spark 数据预处理 特征标准化 归一化模块
#We will also standardise our data as we have done so far when performing distance-based clustering. from pyspark.mllib.feature import StandardScaler standardizer = StandardScaler(True, True) t0 = time() standardizer_model = standardizer.fit(parsed_data_values) tt = time() - t0 standardized_data_values = standardizer_model.transform(parsed_data_values) print "Data standardized in {} seconds".format(round(tt,3)) Data standardized in 9.54 seconds We can now perform k-means clustering. from pyspark.mllib.clustering import KMeans t0 = time() clusters = KMeans.train(standardized_data_values, 80, maxIterations=10, runs=5, initializationMode="random") tt = time() - t0 print "Data clustered in {} seconds".format(round(tt,3)) Data clustered in 137.496 seconds kmeans demo 摘自：http://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#module-pyspark.mllib.feature pyspark.mllib.feature module Python package for feature in MLlib. classpyspark.mllib.feature.Normalizer( p=2.0) [source] Bases:pyspark.mllib.feature.VectorTransformer Normalizes samples individually to unit Lpnorm For any 1 <=p< float(‘inf’), normalizes samples using sum(abs(vector)p)(1/p)as norm. Forp= float(‘inf’), max(abs(vector)) will be used as norm for normalization. Parameters: p– Normalization in L^p^ space, p = 2 by default. >>> 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]) New in version 1.2.0. transform( vector) [source] Applies unit length normalization on a vector. Parameters: vector– vector or RDD of vector to be normalized. Returns: normalized vector. If the norm of the input is zero, it will return the input vector. New in version 1.2.0. classpyspark.mllib.feature.StandardScalerModel( java_model) [source] Bases:pyspark.mllib.feature.JavaVectorTransformer Represents a StandardScaler model that can transform vectors. New in version 1.2.0. mean [source] Return the column mean values. New in version 2.0.0. setWithMean( withMean) [source] Setter of the boolean which decides whether it uses mean or not New in version 1.4.0. setWithStd( withStd) [source] Setter of the boolean which decides whether it uses std or not New in version 1.4.0. std [source] Return the column standard deviation values. New in version 2.0.0. transform( vector) [source] Applies standardization transformation on a vector. Note In Python, transform cannot currently be used within an RDD transformation or action. Call transform directly on the RDD instead. Parameters: vector– Vector or RDD of Vector to be standardized. Returns: Standardized vector. If the variance of a column is zero, it will return default0.0for the column with zero variance. New in version 1.2.0. withMean [source] Returns if the model centers the data before scaling. New in version 2.0.0. withStd [source] Returns if the model scales the data to unit standard deviation. New in version 2.0.0. classpyspark.mllib.feature.StandardScaler( withMean=False, withStd=True) [source] Bases:object Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. Parameters: withMean– False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. withStd– True by default. Scales the data to unit standard deviation. >>> 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]) >>> int(model.std[0]) 4 >>> int(model.mean[0]*10) 9 >>> model.withStd True >>> model.withMean True New in version 1.2.0. fit( dataset) [source] Computes the mean and variance and stores as a model to be used for later scaling. Parameters: dataset– The data used to compute the mean and variance to build the transformation model. Returns: a StandardScalarModel New in version 1.2.0. classpyspark.mllib.feature.HashingTF( numFeatures=1048576) [source] Bases:object 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...). Parameters: numFeatures– number of features (default: 2^20) >>> htf = HashingTF(100) >>> doc = "a a b b c d".split(" ") >>> htf.transform(doc) SparseVector(100, {...}) New in version 1.2.0. indexOf( term) [source] Returns the index of the input term. New in version 1.2.0. setBinary( value) [source] If True, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: False) New in version 2.0.0. transform( document) [source] Transforms the input document (list of terms) to term frequency vectors, or transform the RDD of document to RDD of term frequency vectors. New in version 1.2.0. classpyspark.mllib.feature.IDFModel( java_model) [source] Bases:pyspark.mllib.feature.JavaVectorTransformer Represents an IDF model that can transform term frequency vectors. New in version 1.2.0. idf() [source] Returns the current IDF vector. New in version 1.4.0. transform( x) [source] Transforms term frequency (TF) vectors to TF-IDF vectors. IfminDocFreqwas set for the IDF calculation, the terms which occur in fewer thanminDocFreqdocuments will have an entry of 0. Note In Python, transform cannot currently be used within an RDD transformation or action. Call transform directly on the RDD instead. Parameters: x– an RDD of term frequency vectors or a term frequency vector Returns: an RDD of TF-IDF vectors or a TF-IDF vector New in version 1.2.0. classpyspark.mllib.feature.IDF( minDocFreq=0) [source] Bases:object Inverse document frequency (IDF). The standard formulation is used:idf = log((m + 1) / (d(t) + 1)), wheremis the total number of documents andd(t)is the number of documents that contain termt. This implementation supports filtering out terms which do not appear in a minimum number of documents (controlled by the variableminDocFreq). For terms that are not in at leastminDocFreqdocuments, the IDF is found as 0, resulting in TF-IDFs of 0. Parameters: minDocFreq– minimum of documents in which a term should appear for filtering >>> 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}) >>> model.transform(Vectors.dense([0.0, 1.0, 2.0, 3.0])) DenseVector([0.0, 0.0, 1.3863, 0.863]) >>> model.transform([0.0, 1.0, 2.0, 3.0]) DenseVector([0.0, 0.0, 1.3863, 0.863]) >>> model.transform(Vectors.sparse(n, (1, 3), (1.0, 2.0))) SparseVector(4, {1: 0.0, 3: 0.5754}) New in version 1.2.0. fit( dataset) [source] Computes the inverse document frequency. Parameters: dataset– an RDD of term frequency vectors New in version 1.2.0. classpyspark.mllib.feature.Word2Vec [source] Bases:object Word2Vec creates vector representation of words in a text corpus. The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms. 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, seehttps://code.google.com/p/word2vec/For research papers, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality. >>> sentence = "a b " * 100 + "a c " * 10 >>> localDoc = [sentence, sentence] >>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" ")) >>> model = Word2Vec().setVectorSize(10).setSeed(42).fit(doc) Querying for synonyms of a word will not return that word: >>> syms = model.findSynonyms("a", 2) >>> [s[0] for s in syms] [u'b', u'c'] But querying for synonyms of a vector may return the word whose representation is that vector: >>> vec = model.transform("a") >>> syms = model.findSynonyms(vec, 2) >>> [s[0] for s in syms] [u'a', u'b'] >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = Word2VecModel.load(sc, path) >>> model.transform("a") == sameModel.transform("a") True >>> syms = sameModel.findSynonyms("a", 2) >>> [s[0] for s in syms] [u'b', u'c'] >>> from shutil import rmtree >>> try: ... rmtree(path) ... except OSError: ... pass New in version 1.2.0. fit( data) [source] Computes the vector representation of each word in vocabulary. Parameters: data– training data. RDD of list of string Returns: Word2VecModel instance New in version 1.2.0. setLearningRate( learningRate) [source] Sets initial learning rate (default: 0.025). New in version 1.2.0. setMinCount( minCount) [source] Sets minCount, the minimum number of times a token must appear to be included in the word2vec model’s vocabulary (default: 5). New in version 1.4.0. setNumIterations( numIterations) [source] Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions. New in version 1.2.0. setNumPartitions( numPartitions) [source] Sets number of partitions (default: 1). Use a small number for accuracy. New in version 1.2.0. setSeed( seed) [source] Sets random seed. New in version 1.2.0. setVectorSize( vectorSize) [source] Sets vector size (default: 100). New in version 1.2.0. setWindowSize( windowSize) [source] Sets window size (default: 5). New in version 2.0.0. classpyspark.mllib.feature.Word2VecModel( java_model) [source] Bases:pyspark.mllib.feature.JavaVectorTransformer,pyspark.mllib.util.JavaSaveable,pyspark.mllib.util.JavaLoader class for Word2Vec model New in version 1.2.0. findSynonyms( word, num) [source] Find synonyms of a word Parameters: word– a word or a vector representation of word num– number of synonyms to find Returns: array of (word, cosineSimilarity) Note Local use only New in version 1.2.0. getVectors() [source] Returns a map of words to their vector representations. New in version 1.4.0. classmethodload( sc, path) [source] Load a model from the given path. New in version 1.5.0. transform( word) [source] Transforms a word to its vector representation Note Local use only Parameters: word– a word Returns: vector representation of word(s) New in version 1.2.0. classpyspark.mllib.feature.ChiSqSelector( numTopFeatures=50, selectorType='numTopFeatures', percentile=0.1, fpr=0.05, fdr=0.05, fwe=0.05) [source] Bases:object Creates a ChiSquared feature selector. The selector supports different selection methods:numTopFeatures,percentile,fpr,fdr,fwe. numTopFeatureschooses a fixed number of top features according to a chi-squared test. percentileis similar but chooses a fraction of all features instead of a fixed number. fprchooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection. fdruses theBenjamini-Hochberg procedureto choose all features whose false discovery rate is below a threshold. fwechooses all features whose p-values are below a threshold. The threshold is scaled by 1/numFeatures, thus controlling the family-wise error rate of selection. By default, the selection method isnumTopFeatures, with the default number of top features set to 50. >>> data = sc.parallelize([ ... LabeledPoint(0.0, SparseVector(3, {0: 8.0, 1: 7.0})), ... LabeledPoint(1.0, SparseVector(3, {1: 9.0, 2: 6.0})), ... LabeledPoint(1.0, [0.0, 9.0, 8.0]), ... LabeledPoint(2.0, [7.0, 9.0, 5.0]), ... LabeledPoint(2.0, [8.0, 7.0, 3.0]) ... ]) >>> model = ChiSqSelector(numTopFeatures=1).fit(data) >>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0})) SparseVector(1, {}) >>> model.transform(DenseVector([7.0, 9.0, 5.0])) DenseVector([7.0]) >>> model = ChiSqSelector(selectorType="fpr", fpr=0.2).fit(data) >>> model.transform(SparseVector(3, {1: 9.0, 2: 6.0})) SparseVector(1, {}) >>> model.transform(DenseVector([7.0, 9.0, 5.0])) DenseVector([7.0]) >>> model = ChiSqSelector(selectorType="percentile", percentile=0.34).fit(data) >>> model.transform(DenseVector([7.0, 9.0, 5.0])) DenseVector([7.0]) New in version 1.4.0. fit( data) [source] Returns a ChiSquared feature selector. Parameters: data– anRDD[LabeledPoint]containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value. Apply feature discretizer before using this function. New in version 1.4.0. setFdr( fdr) [source] set FDR [0.0, 1.0] for feature selection by FDR. Only applicable when selectorType = “fdr”. New in version 2.2.0. setFpr( fpr) [source] set FPR [0.0, 1.0] for feature selection by FPR. Only applicable when selectorType = “fpr”. New in version 2.1.0. setFwe( fwe) [source] set FWE [0.0, 1.0] for feature selection by FWE. Only applicable when selectorType = “fwe”. New in version 2.2.0. setNumTopFeatures( numTopFeatures) [source] set numTopFeature for feature selection by number of top features. Only applicable when selectorType = “numTopFeatures”. New in version 2.1.0. setPercentile( percentile) [source] set percentile [0.0, 1.0] for feature selection by percentile. Only applicable when selectorType = “percentile”. New in version 2.1.0. setSelectorType( selectorType) [source] set the selector type of the ChisqSelector. Supported options: “numTopFeatures” (default), “percentile”, “fpr”, “fdr”, “fwe”. New in version 2.1.0. classpyspark.mllib.feature.ChiSqSelectorModel( java_model) [source] Bases:pyspark.mllib.feature.JavaVectorTransformer Represents a Chi Squared selector model. New in version 1.4.0. transform( vector) [source] Applies transformation on a vector. Parameters: vector– Vector or RDD of Vector to be transformed. Returns: transformed vector. New in version 1.4.0. classpyspark.mllib.feature.ElementwiseProduct( scalingVector) [source] Bases:pyspark.mllib.feature.VectorTransformer Scales each column of the vector, with the supplied weight vector. i.e the elementwise product. >>> weight = Vectors.dense([1.0, 2.0, 3.0]) >>> eprod = ElementwiseProduct(weight) >>> a = Vectors.dense([2.0, 1.0, 3.0]) >>> eprod.transform(a) DenseVector([2.0, 2.0, 9.0]) >>> b = Vectors.dense([9.0, 3.0, 4.0]) >>> rdd = sc.parallelize([a, b]) >>> eprod.transform(rdd).collect() [DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])] New in version 1.5.0. transform( vector) [source] Computes the Hadamard product of the vector. New in version 1.5.0. 本文转自张昺华-sky博客园博客，原文链接：http://www.cnblogs.com/bonelee/p/7774142.html，如需转载请自行联系原作者
