%matplotlib inline
from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split 

wine = load_wine()
wine

{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,
         1.065e+03],
        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,
         1.050e+03],
        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,
         1.185e+03],
        ...,
        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,
         8.350e+02],
        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,
         8.400e+02],
        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,
         5.600e+02]]),
 'target': 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, 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, 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]),
 'frame': None,
 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),
 'DESCR': '.. _wine_dataset:\n\nWine recognition dataset\n------------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 178 (50 in each of three classes)\n    :Number of Attributes: 13 numeric, predictive attributes and the class\n    :Attribute Information:\n \t\t- Alcohol\n \t\t- Malic acid\n \t\t- Ash\n\t\t- Alcalinity of ash  \n \t\t- Magnesium\n\t\t- Total phenols\n \t\t- Flavanoids\n \t\t- Nonflavanoid phenols\n \t\t- Proanthocyanins\n\t\t- Color intensity\n \t\t- Hue\n \t\t- OD280/OD315 of diluted wines\n \t\t- Proline\n\n    - class:\n            - class_0\n            - class_1\n            - class_2\n\t\t\n    :Summary Statistics:\n    \n    ============================= ==== ===== ======= =====\n                                   Min   Max   Mean     SD\n    ============================= ==== ===== ======= =====\n    Alcohol:                      11.0  14.8    13.0   0.8\n    Malic Acid:                   0.74  5.80    2.34  1.12\n    Ash:                          1.36  3.23    2.36  0.27\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n    Magnesium:                    70.0 162.0    99.7  14.3\n    Total Phenols:                0.98  3.88    2.29  0.63\n    Flavanoids:                   0.34  5.08    2.03  1.00\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n    Proanthocyanins:              0.41  3.58    1.59  0.57\n    Colour Intensity:              1.3  13.0     5.1   2.3\n    Hue:                          0.48  1.71    0.96  0.23\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n    Proline:                       278  1680     746   315\n    ============================= ==== ===== ======= =====\n\n    :Missing Attribute Values: None\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\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 Wine recognition datasets.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n\nThe data is the results of a chemical analysis of wines grown in the same\nregion in Italy by three different cultivators. There are thirteen different\nmeasurements taken for different constituents found in the three types of\nwine.\n\nOriginal Owners: \n\nForina, M. et al, PARVUS - \nAn Extendible Package for Data Exploration, Classification and Correlation. \nInstitute of Pharmaceutical and Food Analysis and Technologies,\nVia Brigata Salerno, 16147 Genoa, Italy.\n\nCitation:\n\nLichman, M. (2013). UCI Machine Learning Repository\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\nSchool of Information and Computer Science. \n\n.. topic:: References\n\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \n  Comparison of Classifiers in High Dimensional Settings, \n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n  Mathematics and Statistics, James Cook University of North Queensland. \n  (Also submitted to Technometrics). \n\n  The data was used with many others for comparing various \n  classifiers. The classes are separable, though only RDA \n  has achieved 100% correct classification. \n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n  (All results using the leave-one-out technique) \n\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \n  "THE CLASSIFICATION PERFORMANCE OF RDA" \n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n  Mathematics and Statistics, James Cook University of North Queensland. \n  (Also submitted to Journal of Chemometrics).\n',
 'feature_names': ['alcohol',
  'malic_acid',
  'ash',
  'alcalinity_of_ash',
  'magnesium',
  'total_phenols',
  'flavanoids',
  'nonflavanoid_phenols',
  'proanthocyanins',
  'color_intensity',
  'hue',
  'od280/od315_of_diluted_wines',
  'proline']}

# 特征
wine.data

array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,
        1.065e+03],
       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,
        1.050e+03],
       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,
        1.185e+03],
       ...,
       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,
        8.350e+02],
       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,
        8.400e+02],
       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,
        5.600e+02]])

# 标签(三分类)
wine.target

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, 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, 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])

# 13 个特征
wine.data.shape

(178, 13)

# 字典转换成表
import pandas as pd
pd.concat([pd.DataFrame(wine.data), pd.DataFrame(wine.target)], axis=1)

	0	1	2	3	4	5	6	7	8	9	10	11	12	0
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
173	13.71	5.65	2.45	20.5	95.0	1.68	0.61	0.52	1.06	7.70	0.64	1.74	740.0	2
174	13.40	3.91	2.48	23.0	102.0	1.80	0.75	0.43	1.41	7.30	0.70	1.56	750.0	2
175	13.27	4.28	2.26	20.0	120.0	1.59	0.69	0.43	1.35	10.20	0.59	1.56	835.0	2
176	13.17	2.59	2.37	20.0	120.0	1.65	0.68	0.53	1.46	9.30	0.60	1.62	840.0	2
177	14.13	4.10	2.74	24.5	96.0	2.05	0.76	0.56	1.35	9.20	0.61	1.60	560.0	2

178 rows × 14 columns

# 特征的名字
# 建树时候如果不提供特征名字，则会用特征索引代替
wine.feature_names

['alcohol',
 'malic_acid',
 'ash',
 'alcalinity_of_ash',
 'magnesium',
 'total_phenols',
 'flavanoids',
 'nonflavanoid_phenols',
 'proanthocyanins',
 'color_intensity',
 'hue',
 'od280/od315_of_diluted_wines',
 'proline']

# 标签名字
wine.target_names

array(['class_0', 'class_1', 'class_2'], dtype='<U7')

# 将数据分成训练集和测试集
XTrain, XTest, YTrain, YTest = train_test_split(wine.data, wine.target, test_size=0.3)

print(XTrain.shape)
print(YTrain.shape)
YTrain.squeeze()
print(YTrain.shape)

(124, 13)
(124,)
(124,)

XTest.shape

(54, 13)

# 建模三部曲1
clf = tree.DecisionTreeClassifier(criterion="entropy")
clf = clf.fit(XTrain, YTrain)
score = clf.score(XTest, YTest)

print(score)

0.8888888888888888

# 将树画出来
import graphviz
feature_names = ['酒精', '苹果酸', '灰', '灰的碱性', '镁', '总酚', '类黄酮', '非黄烷类酚类', '花青素', '颜色强度', '色调', 'od280/od315稀释葡萄酒', '脯氨酸']
dot_data = tree.export_graphviz(clf
                                ,feature_names=feature_names
                                ,class_names=["第一类", "第二类", "第三类"]
                                ,filled=True
                                ,rounded=True
                                )
graph = graphviz.Source(dot_data)
graph

# 特征的重要性
clf.feature_importances_

array([0.02508043, 0.        , 0.01026707, 0.        , 0.        ,
       0.        , 0.46922583, 0.        , 0.        , 0.15469768,
       0.        , 0.        , 0.34072898])

# 特征的重要性
[*zip(clf.feature_importances_)]

[(0.02508043479477153,),
 (0.0,),
 (0.010267070595943267,),
 (0.0,),
 (0.0,),
 (0.0,),
 (0.46922583159886455,),
 (0.0,),
 (0.0,),
 (0.15469768267666437,),
 (0.0,),
 (0.0,),
 (0.3407289803337563,)]

设置随机种子 random_state，使得结果可复现

# 设置随机数种子random_state之后，每次的结果都一样了(会一直长出同一棵树)
clf1 = tree.DecisionTreeClassifier(criterion="entropy", random_state=20)
clf1 = clf1.fit(XTrain, YTrain)
score = clf1.score(XTest, YTest)
print(score)

0.8888888888888888

控制随机性的参数 splitter

• best 在分支时优先选择更重要的特征
• random 更随机，降低过拟合

# splitter 也是控制随机性的，best 在分支时优先选择更重要的特征；random 更随机，降低过拟合
clf2 = tree.DecisionTreeClassifier(criterion="entropy"
                                  ,random_state=20
                                  ,splitter="random"
                                 )
clf2 = clf2.fit(XTrain, YTrain)
score = clf2.score(XTest, YTest)
score

0.9444444444444444

# 加了随机之后，将变宽变深了
import graphviz
feature_names = ['酒精', '苹果酸', '灰', '灰的碱性', '镁', '总酚', '类黄酮', '非黄烷类酚类', '花青素', '颜色强度', '色调', 'od280/od315稀释葡萄酒', '脯氨酸']
dot_data = tree.export_graphviz(clf2
                                ,feature_names=feature_names
                                ,class_names=["第一类", "第二类", "第三类"]
                                ,filled=True
                                ,rounded=True
                                )
graph = graphviz.Source(dot_data)
graph

# 对训练集的拟合程度
score_train = clf2.score(XTrain, YTrain)
print(score_train)

1.0

剪枝参数

• max_depth
• min_samples_leat
• min_samples_split

# 剪枝参数
clf3 = tree.DecisionTreeClassifier(criterion="entropy"
                                  ,random_state=20
                                  ,splitter="random"
                                  ,max_depth=3
                                  #,min_samples_leaf=10
                                  #,min_samples_split=10
                                 )
clf3 = clf3.fit(XTrain, YTrain)
score = clf3.score(XTest, YTest)
score

1.0

import graphviz
feature_names = ['酒精', '苹果酸', '灰', '灰的碱性', '镁', '总酚', '类黄酮', '非黄烷类酚类', '花青素', '颜色强度', '色调', 'od280/od315稀释葡萄酒', '脯氨酸']
dot_data = tree.export_graphviz(clf3
                                ,feature_names=feature_names
                                ,class_names=["第一类", "第二类", "第三类"]
                                ,filled=True
                                ,rounded=True
                                )
graph = graphviz.Source(dot_data)
graph

调参 -- 确认最优剪枝参数

# 确认最优剪枝参数
import matplotlib.pyplot as plt

test = []
for i in range(20):
    clf = tree.DecisionTreeClassifier(max_depth=i+1
                                      ,criterion="entropy"
                                      ,random_state=30
                                      ,splitter="random"
                                     )
    clf = clf.fit(XTrain, YTrain)
    score = clf.score(XTest, YTest)
    test.append(score)


plt.plot(range(1, 21), test, color="red", label="max_depth")
plt.legend()
plt.show()