LuciferYang commented on code in PR #40255: URL: https://github.com/apache/spark/pull/40255#discussion_r1124788183
########## connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala: ########## @@ -0,0 +1,605 @@ +/* + * 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. + */ + +package org.apache.spark.sql + +import java.{lang => jl, util => ju} + +import scala.collection.JavaConverters._ + +import org.apache.spark.connect.proto.{Relation, StatSampleBy} +import org.apache.spark.sql.DataFrameStatFunctions.approxQuantileResultEncoder +import org.apache.spark.sql.catalyst.encoders.AgnosticEncoders.{ArrayEncoder, BinaryEncoder, PrimitiveDoubleEncoder} +import org.apache.spark.sql.functions.lit +import org.apache.spark.util.sketch.CountMinSketch + +/** + * Statistic functions for `DataFrame`s. + * + * @since 3.4.0 + */ +final class DataFrameStatFunctions private[sql] (sparkSession: SparkSession, root: Relation) { + + /** + * Calculates the approximate quantiles of a numerical column of a DataFrame. + * + * The result of this algorithm has the following deterministic bound: If the DataFrame has N + * elements and if we request the quantile at probability `p` up to error `err`, then the + * algorithm will return a sample `x` from the DataFrame so that the *exact* rank of `x` is + * close to (p * N). More precisely, + * + * {{{ + * floor((p - err) * N) <= rank(x) <= ceil((p + err) * N) + * }}} + * + * This method implements a variation of the Greenwald-Khanna algorithm (with some speed + * optimizations). The algorithm was first present in <a + * href="https://doi.org/10.1145/375663.375670";> Space-efficient Online Computation of Quantile + * Summaries</a> by Greenwald and Khanna. + * + * @param col + * the name of the numerical column + * @param probabilities + * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the + * minimum, 0.5 is the median, 1 is the maximum. + * @param relativeError + * The relative target precision to achieve (greater than or equal to 0). If set to zero, the + * exact quantiles are computed, which could be very expensive. Note that values greater than + * 1 are accepted but give the same result as 1. + * @return + * the approximate quantiles at the given probabilities + * + * @note + * null and NaN values will be removed from the numerical column before calculation. If the + * dataframe is empty or the column only contains null or NaN, an empty array is returned. + * + * @since 3.4.0 + */ + def approxQuantile( + col: String, + probabilities: Array[Double], + relativeError: Double): Array[Double] = { + approxQuantile(Array(col), probabilities, relativeError).head + } + + /** + * Calculates the approximate quantiles of numerical columns of a DataFrame. + * @see + * `approxQuantile(col:Str* approxQuantile)` for detailed description. + * + * @param cols + * the names of the numerical columns + * @param probabilities + * a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the + * minimum, 0.5 is the median, 1 is the maximum. + * @param relativeError + * The relative target precision to achieve (greater than or equal to 0). If set to zero, the + * exact quantiles are computed, which could be very expensive. Note that values greater than + * 1 are accepted but give the same result as 1. + * @return + * the approximate quantiles at the given probabilities of each column + * + * @note + * null and NaN values will be ignored in numerical columns before calculation. For columns + * only containing null or NaN values, an empty array is returned. + * + * @since 3.4.0 + */ + def approxQuantile( + cols: Array[String], + probabilities: Array[Double], + relativeError: Double): Array[Array[Double]] = { + require( + probabilities.forall(p => p >= 0.0 && p <= 1.0), + "percentile should be in the range [0.0, 1.0]") + require(relativeError >= 0, s"Relative Error must be non-negative but got $relativeError") + sparkSession + .newDataset(approxQuantileResultEncoder) { builder => + val approxQuantileBuilder = builder.getApproxQuantileBuilder + .setInput(root) + .setRelativeError(relativeError) + cols.foreach(approxQuantileBuilder.addCols) + probabilities.foreach(approxQuantileBuilder.addProbabilities) + } + .head() + } + + /** + * Python-friendly version of [[approxQuantile()]] + */ + private[spark] def approxQuantile( + cols: List[String], + probabilities: List[Double], + relativeError: Double): java.util.List[java.util.List[Double]] = { + approxQuantile(cols.toArray, probabilities.toArray, relativeError) + .map(_.toList.asJava) + .toList + .asJava + } + + /** + * Calculate the sample covariance of two numerical columns of a DataFrame. + * @param col1 + * the name of the first column + * @param col2 + * the name of the second column + * @return + * the covariance of the two columns. + * + * {{{ + * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) + * .withColumn("rand2", rand(seed=27)) + * df.stat.cov("rand1", "rand2") + * res1: Double = 0.065... + * }}} + * + * @since 3.4.0 + */ + def cov(col1: String, col2: String): Double = { + sparkSession + .newDataset(PrimitiveDoubleEncoder) { builder => + builder.getCovBuilder.setInput(root).setCol1(col1).setCol2(col2) + } + .head() + } + + /** + * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson + * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in + * MLlib's Statistics. + * + * @param col1 + * the name of the column + * @param col2 + * the name of the column to calculate the correlation against + * @return + * The Pearson Correlation Coefficient as a Double. + * + * {{{ + * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) + * .withColumn("rand2", rand(seed=27)) + * df.stat.corr("rand1", "rand2") + * res1: Double = 0.613... + * }}} + * + * @since 3.4.0 + */ + def corr(col1: String, col2: String, method: String): Double = { + require( + method == "pearson", + "Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + sparkSession + .newDataset(PrimitiveDoubleEncoder) { builder => + builder.getCorrBuilder.setInput(root).setCol1(col1).setCol2(col2) + } + .head() + } + + /** + * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame. + * + * @param col1 + * the name of the column + * @param col2 + * the name of the column to calculate the correlation against + * @return + * The Pearson Correlation Coefficient as a Double. + * + * {{{ + * val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) + * .withColumn("rand2", rand(seed=27)) + * df.stat.corr("rand1", "rand2", "pearson") + * res1: Double = 0.613... + * }}} + * + * @since 3.4.0 + */ + def corr(col1: String, col2: String): Double = { + corr(col1, col2, "pearson") + } + + /** + * Computes a pair-wise frequency table of the given columns. Also known as a contingency table. + * The first column of each row will be the distinct values of `col1` and the column names will + * be the distinct values of `col2`. The name of the first column will be `col1_col2`. Counts + * will be returned as `Long`s. Pairs that have no occurrences will have zero as their counts. + * Null elements will be replaced by "null", and back ticks will be dropped from elements if + * they exist. + * + * @param col1 + * The name of the first column. Distinct items will make the first item of each row. + * @param col2 + * The name of the second column. Distinct items will make the column names of the DataFrame. + * @return + * A DataFrame containing for the contingency table. + * + * {{{ + * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))) + * .toDF("key", "value") + * val ct = df.stat.crosstab("key", "value") + * ct.show() + * +---------+---+---+---+ + * |key_value| 1| 2| 3| + * +---------+---+---+---+ + * | 2| 2| 0| 1| + * | 1| 1| 1| 0| + * | 3| 0| 1| 1| + * +---------+---+---+---+ + * }}} + * + * @since 3.4.0 + */ + def crosstab(col1: String, col2: String): DataFrame = { + sparkSession.newDataFrame { builder => + builder.getCrosstabBuilder.setInput(root).setCol1(col1).setCol2(col2) + } + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the frequent element + * count algorithm described in <a href="https://doi.org/10.1145/762471.762473";>here</a>, + * proposed by Karp, Schenker, and Papadimitriou. The `support` should be greater than 1e-4. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting `DataFrame`. + * + * @param cols + * the names of the columns to search frequent items in. + * @param support + * The minimum frequency for an item to be considered `frequent`. Should be greater than 1e-4. + * @return + * A Local DataFrame with the Array of frequent items for each column. + * + * {{{ + * val rows = Seq.tabulate(100) { i => + * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) + * } + * val df = spark.createDataFrame(rows).toDF("a", "b") + * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns + * // "a" and "b" + * val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4) + * freqSingles.show() + * +-----------+-------------+ + * |a_freqItems| b_freqItems| + * +-----------+-------------+ + * | [1, 99]|[-1.0, -99.0]| + * +-----------+-------------+ + * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b" + * val pairDf = df.select(struct("a", "b").as("a-b")) + * val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1) + * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show() + * +----------+ + * | freq_ab| + * +----------+ + * | [1,-1.0]| + * | ... | + * +----------+ + * }}} + * + * @since 3.4.0 + */ + def freqItems(cols: Array[String], support: Double): DataFrame = { + sparkSession.newDataFrame { builder => + val freqItemsBuilder = builder.getFreqItemsBuilder.setInput(root).setSupport(support) + cols.foreach(freqItemsBuilder.addCols) + } + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the frequent element + * count algorithm described in <a href="https://doi.org/10.1145/762471.762473";>here</a>, + * proposed by Karp, Schenker, and Papadimitriou. Uses a `default` support of 1%. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting `DataFrame`. + * + * @param cols + * the names of the columns to search frequent items in. + * @return + * A Local DataFrame with the Array of frequent items for each column. + * + * @since 3.4.0 + */ + def freqItems(cols: Array[String]): DataFrame = { + freqItems(cols, 0.01) + } + + /** + * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in <a + * href="https://doi.org/10.1145/762471.762473";>here</a>, proposed by Karp, Schenker, and + * Papadimitriou. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting `DataFrame`. + * + * @param cols + * the names of the columns to search frequent items in. + * @return + * A Local DataFrame with the Array of frequent items for each column. + * + * {{{ + * val rows = Seq.tabulate(100) { i => + * if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) + * } + * val df = spark.createDataFrame(rows).toDF("a", "b") + * // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns + * // "a" and "b" + * val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4) + * freqSingles.show() + * +-----------+-------------+ + * |a_freqItems| b_freqItems| + * +-----------+-------------+ + * | [1, 99]|[-1.0, -99.0]| + * +-----------+-------------+ + * // find the pair of items with a frequency greater than 0.1 in columns "a" and "b" + * val pairDf = df.select(struct("a", "b").as("a-b")) + * val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1) + * freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show() + * +----------+ + * | freq_ab| + * +----------+ + * | [1,-1.0]| + * | ... | + * +----------+ + * }}} + * + * @since 3.4.0 + */ + def freqItems(cols: Seq[String], support: Double): DataFrame = { + freqItems(cols.toArray, support) + } + + /** + * (Scala-specific) Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in <a + * href="https://doi.org/10.1145/762471.762473";>here</a>, proposed by Karp, Schenker, and + * Papadimitriou. Uses a `default` support of 1%. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting `DataFrame`. + * + * @param cols + * the names of the columns to search frequent items in. + * @return + * A Local DataFrame with the Array of frequent items for each column. + * + * @since 3.4.0 + */ + def freqItems(cols: Seq[String]): DataFrame = { + freqItems(cols.toArray, 0.01) + } + + /** + * Returns a stratified sample without replacement based on the fraction given on each stratum. + * @param col + * column that defines strata + * @param fractions + * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as + * zero. + * @param seed + * random seed + * @tparam T + * stratum type + * @return + * a new `DataFrame` that represents the stratified sample + * + * {{{ + * val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), + * (3, 3))).toDF("key", "value") + * val fractions = Map(1 -> 1.0, 3 -> 0.5) + * df.stat.sampleBy("key", fractions, 36L).show() + * +---+-----+ + * |key|value| + * +---+-----+ + * | 1| 1| + * | 1| 2| + * | 3| 2| + * +---+-----+ + * }}} + * + * @since 3.4.0 + */ + def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = { + sampleBy(Column(col), fractions, seed) + } + + /** + * Returns a stratified sample without replacement based on the fraction given on each stratum. + * @param col + * column that defines strata + * @param fractions + * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as + * zero. + * @param seed + * random seed + * @tparam T + * stratum type + * @return + * a new `DataFrame` that represents the stratified sample + * + * @since 3.4.0 + */ + def sampleBy[T](col: String, fractions: ju.Map[T, jl.Double], seed: Long): DataFrame = { + sampleBy(col, fractions.asScala.toMap.asInstanceOf[Map[T, Double]], seed) + } + + /** + * Returns a stratified sample without replacement based on the fraction given on each stratum. + * @param col + * column that defines strata + * @param fractions + * sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as + * zero. + * @param seed + * random seed + * @tparam T + * stratum type + * @return + * a new `DataFrame` that represents the stratified sample + * + * The stratified sample can be performed over multiple columns: + * {{{ + * import org.apache.spark.sql.Row + * import org.apache.spark.sql.functions.struct + * + * val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17), + * ("Alice", 10))).toDF("name", "age") + * val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0) + * df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show() + * +-----+---+ + * | name|age| + * +-----+---+ + * | Nico| 8| + * |Alice| 10| + * +-----+---+ + * }}} + * + * @since 3.4.0 + */ + def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame = { + require( + fractions.values.forall(p => p >= 0.0 && p <= 1.0), + s"Fractions must be in [0, 1], but got $fractions.") + sparkSession.newDataFrame { builder => Review Comment: @hvanhovell Is there any error in the construction of this dataframe? -- This is an automated message from the Apache Git Service. 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