Source code for pyspark.mllib.util

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import numpy as np
import warnings

from pyspark.mllib.common import callMLlibFunc
from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
from pyspark.mllib.regression import LabeledPoint


[docs]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
[docs] 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
[docs] 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
[docs] 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)
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()