Spark随机森林-无法将float转换为int错误

时间:2019-03-21 14:53:13

标签: numpy machine-learning pyspark random-forest apache-spark-ml

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```

1 个答案:

答案 0 :(得分:2)

maxDepthnumTrees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])