我正在尝试使用Binaryclassification评估程序评估我的模型，但是尽管实际上存在“ rawPrediction”，但我仍然收到上述错误。
我正在使用Logistic回归运行NLP模型，拟合和变换进行得很好，但评估一直是问题所在
我尝试将评估器更改为多类和回归，即使其存在分类问题，但仍然存在相同的错误
# creating bag of word model
from pyspark.ml.feature import CountVectorizer, StringIndexer
from pyspark.ml.classification import LogisticRegression
from pyspark.ml import Pipeline
cv = CountVectorizer(inputCol = 'bagofwords', outputCol='vectors') #features
si = StringIndexer(inputCol = 'Sentiment', outputCol = 'label') #labels
lr = LogisticRegression(featuresCol = 'vectors', labelCol = 'label', maxIter = 10, regParam = 0.3, elasticNetParam = 0.8)
pipeline = Pipeline(stages = [cv, si, lr])
model = pipeline.fit(df_train)
prediction = model.transform(df_test)
prediction.take(1)
[Row(Sentiment='negative', text='!!!!!!TO AVOID!!!!!\n\nFirst of all, as we enter, the host was very rude (and seemed not to like his job). We get seated, and then my husband had to GET UP to ask for menus and water. After getting it, we ask for the 2 for 22$ menu, we were told the boss at this location was not offering it anymore. The 2 for 22$ being the main appeal of the restaurant, we got up and left to go to the Sherbrooke location. \n\n\nI have been to the sherbrooke location before and still now i have nothing to complain about. A1 service.\n\nWhat a shame for the Madisons restaurant line.', bagofwords=['main', 'still', 'seated', 'what', 'rude', 'not', 'nothing', 'then', 'location', 'got', 'sherbrooke', 'get', 'appeal', 'as', 'enter', 'offering', 'left', 'first', 'madisons', 'been', 'husband', 'told', 'the', 'sherbrooke', 'ask', 'get', 'to', 'being', 'go', 'after', 'anymore', 'a', 'very', 'like', 'menus', 'menu', 'getting', 'service', 'line', 'restaurant', 'avoid', 'complain', 'job', 'host', 'seemed', 'shame', 'boss', 'up', 'water'], vectors=SparseVector(191243, {0: 1.0, 1: 1.0, 7: 1.0, 9: 1.0, 12: 1.0, 13: 2.0, 14: 1.0, 25: 1.0, 31: 1.0, 40: 1.0, 65: 1.0, 78: 1.0, 81: 1.0, 87: 1.0, 98: 1.0, 105: 1.0, 120: 1.0, 125: 1.0, 146: 1.0, 187: 1.0, 193: 1.0, 209: 1.0, 215: 1.0, 218: 1.0, 228: 1.0, 282: 1.0, 283: 1.0, 308: 1.0, 317: 1.0, 330: 1.0, 420: 1.0, 422: 1.0, 463: 1.0, 503: 1.0, 612: 1.0, 876: 1.0, 1202: 1.0, 1277: 1.0, 1584: 1.0, 1588: 1.0, 1602: 1.0, 1714: 1.0, 1968: 1.0, 3140: 1.0, 4329: 1.0, 19856: 2.0, 51462: 1.0}), label=1.0, rawPrediction=DenseVector([1.0612, -1.0612]), probability=DenseVector([0.7429, 0.2571]), prediction=0.0)]
from pyspark.ml.evaluation import BinaryClassificationEvaluator
assess = BinaryClassificationEvaluator().setMetricName('areaUnderROC').setLabelCol('label').setRawPredictionCol('rawPrediction')
assess.evaluate(df_test)
我希望获得一个指示准确性或相似性的值或度量，但我不断收到此错误：
IllegalArgumentException Traceback (most recent call last)
<ipython-input-41-d1448386660e> in <module>()
5 #eval = RegressionEvaluator().setMetricName('rmse').setLabelCol('label').setPredictionCol('prediction')
6
----> 7 assess.evaluate(df_test)
/usr/local/src/spark21master/spark/python/pyspark/ml/evaluation.py in evaluate(self, dataset, params)
67 return self.copy(params)._evaluate(dataset)
68 else:
---> 69 return self._evaluate(dataset)
70 else:
71 raise ValueError("Params must be a param map but got %s." % type(params))
/usr/local/src/spark21master/spark/python/pyspark/ml/evaluation.py in _evaluate(self, dataset)
97 """
98 self._transfer_params_to_java()
---> 99 return self._java_obj.evaluate(dataset._jdf)
100
101 def isLargerBetter(self):
/usr/local/src/spark21master/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py in __call__(self, *args)
1255 answer = self.gateway_client.send_command(command)
1256 return_value = get_return_value(
-> 1257 answer, self.gateway_client, self.target_id, self.name)
1258
1259 for temp_arg in temp_args:
/usr/local/src/spark21master/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
77 raise QueryExecutionException(s.split(': ', 1)[1], stackTrace)
78 if s.startswith('java.lang.IllegalArgumentException: '):
---> 79 raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
80 raise
81 return deco
IllegalArgumentException: 'Field "rawPrediction" does not exist.'