I have features which are numeric and a binary response. I am trying to build ensemble decision trees such as random forest and gradient-boosted trees. However, I get an error. I have reproduced the error with iris data. The error is below and the whole error message is at the bottom.
TypeError: Could not convert 12.631578947368421 to 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```
Both maxDepth and numTrees need to be integers; Numpy linspace procudes floats:
import numpy as np
np.linspace(10, 60, 20)
Result:
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. ])
So, your code bumps upon the first non-integer value (here 12.63157895), and produces an error.
Use arange instead:
np.arange(10, 60, 20)
# array([10, 30, 50])