Has anyone see this issue? I am calling the
LogisticRegressionWithLBFGS.train API and about 7 out of 10 times, I get
an ""Input validation failed" error". The exact same code and dataset works
sometimes but fails at other times. It is odd. I can't seem to find any
info on this. Below is the pyspark code and the error message. I did check
the dataset and all values are zero or greater. There are no blank spaces
or nulls. This code below is pretty much the sample code from the Spark
site.
Thanks in advance for any help or pointers in how to investigate this
issue.
--
*Francis *
*CODE:*
from pyspark.mllib.classification import LogisticRegressionWithLBFGS
from pyspark.mllib.regression import LabeledPoint
from numpy import array
# Load and parse the data
def parsePoint(line):
#values = [float(x) for x in line.split(' ')]
values = [float(x) for x in line.asDict().values()] # need to convert
from Row to Array
return LabeledPoint(values[0], values[1:])
# convert SQL to format needed for training model
regData = sqlContext.sql("select statement")
df = regData.limit(1000)
data = df.rdd
parsedData = data.map(parsePoint)
# Build the model
model = LogisticRegressionWithLBFGS.train(parsedData)
# Evaluating the model on training data
labelsAndPreds = parsedData.map(lambda p: (p.label,
model.predict(p.features)))
trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() /
float(parsedData.count())
print("Training Error = " + str(trainErr))
print "Intercept: " + str(model.intercept)
print "Weights: " + str(model.weights)
*ERROR:*
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-134-b31b9c04499a> in <module>()
20
21 # Build the model
---> 22 model = LogisticRegressionWithLBFGS.train(parsedData)
23
24 # Evaluating the model on training data
/home/ubuntu/databricks/spark/python/pyspark/mllib/classification.py in
train(cls, data, iterations, initialWeights, regParam, regType, intercept,
corrections, tolerance, validateData, numClasses)
344 else:
345 initialWeights = [0.0] *
len(data.first().features) * (numClasses - 1)
--> 346 return _regression_train_wrapper(train,
LogisticRegressionModel, data, initialWeights)
347
348
/home/ubuntu/databricks/spark/python/pyspark/mllib/regression.py in
_regression_train_wrapper(train_func, modelClass, data, initial_weights)
186 if (modelClass == LogisticRegressionModel):
187 weights, intercept, numFeatures, numClasses = train_func(
--> 188 data, _convert_to_vector(initial_weights))
189 return modelClass(weights, intercept, numFeatures,
numClasses)
190 else:
/home/ubuntu/databricks/spark/python/pyspark/mllib/classification.py in
train(rdd, i)
334 return
callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd,
int(iterations), i,
335 float(regParam), regType,
bool(intercept), int(corrections),
--> 336 float(tolerance),
bool(validateData), int(numClasses))
337
338 if initialWeights is None:
/home/ubuntu/databricks/spark/python/pyspark/mllib/common.py in
callMLlibFunc(name, *args)
126 sc = SparkContext._active_spark_context
127 api = getattr(sc._jvm.PythonMLLibAPI(), name)
--> 128 return callJavaFunc(sc, api, *args)
129
130
/home/ubuntu/databricks/spark/python/pyspark/mllib/common.py in
callJavaFunc(sc, func, *args)
119 """ Call Java Function """
120 args = [_py2java(sc, a) for a in args]
--> 121 return _java2py(sc, func(*args))
122
123
/home/ubuntu/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/home/ubuntu/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling
o5803.trainLogisticRegressionModelWithLBFGS.
: org.apache.spark.SparkException: *Input validation failed.*
at
org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:225)
at
org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRegressionModel(PythonMLLibAPI.scala:81)
at
org.apache.spark.mllib.api.python.PythonMLLibAPI.trainLogisticRegressionModelWithLBFGS(PythonMLLibAPI.scala:270)
at sun.reflect.GeneratedMethodAccessor155.invoke(Unknown Source)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
