01321bc0fe
### What changes were proposed in this pull request? This PR proposes migration of `pyspark.mllib` to NumPy documentation style. ### Why are the changes needed? To improve documentation style. Before:  After:  ### Does this PR introduce _any_ user-facing change? Yes, this changes both rendered HTML docs and console representation (SPARK-33243). ### How was this patch tested? `dev/lint-python` and manual inspection. Closes #30413 from zero323/SPARK-33252. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
610 lines
21 KiB
Python
610 lines
21 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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from pyspark import SparkContext, since
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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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from pyspark.sql import DataFrame
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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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.. versionadded:: 1.0.0
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"""
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@staticmethod
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def _parse_libsvm_line(line):
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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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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 range(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 range(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 range(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):
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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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.. versionadded:: 1.0.0
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Parameters
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----------
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sc : :py:class:`pyspark.SparkContext`
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Spark context
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path : str
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file or directory path in any Hadoop-supported file system URI
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numFeatures : int, optional
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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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minPartitions : int, optional
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min number of partitions
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Returns
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-------
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:py:class:`pyspark.RDD`
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labeled data stored as an RDD of LabeledPoint
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Examples
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--------
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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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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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.. versionadded:: 1.0.0
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Parameters
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----------
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data : :py:class:`pyspark.RDD`
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an RDD of LabeledPoint to be saved
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dir : str
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directory to save the data
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Examples
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--------
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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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.. versionadded:: 1.0.0
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Parameters
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----------
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sc : :py:class:`pyspark.SparkContext`
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Spark context
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path : str
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file or directory path in any Hadoop-supported file system URI
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minPartitions : int, optional
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min number of partitions
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Returns
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-------
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:py:class:`pyspark.RDD`
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labeled data stored as an RDD of LabeledPoint
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Examples
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--------
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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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@since("1.5.0")
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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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@since("1.5.0")
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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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@staticmethod
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def convertVectorColumnsToML(dataset, *cols):
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"""
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Converts vector columns in an input DataFrame from the
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:py:class:`pyspark.mllib.linalg.Vector` type to the new
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:py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
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package.
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.. versionadded:: 2.0.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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\\*cols : str
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Vector columns to be converted.
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New vector columns will be ignored. If unspecified, all old
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vector columns will be converted excepted nested ones.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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the input dataset with old vector columns converted to the
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new vector type
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Examples
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--------
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>>> import pyspark
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>>> from pyspark.mllib.linalg import Vectors
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>>> from pyspark.mllib.util import MLUtils
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>>> df = spark.createDataFrame(
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... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],
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... ["id", "x", "y"])
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>>> r1 = MLUtils.convertVectorColumnsToML(df).first()
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>>> isinstance(r1.x, pyspark.ml.linalg.SparseVector)
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True
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>>> isinstance(r1.y, pyspark.ml.linalg.DenseVector)
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True
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>>> r2 = MLUtils.convertVectorColumnsToML(df, "x").first()
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>>> isinstance(r2.x, pyspark.ml.linalg.SparseVector)
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True
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>>> isinstance(r2.y, pyspark.mllib.linalg.DenseVector)
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True
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"""
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if not isinstance(dataset, DataFrame):
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raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
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return callMLlibFunc("convertVectorColumnsToML", dataset, list(cols))
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@staticmethod
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def convertVectorColumnsFromML(dataset, *cols):
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"""
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Converts vector columns in an input DataFrame to the
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:py:class:`pyspark.mllib.linalg.Vector` type from the new
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:py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
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package.
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.. versionadded:: 2.0.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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\\*cols : str
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Vector columns to be converted.
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Old vector columns will be ignored. If unspecified, all new
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vector columns will be converted except nested ones.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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the input dataset with new vector columns converted to the
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old vector type
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Examples
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--------
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>>> import pyspark
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>>> from pyspark.ml.linalg import Vectors
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>>> from pyspark.mllib.util import MLUtils
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>>> df = spark.createDataFrame(
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... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))],
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... ["id", "x", "y"])
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>>> r1 = MLUtils.convertVectorColumnsFromML(df).first()
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>>> isinstance(r1.x, pyspark.mllib.linalg.SparseVector)
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True
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>>> isinstance(r1.y, pyspark.mllib.linalg.DenseVector)
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True
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>>> r2 = MLUtils.convertVectorColumnsFromML(df, "x").first()
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>>> isinstance(r2.x, pyspark.mllib.linalg.SparseVector)
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True
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>>> isinstance(r2.y, pyspark.ml.linalg.DenseVector)
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True
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"""
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if not isinstance(dataset, DataFrame):
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raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
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return callMLlibFunc("convertVectorColumnsFromML", dataset, list(cols))
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@staticmethod
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def convertMatrixColumnsToML(dataset, *cols):
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"""
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Converts matrix columns in an input DataFrame from the
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:py:class:`pyspark.mllib.linalg.Matrix` type to the new
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:py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`
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package.
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.. versionadded:: 2.0.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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\\*cols : str
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Matrix columns to be converted.
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New matrix columns will be ignored. If unspecified, all old
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matrix columns will be converted excepted nested ones.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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the input dataset with old matrix columns converted to the
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new matrix type
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Examples
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--------
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>>> import pyspark
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>>> from pyspark.mllib.linalg import Matrices
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>>> from pyspark.mllib.util import MLUtils
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>>> df = spark.createDataFrame(
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... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),
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... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])
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>>> r1 = MLUtils.convertMatrixColumnsToML(df).first()
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>>> isinstance(r1.x, pyspark.ml.linalg.SparseMatrix)
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True
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>>> isinstance(r1.y, pyspark.ml.linalg.DenseMatrix)
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True
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>>> r2 = MLUtils.convertMatrixColumnsToML(df, "x").first()
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>>> isinstance(r2.x, pyspark.ml.linalg.SparseMatrix)
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True
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>>> isinstance(r2.y, pyspark.mllib.linalg.DenseMatrix)
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True
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"""
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if not isinstance(dataset, DataFrame):
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raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
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return callMLlibFunc("convertMatrixColumnsToML", dataset, list(cols))
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@staticmethod
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def convertMatrixColumnsFromML(dataset, *cols):
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"""
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Converts matrix columns in an input DataFrame to the
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:py:class:`pyspark.mllib.linalg.Matrix` type from the new
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:py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml`
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package.
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.. versionadded:: 2.0.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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input dataset
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\\*cols : str
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Matrix columns to be converted.
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Old matrix columns will be ignored. If unspecified, all new
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matrix columns will be converted except nested ones.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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the input dataset with new matrix columns converted to the
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old matrix type
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Examples
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--------
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>>> import pyspark
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>>> from pyspark.ml.linalg import Matrices
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>>> from pyspark.mllib.util import MLUtils
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>>> df = spark.createDataFrame(
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... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]),
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... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"])
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>>> r1 = MLUtils.convertMatrixColumnsFromML(df).first()
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>>> isinstance(r1.x, pyspark.mllib.linalg.SparseMatrix)
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True
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>>> isinstance(r1.y, pyspark.mllib.linalg.DenseMatrix)
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True
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>>> r2 = MLUtils.convertMatrixColumnsFromML(df, "x").first()
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>>> isinstance(r2.x, pyspark.mllib.linalg.SparseMatrix)
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True
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>>> isinstance(r2.y, pyspark.ml.linalg.DenseMatrix)
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True
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"""
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if not isinstance(dataset, DataFrame):
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raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
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return callMLlibFunc("convertMatrixColumnsFromML", dataset, list(cols))
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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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.. versionadded:: 1.3.0
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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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Parameters
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----------
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sc : :py:class:`pyspark.SparkContext`
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Spark context used to save model data.
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path : str
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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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.. versionadded:: 1.3.0
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"""
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@since("1.3.0")
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def save(self, sc, path):
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"""Save this model to the given path."""
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if not isinstance(sc, SparkContext):
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raise TypeError("sc should be a SparkContext, got type %s" % type(sc))
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if not isinstance(path, str):
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raise TypeError("path should be a string, got type %s" % type(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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.. versionadded:: 1.3.0
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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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Parameters
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----------
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sc : :py:class:`pyspark.SparkContext`
|
|
Spark context used for loading model files.
|
|
path : str
|
|
Path specifying the directory to which the model was saved.
|
|
|
|
Returns
|
|
-------
|
|
object
|
|
model instance
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
|
|
@inherit_doc
|
|
class JavaLoader(Loader):
|
|
"""
|
|
Mixin for classes which can load saved models using its Scala
|
|
implementation.
|
|
|
|
.. versionadded:: 1.3.0
|
|
"""
|
|
|
|
@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
|
|
@since("1.3.0")
|
|
def load(cls, sc, path):
|
|
"""Load a model from the given path."""
|
|
java_model = cls._load_java(sc, path)
|
|
return cls(java_model)
|
|
|
|
|
|
class LinearDataGenerator(object):
|
|
"""Utils for generating linear data.
|
|
|
|
.. versionadded:: 1.5.0
|
|
"""
|
|
|
|
@staticmethod
|
|
def generateLinearInput(intercept, weights, xMean, xVariance,
|
|
nPoints, seed, eps):
|
|
"""
|
|
.. versionadded:: 1.5.0
|
|
|
|
Parameters
|
|
----------
|
|
intercept : float
|
|
bias factor, the term c in X'w + c
|
|
weights : :py:class:`pyspark.mllib.linalg.Vector` or convertible
|
|
feature vector, the term w in X'w + c
|
|
xMean : :py:class:`pyspark.mllib.linalg.Vector` or convertible
|
|
Point around which the data X is centered.
|
|
xVariance : :py:class:`pyspark.mllib.linalg.Vector` or convertible
|
|
Variance of the given data
|
|
nPoints : int
|
|
Number of points to be generated
|
|
seed : int
|
|
Random Seed
|
|
eps : float
|
|
Used to scale the noise. If eps is set high,
|
|
the amount of gaussian noise added is more.
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
of :py:class:`pyspark.mllib.regression.LabeledPoints` of length nPoints
|
|
"""
|
|
weights = [float(weight) for weight in weights]
|
|
xMean = [float(mean) for mean in xMean]
|
|
xVariance = [float(var) for var in xVariance]
|
|
return list(callMLlibFunc(
|
|
"generateLinearInputWrapper", float(intercept), weights, xMean,
|
|
xVariance, int(nPoints), int(seed), float(eps)))
|
|
|
|
@staticmethod
|
|
@since("1.5.0")
|
|
def generateLinearRDD(sc, nexamples, nfeatures, eps,
|
|
nParts=2, intercept=0.0):
|
|
"""
|
|
Generate an RDD of LabeledPoints.
|
|
"""
|
|
return callMLlibFunc(
|
|
"generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures),
|
|
float(eps), int(nParts), float(intercept))
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
from pyspark.sql import SparkSession
|
|
globs = globals().copy()
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
spark = SparkSession.builder\
|
|
.master("local[2]")\
|
|
.appName("mllib.util tests")\
|
|
.getOrCreate()
|
|
globs['spark'] = spark
|
|
globs['sc'] = spark.sparkContext
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
spark.stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|