Inspect intermediate data during processing

Learn how to inspect intermediate data during loading, processing, and model training steps in ML.NET. Intermediate data is the output of each stage in the machine learning pipeline.

Consider the following housing data:

HousingData[] housingData = new HousingData[]
{
    new HousingData
    {
        Size = 600f,
        HistoricalPrices = new float[] { 100000f ,125000f ,122000f },
        CurrentPrice = 170000f
    },
    new HousingData
    {
        Size = 1000f,
        HistoricalPrices = new float[] { 200000f, 250000f, 230000f },
        CurrentPrice = 225000f
    },
    new HousingData
    {
        Size = 1000f,
        HistoricalPrices = new float[] { 126000f, 130000f, 200000f },
        CurrentPrice = 195000f
    },
    new HousingData
    {
        Size = 850f,
        HistoricalPrices = new float[] { 150000f,175000f,210000f },
        CurrentPrice = 205000f
    },
    new HousingData
    {
        Size = 900f,
        HistoricalPrices = new float[] { 155000f, 190000f, 220000f },
        CurrentPrice = 210000f
    },
    new HousingData
    {
        Size = 550f,
        HistoricalPrices = new float[] { 99000f, 98000f, 130000f },
        CurrentPrice = 180000f
    }
};

In ML.NET, you can inspect intermediate data like this that's loaded into an IDataView in various ways, as described in the following sections.

Convert IDataView to IEnumerable

One of the quickest ways to inspect an IDataView is to convert it to an IEnumerable. To do this conversion, use the CreateEnumerable method.

To optimize performance, set reuseRowObject to true. Doing so lazily populates the same object with the data of the current row as it's being evaluated as opposed to creating a new object for each row in the dataset.

// Create an IEnumerable of HousingData objects from IDataView
IEnumerable<HousingData> housingDataEnumerable =
    mlContext.Data.CreateEnumerable<HousingData>(data, reuseRowObject: true);

// Iterate over each row
foreach (HousingData row in housingDataEnumerable)
{
    // Do something (print out Size property) with current Housing Data object being evaluated
    Console.WriteLine(row.Size);
}

Access specific indices with IEnumerable

If you only need access to a portion of the data or specific indices, use CreateEnumerable and set the reuseRowObject parameter value to false so a new object is created for each of the requested rows in the dataset. Then, convert the IEnumerable to an array or list.

Warning

Converting the result of CreateEnumerable to an array or list loads all the requested IDataView rows into memory, which might affect performance.

Once the collection has been created, you can perform operations on the data. The following code snippet takes the first three rows in the dataset and calculates the average current price.

// Create an Array of HousingData objects from IDataView
HousingData[] housingDataArray =
    mlContext.Data.CreateEnumerable<HousingData>(data, reuseRowObject: false)
        .Take(3)
        .ToArray();

// Calculate Average CurrentPrice of First Three Elements
HousingData firstRow = housingDataArray[0];
HousingData secondRow = housingDataArray[1];
HousingData thirdRow = housingDataArray[2];
float averageCurrentPrice = (firstRow.CurrentPrice + secondRow.CurrentPrice + thirdRow.CurrentPrice) / 3;

Inspect values in a single column

At any point in the model building process, values in a single column of an IDataView can be accessed using the GetColumn method. The GetColumn method returns all of the values in a single column as an IEnumerable.

IEnumerable<float> sizeColumn = data.GetColumn<float>("Size").ToList();

Inspect IDataView values one row at a time

IDataView is lazily evaluated. To iterate over the rows of an IDataView without converting to an IEnumerable as demonstrated in previous sections of this document, create a DataViewRowCursor by using the GetRowCursor method and passing in the DataViewSchema of your IDataView as a parameter. Then, to iterate over rows, use the MoveNext cursor method along with ValueGetter delegates to extract the respective values from each of the columns.

Important

For performance purposes, vectors in ML.NET use VBuffer instead of native collection types (that is, Vector and float[]).

// Get DataViewSchema of IDataView
DataViewSchema columns = data.Schema;

// Create DataViewCursor
using (DataViewRowCursor cursor = data.GetRowCursor(columns))
{
    // Define variables where extracted values will be stored to
    float size = default;
    VBuffer<float> historicalPrices = default;
    float currentPrice = default;

    // Define delegates for extracting values from columns
    ValueGetter<float> sizeDelegate = cursor.GetGetter<float>(columns[0]);
    ValueGetter<VBuffer<float>> historicalPriceDelegate = cursor.GetGetter<VBuffer<float>>(columns[1]);
    ValueGetter<float> currentPriceDelegate = cursor.GetGetter<float>(columns[2]);

    // Iterate over each row
    while (cursor.MoveNext())
    {
        //Get values from respective columns
        sizeDelegate.Invoke(ref size);
        historicalPriceDelegate.Invoke(ref historicalPrices);
        currentPriceDelegate.Invoke(ref currentPrice);
    }
}

Preview result of preprocessing or training on a subset of the data

Warning

Do not use Preview in production code because it is intended for debugging and may reduce performance.

The model building process is experimental and iterative. To preview what data would look like after preprocessing or training a machine learning model on a subset of the data, use the Preview method, which returns a DataDebuggerPreview. The result is an object with ColumnView and RowView properties that are both an IEnumerable and contain the values in a particular column or row. Specify the number of rows to apply the transformation to with the maxRows parameter.

The result of inspecting an IDataView looks similar to the following image: