DenseVector是PySpark中MLlib库提供的一种向量表示方式,用于存储连续的数值数据。然而,在某些情况下,我们可能需要将这些DenseVector转换为Python的原生数组或浮点数列表,以便进行进一步的处理或分析。 DenseVector转换为数组 PySpark的DenseVector类提供了toArray方法,可以直接将DenseVector转换为一个NumPy数组或Python的原生列...
1、将一个字符或数字列转换为vector/array from pyspark.sql.functions import col,udf from pyspark.ml.linalg import Vectors, _convert_to_vector, VectorUDT, DenseVector # 数字的可转为vector,但字符串转为vector会报错 to_vec = udf(lambda x: DenseVector([x]), VectorUDT()) # 字符串转为array to_...
X:numpy array格式的数据[n_samples,n_features] 返回值:转换后指定维度的array 降维案例 流程图
(Vectors.sparse(10, [0,1,2,4,5], [1.0,5.0,3.0,5.0,7]))# >> SparseVector(10, {0: 1.0, 1: 5.0, 2: 3.0, 4: 5.0, 5: 7.0})print(Vectors.sparse(10, [0,1,2,4,5], [1.0,5.0,3.0,5.0,7]).toArray())# >> array([1., 5., 3., 0., 5., 7., 0., 0., 0., ...
array(item) return (result / len(word_seq)).tolist() avg_word_embbeding_2_udf = udf(avg_word_embbeding_2, ArrayType(FloatType())) person_behavior_vector_all_df = person_behavior_vector_df.groupBy("id").agg( avg_word_embbeding_2_udf(collect_list("person_behavior_article_vector"))...
position_vectorARRAY<double>comment "keyword")rowformat delimited fields terminatedby"/t" collection items terminatedby','; 新建文件word2vec.ipynb文件,计算职位画像结果和职位相似度 见word2vec.ipynb代码 hbase新建表 disable'position_similar'drop'position_similar'create'position_similar','similar'# 存储...
finalSample Samples: root |-- movieId: string (nullable = true) |-- genreIndexes: array (nullable = true) | |-- element: integer (containsNull = false) |-- indexSize: integer (nullable = false) |-- vector: vector (nullable = true) +---+---+---+---+ |movieId|genreIndexes|...
# put features into a feature vector column assembler = VectorAssembler(inputCols=featureCols, outputCol="features") # Initialize the `standardScaler` standardScaler=StandardScaler(inputCol="features",outputCol="features_scaled") assembled_df = assembler.transform(housing_df) ...
查看计算结果rescaledData.select("id","features").show(truncate=False)forvecinrescaledData.collect():print("text: ",vec.text)print("vector: ",list(vec.features.toArray()))print("===") 代码运行结果: +---+---+ | id| text| ...
(Vectors.sparse(10, [0,1,2,4,5], [1.0,5.0,3.0,5.0,7])) # >> SparseVector(10, {0: 1.0, 1: 5.0, 2: 3.0, 4: 5.0, 5: 7.0}) print(Vectors.sparse(10, [0,1,2,4,5], [1.0,5.0,3.0,5.0,7]).toArray()) # >> array([1., 5., 3., 0., 5., 7., 0., 0., 0...