public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string irisDataPath = GetBenchmarkDataPathAndEnsureData("iris.txt");
var env = new MLContext(seed: 1);
// Create text loader.
var options = new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("SepalLength", DataKind.Single, 1),
new TextLoader.Column("SepalWidth", DataKind.Single, 2),
new TextLoader.Column("PetalLength", DataKind.Single, 3),
new TextLoader.Column("PetalWidth", DataKind.Single, 4),
},
HasHeader = true,
};
var loader = new TextLoader(env, options: options);
IDataView data = loader.Load(irisDataPath);
var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(env.Transforms.Conversion.MapValueToKey("Label"))
.Append(env.MulticlassClassification.Trainers.SdcaMaximumEntropy(
new SdcaMaximumEntropyMulticlassTrainer.Options {
NumberOfThreads = 1, ConvergenceTolerance = 1e-2f,
}));
var model = pipeline.Fit(data);
_irisModel = env.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
}
public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_predictionEngine = _mlContext.Model.CreatePredictionEngine <IrisData, IrisPrediction>(_trainedModel);
_consumer.Consume(_predictionEngine.Predict(_example));
// Create text loader.
var options = new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("SepalLength", DataKind.Single, 1),
new TextLoader.Column("SepalWidth", DataKind.Single, 2),
new TextLoader.Column("PetalLength", DataKind.Single, 3),
new TextLoader.Column("PetalWidth", DataKind.Single, 4),
},
HasHeader = true,
};
var loader = new TextLoader(_mlContext, options: options);
IDataView testData = loader.Load(_dataPath);
_scoredIrisTestData = _trainedModel.Transform(testData);
_evaluator = new MulticlassClassificationEvaluator(_mlContext, new MulticlassClassificationEvaluator.Arguments());
_metrics = _evaluator.Evaluate(_scoredIrisTestData, DefaultColumnNames.Label, DefaultColumnNames.Score, DefaultColumnNames.PredictedLabel);
_batches = new IrisData[_batchSizes.Length][];
for (int i = 0; i < _batches.Length; i++)
{
var batch = new IrisData[_batchSizes[i]];
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = _example;
}
_batches[i] = batch;
}
}
