我具有数字和二进制响应的功能。我正在尝试构建整体决策树,例如随机森林和梯度增强树。但是,我得到一个错误。我已经用虹膜数据重现了错误。 错误在下面,整个错误消息在底部。
TypeError:无法将12.631578947368421转换为int
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.classification import GBTClassifier
import pandas as pd
from sklearn import datasets
iris = datasets.load_iris()
y = list(iris.target)
df = pd.read_csv("https://raw.githubusercontent.com/venky14/Machine- Learning-with-Iris-Dataset/master/Iris.csv")
df = df.drop(['Species'], axis = 1)
df['label'] = y
spark_df = spark.createDataFrame(df).drop('Id')
cols = spark_df.drop('label').columns
assembler = VectorAssembler(inputCols = cols, outputCol = 'features')
output_dat = assembler.transform(spark_df).select('label', 'features')
rf = RandomForestClassifier(labelCol = "label", featuresCol = "features")
paramGrid_rf = ParamGridBuilder() \
.addGrid(rf.maxDepth, np.linspace(5, 30, 6)) \
.addGrid(rf.numTrees, np.linspace(10, 60, 20)).build()
crossval_rf = CrossValidator(estimator = rf,
estimatorParamMaps = paramGrid_rf,
evaluator = BinaryClassificationEvaluator(),
numFolds = 5)
cvModel_rf = crossval_rf.fit(output_dat)
TypeError Traceback (most recent call last)
<ipython-input-24-44f8f759ed8e> in <module>
2 paramGrid_rf = ParamGridBuilder() \
3 .addGrid(rf.maxDepth, np.linspace(5, 30, 6)) \
----> 4 .addGrid(rf.numTrees, np.linspace(10, 60, 20)) \
5 .build()
6
~/spark-2.4.0-bin-hadoop2.7/python/pyspark/ml/tuning.py in build(self)
120 return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
121
--> 122 return [dict(to_key_value_pairs(keys, prod)) for prod in itertools.product(*grid_values)]
123
124
~/spark-2.4.0-bin-hadoop2.7/python/pyspark/ml/tuning.py in <listcomp>(.0)
120 return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
121
--> 122 return [dict(to_key_value_pairs(keys, prod)) for prod in itertools.product(*grid_values)]
123
124
~/spark-2.4.0-bin-hadoop2.7/python/pyspark/ml/tuning.py in to_key_value_pairs(keys, values)
118
119 def to_key_value_pairs(keys, values):
--> 120 return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
121
122 return [dict(to_key_value_pairs(keys, prod)) for prod in itertools.product(*grid_values)]
~/spark-2.4.0-bin-hadoop2.7/python/pyspark/ml/tuning.py in <listcomp>(.0)
118
119 def to_key_value_pairs(keys, values):
--> 120 return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
121
122 return [dict(to_key_value_pairs(keys, prod)) for prod in itertools.product(*grid_values)]
~/spark-2.4.0-bin-hadoop2.7/python/pyspark/ml/param/__init__.py in toInt(value)
197 return int(value)
198 else:
--> 199 raise TypeError("Could not convert %s to int" % value)
200
201 @staticmethod
TypeError: Could not convert 12.631578947368421 to int```
答案 0 :(得分:2)
maxDepth
和numTrees
都必须是 integers ;脾气暴躁的linspace
会浮动:
import numpy as np
np.linspace(10, 60, 20)
结果:
array([ 10. , 12.63157895, 15.26315789, 17.89473684,
20.52631579, 23.15789474, 25.78947368, 28.42105263,
31.05263158, 33.68421053, 36.31578947, 38.94736842,
41.57894737, 44.21052632, 46.84210526, 49.47368421,
52.10526316, 54.73684211, 57.36842105, 60. ])
因此,您的代码碰到第一个非整数值(此处为12.63157895
),并产生错误。
改为使用arange
:
np.arange(10, 60, 20)
# array([10, 30, 50])