DataVec Analysis

Analysis of data

Sometimes datasets are too large or too abstract in their format to manually analyze and estimate statistics on certain columns or patterns. DataVec comes with some helper utilities for performing a data analysis, and maximums, means, minimums, and other useful metrics.

Using Spark for analysis

If you have loaded your data into Apache Spark, DataVec has a special AnalyzeSpark class which can generate histograms, collect statistics, and return information about the quality of the data. Assuming you have already loaded your data into a Spark RDD, pass the JavaRDD and Schema to the class.

If you are using DataVec in Scala and your data was loaded into a regular RDD class, you can convert it by calling .toJavaRDD() which returns a JavaRDD. If you need to convert it back, call rdd().

The code below demonstrates some of many analyses for a 2D dataset in Spark analysis using the RDD javaRdd and the schema mySchema:

import org.datavec.spark.transform.AnalyzeSpark;
import org.datavec.api.writable.Writable;
import org.datavec.api.transform.analysis.*;

int maxHistogramBuckets = 10
DataAnalysis analysis = AnalyzeSpark.analyze(mySchema, javaRdd, maxHistogramBuckets)

DataQualityAnalysis analysis = AnalyzeSpark.analyzeQuality(mySchema, javaRdd)

Writable max = AnalyzeSpark.max(javaRdd, "myColumn", mySchema)

int numSamples = 5
List<Writable> sample = AnalyzeSpark.sampleFromColumn(numSamples, "myColumn", mySchema, javaRdd)

Note that if you have sequence data, there are special methods for that as well:

SequenceDataAnalysis seqAnalysis = AnalyzeSpark.analyzeSequence(mySchema, sequenceRdd)

List<Writable> uniqueSequence = AnalyzeSpark.getUniqueSequence("myColumn", seqSchema, sequenceRdd)

Analyzing locally

The AnalyzeLocal class works very similarly to its Spark counterpart and has a similar API. Instead of passing an RDD, it accepts a RecordReader which allows it to iterate over the dataset.

import org.datavec.local.transforms.AnalyzeLocal;

int maxHistogramBuckets = 10
DataAnalysis analysis = AnalyzeLocal.analyze(mySchema, csvRecordReader, maxHistogramBuckets)

Utilities


AnalyzeLocal

[source]

Analyse the specified data - returns a DataAnalysis object with summary information about each column

analyze
public static DataAnalysis analyze(Schema schema, RecordReader rr, int maxHistogramBuckets)

Analyse the specified data - returns a DataAnalysis object with summary information about each column

  • param schema Schema for data
  • param rr Data to analyze
  • return DataAnalysis for data
analyzeQualitySequence
public static DataQualityAnalysis analyzeQualitySequence(Schema schema, SequenceRecordReader data) 

Analyze the data quality of sequence data - provides a report on missing values, values that don’t comply with schema, etc

  • param schema Schema for data
  • param data Data to analyze
  • return DataQualityAnalysis object
analyzeQuality
public static DataQualityAnalysis analyzeQuality(final Schema schema, final RecordReader data) 

Analyze the data quality of data - provides a report on missing values, values that don’t comply with schema, etc

  • param schema Schema for data
  • param data Data to analyze
  • return DataQualityAnalysis object

AnalyzeSpark

[source]

AnalizeSpark: static methods for analyzing and

analyzeSequence
public static SequenceDataAnalysis analyzeSequence(Schema schema, JavaRDD<List<List<Writable>>> data,
                    int maxHistogramBuckets) 
  • param schema
  • param data
  • param maxHistogramBuckets
  • return
analyze
public static DataAnalysis analyze(Schema schema, JavaRDD<List<Writable>> data) 

Analyse the specified data - returns a DataAnalysis object with summary information about each column

  • param schema Schema for data
  • param data Data to analyze
  • return DataAnalysis for data
analyzeQualitySequence
public static DataQualityAnalysis analyzeQualitySequence(Schema schema, JavaRDD<List<List<Writable>>> data) 

Randomly sample values from a single column

  • param count Number of values to sample
  • param columnName Name of the column to sample from
  • param schema Schema
  • param data Data to sample from
  • return A list of random samples
analyzeQuality
public static DataQualityAnalysis analyzeQuality(final Schema schema, final JavaRDD<List<Writable>> data) 

Analyze the data quality of data - provides a report on missing values, values that don’t comply with schema, etc

  • param schema Schema for data
  • param data Data to analyze
  • return DataQualityAnalysis object
min
public static Writable min(JavaRDD<List<Writable>> allData, String columnName, Schema schema)

Randomly sample a set of invalid values from a specified column. Values are considered invalid according to the Schema / ColumnMetaData

  • param numToSample Maximum number of invalid values to sample
  • param columnName Same of the column from which to sample invalid values
  • param schema Data schema
  • param data Data
  • return List of invalid examples
max
public static Writable max(JavaRDD<List<Writable>> allData, String columnName, Schema schema)

Get the maximum value for the specified column

  • param allData All data
  • param columnName Name of the column to get the minimum value for
  • param schema Schema of the data
  • return Maximum value for the column

API Reference

API Reference

Detailed API docs for all libraries including DL4J, ND4J, DataVec, and Arbiter.

Examples

Examples

Explore sample projects and demos for DL4J, ND4J, and DataVec in multiple languages including Java and Kotlin.

Tutorials

Tutorials

Step-by-step tutorials for learning concepts in deep learning while using the DL4J API.

Guide

Guide

In-depth documentation on different scenarios including import, distributed training, early stopping, and GPU setup.

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