public PipelineProposer(AutoMLExperimentSettings settings)
{
// this cost is used to initialize eci when started, the smaller the number, the less cost this trainer will use at start, and more likely it will be
// picked.
_estimatorCost = new Dictionary <EstimatorType, double>()
{
{ EstimatorType.LightGbmRegression, 0.788 },
{ EstimatorType.FastTreeRegression, 0.382 },
{ EstimatorType.FastForestRegression, 0.374 },
{ EstimatorType.SdcaRegression, 0.566 },
{ EstimatorType.FastTreeTweedieRegression, 0.401 },
{ EstimatorType.LbfgsPoissonRegressionRegression, 4.73 },
{ EstimatorType.FastForestOva, 4.283 },
{ EstimatorType.FastTreeOva, 3.701 },
{ EstimatorType.LightGbmMulti, 4.765 },
{ EstimatorType.SdcaMaximumEntropyMulti, 10.129 },
{ EstimatorType.SdcaLogisticRegressionOva, 13.16 },
{ EstimatorType.LbfgsMaximumEntropyMulti, 7.980 },
{ EstimatorType.LbfgsLogisticRegressionOva, 11.513 },
{ EstimatorType.LightGbmBinary, 4.765 },
{ EstimatorType.FastTreeBinary, 3.701 },
{ EstimatorType.FastForestBinary, 4.283 },
{ EstimatorType.SdcaLogisticRegressionBinary, 13.16 },
{ EstimatorType.LbfgsLogisticRegressionBinary, 11.513 },
{ EstimatorType.ForecastBySsa, 1 },
{ EstimatorType.ImageClassificationMulti, 1 },
{ EstimatorType.MatrixFactorization, 1 },
};
_rand = new Random(settings.Seed ?? 0);
_multiModelPipeline = null;
}
public AutoMLExperiment(MLContext context, AutoMLExperimentSettings settings)
{
_context = context;
_settings = settings;
_serviceCollection = new ServiceCollection();
}
