Spark 随机森林 - 无法将浮点数转换为 int 错误

Question

我有数字和二进制响应的特征。我正在尝试构建集成决策树，例如随机森林和梯度提升树。但是，我得到一个错误。我已经用 iris 数据重现了这个错误。 错误在下方，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```

Answer 1

maxDepth 和numTrees 都需要整数； Numpy linspace 产生浮点数：

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，您的代码会碰到第一个非整数值（此处为 12.63157895），并产生错误。

改用arange：

np.arange(10, 60, 20)
# array([10, 30, 50])