#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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#
import numpy as np
import warnings
from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper, inherit_doc
from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
from pyspark.mllib.regression import LabeledPoint
class MLUtils(object):
"""
Helper methods to load, save and pre-process data used in MLlib.
"""
@staticmethod
def _parse_libsvm_line(line, multiclass=None):
"""
Parses a line in LIBSVM format into (label, indices, values).
"""
if multiclass is not None:
warnings.warn("deprecated", DeprecationWarning)
items = line.split(None)
label = float(items[0])
nnz = len(items) - 1
indices = np.zeros(nnz, dtype=np.int32)
values = np.zeros(nnz)
for i in xrange(nnz):
index, value = items[1 + i].split(":")
indices[i] = int(index) - 1
values[i] = float(value)
return label, indices, values
@staticmethod
def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
assert isinstance(p, LabeledPoint)
items = [str(p.label)]
v = _convert_to_vector(p.features)
if isinstance(v, SparseVector):
nnz = len(v.indices)
for i in xrange(nnz):
items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
else:
for i in xrange(len(v)):
items.append(str(i + 1) + ":" + str(v[i]))
return " ".join(items)
@staticmethod
def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None):
"""
Loads labeled data in the LIBSVM format into an RDD of
LabeledPoint. The LIBSVM format is a text-based format used by
LIBSVM and LIBLINEAR. Each line represents a labeled sparse
feature vector using the following format:
label index1:value1 index2:value2 ...
where the indices are one-based and in ascending order. This
method parses each line into a LabeledPoint, where the feature
indices are converted to zero-based.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param numFeatures: number of features, which will be determined
from the input data if a nonpositive value
is given. This is useful when the dataset is
already split into multiple files and you
want to load them separately, because some
features may not present in certain files,
which leads to inconsistent feature
dimensions.
:param minPartitions: min number of partitions
@return: labeled data stored as an RDD of LabeledPoint
>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")
>>> tempFile.flush()
>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
>>> tempFile.close()
>>> type(examples[0]) == LabeledPoint
True
>>> print examples[0]
(1.0,(6,[0,2,4],[1.0,2.0,3.0]))
>>> type(examples[1]) == LabeledPoint
True
>>> print examples[1]
(-1.0,(6,[],[]))
>>> type(examples[2]) == LabeledPoint
True
>>> print examples[2]
(-1.0,(6,[1,3,5],[4.0,5.0,6.0]))
"""
if multiclass is not None:
warnings.warn("deprecated", DeprecationWarning)
lines = sc.textFile(path, minPartitions)
parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l))
if numFeatures <= 0:
parsed.cache()
numFeatures = parsed.map(lambda x: -1 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
@staticmethod
def saveAsLibSVMFile(data, dir):
"""
Save labeled data in LIBSVM format.
:param data: an RDD of LabeledPoint to be saved
:param dir: directory to save the data
>>> from tempfile import NamedTemporaryFile
>>> from fileinput import input
>>> from glob import glob
>>> from pyspark.mllib.util import MLUtils
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \
LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
'0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'
"""
lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
lines.saveAsTextFile(dir)
@staticmethod
def loadLabeledPoints(sc, path, minPartitions=None):
"""
Load labeled points saved using RDD.saveAsTextFile.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param minPartitions: min number of partitions
@return: labeled data stored as an RDD of LabeledPoint
>>> from tempfile import NamedTemporaryFile
>>> from pyspark.mllib.util import MLUtils
>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
>>> tempFile = NamedTemporaryFile(delete=True)
>>> tempFile.close()
>>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
>>> MLUtils.loadLabeledPoints(sc, tempFile.name).collect()
[LabeledPoint(1.1, (3,[0,2],[-1.23,4.56e-07])), LabeledPoint(0.0, [1.01,2.02,3.03])]
"""
minPartitions = minPartitions or min(sc.defaultParallelism, 2)
return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
class Saveable(object):
"""
Mixin for models and transformers which may be saved as files.
"""
def save(self, sc, path):
"""
Save this model to the given path.
This saves:
* human-readable (JSON) model metadata to path/metadata/
* Parquet formatted data to path/data/
The model may be loaded using py:meth:`Loader.load`.
:param sc: Spark context used to save model data.
:param path: Path specifying the directory in which to save
this model. If the directory already exists,
this method throws an exception.
"""
raise NotImplementedError
@inherit_doc
class JavaSaveable(Saveable):
"""
Mixin for models that provide save() through their Scala
implementation.
"""
def save(self, sc, path):
self._java_model.save(sc._jsc.sc(), path)
class Loader(object):
"""
Mixin for classes which can load saved models from files.
"""
@classmethod
def load(cls, sc, path):
"""
Load a model from the given path. The model should have been
saved using py:meth:`Saveable.save`.
:param sc: Spark context used for loading model files.
:param path: Path specifying the directory to which the model
was saved.
:return: model instance
"""
raise NotImplemented
@inherit_doc
class JavaLoader(Loader):
"""
Mixin for classes which can load saved models using its Scala
implementation.
"""
@classmethod
def _java_loader_class(cls):
"""
Returns the full class name of the Java loader. The default
implementation replaces "pyspark" by "org.apache.spark" in
the Python full class name.
"""
java_package = cls.__module__.replace("pyspark", "org.apache.spark")
return ".".join([java_package, cls.__name__])
@classmethod
def _load_java(cls, sc, path):
"""
Load a Java model from the given path.
"""
java_class = cls._java_loader_class()
java_obj = sc._jvm
for name in java_class.split("."):
java_obj = getattr(java_obj, name)
return java_obj.load(sc._jsc.sc(), path)
@classmethod
def load(cls, sc, path):
java_model = cls._load_java(sc, path)
return cls(java_model)
def _test():
import doctest
from pyspark.context import SparkContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
