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# # 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. #
""" Helper methods to load, save and pre-process data used in MLlib.
.. versionadded:: 1.0.0 """
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 range(nnz): index, value = items[1 + i].split(":") indices[i] = int(index) - 1 values[i] = float(value) return label, indices, values
def _convert_labeled_point_to_libsvm(p): """Converts a LabeledPoint to a string in LIBSVM format.""" from pyspark.mllib.regression import LabeledPoint 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 range(nnz): items.append(str(v.indices[i] + 1) + ":" + str(v.values[i])) else: for i in range(len(v)): items.append(str(i + 1) + ":" + str(v[i])) return " ".join(items)
""" 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.
.. versionadded:: 1.0.0
Parameters ---------- sc : :py:class:`pyspark.SparkContext` Spark context path : str file or directory path in any Hadoop-supported file system URI numFeatures : int, optional 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. minPartitions : int, optional min number of partitions
Returns ------- :py:class:`pyspark.RDD` labeled data stored as an RDD of LabeledPoint
Examples -------- >>> from tempfile import NamedTemporaryFile >>> from pyspark.mllib.util import MLUtils >>> from pyspark.mllib.regression import LabeledPoint >>> tempFile = NamedTemporaryFile(delete=True) >>> _ = 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") >>> tempFile.flush() >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect() >>> tempFile.close() >>> examples[0] LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0])) >>> examples[1] LabeledPoint(-1.0, (6,[],[])) >>> examples[2] LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0])) """
def saveAsLibSVMFile(data, dir): """ Save labeled data in LIBSVM format.
.. versionadded:: 1.0.0
Parameters ---------- data : :py:class:`pyspark.RDD` an RDD of LabeledPoint to be saved dir : str directory to save the data
Examples -------- >>> from tempfile import NamedTemporaryFile >>> from fileinput import input >>> from pyspark.mllib.regression import LabeledPoint >>> 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' """
""" Load labeled points saved using RDD.saveAsTextFile.
.. versionadded:: 1.0.0
Parameters ---------- sc : :py:class:`pyspark.SparkContext` Spark context path : str file or directory path in any Hadoop-supported file system URI minPartitions : int, optional min number of partitions
Returns ------- :py:class:`pyspark.RDD` labeled data stored as an RDD of LabeledPoint
Examples -------- >>> from tempfile import NamedTemporaryFile >>> from pyspark.mllib.util import MLUtils >>> from pyspark.mllib.regression import LabeledPoint >>> 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])] """
def appendBias(data): """ Returns a new vector with `1.0` (bias) appended to the end of the input vector. """ else:
def loadVectors(sc, path): """ Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions. """
def convertVectorColumnsToML(dataset, *cols): """ Converts vector columns in an input DataFrame from the :py:class:`pyspark.mllib.linalg.Vector` type to the new :py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml` package.
.. versionadded:: 2.0.0
Parameters ---------- dataset : :py:class:`pyspark.sql.DataFrame` input dataset \\*cols : str Vector columns to be converted.
New vector columns will be ignored. If unspecified, all old vector columns will be converted excepted nested ones.
Returns ------- :py:class:`pyspark.sql.DataFrame` the input dataset with old vector columns converted to the new vector type
Examples -------- >>> import pyspark >>> from pyspark.mllib.linalg import Vectors >>> from pyspark.mllib.util import MLUtils >>> df = spark.createDataFrame( ... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))], ... ["id", "x", "y"]) >>> r1 = MLUtils.convertVectorColumnsToML(df).first() >>> isinstance(r1.x, pyspark.ml.linalg.SparseVector) True >>> isinstance(r1.y, pyspark.ml.linalg.DenseVector) True >>> r2 = MLUtils.convertVectorColumnsToML(df, "x").first() >>> isinstance(r2.x, pyspark.ml.linalg.SparseVector) True >>> isinstance(r2.y, pyspark.mllib.linalg.DenseVector) True """ raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
def convertVectorColumnsFromML(dataset, *cols): """ Converts vector columns in an input DataFrame to the :py:class:`pyspark.mllib.linalg.Vector` type from the new :py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml` package.
.. versionadded:: 2.0.0
Parameters ---------- dataset : :py:class:`pyspark.sql.DataFrame` input dataset \\*cols : str Vector columns to be converted.
Old vector columns will be ignored. If unspecified, all new vector columns will be converted except nested ones.
Returns ------- :py:class:`pyspark.sql.DataFrame` the input dataset with new vector columns converted to the old vector type
Examples -------- >>> import pyspark >>> from pyspark.ml.linalg import Vectors >>> from pyspark.mllib.util import MLUtils >>> df = spark.createDataFrame( ... [(0, Vectors.sparse(2, [1], [1.0]), Vectors.dense(2.0, 3.0))], ... ["id", "x", "y"]) >>> r1 = MLUtils.convertVectorColumnsFromML(df).first() >>> isinstance(r1.x, pyspark.mllib.linalg.SparseVector) True >>> isinstance(r1.y, pyspark.mllib.linalg.DenseVector) True >>> r2 = MLUtils.convertVectorColumnsFromML(df, "x").first() >>> isinstance(r2.x, pyspark.mllib.linalg.SparseVector) True >>> isinstance(r2.y, pyspark.ml.linalg.DenseVector) True """ raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
def convertMatrixColumnsToML(dataset, *cols): """ Converts matrix columns in an input DataFrame from the :py:class:`pyspark.mllib.linalg.Matrix` type to the new :py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml` package.
.. versionadded:: 2.0.0
Parameters ---------- dataset : :py:class:`pyspark.sql.DataFrame` input dataset \\*cols : str Matrix columns to be converted.
New matrix columns will be ignored. If unspecified, all old matrix columns will be converted excepted nested ones.
Returns ------- :py:class:`pyspark.sql.DataFrame` the input dataset with old matrix columns converted to the new matrix type
Examples -------- >>> import pyspark >>> from pyspark.mllib.linalg import Matrices >>> from pyspark.mllib.util import MLUtils >>> df = spark.createDataFrame( ... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]), ... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"]) >>> r1 = MLUtils.convertMatrixColumnsToML(df).first() >>> isinstance(r1.x, pyspark.ml.linalg.SparseMatrix) True >>> isinstance(r1.y, pyspark.ml.linalg.DenseMatrix) True >>> r2 = MLUtils.convertMatrixColumnsToML(df, "x").first() >>> isinstance(r2.x, pyspark.ml.linalg.SparseMatrix) True >>> isinstance(r2.y, pyspark.mllib.linalg.DenseMatrix) True """ raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
def convertMatrixColumnsFromML(dataset, *cols): """ Converts matrix columns in an input DataFrame to the :py:class:`pyspark.mllib.linalg.Matrix` type from the new :py:class:`pyspark.ml.linalg.Matrix` type under the `spark.ml` package.
.. versionadded:: 2.0.0
Parameters ---------- dataset : :py:class:`pyspark.sql.DataFrame` input dataset \\*cols : str Matrix columns to be converted.
Old matrix columns will be ignored. If unspecified, all new matrix columns will be converted except nested ones.
Returns ------- :py:class:`pyspark.sql.DataFrame` the input dataset with new matrix columns converted to the old matrix type
Examples -------- >>> import pyspark >>> from pyspark.ml.linalg import Matrices >>> from pyspark.mllib.util import MLUtils >>> df = spark.createDataFrame( ... [(0, Matrices.sparse(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4]), ... Matrices.dense(2, 2, range(4)))], ["id", "x", "y"]) >>> r1 = MLUtils.convertMatrixColumnsFromML(df).first() >>> isinstance(r1.x, pyspark.mllib.linalg.SparseMatrix) True >>> isinstance(r1.y, pyspark.mllib.linalg.DenseMatrix) True >>> r2 = MLUtils.convertMatrixColumnsFromML(df, "x").first() >>> isinstance(r2.x, pyspark.mllib.linalg.SparseMatrix) True >>> isinstance(r2.y, pyspark.ml.linalg.DenseMatrix) True """ raise TypeError("Input dataset must be a DataFrame but got {}.".format(type(dataset)))
""" Mixin for models and transformers which may be saved as files.
.. versionadded:: 1.3.0 """
""" 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`.
Parameters ---------- sc : :py:class:`pyspark.SparkContext` Spark context used to save model data. path : str Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception. """ raise NotImplementedError
""" Mixin for models that provide save() through their Scala implementation.
.. versionadded:: 1.3.0 """
def save(self, sc, path): """Save this model to the given path.""" raise TypeError("sc should be a SparkContext, got type %s" % type(sc)) raise TypeError("path should be a string, got type %s" % type(path))
""" Mixin for classes which can load saved models from files.
.. versionadded:: 1.3.0 """
def load(cls, sc, path): """ Load a model from the given path. The model should have been saved using :py:meth:`Saveable.save`.
Parameters ---------- 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
""" Mixin for classes which can load saved models using its Scala implementation.
.. versionadded:: 1.3.0 """
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. """
def _load_java(cls, sc, path): """ Load a Java model from the given path. """
def load(cls, sc, path): """Load a model from the given path."""
"""Utils for generating linear data.
.. versionadded:: 1.5.0 """
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 """ "generateLinearInputWrapper", float(intercept), weights, xMean, xVariance, int(nPoints), int(seed), float(eps)))
nParts=2, intercept=0.0): """ Generate an RDD of LabeledPoints. """ "generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures), float(eps), int(nParts), float(intercept))
# The small batch size here ensures that we see multiple batches, # even in these small test examples: .master("local[2]")\ .appName("mllib.util tests")\ .getOrCreate() sys.exit(-1)
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