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(); }