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Pete Luferenko
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Added an ad hoc test playground
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‎src/Microsoft.ML.Core/Data/IEstimator.cs

Lines changed: 5 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -149,9 +149,12 @@ public interface IDataTransformer : ITransformer<IDataView>
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ISchema GetOutputSchema(ISchema inputSchema);
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}
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public interface IDataEstimator : IEstimator<IDataView>
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public interface IDataEstimator
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{
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new IDataTransformer Fit(IDataView input);
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/// <summary>
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/// Train and return a transformer.
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/// </summary>
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IDataTransformer Fit(IDataView input);
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/// <summary>
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/// Schema propagation for estimators.

‎test/Microsoft.ML.Core.Tests/UnitTests/AdHocTest.cs

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@@ -0,0 +1,335 @@
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using Microsoft.ML.Core.Data;
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using Microsoft.ML.Runtime;
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using Microsoft.ML.Runtime.Api;
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using Microsoft.ML.Runtime.Data;
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using Microsoft.ML.Runtime.Data.IO;
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using Microsoft.ML.Runtime.Learners;
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using System.Collections.Generic;
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using System.Linq;
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using Xunit;
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namespace Microsoft.ML.Core.Tests.UnitTests
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{
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public class AdHocTest
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{
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private static TextLoader.Arguments MakeTextLoaderArgs()
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{
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return new TextLoader.Arguments()
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{
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HasHeader = false,
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Column = new[] {
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new TextLoader.Column()
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{
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Name = "Label",
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Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} },
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Type = DataKind.R4
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},
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new TextLoader.Column()
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{
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Name = "SepalLength",
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Source = new [] { new TextLoader.Range() { Min = 1, Max = 1} },
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Type = DataKind.R4
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},
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new TextLoader.Column()
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{
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Name = "SepalWidth",
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Source = new [] { new TextLoader.Range() { Min = 2, Max = 2} },
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Type = DataKind.R4
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},
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new TextLoader.Column()
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{
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Name = "PetalLength",
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Source = new [] { new TextLoader.Range() { Min = 3, Max = 3} },
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Type = DataKind.R4
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},
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new TextLoader.Column()
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{
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Name = "PetalWidth",
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Source = new [] { new TextLoader.Range() { Min = 4, Max = 4} },
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Type = DataKind.R4
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}
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}
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};
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}
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public class MyTextLoader : IEstimator<IMultiStreamSource>, ITransformer<IMultiStreamSource>
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{
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private readonly TextLoader.Arguments _args;
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private readonly IHostEnvironment _env;
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public MyTextLoader(IHostEnvironment env, TextLoader.Arguments args)
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{
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_env = env;
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_args = args;
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}
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public ITransformer<IMultiStreamSource> Fit(IMultiStreamSource input)
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{
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return this;
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}
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public SchemaShape GetOutputSchema()
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{
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var emptyData = new TextLoader(new TlcEnvironment(), _args, new MultiFileSource(null));
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return SchemaShape.Create(emptyData.Schema);
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}
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public IDataView Transform(IMultiStreamSource input)
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{
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return new TextLoader(new TlcEnvironment(), _args, input);
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}
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}
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public class TransformerPipe<TIn> : ITransformer<TIn>
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{
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private readonly ITransformer<TIn> _start;
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private readonly IDataTransformer[] _chain;
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public TransformerPipe(ITransformer<TIn> start, IDataTransformer[] chain)
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{
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_start = start;
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_chain = chain;
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}
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public IDataView Transform(TIn input)
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{
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var idv = _start.Transform(input);
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foreach (var xf in _chain)
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idv = xf.Transform(idv);
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return idv;
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}
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}
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public class EstimatorPipe<TIn> : IEstimator<TIn>
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{
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private readonly IEstimator<TIn> _start;
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private readonly List<IDataEstimator> _estimatorChain = new List<IDataEstimator>();
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private readonly IHostEnvironment _env = new TlcEnvironment();
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public EstimatorPipe(IEstimator<TIn> start)
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{
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_start = start;
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}
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public EstimatorPipe<TIn> Append(IDataEstimator est)
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{
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_estimatorChain.Add(est);
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return this;
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}
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public ITransformer<TIn> Fit(TIn input)
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{
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var start = _start.Fit(input);
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var idv = start.Transform(input);
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var xfs = new List<IDataTransformer>();
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foreach (var est in _estimatorChain)
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{
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var xf = est.Fit(idv);
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xfs.Add(xf);
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idv = xf.Transform(idv);
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}
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return new TransformerPipe<TIn>(start, xfs.ToArray());
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}
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public IEstimator<TIn> GetEstimator()
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{
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return this;
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}
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public SchemaShape GetOutputSchema()
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{
143+
throw new System.NotImplementedException();
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}
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}
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public class MyConcatTransformer : IDataEstimator, IDataTransformer
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{
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private readonly ConcatTransform _xf;
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private readonly IHostEnvironment _env;
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private readonly string _name;
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private readonly string[] _source;
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public MyConcatTransformer(IHostEnvironment env, string name, params string[] source)
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{
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_env = env;
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_name = name;
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_source = source;
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}
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private MyConcatTransformer(IHostEnvironment env, ConcatTransform xf)
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{
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_env = env;
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_xf = xf;
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}
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public IDataTransformer Fit(IDataView input)
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{
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var xf = new ConcatTransform(_env, input, _name, _source);
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return new MyConcatTransformer(_env, xf);
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}
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public SchemaShape GetOutputSchema(SchemaShape inputSchema)
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{
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var cols = inputSchema.Columns.ToList();
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var selectedCols = cols.Where(x => _source.Contains(x.Name)).Cast<SchemaShape.RelaxedColumn>();
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var isFixed = selectedCols.All(x => x.Kind != SchemaShape.RelaxedColumn.VectorKind.VariableVector);
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var newCol = new SchemaShape.RelaxedColumn(_name,
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isFixed ? SchemaShape.RelaxedColumn.VectorKind.Vector : SchemaShape.RelaxedColumn.VectorKind.VariableVector,
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selectedCols.First().ItemKind, selectedCols.First().IsKey);
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cols.Add(newCol);
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return new SchemaShape(cols.ToArray());
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}
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public ISchema GetOutputSchema(ISchema inputSchema)
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{
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var dv = new EmptyDataView(_env, inputSchema);
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var output = ApplyTransformUtils.ApplyTransformToData(_env, _xf, dv);
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return output.Schema;
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}
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public IDataView Transform(IDataView input)
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{
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return ApplyTransformUtils.ApplyTransformToData(_env, _xf, input);
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}
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}
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public class MyNormalizer : IDataEstimator
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{
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private readonly IHostEnvironment _env;
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private readonly string _col;
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public MyNormalizer(IHostEnvironment env, string col)
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{
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_env = env;
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_col = col;
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}
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public IDataTransformer Fit(IDataView input)
212+
{
213+
return new Transformer(_env, input, _col);
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}
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public SchemaShape GetOutputSchema(SchemaShape inputSchema)
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{
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return inputSchema;
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}
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private class Transformer : IDataTransformer
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{
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private IHostEnvironment _env;
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private IDataTransform _xf;
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public Transformer(IHostEnvironment env, IDataView input, string col)
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{
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_env = env;
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_xf = NormalizeTransform.CreateMinMaxNormalizer(env, input, col);
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}
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public ISchema GetOutputSchema(ISchema inputSchema)
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{
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var dv = new EmptyDataView(_env, inputSchema);
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var output = ApplyTransformUtils.ApplyTransformToData(_env, _xf, dv);
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return output.Schema;
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}
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public IDataView Transform(IDataView input)
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{
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return ApplyTransformUtils.ApplyTransformToData(_env, _xf, input);
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}
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}
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}
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public class MySdca : IDataEstimator
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{
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private readonly IHostEnvironment _env;
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public MySdca(IHostEnvironment env)
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{
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_env = env;
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}
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public IDataTransformer Fit(IDataView input)
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{
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// Train
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var trainer = new SdcaMultiClassTrainer(_env, new SdcaMultiClassTrainer.Arguments() { NumThreads = 1 });
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// Explicity adding CacheDataView since caching is not working though trainer has 'Caching' On/Auto
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var cached = new CacheDataView(_env, input, prefetch: null);
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var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
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var pred = trainer.Train(trainRoles);
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var scoreRoles = new RoleMappedData(input, label: "Label", feature: "Features");
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IDataScorerTransform scorer = ScoreUtils.GetScorer(pred, scoreRoles, _env, trainRoles.Schema);
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return new Transformer(_env, pred, scorer);
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}
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public SchemaShape GetOutputSchema(SchemaShape inputSchema)
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{
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throw new System.NotImplementedException();
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}
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private sealed class Transformer : IDataTransformer
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{
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private IHostEnvironment _env;
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private IPredictor _pred;
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private IDataScorerTransform _xf;
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public Transformer(IHostEnvironment env, IPredictorProducing<VBuffer<float>> pred, IDataScorerTransform scorer)
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{
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_env = env;
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_pred = pred;
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_xf = scorer;
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}
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public ISchema GetOutputSchema(ISchema inputSchema)
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{
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var dv = new EmptyDataView(_env, inputSchema);
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var output = ApplyTransformUtils.ApplyTransformToData(_env, _xf, dv);
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return output.Schema;
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}
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public IDataView Transform(IDataView input)
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{
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return ApplyTransformUtils.ApplyTransformToData(_env, _xf, input);
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}
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}
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}
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public class IrisPrediction
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{
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[ColumnName("Score")]
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public float[] PredictedLabels;
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}
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public class IrisData
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{
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public float SepalLength;
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public float SepalWidth;
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public float PetalLength;
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public float PetalWidth;
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}
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[Fact]
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public void TestEstimatorPipe()
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{
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var env = new TlcEnvironment();
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var pipeline = new EstimatorPipe<IMultiStreamSource>(new MyTextLoader(env, MakeTextLoaderArgs()));
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pipeline.Append(new MyConcatTransformer(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
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.Append(new MyNormalizer(env, "Features"))
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.Append(new MySdca(env));
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var model = pipeline.Fit(new MultiFileSource(@"e:\data\iris.txt"));
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var scoredTrainData = model.Transform(new MultiFileSource(@"e:\data\iris.txt"))
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.AsEnumerable<IrisPrediction>(env, reuseRowObject: false)
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.ToArray();
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}
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}
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}

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