, 3.5, 1.4, 0.3, 0. ],
       [5.7, 3.8, 1.7, 0.3, 0. ],
       [5.1, 3.8, 1.5, 0.3, 0. ],
       [5.4, 3.4, 1.7, 0.2, 0. ],
       [5.1, 3.7, 1.5, 0.4, 0. ],
       [4.6, 3.6, 1. , 0.2, 0. ],
       [5.1, 3.3, 1.7, 0.5, 0. ],
       [4.8, 3.4, 1.9, 0.2, 0. ],
       [5. , 3. , 1.6, 0.2, 0. ],
       [5. , 3.4, 1.6, 0.4, 0. ],
       [5.2, 3.5, 1.5, 0.2, 0. ],
       [5.2, 3.4, 1.4, 0.2, 0. ],
       [4.7, 3.2, 1.6, 0.2, 0. ],
       [4.8, 3.1, 1.6, 0.2, 0. ],
       [5.4, 3.4, 1.5, 0.4, 0. ],
       [5.2, 4.1, 1.5, 0.1, 0. ],
       [5.5, 4.2, 1.4, 0.2, 0. ],
       [4.9, 3.1, 1.5, 0.1, 0. ],
       [5. , 3.2, 1.2, 0.2, 0. ],
       [5.5, 3.5, 1.3, 0.2, 0. ],
       [4.9, 3.1, 1.5, 0.1, 0. ],
       [4.4, 3. , 1.3, 0.2, 0. ],
       [5.1, 3.4, 1.5, 0.2, 0. ],
       [5. , 3.5, 1.3, 0.3, 0. ],
       [4.5, 2.3, 1.3, 0.3, 0. ],
       [4.4, 3.2, 1.3, 0.2, 0. ],
       [5. , 3.5, 1.6, 0.6, 0. ],
       [5.1, 3.8, 1.9, 0.4, 0. ],
       [4.8, 3. , 1.4, 0.3, 0. ],
       [5.1, 3.8, 1.6, 0.2, 0. ],
       [4.6, 3.2, 1.4, 0.2, 0. ],
       [5.3, 3.7, 1.5, 0.2, 0. ],
       [5. , 3.3, 1.4, 0.2, 0. ],
       [7. , 3.2, 4.7, 1.4, 1. ],
       [6.4, 3.2, 4.5, 1.5, 1. ],
       [6.9, 3.1, 4.9, 1.5, 1. ],
       [5.5, 2.3, 4. , 1.3, 1. ],
       [6.5, 2.8, 4.6, 1.5, 1. ],
       [5.7, 2.8, 4.5, 1.3, 1. ],
       [6.3, 3.3, 4.7, 1.6, 1. ],
       [4.9, 2.4, 3.3, 1. , 1. ],
       [6.6, 2.9, 4.6, 1.3, 1. ],
       [5.2, 2.7, 3.9, 1.4, 1. ],
       [5. , 2. , 3.5, 1. , 1. ],
       [5.9, 3. , 4.2, 1.5, 1. ],
       [6. , 2.2, 4. , 1. , 1. ],
       [6.1, 2.9, 4.7, 1.4, 1. ],
       [5.6, 2.9, 3.6, 1.3, 1. ],
       [6.7, 3.1, 4.4, 1.4, 1. ],
       [5.6, 3. , 4.5, 1.5, 1. ],
       [5.8, 2.7, 4.1, 1. , 1. ],
       [6.2, 2.2, 4.5, 1.5, 1. ],
       [5.6, 2.5, 3.9, 1.1, 1. ],
       [5.9, 3.2, 4.8, 1.8, 1. ],
       [6.1, 2.8, 4. , 1.3, 1. ],
       [6.3, 2.5, 4.9, 1.5, 1. ],
       [6.1, 2.8, 4.7, 1.2, 1. ],
       [6.4, 2.9, 4.3, 1.3, 1. ],
       [6.6, 3. , 4.4, 1.4, 1. ],
       [6.8, 2.8, 4.8, 1.4, 1. ],
       [6.7, 3. , 5. , 1.7, 1. ],
       [6. , 2.9, 4.5, 1.5, 1. ],
       [5.7, 2.6, 3.5, 1. , 1. ],
       [5.5, 2.4, 3.8, 1.1, 1. ],
       [5.5, 2.4, 3.7, 1. , 1. ],
       [5.8, 2.7, 3.9, 1.2, 1. ],
       [6. , 2.7, 5.1, 1.6, 1. ],
       [5.4, 3. , 4.5, 1.5, 1. ],
       [6. , 3.4, 4.5, 1.6, 1. ],
       [6.7, 3.1, 4.7, 1.5, 1. ],
       [6.3, 2.3, 4.4, 1.3, 1. ],
       [5.6, 3. , 4.1, 1.3, 1. ],
       [5.5, 2.5, 4. , 1.3, 1. ],
       [5.5, 2.6, 4.4, 1.2, 1. ],
       [6.1, 3. , 4.6, 1.4, 1. ],
       [5.8, 2.6, 4. , 1.2, 1. ],
       [5. , 2.3, 3.3, 1. , 1. ],
       [5.6, 2.7, 4.2, 1.3, 1. ],
       [5.7, 3. , 4.2, 1.2, 1. ],
       [5.7, 2.9, 4.2, 1.3, 1. ],
       [6.2, 2.9, 4.3, 1.3, 1. ],
       [5.1, 2.5, 3. , 1.1, 1. ],
       [5.7, 2.8, 4.1, 1.3, 1. ],
       [6.3, 3.3, 6. , 2.5, 2. ],
       [5.8, 2.7, 5.1, 1.9, 2. ],
       [7.1, 3. , 5.9, 2.1, 2. ],
       [6.3, 2.9, 5.6, 1.8, 2. ],
       [6.5, 3. , 5.8, 2.2, 2. ],
       [7.6, 3. , 6.6, 2.1, 2. ],
       [4.9, 2.5, 4.5, 1.7, 2. ],
       [7.3, 2.9, 6.3, 1.8, 2. ],
       [6.7, 2.5, 5.8, 1.8, 2. ],
       [7.2, 3.6, 6.1, 2.5, 2. ],
       [6.5, 3.2, 5.1, 2. , 2. ],
       [6.4, 2.7, 5.3, 1.9, 2. ],
       [6.8, 3. , 5.5, 2.1, 2. ],
       [5.7, 2.5, 5. , 2. , 2. ],
       [5.8, 2.8, 5.1, 2.4, 2. ],
       [6.4, 3.2, 5.3, 2.3, 2. ],
       [6.5, 3. , 5.5, 1.8, 2. ],
       [7.7, 3.8, 6.7, 2.2, 2. ],
       [7.7, 2.6, 6.9, 2.3, 2. ],
       [6. , 2.2, 5. , 1.5, 2. ],
       [6.9, 3.2, 5.7, 2.3, 2. ],
       [5.6, 2.8, 4.9, 2. , 2. ],
       [7.7, 2.8, 6.7, 2. , 2. ],
       [6.3, 2.7, 4.9, 1.8, 2. ],
       [6.7, 3.3, 5.7, 2.1, 2. ],
       [7.2, 3.2, 6. , 1.8, 2. ],
       [6.2, 2.8, 4.8, 1.8, 2. ],
       [6.1, 3. , 4.9, 1.8, 2. ],
       [6.4, 2.8, 5.6, 2.1, 2. ],
       [7.2, 3. , 5.8, 1.6, 2. ],
       [7.4, 2.8, 6.1, 1.9, 2. ],
       [7.9, 3.8, 6.4, 2. , 2. ],
       [6.4, 2.8, 5.6, 2.2, 2. ],
       [6.3, 2.8, 5.1, 1.5, 2. ],
       [6.1, 2.6, 5.6, 1.4, 2. ],
       [7.7, 3. , 6.1, 2.3, 2. ],
       [6.3, 3.4, 5.6, 2.4, 2. ],
       [6.4, 3.1, 5.5, 1.8, 2. ],
       [6. , 3. , 4.8, 1.8, 2. ],
       [6.9, 3.1, 5.4, 2.1, 2. ],
       [6.7, 3.1, 5.6, 2.4, 2. ],
       [6.9, 3.1, 5.1, 2.3, 2. ],
       [5.8, 2.7, 5.1, 1.9, 2. ],
       [6.8, 3.2, 5.9, 2.3, 2. ],
       [6.7, 3.3, 5.7, 2.5, 2. ],
       [6.7, 3. , 5.2, 2.3, 2. ],
       [6.3, 2.5, 5. , 1.9, 2. ],
       [6.5, 3. , 5.2, 2. , 2. ],
       [6.2, 3.4, 5.4, 2.3, 2. ],
       [5.9, 3. , 5.1, 1.8, 2. ]])

In [16]:

x, y = np.split(data, (4,), axis=1)  # 前四列是x最后一列是y
x

Out[16]:

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 [17]:

y

Out[17]:

array([[0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [0.],
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       [0.],
       [0.],
       [0.],
       [1.],
       [1.],
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       [1.],
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       [1.],
       [1.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
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       [2.],
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       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.]])

In [18]:

x = x[:, :2]  # 只取前两列作为x
x

Out[18]:

array([[5.1, 3.5],
       [4.9, 3. ],
       [4.7, 3.2],
       [4.6, 3.1],
       [5. , 3.6],
       [5.4, 3.9],
       [4.6, 3.4],
       [5. , 3.4],
       [4.4, 2.9],
       [4.9, 3.1],
       [5.4, 3.7],
       [4.8, 3.4],
       [4.8, 3. ],
       [4.3, 3. ],
       [5.8, 4. ],
       [5.7, 4.4],
       [5.4, 3.9],
       [5.1, 3.5],
       [5.7, 3.8],
       [5.1, 3.8],
       [5.4, 3.4],
       [5.1, 3.7],
       [4.6, 3.6],
       [5.1, 3.3],
       [4.8, 3.4],
       [5. , 3. ],
       [5. , 3.4],
       [5.2, 3.5],
       [5.2, 3.4],
       [4.7, 3.2],
       [4.8, 3.1],
       [5.4, 3.4],
       [5.2, 4.1],
       [5.5, 4.2],
       [4.9, 3.1],
       [5. , 3.2],
       [5.5, 3.5],
       [4.9, 3.1],
       [4.4, 3. ],
       [5.1, 3.4],
       [5. , 3.5],
       [4.5, 2.3],
       [4.4, 3.2],
       [5. , 3.5],
       [5.1, 3.8],
       [4.8, 3. ],
       [5.1, 3.8],
       [4.6, 3.2],
       [5.3, 3.7],
       [5. , 3.3],
       [7. , 3.2],
       [6.4, 3.2],
       [6.9, 3.1],
       [5.5, 2.3],
       [6.5, 2.8],
       [5.7, 2.8],
       [6.3, 3.3],
       [4.9, 2.4],
       [6.6, 2.9],
       [5.2, 2.7],
       [5. , 2. ],
       [5.9, 3. ],
       [6. , 2.2],
       [6.1, 2.9],
       [5.6, 2.9],
       [6.7, 3.1],
       [5.6, 3. ],
       [5.8, 2.7],
       [6.2, 2.2],
       [5.6, 2.5],
       [5.9, 3.2],
       [6.1, 2.8],
       [6.3, 2.5],
       [6.1, 2.8],
       [6.4, 2.9],
       [6.6, 3. ],
       [6.8, 2.8],
       [6.7, 3. ],
       [6. , 2.9],
       [5.7, 2.6],
       [5.5, 2.4],
       [5.5, 2.4],
       [5.8, 2.7],
       [6. , 2.7],
       [5.4, 3. ],
       [6. , 3.4],
       [6.7, 3.1],
       [6.3, 2.3],
       [5.6, 3. ],
       [5.5, 2.5],
       [5.5, 2.6],
       [6.1, 3. ],
       [5.8, 2.6],
       [5. , 2.3],
       [5.6, 2.7],
       [5.7, 3. ],
       [5.7, 2.9],
       [6.2, 2.9],
       [5.1, 2.5],
       [5.7, 2.8],
       [6.3, 3.3],
       [5.8, 2.7],
       [7.1, 3. ],
       [6.3, 2.9],
       [6.5, 3. ],
       [7.6, 3. ],
       [4.9, 2.5],
       [7.3, 2.9],
       [6.7, 2.5],
       [7.2, 3.6],
       [6.5, 3.2],
       [6.4, 2.7],
       [6.8, 3. ],
       [5.7, 2.5],
       [5.8, 2.8],
       [6.4, 3.2],
       [6.5, 3. ],
       [7.7, 3.8],
       [7.7, 2.6],
       [6. , 2.2],
       [6.9, 3.2],
       [5.6, 2.8],
       [7.7, 2.8],
       [6.3, 2.7],
       [6.7, 3.3],
       [7.2, 3.2],
       [6.2, 2.8],
       [6.1, 3. ],
       [6.4, 2.8],
       [7.2, 3. ],
       [7.4, 2.8],
       [7.9, 3.8],
       [6.4, 2.8],
       [6.3, 2.8],
       [6.1, 2.6],
       [7.7, 3. ],
       [6.3, 3.4],
       [6.4, 3.1],
       [6. , 3. ],
       [6.9, 3.1],
       [6.7, 3.1],
       [6.9, 3.1],
       [5.8, 2.7],
       [6.8, 3.2],
       [6.7, 3.3],
       [6.7, 3. ],
       [6.3, 2.5],
       [6.5, 3. ],
       [6.2, 3.4],
       [5.9, 3. ]])

In [19]:

gnb = Pipeline([
    ('sc', StandardScaler()),  # 把数据进行高斯标准化，以0为均值，1为方差
    ('clf', GaussianNB())])  # 假定数据为高斯分布

In [20]:

y.ravel()  # 转化为行向量

Out[20]:

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 [21]:

gnb.fit(x, y.ravel())

Out[21]:

Pipeline(memory=None,
     steps=[('sc', StandardScaler(copy=True, with_mean=True, with_std=True)), ('clf', GaussianNB(priors=None, var_smoothing=1e-09))])

In [23]:

y_hat = gnb.predict(x)
y_hat

Out[23]:

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., 1., 0., 0., 0., 0., 0., 0., 0., 0., 2.,
       2., 2., 1., 2., 1., 2., 1., 2., 1., 1., 1., 1., 1., 1., 2., 1., 1.,
       1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 1., 1., 1., 1., 1., 1., 1.,
       2., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1.,
       2., 1., 2., 2., 1., 2., 2., 2., 2., 1., 2., 1., 1., 2., 2., 2., 2.,
       1., 2., 1., 2., 1., 2., 2., 1., 1., 1., 2., 2., 2., 1., 1., 1., 2.,
       2., 2., 1., 2., 2., 2., 1., 2., 2., 2., 1., 2., 2., 1.])

In [24]:

y = y.reshape(-1)  # 相当于y.ravel()
y

Out[24]:

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 [25]:

result = y_hat == y
result

Out[25]:

array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True, False,  True,  True,  True,
        True,  True,  True,  True,  True, False, False, False,  True,
       False,  True, False,  True, False,  True,  True,  True,  True,
        True,  True, False,  True,  True,  True,  True,  True,  True,
        True,  True, False, False, False, False,  True,  True,  True,
        True,  True,  True,  True, False, False,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False,  True, False,  True,  True, False,  True,
        True,  True,  True, False,  True, False, False,  True,  True,
        True,  True, False,  True, False,  True, False,  True,  True,
       False, False, False,  True,  True,  True, False, False, False,
        True,  True,  True, False,  True,  True,  True, False,  True,
        True,  True, False,  True,  True, False])

In [27]:

acc = np.mean(result)  # 相当于把true当成1，false为0，求平均值，即为准确率
acc

Out[27]:

0.78

 

以下为版本2

In [29]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures
from sklearn.naive_bayes import GaussianNB, MultinomialNB  #高斯贝叶斯和多项式朴素贝叶斯
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False

# 花萼长度、花萼宽度，花瓣长度，花瓣宽度
iris_feature_E = 'sepal length', 'sepal width', 'petal length', 'petal width'
iris_feature_C = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
iris_class = 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'
features = [2, 3]

# 读取数据
path = 'D:\\mlInAction\\8.iris.data'  # 数据文件路径
data = pd.read_csv(path, header=None)
data

Out[29]:

 

	0	1	2	3	4
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa
5	5.4	3.9	1.7	0.4	Iris-setosa
6	4.6	3.4	1.4	0.3	Iris-setosa
7	5.0	3.4	1.5	0.2	Iris-setosa
8	4.4	2.9	1.4	0.2	Iris-setosa
9	4.9	3.1	1.5	0.1	Iris-setosa
10	5.4	3.7	1.5	0.2	Iris-setosa
11	4.8	3.4	1.6	0.2	Iris-setosa
12	4.8	3.0	1.4	0.1	Iris-setosa
13	4.3	3.0	1.1	0.1	Iris-setosa
14	5.8	4.0	1.2	0.2	Iris-setosa
15	5.7	4.4	1.5	0.4	Iris-setosa
16	5.4	3.9	1.3	0.4	Iris-setosa
17	5.1	3.5	1.4	0.3	Iris-setosa
18	5.7	3.8	1.7	0.3	Iris-setosa
19	5.1	3.8	1.5	0.3	Iris-setosa
20	5.4	3.4	1.7	0.2	Iris-setosa
21	5.1	3.7	1.5	0.4	Iris-setosa
22	4.6	3.6	1.0	0.2	Iris-setosa
23	5.1	3.3	1.7	0.5	Iris-setosa
24	4.8	3.4	1.9	0.2	Iris-setosa
25	5.0	3.0	1.6	0.2	Iris-setosa
26	5.0	3.4	1.6	0.4	Iris-setosa
27	5.2	3.5	1.5	0.2	Iris-setosa
28	5.2	3.4	1.4	0.2	Iris-setosa
29	4.7	3.2	1.6	0.2	Iris-setosa
...	...	...	...	...	...
120	6.9	3.2	5.7	2.3	Iris-virginica
121	5.6	2.8	4.9	2.0	Iris-virginica
122	7.7	2.8	6.7	2.0	Iris-virginica
123	6.3	2.7	4.9	1.8	Iris-virginica
124	6.7	3.3	5.7	2.1	Iris-virginica
125	7.2	3.2	6.0	1.8	Iris-virginica
126	6.2	2.8	4.8	1.8	Iris-virginica
127	6.1	3.0	4.9	1.8	Iris-virginica
128	6.4	2.8	5.6	2.1	Iris-virginica
129	7.2	3.0	5.8	1.6	Iris-virginica
130	7.4	2.8	6.1	1.9	Iris-virginica
131	7.9	3.8	6.4	2.0	Iris-virginica
132	6.4	2.8	5.6	2.2	Iris-virginica
133	6.3	2.8	5.1	1.5	Iris-virginica
134	6.1	2.6	5.6	1.4	Iris-virginica
135	7.7	3.0	6.1	2.3	Iris-virginica
136	6.3	3.4	5.6	2.4	Iris-virginica
137	6.4	3.1	5.5	1.8	Iris-virginica
138	6.0	3.0	4.8	1.8	Iris-virginica
139	6.9	3.1	5.4	2.1	Iris-virginica
140	6.7	3.1	5.6	2.4	Iris-virginica
141	6.9	3.1	5.1	2.3	Iris-virginica
142	5.8	2.7	5.1	1.9	Iris-virginica
143	6.8	3.2	5.9	2.3	Iris-virginica
144	6.7	3.3	5.7	2.5	Iris-virginica
145	6.7	3.0	5.2	2.3	Iris-virginica
146	6.3	2.5	5.0	1.9	Iris-virginica
147	6.5	3.0	5.2	2.0	Iris-virginica
148	6.2	3.4	5.4	2.3	Iris-virginica
149	5.9	3.0	5.1	1.8	Iris-virginica

150 rows × 5 columns

In [35]:

x = data[list(range(4))]  # 此处为pd，不能用切片
x

Out[35]:

 

	0	1	2	3
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
5	5.4	3.9	1.7	0.4
6	4.6	3.4	1.4	0.3
7	5.0	3.4	1.5	0.2
8	4.4	2.9	1.4	0.2
9	4.9	3.1	1.5	0.1
10	5.4	3.7	1.5	0.2
11	4.8	3.4	1.6	0.2
12	4.8	3.0	1.4	0.1
13	4.3	3.0	1.1	0.1
14	5.8	4.0	1.2	0.2
15	5.7	4.4	1.5	0.4
16	5.4	3.9	1.3	0.4
17	5.1	3.5	1.4	0.3
18	5.7	3.8	1.7	0.3
19	5.1	3.8	1.5	0.3
20	5.4	3.4	1.7	0.2
21	5.1	3.7	1.5	0.4
22	4.6	3.6	1.0	0.2
23	5.1	3.3	1.7	0.5
24	4.8	3.4	1.9	0.2
25	5.0	3.0	1.6	0.2
26	5.0	3.4	1.6	0.4
27	5.2	3.5	1.5	0.2
28	5.2	3.4	1.4	0.2
29	4.7	3.2	1.6	0.2
...	...	...	...	...
120	6.9	3.2	5.7	2.3
121	5.6	2.8	4.9	2.0
122	7.7	2.8	6.7	2.0
123	6.3	2.7	4.9	1.8
124	6.7	3.3	5.7	2.1
125	7.2	3.2	6.0	1.8
126	6.2	2.8	4.8	1.8
127	6.1	3.0	4.9	1.8
128	6.4	2.8	5.6	2.1
129	7.2	3.0	5.8	1.6
130	7.4	2.8	6.1	1.9
131	7.9	3.8	6.4	2.0
132	6.4	2.8	5.6	2.2
133	6.3	2.8	5.1	1.5
134	6.1	2.6	5.6	1.4
135	7.7	3.0	6.1	2.3
136	6.3	3.4	5.6	2.4
137	6.4	3.1	5.5	1.8
138	6.0	3.0	4.8	1.8
139	6.9	3.1	5.4	2.1
140	6.7	3.1	5.6	2.4
141	6.9	3.1	5.1	2.3
142	5.8	2.7	5.1	1.9
143	6.8	3.2	5.9	2.3
144	6.7	3.3	5.7	2.5
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

150 rows × 4 columns

In [36]:

x = x[features]
x

Out[36]:

 

	2	3
0	1.4	0.2
1	1.4	0.2
2	1.3	0.2
3	1.5	0.2
4	1.4	0.2
5	1.7	0.4
6	1.4	0.3
7	1.5	0.2
8	1.4	0.2
9	1.5	0.1
10	1.5	0.2
11	1.6	0.2
12	1.4	0.1
13	1.1	0.1
14	1.2	0.2
15	1.5	0.4
16	1.3	0.4
17	1.4	0.3
18	1.7	0.3
19	1.5	0.3
20	1.7	0.2
21	1.5	0.4
22	1.0	0.2
23	1.7	0.5
24	1.9	0.2
25	1.6	0.2
26	1.6	0.4
27	1.5	0.2
28	1.4	0.2
29	1.6	0.2
...	...	...
120	5.7	2.3
121	4.9	2.0
122	6.7	2.0
123	4.9	1.8
124	5.7	2.1
125	6.0	1.8
126	4.8	1.8
127	4.9	1.8
128	5.6	2.1
129	5.8	1.6
130	6.1	1.9
131	6.4	2.0
132	5.6	2.2
133	5.1	1.5
134	5.6	1.4
135	6.1	2.3
136	5.6	2.4
137	5.5	1.8
138	4.8	1.8
139	5.4	2.1
140	5.6	2.4
141	5.1	2.3
142	5.1	1.9
143	5.9	2.3
144	5.7	2.5
145	5.2	2.3
146	5.0	1.9
147	5.2	2.0
148	5.4	2.3
149	5.1	1.8

150 rows × 2 columns

In [37]:

y = pd.Categorical(data[4]).codes  # 直接将数据特征转换为0，1,2
y

Out[37]:

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], dtype=int8)

In [38]:

print("总样本数目：%d；特征属性数目：%d" % x.shape)

 

总样本数目：150；特征属性数目：2

In [40]:

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=14)
print("训练数据集样本数目：%d, 测试数据集样本数目：%d" % (x_train.shape[0], x_test.shape[0]))

 

训练数据集样本数目：120, 测试数据集样本数目：30

In [41]:

clf = Pipeline([
    ('sc', StandardScaler()),  # 标准化，把它转化成了高斯分布
    ('poly', PolynomialFeatures(degree=1)),
    ('clf', GaussianNB())])  # MultinomialNB多项式贝叶斯算法中要求特征属性的取值不能为负数
# 训练模型
clf.fit(x_train, y_train)

Out[41]:

Pipeline(memory=None,
     steps=[('sc', StandardScaler(copy=True, with_mean=True, with_std=True)), ('poly', PolynomialFeatures(degree=1, include_bias=True, interaction_only=False)), ('clf', GaussianNB(priors=None, var_smoothing=1e-09))])

In [42]:

y_train_hat = clf.predict(x_train)
print('训练集准确度: %.2f%%' % (100 * accuracy_score(y_train, y_train_hat)))
y_test_hat = clf.predict(x_test)
print('测试集准确度：%.2f%%' % (100 * accuracy_score(y_test, y_test_hat)))

 

训练集准确度: 95.83%
测试集准确度：96.67%