我正在使用Spark MLlib 1.4.1来创建decisionTree模型。现在我想从决策树中提取规则。
如何提取规则？
答案 0 :(得分：3)
您可以通过调用model.toDebugString（）将完整模型作为字符串获取，或者通过调用model.save（sc，filePath）将其保存为JSON。
The documentation is here，其中包含一个示例数据示例，您可以在命令行中检查输出格式。在这里，我格式化了您可以直接过去并运行的脚本。
from numpy import array
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.tree import DecisionTree
data = [
LabeledPoint(0.0, [0.0]),
LabeledPoint(1.0, [1.0]),
LabeledPoint(1.0, [2.0]),
LabeledPoint(1.0, [3.0])
]
model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {})
print(model)
print(model.toDebugString())
输出是：
DecisionTreeModel classifier of depth 1 with 3 nodes
DecisionTreeModel classifier of depth 1 with 3 nodes
If (feature 0 <= 0.0)
Predict: 0.0
Else (feature 0 > 0.0)
Predict: 1.0
在实际应用中，模型可能非常大并且包含许多行。所以直接使用dtModel.toDebugString（）会导致IPython笔记本停止。所以我建议把它作为文本文件。
以下是如何将模型dtModel导出到文本文件的示例代码。假设我们得到像这样的dtModel：
dtModel = DecisionTree.trainClassifier(parsedTrainData, numClasses=7, categoricalFeaturesInfo={},impurity='gini', maxDepth=20, maxBins=24)
modelFile = ~/decisionTreeModel.txt"
f = open(modelFile,"w")
f.write(dtModel.toDebugString())
f.close()
以下是我的dtMmodel上面脚本的示例输出：
DecisionTreeModel classifier of depth 20 with 20031 nodes
If (feature 0 <= -35.0)
If (feature 24 <= 176.0)
If (feature 0 <= -200.0)
If (feature 29 <= 109.0)
If (feature 6 <= -156.0)
If (feature 9 <= 0.0)
If (feature 20 <= -116.0)
If (feature 16 <= 203.0)
If (feature 11 <= 163.0)
If (feature 5 <= 384.0)
If (feature 15 <= 325.0)
If (feature 13 <= -248.0)
If (feature 20 <= -146.0)
Predict: 0.0
Else (feature 20 > -146.0)
If (feature 19 <= -58.0)
Predict: 6.0
Else (feature 19 > -58.0)
Predict: 0.0
Else (feature 13 > -248.0)
If (feature 9 <= -26.0)
Predict: 0.0
Else (feature 9 > -26.0)
If (feature 10 <= 218.0)
...
...
...
...
答案 1 :(得分：0)
import networkx as nx
加载模型数据，如果您以前在该位置使用过model.save（location），则该数据会出现在hadoop中
modeldf = spark.read.parquet(location+"/data/*")
noderows = modeldf.select("id","prediction","leftChild","rightChild","split").collect()
创建虚拟特征数组
features = ["feature"+str(i) for i in range(0,700)]
初始化图形
G = nx.DiGraph()
for rw in noderows:
if rw['leftChild'] < 0 and rw['rightChild'] < 0:
G.add_node(rw['id'], cat="Prediction", predval=rw['prediction'])
else:
G.add_node(rw['id'], cat="splitter", featureIndex=rw['split']['featureIndex'], thresh=rw['split']['leftCategoriesOrThreshold'], leftChild=rw['leftChild'], rightChild=rw['rightChild'], numCat=rw['split']['numCategories'])
for rw in modeldf.where("leftChild > 0 and rightChild > 0").collect():
tempnode = G.nodes(data="True")[rw['id']][1]
#print(tempnode)
G.add_edge(rw['id'], rw['leftChild'], reason="{0} less than {1}".format(features[tempnode['featureIndex']],tempnode['thresh']))
G.add_edge(rw['id'], rw['rightChild'], reason="{0} greater than {1}".format(features[tempnode['featureIndex']],tempnode['thresh']))
上面的代码将所有规则转换为图形网络。 要以if和else格式打印所有规则，我们可以找到所有叶节点的路径，并列出边缘原因以提取最终规则
nodes = [x for x in G.nodes() if G.out_degree(x)==0 and G.in_degree(x)==1]
for n in nodes:
p = nx.shortest_path(G,0,n)
print("Rule No:",n)
print(" & ".join([G.get_edge_data(p[i],p[i+1])['reason'] for i in range(0,len(p)-1)]))
输出看起来像这样：
（“规则编号：”，5）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature367小于 [1.0]
（“规则编号：”，8）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature367大于 [1.0]和feature318小于[0.0]且feature385小于[0.0]
（“规则编号：”，9）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature367大于 [1.0]和feature318小于[0.0]且Feature385大于[0.0]
（“规则编号：”，11）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature367大于 [1.0]和feature318大于[0.0]，feature266小于[0.0]
（“规则编号：”，12）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature367大于 [1.0]和feature318大于[0.0]和feature266大于[0.0]
（“规则编号：”，16）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature158小于 [1.0]和feature274小于[0.0]和feature89小于[1.0]
（“规则编号：”，17）
feature457小于[0.0]和feature353小于[0.0]和feature185 小于[1.0]和feature294小于[1.0]和feature158小于 [1.0]和feature274小于[0.0]，feature89大于[1.0]
修改了出现的初始代码here
答案 2 :(得分：0)
我们可以使用model.debugString属性提取规则。完整示例如下：
注意：如果您需要以下代码的详细信息，请检查https://medium.com/@dipaweshpawar/decoding-decision-tree-in-pyspark-bdd98dcd1ddf
from pyspark.sql.functions import to_date,datediff,lit,udf,sum,avg,col,count,lag
from pyspark.sql.types import StringType,LongType,StructType,StructField,DateType,IntegerType,DoubleType
from datetime import datetime
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml import Pipeline
import pandas as pd
from pyspark.sql import DataFrame
from pyspark.sql.functions import udf, lit, avg, max, min
from pyspark.sql.types import StringType, ArrayType, DoubleType
from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
import operator
import ast
operators = {
">=": operator.ge,
"<=": operator.le,
">": operator.gt,
"<": operator.lt,
"==": operator.eq,
'and': operator.and_,
'or': operator.or_
}
data = pd.DataFrame({
'ball': [0, 1, 1, 3, 1, 0, 1, 3],
'keep': [4, 5, 6, 7, 7, 4, 6, 7],
'hall': [8, 9, 10, 11, 2, 6, 10, 11],
'fall': [12, 13, 14, 15, 15, 12, 14, 15],
'mall': [16, 17, 18, 10, 10, 16, 18, 10],
'label': [21, 31, 41, 51, 51, 51, 21, 31]
})
df = spark.createDataFrame(data)
f_list = ['ball','keep','mall','hall','fall']
assemble_numerical_features = VectorAssembler(inputCols=f_list, outputCol='features',
handleInvalid='skip')
dt = DecisionTreeClassifier(featuresCol='features', labelCol='label')
pipeline = Pipeline(stages=[assemble_numerical_features, dt])
model = pipeline.fit(df)
df = model.transform(df)
dt_m = model.stages[-1]
# Step 1: convert model.debugString output to dictionary of nodes and children
def parse_debug_string_lines(lines):
block = []
while lines:
if lines[0].startswith('If'):
bl = ' '.join(lines.pop(0).split()[1:]).replace('(', '').replace(')', '')
block.append({'name': bl, 'children': parse_debug_string_lines(lines)})
if lines[0].startswith('Else'):
be = ' '.join(lines.pop(0).split()[1:]).replace('(', '').replace(')', '')
block.append({'name': be, 'children': parse_debug_string_lines(lines)})
elif not lines[0].startswith(('If', 'Else')):
block2 = lines.pop(0)
block.append({'name': block2})
else:
break
return block
def debug_str_to_json(debug_string):
data = []
for line in debug_string.splitlines():
if line.strip():
line = line.strip()
data.append(line)
else:
break
if not line: break
json = {'name': 'Root', 'children': parse_debug_string_lines(data[1:])}
return json
# Step 2 : Using metadata stored in features column, build dictionary which maps each feature in features column of df to its index in feature vector
f_type_to_flist_dict = df.schema['features'].metadata["ml_attr"]["attrs"]
f_index_to_name_dict = {}
for f_type, f_list in f_type_to_flist_dict.items():
for f in f_list:
f_index = f['idx']
f_name = f['name']
f_index_to_name_dict[f_index] = f_name
def generate_explanations(dt_as_json, df:DataFrame, f_index_to_name_dict, operators):
dt_as_json_str = str(dt_as_json)
cond_parsing_exception_occured = False
df = df.withColumn('features'+'_list',
udf(lambda x: x.toArray().tolist(), ArrayType(DoubleType()))
(df['features'])
)
# step 3 : parse and check whether current instance follows condition in perticular node
def parse_validate_cond(cond: str, f_vector: list):
cond_parts = cond.split()
condition_f_index = int(cond_parts[1])
condition_op = cond_parts[2]
condition_value = float(cond_parts[3])
f_value = f_vector[condition_f_index]
f_name = f_index_to_name_dict[condition_f_index].replace('numerical_features_', '').replace('encoded_numeric_', '').lower()
if operators[condition_op](f_value, condition_value):
return True, f_name + ' ' + condition_op + ' ' + str(round(condition_value,2))
return False, ''
# Step 4 : extract rules for an instance in a dataframe, going through nodes in a tree where instance is satisfying the rule, finally leading to a prediction node
def extract_rule(dt_as_json_str: str, f_vector: list, rule=""):
# variable declared in outer function is read only
# in inner if not explicitly declared to be nonlocal
nonlocal cond_parsing_exception_occured
dt_as_json = ast.literal_eval(dt_as_json_str)
child_l = dt_as_json['children']
for child in child_l:
name = child['name'].strip()
if name.startswith('Predict:'):
# remove last comma
return rule[0:rule.rindex(',')]
if name.startswith('feature'):
try:
res, cond = parse_validate_cond(child['name'], f_vector)
except Exception as e:
res = False
cond_parsing_exception_occured = True
if res:
rule += cond +', '
rule = extract_rule(str(child), f_vector, rule=rule)
return rule
df = df.withColumn('explanation',
udf(lambda dt, fv:extract_rule(dt, fv) ,StringType())
(lit(dt_as_json_str), df['features'+'_list'])
)
# log exception occured while trying to parse
# condition in decision tree node
if cond_parsing_exception_occured:
print('some node in decision tree has unexpected format')
return df
df = generate_explanations(debug_str_to_json(dt_m.toDebugString), df, f_index_to_name_dict, operators)
rows = df.select(['ball','keep','mall','hall','fall','explanation','prediction']).collect()
output :
-----------------------
[Row(ball=0, keep=4, mall=16, hall=8, fall=12, explanation='hall > 7.0, mall > 13.0, ball <= 0.5', prediction=21.0),
Row(ball=1, keep=5, mall=17, hall=9, fall=13, explanation='hall > 7.0, mall > 13.0, ball > 0.5, keep <= 5.5', prediction=31.0),
Row(ball=1, keep=6, mall=18, hall=10, fall=14, explanation='hall > 7.0, mall > 13.0, ball > 0.5, keep > 5.5', prediction=21.0),
Row(ball=3, keep=7, mall=10, hall=11, fall=15, explanation='hall > 7.0, mall <= 13.0', prediction=31.0),
Row(ball=1, keep=7, mall=10, hall=2, fall=15, explanation='hall <= 7.0', prediction=51.0),
Row(ball=0, keep=4, mall=16, hall=6, fall=12, explanation='hall <= 7.0', prediction=51.0),
Row(ball=1, keep=6, mall=18, hall=10, fall=14, explanation='hall > 7.0, mall > 13.0, ball > 0.5, keep > 5.5', prediction=21.0),
Row(ball=3, keep=7, mall=10, hall=11, fall=15, explanation='hall > 7.0, mall <= 13.0', prediction=31.0)]
output of dt_m.toDebugString:
-----------------------------------
'DecisionTreeClassificationModel (uid=DecisionTreeClassifier_2a17ae7633b9) of depth 4 with 9 nodes\n If (feature 3 <= 7.0)\n Predict: 51.0\n Else (feature 3 > 7.0)\n If (feature 2 <= 13.0)\n Predict: 31.0\n Else (feature 2 > 13.0)\n If (feature 0 <= 0.5)\n Predict: 21.0\n Else (feature 0 > 0.5)\n If (feature 1 <= 5.5)\n Predict: 31.0\n Else (feature 1 > 5.5)\n Predict: 21.0\n'
output of debug_str_to_json(dt_m.toDebugString):
------------------------------------
{'name': 'Root',
'children': [{'name': 'feature 3 <= 7.0',
'children': [{'name': 'Predict: 51.0'}]},
{'name': 'feature 3 > 7.0',
'children': [{'name': 'feature 2 <= 13.0',
'children': [{'name': 'Predict: 31.0'}]},
{'name': 'feature 2 > 13.0',
'children': [{'name': 'feature 0 <= 0.5',
'children': [{'name': 'Predict: 21.0'}]},
{'name': 'feature 0 > 0.5',
'children': [{'name': 'feature 1 <= 5.5',
'children': [{'name': 'Predict: 31.0'}]},
{'name': 'feature 1 > 5.5',
'children': [{'name': 'Predict: 21.0'}]}]}]}]}]}