C# MapReduce Based Co-occurrence Item Based Recommender

Artykuł
07/09/2012

As promised, to conclude the Co-occurrence Approach to an Item Based Recommender posts I wanted to port the MapReduce code to C#; just for kicks and to prove the code is also easy to write in C#. For an explanation of the MapReduce post review the previous article:

https://blogs.msdn.com/b/carlnol/archive/2012/07/07/mapreduce-based-co-occurrence-approach-to-an-item-based-recommender.aspx

The latest version of the code now includes the MSDN.Recommender.MapReduceCs project, that is a full C# implementation of the corresponding F# project; MSDN.Recommender.MapReduce. These code variants are intentionally similar in structure.

The code for the Order Mapper is as follows:

Order Mapper

public class OrderVectorMapper : MapperBaseText<Types.ProductQuantityList>
{
// Configuration values
static double qtyMaximum = 5.0; // Maximum rating contribution for an item
static double recentFactor = 2.0; // Quantity increase factor for recent items
static DateTime baseDate = DateTime.Today.AddMonths(-3); // Date for a recent item
Types.ProductQuantityList products = new Types.ProductQuantityList();
int? currentOrder = null;
// Converts an order Id to a string key
private static string getOrderKey(int orderId)
{
return String.Format("{0:D}", orderId);
}
// Adds a quantity factor based on recent orders
private static double orderFactor(Types.OrderDetail order)
{
if (DateTime.Compare(order.OrderDate, OrderVectorMapper.baseDate) > 0)
{
return OrderVectorMapper.recentFactor;
}
else
{
return 1.0;
}
}
/// Map the data from input name/value to output name/value
public override IEnumerable<Tuple<string, Types.ProductQuantityList>> Map(string value)
{
// Ensure order can be derived
Types.OrderDetail order = null;
try
{
order = Types.Helpers.ParseInputData(value);
}
catch (ArgumentException)
{
order = null;
}
// Process the order
if (order != null)
{
double quantity = Math.Min(((double)order.OrderQty), OrderVectorMapper.qtyMaximum) * OrderVectorMapper.orderFactor(order);
Types.ProductQuantity product = new Types.ProductQuantity(order.ProductId, quantity);
if (this.currentOrder.HasValue && (order.OrderId != this.currentOrder.Value))
{
yield return Tuple.Create(OrderVectorMapper.getOrderKey(currentOrder.Value), this.products);
this.products.Clear();
}
this.currentOrder = order.OrderId;
this.products.Add(product);
}
else
{
Context.IncrementCounter("ORDERS", "Skipped Lines");
}
}
/// Output remaining Map items
public override IEnumerable<Tuple<string, Types.ProductQuantityList>> Cleanup()
{
if (this.currentOrder.HasValue)
{
yield return Tuple.Create(OrderVectorMapper.getOrderKey(currentOrder.Value), this.products);
}
}
}

And that of the Order Reducer is:

Order Reducer

class OrderVectorReducer : ReducerBase<Types.ProductQuantityList, Types.ProductQuantityList>
{
/// Reduce the order data into a product list
public override IEnumerable<Tuple<string, Types.ProductQuantityList>> Reduce(string key, IEnumerable<Types.ProductQuantityList> values)
{
var products = new Types.ProductQuantityList();
foreach (var prodList in values)
{
products.AddRange(prodList);
}
yield return Tuple.Create(key, products);
}
}

Your friend when writing the C# code is the yield return command for emitting data, and the Tuple.Create() command for creating the key/value pairs. The two commands enable one to return a key/value sequence; namely a .Net IEnumerable.

For completeness the code for the Product Mapper is:

Product Mapper

class ProductVectorMapper : MapperBaseText<Types.ProductQuantity>
{
static int maxSize = 1024 * 1024;
Dictionary<Tuple<int, int>, double> prodPairs = new Dictionary<Tuple<int, int>, double>(ProductVectorMapper.maxSize);
// Converts an order Id to a string key
static private string getProductKey(int productId)
{
return String.Format("{0:D}", productId);
}
// Parses an input line of format List<ProductQuantity>
static private Types.ProductQuantityList deserialize(string input)
{
string[] keyValue = input.Split('\t');
return Types.Helpers.ParseProductQuantityList(keyValue[1].Trim());
}
// calculates the pairs for an order
static private IEnumerable<Tuple<T, T>> pairs<T>(List<T> items)
{
int count = items.Count;
if (count == 2)
{
yield return Tuple.Create(items[0], items[1]);
}
else if (count > 2)
{
for (int idxOut = 0; idxOut <= (count - 2); idxOut++)
{
for (int idxIn = (idxOut + 1); idxIn <= (count - 1); idxIn++)
{
yield return Tuple.Create(items[idxOut], items[idxIn]);
}
}
}
}
// Adds to the table
private void addRow(Tuple<int, int> idx, double qty)
{
if (this.prodPairs.ContainsKey(idx))
{
this.prodPairs[idx] = this.prodPairs[idx] + qty;
}
else
{
this.prodPairs[idx] = qty;
}
}
// Defines a sequence of the current pairs
private IEnumerable<Tuple<string, Types.ProductQuantity>> currents()
{
foreach (var item in prodPairs)
{
int product1 = item.Key.Item1;
int product2 = item.Key.Item2;
yield return Tuple.Create(ProductVectorMapper.getProductKey(product1), new Types.ProductQuantity(product2, item.Value));
}
prodPairs.Clear();
}
/// Map the data from input name/value to output name/value
public override IEnumerable<Tuple<string, Types.ProductQuantity>> Map(string value)
{
var products = ProductVectorMapper.pairs<Types.ProductQuantity>(ProductVectorMapper.deserialize(value));
foreach (var product in products)
{
Types.ProductQuantity product1 = product.Item1;
Types.ProductQuantity product2 = product.Item2;
double qty = Math.Max(product1.Quantity, product2.Quantity);
this.addRow(Tuple.Create(product1.ProductId, product2.ProductId), qty);
this.addRow(Tuple.Create(product2.ProductId, product1.ProductId), qty);
}
if (prodPairs.Count > ProductVectorMapper.maxSize)
{
return currents();
}
else
{
return Enumerable.Empty<Tuple<string, Types.ProductQuantity>>();
}
}
/// Output remaining Map items
public override IEnumerable<Tuple<string, Types.ProductQuantity>> Cleanup()
{
return currents();
}
}

That of the Product Combiner is:

Product Combiner

class ProductVectorCombiner : CombinerBase<Types.ProductQuantity>
{
/// Combine the data from input name/value to output name/value
public override IEnumerable<Tuple<string, Types.ProductQuantity>> Combine(string key, IEnumerable<Types.ProductQuantity> values)
{
int maxSize = 10000; // Expected number of product correlations
var products = new Dictionary<int, double>(maxSize);
// Process the combiner input adding to the table
foreach (var product in values)
{
if (products.ContainsKey(product.ProductId))
{
products[product.ProductId] = products[product.ProductId] + product.Quantity;
}
else
{
products[product.ProductId] = product.Quantity;
}
}
// Return the combiner input
return products.Select(item => Tuple.Create(key, new Types.ProductQuantity(item.Key, item.Value)));
}
}

And finally, that of the Product Reducer is:

Product Reducer

class ProductVectorReducer : ReducerBase<Types.ProductQuantity, Types.ProductRecommendations>
{
/// Reduce the data from input name/value to output name/value
public override IEnumerable<Tuple<string, Types.ProductRecommendations>> Reduce(string key, IEnumerable<Types.ProductQuantity> values)
{
// Configuration values
double entryThreshold = 20.0; // Minimum correlations for matrix inclusion
int matrixSize = 10000; // Expected Correlations for Hash Table init
int minItem = Int32.MaxValue;
int maxItem = 0;
var rcTable = new Dictionary<int, double>(matrixSize);
// Process the combiner input adding to the table
foreach (var product in values)
{
int idx = product.ProductId;
minItem = Math.Min(idx, minItem);
maxItem = Math.Max(idx, maxItem);
if (rcTable.ContainsKey(product.ProductId))
{
rcTable[product.ProductId] = rcTable[product.ProductId] + product.Quantity;
}
else
{
rcTable[product.ProductId] = product.Quantity;
}
}
int offset = minItem;
int size = maxItem + 1 - minItem;
// Build the sparse vector
var vector = new SparseVector(size);
foreach (var item in rcTable)
{
if (item.Value > entryThreshold)
{
vector[item.Key - offset] = item.Value;
}
}
var recommendations = new Types.ProductRecommendations(Int32.Parse(key), offset, vector);
Context.IncrementCounter("PRODUCTS", "Recommendations Written");
yield return Tuple.Create(key, recommendations);
}
}

A lot of the processing is based upon processing IEnumerable sequences. For this reason foreach and Linq expressions are used a lot. I could have defined a ForEach IEnumerable extension, to use Linq exclusively, but have preferred the C# syntax.

One difference in the final Reducer is how the Sparse Matrix is defined. In the F# code an F# extension allows one to build a SparseMatrix type by providing a sequence of index/value tuples. In the C# code one has to define an empty SparseMatrix and then interactively set the element values.

Note: There may be some confusion in the usage of the float type for .Net programmers, when switching between C# and F#, due to F#’s naming for floating-point number types. F# float type corresponds to System.Double in .NET (which is called double in C#), and F# float32 type corresponds to System.Single in .NET (which is called float in C#). For this reason I have switched to using the type double in F#, as it is another type alias for the System.Double type.

Of course the recommendations produced by the C# code are the same as the F# code, and clearly demonstrate that the Framework cleanly support writing MapReduce jobs in C#. I personally find the F# code more succinct and easier to read, but this is my personal preference.

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C# MapReduce Based Co-occurrence Item Based Recommender

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