public void CacheTest()
{
var src = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
var data = ML.CreateDataView(src);
data.GetColumn <float[]>(ML, "Features").ToArray();
data.GetColumn <float[]>(ML, "Features").ToArray();
Assert.True(src.All(x => x.AccessCount == 2));
src = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
data = ML.CreateDataView(src);
data = ML.Data.Cache(data);
data.GetColumn <float[]>(ML, "Features").ToArray();
data.GetColumn <float[]>(ML, "Features").ToArray();
Assert.True(src.All(x => x.AccessCount == 1));
}
public void CacheCheckpointTest()
{
var trainData = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
var pipe = ML.Transforms.CopyColumns("Features", "F1")
.Append(ML.Transforms.Normalize("F1", "Norm1"))
.Append(ML.Transforms.Normalize("F1", "Norm2", Transforms.Normalizers.NormalizingEstimator.NormalizerMode.MeanVariance));
pipe.Fit(ML.CreateDataView(trainData));
Assert.True(trainData.All(x => x.AccessCount == 2));
trainData = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
pipe = ML.Transforms.CopyColumns("Features", "F1")
.AppendCacheCheckpoint(ML)
.Append(ML.Transforms.Normalize("F1", "Norm1"))
.Append(ML.Transforms.Normalize("F1", "Norm2", Transforms.Normalizers.NormalizingEstimator.NormalizerMode.MeanVariance));
pipe.Fit(ML.CreateDataView(trainData));
Assert.True(trainData.All(x => x.AccessCount == 1));
}
