Implement missing API in pyspark. MLUtils * appendBias * loadVectors `kFold` is also missing however I am not sure `ClassTag` can be passed or restored through python. Author: lewuathe <lewuathe@me.com> Closes #5707 from Lewuathe/SPARK-6263 and squashes the following commits: 16863ea [lewuathe] Merge master 3fc27e7 [lewuathe] Merge branch 'master' into SPARK-6263 6084e9c [lewuathe] Resolv conflict d2aa2a0 [lewuathe] Resolv conflict 9c329d8 [lewuathe] Fix efficiency 3a12a2d [lewuathe] Merge branch 'master' into SPARK-6263 1d4714b [lewuathe] Fix style b29e2bc [lewuathe] Remove scipy dependencies e32eb40 [lewuathe] Merge branch 'master' into SPARK-6263 25d3c9d [lewuathe] Remove unnecessary imports 7ec04db [lewuathe] Resolv conflict 1502d13 [lewuathe] Resolv conflict d6bd416 [lewuathe] Check existence of scipy.sparse 5d555b1 [lewuathe] Construct scipy.sparse matrix c345a44 [lewuathe] Merge branch 'master' into SPARK-6263 b8b5ef7 [lewuathe] Fix unnecessary sort method d254be7 [lewuathe] Merge branch 'master' into SPARK-6263 62a9c7e [lewuathe] Fix appendBias return type 454c73d [lewuathe] Merge branch 'master' into SPARK-6263 a353354 [lewuathe] Remove unnecessary appendBias implementation 44295c2 [lewuathe] Merge branch 'master' into SPARK-6263 64f72ad [lewuathe] Merge branch 'master' into SPARK-6263 c728046 [lewuathe] Fix style 2980569 [lewuathe] [SPARK-6263] Python MLlib API missing items: Utils
332 lines
12 KiB
Python
332 lines
12 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import numpy as np
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import warnings
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if sys.version > '3':
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xrange = range
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from pyspark.mllib.common import callMLlibFunc, inherit_doc
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from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector
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class MLUtils(object):
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"""
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Helper methods to load, save and pre-process data used in MLlib.
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"""
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@staticmethod
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def _parse_libsvm_line(line, multiclass=None):
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"""
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Parses a line in LIBSVM format into (label, indices, values).
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"""
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if multiclass is not None:
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warnings.warn("deprecated", DeprecationWarning)
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items = line.split(None)
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label = float(items[0])
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nnz = len(items) - 1
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indices = np.zeros(nnz, dtype=np.int32)
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values = np.zeros(nnz)
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for i in xrange(nnz):
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index, value = items[1 + i].split(":")
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indices[i] = int(index) - 1
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values[i] = float(value)
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return label, indices, values
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@staticmethod
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def _convert_labeled_point_to_libsvm(p):
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"""Converts a LabeledPoint to a string in LIBSVM format."""
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from pyspark.mllib.regression import LabeledPoint
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assert isinstance(p, LabeledPoint)
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items = [str(p.label)]
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v = _convert_to_vector(p.features)
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if isinstance(v, SparseVector):
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nnz = len(v.indices)
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for i in xrange(nnz):
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items.append(str(v.indices[i] + 1) + ":" + str(v.values[i]))
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else:
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for i in xrange(len(v)):
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items.append(str(i + 1) + ":" + str(v[i]))
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return " ".join(items)
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@staticmethod
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def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None):
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"""
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Loads labeled data in the LIBSVM format into an RDD of
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LabeledPoint. The LIBSVM format is a text-based format used by
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LIBSVM and LIBLINEAR. Each line represents a labeled sparse
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feature vector using the following format:
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label index1:value1 index2:value2 ...
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where the indices are one-based and in ascending order. This
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method parses each line into a LabeledPoint, where the feature
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indices are converted to zero-based.
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:param sc: Spark context
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:param path: file or directory path in any Hadoop-supported file
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system URI
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:param numFeatures: number of features, which will be determined
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from the input data if a nonpositive value
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is given. This is useful when the dataset is
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already split into multiple files and you
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want to load them separately, because some
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features may not present in certain files,
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which leads to inconsistent feature
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dimensions.
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:param minPartitions: min number of partitions
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@return: labeled data stored as an RDD of LabeledPoint
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>>> from tempfile import NamedTemporaryFile
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>>> from pyspark.mllib.util import MLUtils
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> tempFile = NamedTemporaryFile(delete=True)
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>>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0")
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>>> tempFile.flush()
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>>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect()
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>>> tempFile.close()
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>>> examples[0]
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LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0]))
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>>> examples[1]
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LabeledPoint(-1.0, (6,[],[]))
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>>> examples[2]
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LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0]))
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"""
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from pyspark.mllib.regression import LabeledPoint
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if multiclass is not None:
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warnings.warn("deprecated", DeprecationWarning)
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lines = sc.textFile(path, minPartitions)
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parsed = lines.map(lambda l: MLUtils._parse_libsvm_line(l))
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if numFeatures <= 0:
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parsed.cache()
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numFeatures = parsed.map(lambda x: -1 if x[1].size == 0 else x[1][-1]).reduce(max) + 1
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return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2])))
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@staticmethod
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def saveAsLibSVMFile(data, dir):
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"""
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Save labeled data in LIBSVM format.
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:param data: an RDD of LabeledPoint to be saved
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:param dir: directory to save the data
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>>> from tempfile import NamedTemporaryFile
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>>> from fileinput import input
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> from glob import glob
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>>> from pyspark.mllib.util import MLUtils
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>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \
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LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
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>>> tempFile = NamedTemporaryFile(delete=True)
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>>> tempFile.close()
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>>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
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>>> ''.join(sorted(input(glob(tempFile.name + "/part-0000*"))))
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'0.0 1:1.01 2:2.02 3:3.03\\n1.1 1:1.23 3:4.56\\n'
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"""
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lines = data.map(lambda p: MLUtils._convert_labeled_point_to_libsvm(p))
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lines.saveAsTextFile(dir)
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@staticmethod
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def loadLabeledPoints(sc, path, minPartitions=None):
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"""
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Load labeled points saved using RDD.saveAsTextFile.
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:param sc: Spark context
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:param path: file or directory path in any Hadoop-supported file
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system URI
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:param minPartitions: min number of partitions
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@return: labeled data stored as an RDD of LabeledPoint
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>>> from tempfile import NamedTemporaryFile
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>>> from pyspark.mllib.util import MLUtils
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
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LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
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>>> tempFile = NamedTemporaryFile(delete=True)
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>>> tempFile.close()
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>>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
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>>> MLUtils.loadLabeledPoints(sc, tempFile.name).collect()
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[LabeledPoint(1.1, (3,[0,2],[-1.23,4.56e-07])), LabeledPoint(0.0, [1.01,2.02,3.03])]
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"""
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minPartitions = minPartitions or min(sc.defaultParallelism, 2)
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return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions)
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@staticmethod
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def appendBias(data):
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"""
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Returns a new vector with `1.0` (bias) appended to
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the end of the input vector.
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"""
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vec = _convert_to_vector(data)
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if isinstance(vec, SparseVector):
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newIndices = np.append(vec.indices, len(vec))
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newValues = np.append(vec.values, 1.0)
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return SparseVector(len(vec) + 1, newIndices, newValues)
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else:
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return _convert_to_vector(np.append(vec.toArray(), 1.0))
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@staticmethod
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def loadVectors(sc, path):
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"""
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Loads vectors saved using `RDD[Vector].saveAsTextFile`
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with the default number of partitions.
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"""
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return callMLlibFunc("loadVectors", sc, path)
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class Saveable(object):
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"""
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Mixin for models and transformers which may be saved as files.
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"""
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def save(self, sc, path):
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"""
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Save this model to the given path.
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This saves:
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* human-readable (JSON) model metadata to path/metadata/
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* Parquet formatted data to path/data/
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The model may be loaded using py:meth:`Loader.load`.
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:param sc: Spark context used to save model data.
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:param path: Path specifying the directory in which to save
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this model. If the directory already exists,
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this method throws an exception.
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"""
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raise NotImplementedError
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@inherit_doc
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class JavaSaveable(Saveable):
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"""
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Mixin for models that provide save() through their Scala
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implementation.
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"""
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def save(self, sc, path):
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self._java_model.save(sc._jsc.sc(), path)
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class Loader(object):
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"""
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Mixin for classes which can load saved models from files.
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"""
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@classmethod
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def load(cls, sc, path):
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"""
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Load a model from the given path. The model should have been
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saved using py:meth:`Saveable.save`.
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:param sc: Spark context used for loading model files.
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:param path: Path specifying the directory to which the model
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was saved.
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:return: model instance
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"""
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raise NotImplemented
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@inherit_doc
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class JavaLoader(Loader):
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"""
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Mixin for classes which can load saved models using its Scala
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implementation.
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"""
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@classmethod
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def _java_loader_class(cls):
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"""
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Returns the full class name of the Java loader. The default
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implementation replaces "pyspark" by "org.apache.spark" in
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the Python full class name.
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"""
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java_package = cls.__module__.replace("pyspark", "org.apache.spark")
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return ".".join([java_package, cls.__name__])
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@classmethod
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def _load_java(cls, sc, path):
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"""
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Load a Java model from the given path.
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"""
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java_class = cls._java_loader_class()
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java_obj = sc._jvm
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for name in java_class.split("."):
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java_obj = getattr(java_obj, name)
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return java_obj.load(sc._jsc.sc(), path)
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@classmethod
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def load(cls, sc, path):
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java_model = cls._load_java(sc, path)
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return cls(java_model)
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class LinearDataGenerator(object):
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"""Utils for generating linear data"""
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@staticmethod
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def generateLinearInput(intercept, weights, xMean, xVariance,
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nPoints, seed, eps):
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"""
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:param: intercept bias factor, the term c in X'w + c
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:param: weights feature vector, the term w in X'w + c
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:param: xMean Point around which the data X is centered.
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:param: xVariance Variance of the given data
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:param: nPoints Number of points to be generated
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:param: seed Random Seed
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:param: eps Used to scale the noise. If eps is set high,
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the amount of gaussian noise added is more.
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Returns a list of LabeledPoints of length nPoints
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"""
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weights = [float(weight) for weight in weights]
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xMean = [float(mean) for mean in xMean]
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xVariance = [float(var) for var in xVariance]
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return list(callMLlibFunc(
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"generateLinearInputWrapper", float(intercept), weights, xMean,
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xVariance, int(nPoints), int(seed), float(eps)))
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@staticmethod
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def generateLinearRDD(sc, nexamples, nfeatures, eps,
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nParts=2, intercept=0.0):
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"""
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Generate a RDD of LabeledPoints.
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"""
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return callMLlibFunc(
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"generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures),
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float(eps), int(nParts), float(intercept))
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def _test():
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import doctest
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from pyspark.context import SparkContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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