Repository: spark Updated Branches: refs/heads/master 46c63417c -> fae095bc7
[SPARK-3961] [MLlib] [PySpark] Python API for mllib.feature Added completed Python API for MLlib.feature Normalizer StandardScalerModel StandardScaler HashTF IDFModel IDF cc mengxr Author: Davies Liu <[email protected]> Author: Davies Liu <[email protected]> Closes #2819 from davies/feature and squashes the following commits: 4f48f48 [Davies Liu] add a note for HashingTF 67f6d21 [Davies Liu] address comments b628693 [Davies Liu] rollback changes in Word2Vec efb4f4f [Davies Liu] Merge branch 'master' into feature 806c7c2 [Davies Liu] address comments 3abb8c2 [Davies Liu] address comments 59781b9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into feature a405ae7 [Davies Liu] fix tests 7a1891a [Davies Liu] fix tests 486795f [Davies Liu] update programming guide, HashTF -> HashingTF 8a50584 [Davies Liu] Python API for mllib.feature Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/fae095bc Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/fae095bc Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/fae095bc Branch: refs/heads/master Commit: fae095bc7c4097859af522ced77f09cf6be17691 Parents: 46c6341 Author: Davies Liu <[email protected]> Authored: Tue Oct 28 03:50:22 2014 -0700 Committer: Xiangrui Meng <[email protected]> Committed: Tue Oct 28 03:50:22 2014 -0700 ---------------------------------------------------------------------- docs/mllib-feature-extraction.md | 85 ++++ .../spark/mllib/api/python/PythonMLLibAPI.scala | 49 ++- .../spark/mllib/feature/VectorTransformer.scala | 11 + .../apache/spark/mllib/feature/Word2Vec.scala | 4 +- python/pyspark/mllib/feature.py | 395 ++++++++++++++++--- python/pyspark/mllib/linalg.py | 16 +- 6 files changed, 499 insertions(+), 61 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/docs/mllib-feature-extraction.md ---------------------------------------------------------------------- diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 1162241..886d71d 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -95,8 +95,49 @@ tf.cache() val idf = new IDF(minDocFreq = 2).fit(tf) val tfidf: RDD[Vector] = idf.transform(tf) {% endhighlight %} +</div> +<div data-lang="python" markdown="1"> + +TF and IDF are implemented in [HashingTF](api/python/pyspark.mllib.html#pyspark.mllib.feature.HashingTF) +and [IDF](api/python/pyspark.mllib.html#pyspark.mllib.feature.IDF). +`HashingTF` takes an RDD of list as the input. +Each record could be an iterable of strings or other types. + +{% highlight python %} +from pyspark import SparkContext +from pyspark.mllib.feature import HashingTF + +sc = SparkContext() +# Load documents (one per line). +documents = sc.textFile("...").map(lambda line: line.split(" ")) + +hashingTF = HashingTF() +tf = hashingTF.transform(documents) +{% endhighlight %} + +While applying `HashingTF` only needs a single pass to the data, applying `IDF` needs two passes: +first to compute the IDF vector and second to scale the term frequencies by IDF. + +{% highlight python %} +from pyspark.mllib.feature import IDF + +# ... continue from the previous example +tf.cache() +idf = IDF().fit(tf) +tfidf = idf.transform(tf) +{% endhighlight %} + +MLLib's IDF implementation provides an option for ignoring terms which occur in less than a +minimum number of documents. In such cases, the IDF for these terms is set to 0. This feature +can be used by passing the `minDocFreq` value to the IDF constructor. +{% highlight python %} +# ... continue from the previous example +tf.cache() +idf = IDF(minDocFreq=2).fit(tf) +tfidf = idf.transform(tf) +{% endhighlight %} </div> </div> @@ -223,6 +264,29 @@ val data1 = data.map(x => (x.label, scaler1.transform(x.features))) val data2 = data.map(x => (x.label, scaler2.transform(Vectors.dense(x.features.toArray)))) {% endhighlight %} </div> + +<div data-lang="python"> +{% highlight python %} +from pyspark.mllib.util import MLUtils +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.feature import StandardScaler + +data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") +label = data.map(lambda x: x.label) +features = data.map(lambda x: x.features) + +scaler1 = StandardScaler().fit(features) +scaler2 = StandardScaler(withMean=True, withStd=True).fit(features) + +# data1 will be unit variance. +data1 = label.zip(scaler1.transform(features)) + +# Without converting the features into dense vectors, transformation with zero mean will raise +# exception on sparse vector. +# data2 will be unit variance and zero mean. +data2 = label.zip(scaler1.transform(features.map(lambda x: Vectors.dense(x.toArray())))) +{% endhighlight %} +</div> </div> ## Normalizer @@ -267,4 +331,25 @@ val data1 = data.map(x => (x.label, normalizer1.transform(x.features))) val data2 = data.map(x => (x.label, normalizer2.transform(x.features))) {% endhighlight %} </div> + +<div data-lang="python"> +{% highlight python %} +from pyspark.mllib.util import MLUtils +from pyspark.mllib.linalg import Vectors +from pyspark.mllib.feature import Normalizer + +data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") +labels = data.map(lambda x: x.label) +features = data.map(lambda x: x.features) + +normalizer1 = Normalizer() +normalizer2 = Normalizer(p=float("inf")) + +# Each sample in data1 will be normalized using $L^2$ norm. +data1 = labels.zip(normalizer1.transform(features)) + +# Each sample in data2 will be normalized using $L^\infty$ norm. +data2 = labels.zip(normalizer2.transform(features)) +{% endhighlight %} +</div> </div> http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index b478c21..485abe2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -31,8 +31,7 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.api.python.{PythonRDD, SerDeUtil} import org.apache.spark.mllib.classification._ import org.apache.spark.mllib.clustering._ -import org.apache.spark.mllib.feature.Word2Vec -import org.apache.spark.mllib.feature.Word2VecModel +import org.apache.spark.mllib.feature._ import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.linalg._ import org.apache.spark.mllib.random.{RandomRDDs => RG} @@ -292,6 +291,43 @@ class PythonMLLibAPI extends Serializable { } /** + * Java stub for Normalizer.transform() + */ + def normalizeVector(p: Double, vector: Vector): Vector = { + new Normalizer(p).transform(vector) + } + + /** + * Java stub for Normalizer.transform() + */ + def normalizeVector(p: Double, rdd: JavaRDD[Vector]): JavaRDD[Vector] = { + new Normalizer(p).transform(rdd) + } + + /** + * Java stub for IDF.fit(). This stub returns a + * handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on + * exit; see the Py4J documentation. + */ + def fitStandardScaler( + withMean: Boolean, + withStd: Boolean, + data: JavaRDD[Vector]): StandardScalerModel = { + new StandardScaler(withMean, withStd).fit(data.rdd) + } + + /** + * Java stub for IDF.fit(). This stub returns a + * handle to the Java object instead of the content of the Java object. + * Extra care needs to be taken in the Python code to ensure it gets freed on + * exit; see the Py4J documentation. + */ + def fitIDF(minDocFreq: Int, dataset: JavaRDD[Vector]): IDFModel = { + new IDF(minDocFreq).fit(dataset) + } + + /** * Java stub for Python mllib Word2Vec fit(). This stub returns a * handle to the Java object instead of the content of the Java object. * Extra care needs to be taken in the Python code to ensure it gets freed on @@ -328,6 +364,15 @@ class PythonMLLibAPI extends Serializable { model.transform(word) } + /** + * Transforms an RDD of words to its vector representation + * @param rdd an RDD of words + * @return an RDD of vector representations of words + */ + def transform(rdd: JavaRDD[String]): JavaRDD[Vector] = { + rdd.rdd.map(model.transform) + } + def findSynonyms(word: String, num: Int): java.util.List[java.lang.Object] = { val vec = transform(word) findSynonyms(vec, num) http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala index 415a845..7358c1c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala @@ -18,6 +18,7 @@ package org.apache.spark.mllib.feature import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector import org.apache.spark.rdd.RDD @@ -48,4 +49,14 @@ trait VectorTransformer extends Serializable { data.map(x => this.transform(x)) } + /** + * Applies transformation on an JavaRDD[Vector]. + * + * @param data JavaRDD[Vector] to be transformed. + * @return transformed JavaRDD[Vector]. + */ + def transform(data: JavaRDD[Vector]): JavaRDD[Vector] = { + transform(data.rdd) + } + } http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index d321994..f5f7ad6 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -432,7 +432,7 @@ class Word2VecModel private[mllib] ( throw new IllegalStateException(s"$word not in vocabulary") } } - + /** * Find synonyms of a word * @param word a word @@ -443,7 +443,7 @@ class Word2VecModel private[mllib] ( val vector = transform(word) findSynonyms(vector,num) } - + /** * Find synonyms of the vector representation of a word * @param vector vector representation of a word http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/python/pyspark/mllib/feature.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index b5a3f22..3243434 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -18,59 +18,357 @@ """ Python package for feature in MLlib. """ +import sys +import warnings + +import py4j.protocol +from py4j.protocol import Py4JJavaError +from py4j.java_gateway import JavaObject + +from pyspark import RDD, SparkContext from pyspark.serializers import PickleSerializer, AutoBatchedSerializer -from pyspark.mllib.linalg import _convert_to_vector, _to_java_object_rdd +from pyspark.mllib.linalg import Vectors, _to_java_object_rdd + +__all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler', + 'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel'] + + +# Hack for support float('inf') in Py4j +_old_smart_decode = py4j.protocol.smart_decode + +_float_str_mapping = { + u'nan': u'NaN', + u'inf': u'Infinity', + u'-inf': u'-Infinity', +} + + +def _new_smart_decode(obj): + if isinstance(obj, float): + s = unicode(obj) + return _float_str_mapping.get(s, s) + return _old_smart_decode(obj) + +py4j.protocol.smart_decode = _new_smart_decode + + +# TODO: move these helper functions into utils +_picklable_classes = [ + 'LinkedList', + 'SparseVector', + 'DenseVector', + 'DenseMatrix', + 'Rating', + 'LabeledPoint', +] + + +def _py2java(sc, a): + """ Convert Python object into Java """ + if isinstance(a, RDD): + a = _to_java_object_rdd(a) + elif not isinstance(a, (int, long, float, bool, basestring)): + bytes = bytearray(PickleSerializer().dumps(a)) + a = sc._jvm.SerDe.loads(bytes) + return a + + +def _java2py(sc, r): + if isinstance(r, JavaObject): + clsName = r.getClass().getSimpleName() + if clsName in ("RDD", "JavaRDD"): + if clsName == "RDD": + r = r.toJavaRDD() + jrdd = sc._jvm.SerDe.javaToPython(r) + return RDD(jrdd, sc, AutoBatchedSerializer(PickleSerializer())) -__all__ = ['Word2Vec', 'Word2VecModel'] + elif clsName in _picklable_classes: + r = sc._jvm.SerDe.dumps(r) + if isinstance(r, bytearray): + r = PickleSerializer().loads(str(r)) + return r -class Word2VecModel(object): + +def _callJavaFunc(sc, func, *args): + """ Call Java Function """ - class for Word2Vec model + args = [_py2java(sc, a) for a in args] + return _java2py(sc, func(*args)) + + +def _callAPI(sc, name, *args): + """ Call API in PythonMLLibAPI """ - def __init__(self, sc, java_model): + api = getattr(sc._jvm.PythonMLLibAPI(), name) + return _callJavaFunc(sc, api, *args) + + +class VectorTransformer(object): + """ + :: DeveloperApi :: + + Base class for transformation of a vector or RDD of vector + """ + def transform(self, vector): + """ + Applies transformation on a vector. + + :param vector: vector to be transformed. + """ + raise NotImplementedError + + +class Normalizer(VectorTransformer): + """ + :: Experimental :: + + Normalizes samples individually to unit L\ :sup:`p`\ norm + + For any 1 <= `p` <= float('inf'), normalizes samples using + sum(abs(vector). :sup:`p`) :sup:`(1/p)` as norm. + + For `p` = float('inf'), max(abs(vector)) will be used as norm for normalization. + + >>> v = Vectors.dense(range(3)) + >>> nor = Normalizer(1) + >>> nor.transform(v) + DenseVector([0.0, 0.3333, 0.6667]) + + >>> rdd = sc.parallelize([v]) + >>> nor.transform(rdd).collect() + [DenseVector([0.0, 0.3333, 0.6667])] + + >>> nor2 = Normalizer(float("inf")) + >>> nor2.transform(v) + DenseVector([0.0, 0.5, 1.0]) + """ + def __init__(self, p=2.0): """ - :param sc: Spark context - :param java_model: Handle to Java model object + :param p: Normalization in L^p^ space, p = 2 by default. """ + assert p >= 1.0, "p should be greater than 1.0" + self.p = float(p) + + def transform(self, vector): + """ + Applies unit length normalization on a vector. + + :param vector: vector to be normalized. + :return: normalized vector. If the norm of the input is zero, it + will return the input vector. + """ + sc = SparkContext._active_spark_context + assert sc is not None, "SparkContext should be initialized first" + return _callAPI(sc, "normalizeVector", self.p, vector) + + +class JavaModelWrapper(VectorTransformer): + """ + Wrapper for the model in JVM + """ + def __init__(self, sc, java_model): self._sc = sc self._java_model = java_model def __del__(self): self._sc._gateway.detach(self._java_model) - def transform(self, word): + def transform(self, dataset): + return _callJavaFunc(self._sc, self._java_model.transform, dataset) + + +class StandardScalerModel(JavaModelWrapper): + """ + :: Experimental :: + + Represents a StandardScaler model that can transform vectors. + """ + def transform(self, vector): """ - :param word: a word - :return: vector representation of word + Applies standardization transformation on a vector. + + :param vector: Vector to be standardized. + :return: Standardized vector. If the variance of a column is zero, + it will return default `0.0` for the column with zero variance. + """ + return JavaModelWrapper.transform(self, vector) + + +class StandardScaler(object): + """ + :: Experimental :: + + Standardizes features by removing the mean and scaling to unit + variance using column summary statistics on the samples in the + training set. + >>> vs = [Vectors.dense([-2.0, 2.3, 0]), Vectors.dense([3.8, 0.0, 1.9])] + >>> dataset = sc.parallelize(vs) + >>> standardizer = StandardScaler(True, True) + >>> model = standardizer.fit(dataset) + >>> result = model.transform(dataset) + >>> for r in result.collect(): r + DenseVector([-0.7071, 0.7071, -0.7071]) + DenseVector([0.7071, -0.7071, 0.7071]) + """ + def __init__(self, withMean=False, withStd=True): + """ + :param withMean: False by default. Centers the data with mean + before scaling. It will build a dense output, so this + does not work on sparse input and will raise an exception. + :param withStd: True by default. Scales the data to unit standard + deviation. + """ + if not (withMean or withStd): + warnings.warn("Both withMean and withStd are false. The model does nothing.") + self.withMean = withMean + self.withStd = withStd + + def fit(self, dataset): + """ + Computes the mean and variance and stores as a model to be used for later scaling. + + :param data: The data used to compute the mean and variance to build + the transformation model. + :return: a StandardScalarModel + """ + sc = dataset.context + jmodel = _callAPI(sc, "fitStandardScaler", self.withMean, self.withStd, dataset) + return StandardScalerModel(sc, jmodel) + + +class HashingTF(object): + """ + :: Experimental :: + + Maps a sequence of terms to their term frequencies using the hashing trick. + + Note: the terms must be hashable (can not be dict/set/list...). + + >>> htf = HashingTF(100) + >>> doc = "a a b b c d".split(" ") + >>> htf.transform(doc) + SparseVector(100, {1: 1.0, 14: 1.0, 31: 2.0, 44: 2.0}) + """ + def __init__(self, numFeatures=1 << 20): + """ + :param numFeatures: number of features (default: 2^20) + """ + self.numFeatures = numFeatures + + def indexOf(self, term): + """ Returns the index of the input term. """ + return hash(term) % self.numFeatures + + def transform(self, document): + """ + Transforms the input document (list of terms) to term frequency vectors, + or transform the RDD of document to RDD of term frequency vectors. + """ + if isinstance(document, RDD): + return document.map(self.transform) + + freq = {} + for term in document: + i = self.indexOf(term) + freq[i] = freq.get(i, 0) + 1.0 + return Vectors.sparse(self.numFeatures, freq.items()) + + +class IDFModel(JavaModelWrapper): + """ + Represents an IDF model that can transform term frequency vectors. + """ + def transform(self, dataset): + """ + Transforms term frequency (TF) vectors to TF-IDF vectors. + + If `minDocFreq` was set for the IDF calculation, + the terms which occur in fewer than `minDocFreq` + documents will have an entry of 0. + + :param dataset: an RDD of term frequency vectors + :return: an RDD of TF-IDF vectors + """ + return JavaModelWrapper.transform(self, dataset) + + +class IDF(object): + """ + :: Experimental :: + + Inverse document frequency (IDF). + + The standard formulation is used: `idf = log((m + 1) / (d(t) + 1))`, + where `m` is the total number of documents and `d(t)` is the number + of documents that contain term `t`. + + This implementation supports filtering out terms which do not appear + in a minimum number of documents (controlled by the variable `minDocFreq`). + For terms that are not in at least `minDocFreq` documents, the IDF is + found as 0, resulting in TF-IDFs of 0. + + >>> n = 4 + >>> freqs = [Vectors.sparse(n, (1, 3), (1.0, 2.0)), + ... Vectors.dense([0.0, 1.0, 2.0, 3.0]), + ... Vectors.sparse(n, [1], [1.0])] + >>> data = sc.parallelize(freqs) + >>> idf = IDF() + >>> model = idf.fit(data) + >>> tfidf = model.transform(data) + >>> for r in tfidf.collect(): r + SparseVector(4, {1: 0.0, 3: 0.5754}) + DenseVector([0.0, 0.0, 1.3863, 0.863]) + SparseVector(4, {1: 0.0}) + """ + def __init__(self, minDocFreq=0): + """ + :param minDocFreq: minimum of documents in which a term + should appear for filtering + """ + self.minDocFreq = minDocFreq + + def fit(self, dataset): + """ + Computes the inverse document frequency. + + :param dataset: an RDD of term frequency vectors + """ + sc = dataset.context + jmodel = _callAPI(sc, "fitIDF", self.minDocFreq, dataset) + return IDFModel(sc, jmodel) + + +class Word2VecModel(JavaModelWrapper): + """ + class for Word2Vec model + """ + def transform(self, word): + """ Transforms a word to its vector representation Note: local use only + + :param word: a word + :return: vector representation of word(s) """ - # TODO: make transform usable in RDD operations from python side - result = self._java_model.transform(word) - return PickleSerializer().loads(str(self._sc._jvm.SerDe.dumps(result))) + try: + return _callJavaFunc(self._sc, self._java_model.transform, word) + except Py4JJavaError: + raise ValueError("%s not found" % word) - def findSynonyms(self, x, num): + def findSynonyms(self, word, num): """ - :param x: a word or a vector representation of word + Find synonyms of a word + + :param word: a word or a vector representation of word :param num: number of synonyms to find :return: array of (word, cosineSimilarity) - Find synonyms of a word - Note: local use only """ - # TODO: make findSynonyms usable in RDD operations from python side - ser = PickleSerializer() - if type(x) == str: - jlist = self._java_model.findSynonyms(x, num) - else: - bytes = bytearray(ser.dumps(_convert_to_vector(x))) - vec = self._sc._jvm.SerDe.loads(bytes) - jlist = self._java_model.findSynonyms(vec, num) - words, similarity = ser.loads(str(self._sc._jvm.SerDe.dumps(jlist))) + words, similarity = _callJavaFunc(self._sc, self._java_model.findSynonyms, word, num) return zip(words, similarity) @@ -85,6 +383,7 @@ class Word2Vec(object): We used skip-gram model in our implementation and hierarchical softmax method to train the model. The variable names in the implementation matches the original C implementation. + For original C implementation, see https://code.google.com/p/word2vec/ For research papers, see Efficient Estimation of Word Representations in Vector Space @@ -95,33 +394,26 @@ class Word2Vec(object): >>> localDoc = [sentence, sentence] >>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" ")) >>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc) + >>> syms = model.findSynonyms("a", 2) - >>> str(syms[0][0]) - 'b' - >>> str(syms[1][0]) - 'c' - >>> len(syms) - 2 + >>> [s[0] for s in syms] + [u'b', u'c'] >>> vec = model.transform("a") - >>> len(vec) - 10 >>> syms = model.findSynonyms(vec, 2) - >>> str(syms[0][0]) - 'b' - >>> str(syms[1][0]) - 'c' - >>> len(syms) - 2 + >>> [s[0] for s in syms] + [u'b', u'c'] """ def __init__(self): """ Construct Word2Vec instance """ + import random # this can't be on the top because of mllib.random + self.vectorSize = 100 self.learningRate = 0.025 self.numPartitions = 1 self.numIterations = 1 - self.seed = 42L + self.seed = random.randint(0, sys.maxint) def setVectorSize(self, vectorSize): """ @@ -164,20 +456,13 @@ class Word2Vec(object): Computes the vector representation of each word in vocabulary. :param data: training data. RDD of subtype of Iterable[String] - :return: python Word2VecModel instance + :return: Word2VecModel instance """ sc = data.context - ser = PickleSerializer() - vectorSize = self.vectorSize - learningRate = self.learningRate - numPartitions = self.numPartitions - numIterations = self.numIterations - seed = self.seed - - model = sc._jvm.PythonMLLibAPI().trainWord2Vec( - _to_java_object_rdd(data), vectorSize, - learningRate, numPartitions, numIterations, seed) - return Word2VecModel(sc, model) + jmodel = _callAPI(sc, "trainWord2Vec", data, int(self.vectorSize), + float(self.learningRate), int(self.numPartitions), + int(self.numIterations), long(self.seed)) + return Word2VecModel(sc, jmodel) def _test(): @@ -191,4 +476,8 @@ def _test(): exit(-1) if __name__ == "__main__": + # remove current path from list of search paths to avoid importing mllib.random + # for C{import random}, which is done in an external dependency of pyspark during doctests. + import sys + sys.path.pop(0) _test() http://git-wip-us.apache.org/repos/asf/spark/blob/fae095bc/python/pyspark/mllib/linalg.py ---------------------------------------------------------------------- diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 773d8d3..1b9bf59 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -111,6 +111,13 @@ def _vector_size(v): raise TypeError("Cannot treat type %s as a vector" % type(v)) +def _format_float(f, digits=4): + s = str(round(f, digits)) + if '.' in s: + s = s[:s.index('.') + 1 + digits] + return s + + class Vector(object): """ Abstract class for DenseVector and SparseVector @@ -228,7 +235,7 @@ class DenseVector(Vector): return "[" + ",".join([str(v) for v in self.array]) + "]" def __repr__(self): - return "DenseVector(%r)" % self.array + return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array)) def __eq__(self, other): return isinstance(other, DenseVector) and self.array == other.array @@ -416,7 +423,7 @@ class SparseVector(Vector): Returns a copy of this SparseVector as a 1-dimensional NumPy array. """ arr = np.zeros((self.size,), dtype=np.float64) - for i in xrange(self.indices.size): + for i in xrange(len(self.indices)): arr[self.indices[i]] = self.values[i] return arr @@ -431,7 +438,8 @@ class SparseVector(Vector): def __repr__(self): inds = self.indices vals = self.values - entries = ", ".join(["{0}: {1}".format(inds[i], vals[i]) for i in xrange(len(inds))]) + entries = ", ".join(["{0}: {1}".format(inds[i], _format_float(vals[i])) + for i in xrange(len(inds))]) return "SparseVector({0}, {{{1}}})".format(self.size, entries) def __eq__(self, other): @@ -491,7 +499,7 @@ class Vectors(object): returns a NumPy array. >>> Vectors.dense([1, 2, 3]) - DenseVector(array('d', [1.0, 2.0, 3.0])) + DenseVector([1.0, 2.0, 3.0]) """ return DenseVector(elements) --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
