I have a LIBSVM scaling model (generated with svm-scale) that I would like to port over to PySpark. I've naively tried the following:
scaler_path = "path to model"
a = MinMaxScaler().load(scaler_path)
But I'm thrown an error, expecting a metadata directory:
Py4JJavaErrorTraceback (most recent call last)
<ipython-input-22-1942e7522174> in <module>()
----> 1 a = MinMaxScaler().load(scaler_path)
/srv/data/spark/spark-2.0.0-bin-hadoop2.6/python/pyspark/ml/util.pyc in load(cls, path)
226 def load(cls, path):
227 """Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
--> 228 return cls.read().load(path)
229
230
/srv/data/spark/spark-2.0.0-bin-hadoop2.6/python/pyspark/ml/util.pyc in load(self, path)
174 if not isinstance(path, basestring):
175 raise TypeError("path should be a basestring, got type %s" % type(path))
--> 176 java_obj = self._jread.load(path)
177 if not hasattr(self._clazz, "_from_java"):
178 raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r"
/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc in __call__(self, *args)
1131 answer = self.gateway_client.send_command(command)
1132 return_value = get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name)
1134
1135 for temp_arg in temp_args:
/srv/data/spark/spark-2.0.0-bin-hadoop2.6/python/pyspark/sql/utils.pyc in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o321.load.
: org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:[filename]/metadata
```
Is there a simple work-around for loading this? The format of the LIBSVM model is
x
0 1
1 -1050 1030
2 0 1
3 0 3
4 0 1
5 0 1
First, the file presented isn't in libsvm format. The correct format of a libsvm file is the following :
<label> <index1>:<value1> <index2>:<value2> ... <indexN>:<valueN>
Thus your data preparation is incorrect to start with.
Secondly, the class method load(path) that you are using with MinMaxScaler reads an ML instance from the input path.
Remember that : MinMaxScaler computes summary statistics on a data set and produces a MinMaxScalerModel. The model can then transform each feature individually such that it is in the given range.
e.g :
from pyspark.ml.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.ml.feature import MinMaxScaler
df = spark.createDataFrame([(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])) ,(0.0, Vectors.dense([1.01, 2.02, 3.03]))],['label','features'])
df.show(truncate=False)
# +-----+---------------------+
# |label|features |
# +-----+---------------------+
# |1.1 |(3,[0,2],[1.23,4.56])|
# |0.0 |[1.01,2.02,3.03] |
# +-----+---------------------+
mmScaler = MinMaxScaler(inputCol="features", outputCol="scaled")
temp_path = "/tmp/spark/"
minMaxScalerPath = temp_path + "min-max-scaler"
mmScaler.save(minMaxScalerPath)
The snippet above will save the MinMaxScaler feature transformer so it can be loaded after with the class method load.
Now, let's take a look at what actually happened. The class method save will create the following file structure :
/tmp/spark/
└── min-max-scaler
└── metadata
├── part-00000
└── _SUCCESS
Let's check the content of that part-0000 file :
$ cat /tmp/spark/min-max-scaler/metadata/part-00000 | python -m json.tool
{
"class": "org.apache.spark.ml.feature.MinMaxScaler",
"paramMap": {
"inputCol": "features",
"max": 1.0,
"min": 0.0,
"outputCol": "scaled"
},
"sparkVersion": "2.0.0",
"timestamp": 1480501003244,
"uid": "MinMaxScaler_42e68455a929c67ba66f"
}
So actually when you load the transformer :
loadedMMScaler = MinMaxScaler.load(minMaxScalerPath)
You are actually load that file. It won't take a libsvm file !
Now you can apply your transformer to create the model and transform your DataFrame :
model = loadedMMScaler.fit(df)
model.transform(df).show(truncate=False)
# +-----+---------------------+-------------+
# |label|features |scaled |
# +-----+---------------------+-------------+
# |1.1 |(3,[0,2],[1.23,4.56])|[1.0,0.0,1.0]|
# |0.0 |[1.01,2.02,3.03] |[0.0,1.0,0.0]|
# +-----+---------------------+-------------+
Now let's get back to that libsvm file and let us create some dummy data and save it to a libsvm format using MLUtils
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.util import MLUtils
data = sc.parallelize([LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))])
MLUtils.saveAsLibSVMFile(data, temp_path + "data")
Back to our file structure :
/tmp/spark/
├── data
│ ├── part-00000
│ ├── part-00001
│ ├── part-00002
│ ├── part-00003
│ ├── part-00004
│ ├── part-00005
│ ├── part-00006
│ ├── part-00007
│ └── _SUCCESS
└── min-max-scaler
└── metadata
├── part-00000
└── _SUCCESS
You can check the content of those file which is in libsvm format now :
$ cat /tmp/spark/data/part-0000*
1.1 1:1.23 3:4.56
0.0 1:1.01 2:2.02 3:3.03
Now let's load that data and apply :
loadedData = MLUtils.loadLibSVMFile(sc, temp_path + "data")
loadedDataDF = spark.createDataFrame(loadedData.map(lambda lp : (lp.label, lp.features.asML())), ['label','features'])
loadedDataDF.show(truncate=False)
# +-----+----------------------------+
# |label|features |
# +-----+----------------------------+
# |1.1 |(3,[0,2],[1.23,4.56]) |
# |0.0 |(3,[0,1,2],[1.01,2.02,3.03])|
# +-----+----------------------------+
Note that converting MLlib Vectors to ML Vectors is very important. You can read more about it here.
model.transform(loadedDataDF).show(truncate=False)
# +-----+----------------------------+-------------+
# |label|features |scaled |
# +-----+----------------------------+-------------+
# |1.1 |(3,[0,2],[1.23,4.56]) |[1.0,0.0,1.0]|
# |0.0 |(3,[0,1,2],[1.01,2.02,3.03])|[0.0,1.0,0.0]|
# +-----+----------------------------+-------------+
I hope that this answers your question!