# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # 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 # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import numpy as np import warnings from pyspark.mllib.linalg import Vectors, SparseVector from pyspark.mllib.regression import LabeledPoint from pyspark.mllib._common import _convert_vector, _deserialize_labeled_point from pyspark.rdd import RDD from pyspark.serializers import NoOpSerializer class MLUtils: """ Helper methods to load, save and pre-process data used in MLlib. """ @staticmethod def _parse_libsvm_line(line, multiclass): warnings.warn("deprecated", DeprecationWarning) return _parse_libsvm_line(line) @staticmethod def _parse_libsvm_line(line): """ Parses a line in LIBSVM format into (label, indices, values). """ 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.""" items = [str(p.label)] v = _convert_vector(p.features) if type(v) == np.ndarray: for i in xrange(len(v)): items.append(str(i + 1) + ":" + str(v[i])) elif type(v) == SparseVector: nnz = len(v.indices) for i in xrange(nnz): items.append(str(v.indices[i] + 1) + ":" + str(v.values[i])) else: raise TypeError("_convert_labeled_point_to_libsvm needs either ndarray or SparseVector" " but got " % type(v)) return " ".join(items) @staticmethod def loadLibSVMFile(sc, path, multiclass=False, numFeatures=-1, minPartitions=None): warnings.warn("deprecated", DeprecationWarning) return loadLibSVMFile(sc, path, numFeatures, minPartitions) @staticmethod def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=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])) """ 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) >>> loaded = MLUtils.loadLabeledPoints(sc, tempFile.name).collect() >>> type(loaded[0]) == LabeledPoint True >>> print examples[0] (1.1,(3,[0,2],[-1.23,4.56e-07])) >>> type(examples[1]) == LabeledPoint True >>> print examples[1] (0.0,[1.01,2.02,3.03]) """ minPartitions = minPartitions or min(sc.defaultParallelism, 2) jSerialized = sc._jvm.PythonMLLibAPI().loadLabeledPoints(sc._jsc, path, minPartitions) serialized = RDD(jSerialized, sc, NoOpSerializer()) return serialized.map(lambda bytes: _deserialize_labeled_point(bytearray(bytes))) 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()