问题描述
我正在尝试将 Python 的 sklearn.tree export_text 输出转换为 SAS 条件。
此处（在页面的最后）已在 Python 中给出了针对此问题的解决方案：How to extract the decision rules from scikit-learn decision-tree?
我尝试修改生成 SAS 代码的代码，但在处理嵌套 DO 时遇到问题；结束;
def get_sas_from_text(tree,tree_id,features,text,spacing=2):
# tree is a decision tree from a RandomForestClassifier for instance
# tree id is a number I use for naming the table I create
# features is a list of features names
# text is the output of the export_text function from sklearn.tree
# spacing is used to handle the file size
skip,dash = ' '*spacing,'-'*(spacing-1)
code = 'data decision_tree_' + str(tree_id) + ';'
code += ' set input_data; '
n_end_last = 0 # Number of 'END;' to add at the end of the data step
splitted_text = text.split('\n')
text_list = []
for i,line in enumerate(splitted_text):
line = line.rstrip().replace('|',' ')
# Handling rows for IF conditions
if '<' in line or '>' in line:
line,val = line.rsplit(maxsplit=1)
line = line.replace(' ' + dash,'if')
line = '{} {:g} THEN DO;'.format(line,float(val))
n_end_last += 1 # need to add an END;
if i > 0 and 'PREDICTED_VALUE' in text_list[i-1]: # If there's a PREDICTED_VALUE in line above,then condition is ELSE DO
line = "ELSE DO; " + line
n_end_last += 1 # add another END
# Handling rows for PREDICTED_VALUE
else:
line = line.replace(' {} class:'.format(dash),'PREDICTED_VALUE =')
line += ';'
line += '\n end;' # Immediately add END after PREDICTED_VALUE = .. ;
n_end_last -= 1
text_list.append(line)
code += skip + line + '\n'
code = code[:-1]
code += 'end; + '\n''* n_end_last # add END;
code += 'run;'
return code
以下是 iris 数据集的示例：
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import _tree
import string
from sklearn.tree import export_text
iris = datasets.load_iris()
data=pd.DataFrame({
'sepal length':iris.data[:,0],'sepal width':iris.data[:,1],'petal length':iris.data[:,2],'petal width':iris.data[:,3],'species':iris.target
})
X=data[['sepal length','sepal width','petal length','petal width']] # Features
y=data['species'] # Labels
# Split dataset into training set and test set
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3) # 70% training and 30% test
clf=RandomForestClassifier(n_estimators=100)
#Train the model using the training sets y_pred=clf.predict(X_test)
clf.fit(X_train,y_train)
# Function to export the tree rules' code : in Python (works) and in SAS (issue with DO; END;
def export_code(tree,feature_names,max_depth=100,spacing=2):
if spacing < 2:
raise ValueError('spacing must be > 1')
# Clean up feature names (for correctness)
nums = string.digits
alnums = string.ascii_letters + nums
clean = lambda s: ''.join(c if c in alnums else '_' for c in s)
features = [clean(x) for x in feature_names]
features = ['_'+x if x[0] in nums else x for x in features if x]
if len(set(features)) != len(feature_names):
raise ValueError('invalid feature names')
# First: export tree to text
res = export_text(tree,feature_names=features,max_depth=max_depth,decimals=6,spacing=spacing-1)
code_sas = get_sas_from_text(tree,res,spacing)
code_py = get_py_from_text(tree,spacing)
return res,code_sas,code_py # to take a look at the different code outputs
# Python function
def get_py_from_text(tree,spacing):
skip,'-'*(spacing-1)
code = 'def decision_tree_'+ str(tree_id) + '({}):\n'.format(','.join(features))
for line in repr(tree).split('\n'):
code += skip + "# " + line + '\n'
for line in text.split('\n'):
line = line.rstrip().replace('|',' ')
if '<' in line or '>' in line:
line,'if')
line = '{} {:g}:'.format(line,float(val))
else:
line = line.replace(' {} class:'.format(dash),'return')
code += skip + line + '\n'
return code
然后我在 SAS 和 Python 中获取生成的代码以用于树的决策规则：
# Rules for first decision tree (there are 100 of them)
exported_text,sas_text,py_text = export_code(clf[0],iris.feature_names)
以下是 Python 中第一棵树的决策规则：
def decision_tree_0(sepal_length__cm_,sepal_width__cm_,petal_length__cm_,petal_width__cm_):
# DecisionTreeClassifier(max_features='auto',random_state=1087864992)
if sepal_length__cm_ <= 5.35:
if sepal_width__cm_ <= 2.7:
return 1.0
if sepal_width__cm_ > 2.7:
return 0.0
if sepal_length__cm_ > 5.35:
if petal_width__cm_ <= 1.75:
if petal_length__cm_ <= 2.5:
return 0.0
if petal_length__cm_ > 2.5:
if sepal_length__cm_ <= 7.1:
if petal_width__cm_ <= 1.45:
if petal_length__cm_ <= 5.15:
return 1.0
if petal_length__cm_ > 5.15:
return 2.0
if petal_width__cm_ > 1.45:
return 1.0
if sepal_length__cm_ > 7.1:
return 2.0
if petal_width__cm_ > 1.75:
return 2.0
我的程序 get_sas_from_text 的问题是“END;”语句在代码中并没有很好地放置，这不符合作为输入给出的决策规则（与相应的 Python 函数相比：
data decision_tree_0;
set input_data;
if sepal_length__cm_ <= 5.35 THEN
DO;
if sepal_width__cm_ <= 2.7 THEN
DO;
PREDICTED_VALUE = 1.0;
end;
ELSE
DO;
if sepal_width__cm_ > 2.7 THEN
DO;
PREDICTED_VALUE = 0.0;
end;
ELSE
DO;
if sepal_length__cm_ > 5.35 THEN
DO;
if petal_width__cm_ <= 1.75 THEN
DO;
if petal_length__cm_ <= 2.5 THEN
DO;
PREDICTED_VALUE = 0.0;
end;
ELSE
DO;
if petal_length__cm_ > 2.5 THEN
DO;
if sepal_length__cm_ <= 7.1 THEN
DO;
if petal_width__cm_ <= 1.45 THEN
DO;
if petal_length__cm_ <= 5.15 THEN
DO;
PREDICTED_VALUE = 1.0;
end;
ELSE
DO;
if petal_length__cm_ > 5.15 THEN
DO;
PREDICTED_VALUE = 2.0;
end;
ELSE
DO;
if petal_width__cm_ > 1.45 THEN
DO;
PREDICTED_VALUE = 1.0;
end;
ELSE
DO;
if sepal_length__cm_ > 7.1 THEN
DO;
PREDICTED_VALUE = 2.0;
end;
ELSE
DO;
if petal_width__cm_ > 1.75 THEN
DO;
PREDICTED_VALUE = 2.0;
end;
;
end;
end;
end;
end;
end;
end;
end;
end;
end;
end;
end;
end;
end;
run;
您知道如何定位“END;”吗？生成的数据步骤中的语句？ 非常感谢您。
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
小编邮箱:dio#foxmail.com (将#修改为@）