Bootstrapping is a statistical resampling method that consists of randomly sampling a dataset with replacement. This technique enables data scientists to estimate the sampling distribution of a wide variety of probability distributions.

**Background**

One key objective of bootstrapping is to compute the accuracy of any statistic such as mean, standard deviation, median, mode or error. These statistics,

The following diagram captures the essence of bootstrapping by resampling.

**s**are known as estimators of an approximate distribution. The most common approximate distribution is known as the empirical distribution function. In the case, the observations are independent and evenly distributed (iid), the empirical or approximate distribution can be estimated through resampling.The following diagram captures the essence of bootstrapping by resampling.

Generation of bootstrap replicates by resampling |

Each of the B bootstrap samples has the same number of observations or data points as the original dataset from which the samples are created. Once the samples are created, a statistical function s such as mean, mode, median or standard deviation is computed for each sample.

The standard deviation for the B statistics should be similar to the standard deviation of the original dataset.

The standard deviation for the B statistics should be similar to the standard deviation of the original dataset.

**Implementation in Scala**

The purpose of this post is to illustrate some basic properties of bootstrapped sampling

Let's implement a bootstrap by resampling in Scala, starting with a class

- Profile of the distribution of statistics
**s**for a given probability distribution - Comparison of the standard deviation of the statistics s with the standard deviation of the original dataset

Let's implement a bootstrap by resampling in Scala, starting with a class

*Bootstrap*.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | class Bootstrap( numSamples: Int = 1000, s: Vector[Double] => Double, inputDistribution: Vector[Double], randomizer: Random ) { lazy val bootstrappedReplicates: Array[Double] = ( 0 until numSamples )./:( new mutable.ArrayBuffer[Double] )( ( buf, _ ) => buf += createBootstrapSample ).toArray def createBootstrapSample: Double {} lazy val mean = bootstrappedReplicates.reduce( _ + _ )/numSamples def error: Double = {} } |

The class

The computation of the bootstrap replicates,

Let's implement the method

*Bootstrap*is instantiated with a predefine number of samples,*numSamples*(line 2), a statistic function*s*(line 3), a dataset generated by a given distribution*inputDistribution*(line 4) and a randomizer (line 5).The computation of the bootstrap replicates,

*bootstrappedReplicates*is central to resampling (lines 8 - 11). As described in the introduction, a replicate,*s*is computed from a sample of the original data set with the method*createBootstrapSample*(line 10).Let's implement the method

*createBootstrapSample*.1 2 3 4 5 6 7 8 9 10 | def createBootstrapSample: Double = s( ( 0 until inputDistribution.size )./:( new mutable.ArrayBuffer[Double] )( ( buf, _ ) => { randomizer.setSeed( randomizer.nextLong ) val randomValueIndex = randomizer.nextInt( inputDistribution.size ) buf += inputDistribution( randomValueIndex ) } ).toVector ) |

The method

- Samples the original dataset using a uniform random function (line 6)

- Applies the statistic function

The last step consists of computing the error (deviation) on the bootstrap replicates

*createBootstrapSample*- Samples the original dataset using a uniform random function (line 6)

- Applies the statistic function

**s**to this sample dataset (line 1 & 11)The last step consists of computing the error (deviation) on the bootstrap replicates

1 2 3 4 5 6 7 8 | def error: Double = { val sumOfSquaredDiff = bootstrappedReplicates.reduce( (s1: Double, s2: Double) => (s1 - mean) (s1 - mean) + (s2 - mean)*(s2 - mean) ) Math.sqrt(sumOfSquaredDiff / (numSamples - 1)) } |

**Evaluation**

The first evaluation consists of comparing the distribution of replicates with the original distribution. To this purpose, we generate an input dataset using

- Normal distribution
- LogNormal distribution

*bootstrapEvaluation*to compare the distribution of the bootstrap replicates with the dataset from which the bootstrap samples are generated.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | def bootstrapEvaluation( dist: RealDistribution, random: Random, gen: (Double, Double) ): (Double, Double) = { val inputDistribution = (0 until 5000)./:(new ArrayBuffer[(Double, Double)]) ( ( buf, _ ) => { val x = gen._1 * random.nextDouble - gen._2 buf += ( ( x, dist.density( x ) ) ) } ).toVector val mean = (x: Vector[Double]) => x.sum/x.length val bootstrap = new Bootstrap( numReplicates, mean, inputDistribution.map( _._2 ), new Random( System.currentTimeMillis) ) val meanS = bootstrap.bootstrappedReplicates.sum / bootstrap.bootstrappedReplicates.size val sProb = bootstrap.bootstrappedReplicates.map(_ - meanS) // .. plotting histogram of distribution sProb (bootstrap.mean, bootstrap.error) } |

We are using the normal and log normal probability density function defined in the Apache Commons Math Java library. These probability density functions are defined in the

The comparative method

Next the

Let's plot the distribution the input dataset generated from a normal density function.

*org.apache.commons.math3.distribution*package.The comparative method

*bootstrapEvaluation*has the following argument:*dist*: A probability density function used to generate the dataset upon which sampling is performed (line 2).*random*: A random number generator (line 3)*gen*: A pair of parameters for the linear transform for the generation of random values (a.r + b) (line 4).

*inputDistribution*{ (x, pdf(x)} is generated for 5000 data points (lines 7 - 13).Next the

*bootstrap*is created with the appropriate number of replicates,*numReplicates*, the*mean*of the input dataset as the statistical function**s**, the input distribution and the generic random number generator of Scala library, as arguments (lines 16 -20).Let's plot the distribution the input dataset generated from a normal density function.

val (meanNormal, errorNormal) = bootstrap( new NormalDistribution, new scala.util.Random, (5.0, 2.5) )

**Normally distributed dataset**

The first graph plots the distribution of the input dataset using the Normal distribution.

The second graph illustrates the distribution (histogram) of the replicates

*s - mean*.
The bootstrap replicates

We repeat the same process for the lognormal distribution. This time around the dataset to sample from follows a log-normal distribution.

*s(x)*are also normally distributed. The mean value for the bootstrap replicates is**0.1978**and the error is**0.001691****Dataset with a log normal distribution**We repeat the same process for the lognormal distribution. This time around the dataset to sample from follows a log-normal distribution.

val (meanLogNormal, errorLogNormal) = bootstrap( new LogNormalDistribution, new scala.util.Random, (2.0, 0.0) )

Although the original dataset used for generated the bootstrap samples is normally distribured, the bootstrap replicates

The error for the bootstrap resampling from a log normal distribution is twice as much as the error related to the normal distribution

The results is not be a surprise: The bootstrap replicates follow a Normal distribution which matches closely the original dataset created using the same probability density function.

*s(x)*are normally distributed. The mean for the bootstrap replicates is**0.3801**and the error is**0.002937**The error for the bootstrap resampling from a log normal distribution is twice as much as the error related to the normal distribution

The results is not be a surprise: The bootstrap replicates follow a Normal distribution which matches closely the original dataset created using the same probability density function.

**References**

*Programming in Scala - 3rd edition*M Odersky, L. Spoon, B. Venners - Artima - 2016

*Elements of Statistics Learning: Data mining, Inference and Prediction - 7.11 Bootstrap method*Springer - 2001