使用Graphviz决策树可视化展示，将DataFrame数据保存到本地
决策树可视化展示
准备数据集
本次使用sklearn的内置数据集
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets.california_housing import fetch_california_housing #导入内置数据集
house_price = fetch_california_housing()
print(house_price.DESCR) #打印关于数据集的描述
print(house_price)
.. _california_housing_dataset:
California Housing dataset
--------------------------
**Data Set Characteristics:**
:Number of Instances: 20640
:Number of Attributes: 8 numeric, predictive attributes and the target
:Attribute Information:
- MedInc median income in block
- HouseAge median house age in block
- AveRooms average number of rooms
- AveBedrms average number of bedrooms
- Population block population
- AveOccup average house occupancy
- Latitude house block latitude
- Longitude house block longitude
:Missing Attribute Values: None
This dataset was obtained from the StatLib repository.
http://lib.stat.cmu.edu/datasets/
The target variable is the median house value for California districts.
This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).
It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.
.. topic:: References
- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297
{'data': array([[ 8.3252 , 41. , 6.98412698, ..., 2.55555556,
37.88 , -122.23 ],
[ 8.3014 , 21. , 6.23813708, ..., 2.10984183,
37.86 , -122.22 ],
[ 7.2574 , 52. , 8.28813559, ..., 2.80225989,
37.85 , -122.24 ],
...,
[ 1.7 , 17. , 5.20554273, ..., 2.3256351 ,
39.43 , -121.22 ],
[ 1.8672 , 18. , 5.32951289, ..., 2.12320917,
39.43 , -121.32 ],
[ 2.3886 , 16. , 5.25471698, ..., 2.61698113,
39.37 , -121.24 ]]), 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]), 'feature_names': ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], 'DESCR': '.. _california_housing_dataset:\n\nCalifornia Housing dataset\n--------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 20640\n\n :Number of Attributes: 8 numeric, predictive attributes and the target\n\n :Attribute Information:\n - MedInc median income in block\n - HouseAge median house age in block\n - AveRooms average number of rooms\n - AveBedrms average number of bedrooms\n - Population block population\n - AveOccup average house occupancy\n - Latitude house block latitude\n - Longitude house block longitude\n\n :Missing Attribute Values: None\n\nThis dataset was obtained from the StatLib repository.\nhttp://lib.stat.cmu.edu/datasets/\n\nThe target variable is the median house value for California districts.\n\nThis dataset was derived from the 1990 U.S. census, using one row per census\nblock group. A block group is the smallest geographical unit for which the U.S.\nCensus Bureau publishes sample data (a block group typically has a population\nof 600 to 3,000 people).\n\nIt can be downloaded/loaded using the\n:func:`sklearn.datasets.fetch_california_housing` function.\n\n.. topic:: References\n\n - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n Statistics and Probability Letters, 33 (1997) 291-297\n'}
如何将fetch_california_housing数据集转化为DataFrame类型生成一张新表
查看数据类型
#查看数据类型
type(house_price)
sklearn.utils.Bunch
查看data
house_price.data
array([[ 8.3252 , 41. , 6.98412698, ..., 2.55555556,
37.88 , -122.23 ],
[ 8.3014 , 21. , 6.23813708, ..., 2.10984183,
37.86 , -122.22 ],
[ 7.2574 , 52. , 8.28813559, ..., 2.80225989,
37.85 , -122.24 ],
...,
[ 1.7 , 17. , 5.20554273, ..., 2.3256351 ,
39.43 , -121.22 ],
[ 1.8672 , 18. , 5.32951289, ..., 2.12320917,
39.43 , -121.32 ],
[ 2.3886 , 16. , 5.25471698, ..., 2.61698113,
39.37 , -121.24 ]])
查看target
house_price.target.reshape(-1,1)
array([[4.526],
[3.585],
[3.521],
...,
[0.923],
[0.847],
[0.894]])
查看feature_names
house_price.feature_names
['MedInc',
'HouseAge',
'AveRooms',
'AveBedrms',
'Population',
'AveOccup',
'Latitude',
'Longitude']
将特征数据转换为DataFrame类型
data = pd.DataFrame(house_price.data)
data.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | Target | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.3252 | 41.0 | 6.984127 | 1.023810 | 322.0 | 2.555556 | 37.88 | -122.23 | 4.526 |
| 1 | 8.3014 | 21.0 | 6.238137 | 0.971880 | 2401.0 | 2.109842 | 37.86 | -122.22 | 3.585 |
| 2 | 7.2574 | 52.0 | 8.288136 | 1.073446 | 496.0 | 2.802260 | 37.85 | -122.24 | 3.521 |
| 3 | 5.6431 | 52.0 | 5.817352 | 1.073059 | 558.0 | 2.547945 | 37.85 | -122.25 | 3.413 |
| 4 | 3.8462 | 52.0 | 6.281853 | 1.081081 | 565.0 | 2.181467 | 37.85 | -122.25 | 3.422 |
将目标值转化为DataFrame类型，并插入data中
data.insert(8,'Target',pd.DataFrame(house_price.target))
data.head()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | Target | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.3252 | 41.0 | 6.984127 | 1.023810 | 322.0 | 2.555556 | 37.88 | -122.23 | 4.526 |
| 1 | 8.3014 | 21.0 | 6.238137 | 0.971880 | 2401.0 | 2.109842 | 37.86 | -122.22 | 3.585 |
| 2 | 7.2574 | 52.0 | 8.288136 | 1.073446 | 496.0 | 2.802260 | 37.85 | -122.24 | 3.521 |
| 3 | 5.6431 | 52.0 | 5.817352 | 1.073059 | 558.0 | 2.547945 | 37.85 | -122.25 | 3.413 |
| 4 | 3.8462 | 52.0 | 6.281853 | 1.081081 | 565.0 | 2.181467 | 37.85 | -122.25 | 3.422 |
查看列的索引值
data.columns
Index([0, 1, 2, 3, 4, 5, 6, 7, 'Target'], dtype='object')
修改列名生成新表
columns = dict(zip([0, 1, 2, 3, 4, 5, 6, 7],house_price.feature_names))
data.rename(columns=columns).head()
| MedInc | HouseAge | AveRooms | AveBedrms | Population | AveOccup | Latitude | Longitude | Target | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.3252 | 41.0 | 6.984127 | 1.023810 | 322.0 | 2.555556 | 37.88 | -122.23 | 4.526 |
| 1 | 8.3014 | 21.0 | 6.238137 | 0.971880 | 2401.0 | 2.109842 | 37.86 | -122.22 | 3.585 |
| 2 | 7.2574 | 52.0 | 8.288136 | 1.073446 | 496.0 | 2.802260 | 37.85 | -122.24 | 3.521 |
| 3 | 5.6431 | 52.0 | 5.817352 | 1.073059 | 558.0 | 2.547945 | 37.85 | -122.25 | 3.413 |
| 4 | 3.8462 | 52.0 | 6.281853 | 1.081081 | 565.0 | 2.181467 | 37.85 | -122.25 | 3.422 |
将数据保存到本地
table = data.rename(columns=columns)
table.to_csv('table.csv',index=False)#保存文件
进行可视化展示
建立决策树
from sklearn import tree
dtr = tree.DecisionTreeRegressor(max_depth = 2) #指定树的深度
dtr.fit(house_price.data[:, [6, 7]], house_price.target) #这里只选择经度和纬度两个特征进行拟合
DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,
max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best')
要可视化显示 首先需要安装 graphviz http://www.Graphviz.org
#要可视化显示 首先需要安装 graphviz http://www.Graphviz.org
dot_data = \
tree.export_graphviz(
dtr,
out_file = None,
feature_names = house_price.feature_names[6:8],
filled = True, #是否填充
impurity = False, #控制是否显示均方误差
rounded = True #控制显示格式、字体
)
绘图并显示
import pydotplus
graph = pydotplus.graph_from_dot_data(dot_data)
nodes = graph.get_nodes()
for i in nodes:
i.set_fillcolor("#FFE1FF")
from IPython.display import Image
Image(graph.create_png())
但是，有些时候会报错InvocationException: GraphViz’s executables not found
这是因为python并没有识别出其环境变量，重新写入一次，然后重新执行绘图显示操作
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/' #注意修改自己的路径 将路径写入环境变量