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author | Yu ISHIKAWA <yuu.ishikawa@gmail.com> | 2015-09-17 08:50:00 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-09-17 08:50:00 -0700 |
commit | 4a0b56e8dbb3713b16e58738201d838ffc4b258b (patch) | |
tree | be96c7be195f02e95ce836f49f32a3f04e114aab | |
parent | 39b44cb52eb225469eb4ccdf696f0bc6405b9184 (diff) | |
download | spark-4a0b56e8dbb3713b16e58738201d838ffc4b258b.tar.gz spark-4a0b56e8dbb3713b16e58738201d838ffc4b258b.tar.bz2 spark-4a0b56e8dbb3713b16e58738201d838ffc4b258b.zip |
[SPARK-10279] [MLLIB] [PYSPARK] [DOCS] Add @since annotation to pyspark.mllib.util
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes #8689 from yu-iskw/SPARK-10279.
-rw-r--r-- | python/pyspark/mllib/util.py | 28 |
1 files changed, 26 insertions, 2 deletions
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index 10a1e4b3eb..39bc6586dd 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -23,7 +23,7 @@ if sys.version > '3': xrange = range basestring = str -from pyspark import SparkContext +from pyspark import SparkContext, since from pyspark.mllib.common import callMLlibFunc, inherit_doc from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector @@ -32,6 +32,8 @@ class MLUtils(object): """ Helper methods to load, save and pre-process data used in MLlib. + + .. versionadded:: 1.0.0 """ @staticmethod @@ -69,6 +71,7 @@ class MLUtils(object): return " ".join(items) @staticmethod + @since("1.0.0") def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None): """ Loads labeled data in the LIBSVM format into an RDD of @@ -123,6 +126,7 @@ class MLUtils(object): return parsed.map(lambda x: LabeledPoint(x[0], Vectors.sparse(numFeatures, x[1], x[2]))) @staticmethod + @since("1.0.0") def saveAsLibSVMFile(data, dir): """ Save labeled data in LIBSVM format. @@ -147,6 +151,7 @@ class MLUtils(object): lines.saveAsTextFile(dir) @staticmethod + @since("1.1.0") def loadLabeledPoints(sc, path, minPartitions=None): """ Load labeled points saved using RDD.saveAsTextFile. @@ -172,6 +177,7 @@ class MLUtils(object): return callMLlibFunc("loadLabeledPoints", sc, path, minPartitions) @staticmethod + @since("1.5.0") def appendBias(data): """ Returns a new vector with `1.0` (bias) appended to @@ -186,6 +192,7 @@ class MLUtils(object): return _convert_to_vector(np.append(vec.toArray(), 1.0)) @staticmethod + @since("1.5.0") def loadVectors(sc, path): """ Loads vectors saved using `RDD[Vector].saveAsTextFile` @@ -197,6 +204,8 @@ class MLUtils(object): class Saveable(object): """ Mixin for models and transformers which may be saved as files. + + .. versionadded:: 1.3.0 """ def save(self, sc, path): @@ -222,9 +231,13 @@ class JavaSaveable(Saveable): """ Mixin for models that provide save() through their Scala implementation. + + .. versionadded:: 1.3.0 """ + @since("1.3.0") def save(self, sc, path): + """Save this model to the given path.""" if not isinstance(sc, SparkContext): raise TypeError("sc should be a SparkContext, got type %s" % type(sc)) if not isinstance(path, basestring): @@ -235,6 +248,8 @@ class JavaSaveable(Saveable): class Loader(object): """ Mixin for classes which can load saved models from files. + + .. versionadded:: 1.3.0 """ @classmethod @@ -256,6 +271,8 @@ class JavaLoader(Loader): """ Mixin for classes which can load saved models using its Scala implementation. + + .. versionadded:: 1.3.0 """ @classmethod @@ -280,15 +297,21 @@ class JavaLoader(Loader): 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""" + """Utils for generating linear data. + + .. versionadded:: 1.5.0 + """ @staticmethod + @since("1.5.0") def generateLinearInput(intercept, weights, xMean, xVariance, nPoints, seed, eps): """ @@ -311,6 +334,7 @@ class LinearDataGenerator(object): xVariance, int(nPoints), int(seed), float(eps))) @staticmethod + @since("1.5.0") def generateLinearRDD(sc, nexamples, nfeatures, eps, nParts=2, intercept=0.0): """ |