public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = Program.GetInvariantCultureDataPath("iris.txt");
var env = new MLContext(seed: 1, conc: 1);
var reader = new TextLoader(env,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
},
hasHeader: true
);
IDataView data = reader.Read(_irisDataPath);
var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(new SdcaMultiClassTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; }));
var model = pipeline.Fit(data);
_irisModel = model.CreatePredictionEngine <IrisData, IrisPrediction>(env);
}
public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_predictionEngine = _trainedModel.CreatePredictionEngine <IrisData, IrisPrediction>(_env);
_consumer.Consume(_predictionEngine.Predict(_example));
var reader = new TextLoader(_env,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
},
hasHeader: true
);
IDataView testData = reader.Read(_dataPath);
IDataView scoredTestData = _trainedModel.Transform(testData);
var evaluator = new MultiClassClassifierEvaluator(_env, new MultiClassClassifierEvaluator.Arguments());
_metrics = evaluator.Evaluate(scoredTestData, 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;
}
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string irisDataPath = GetBenchmarkDataPath("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 SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = BaseTestClass.GetDataPath("iris.txt");
var env = new MLContext(seed: 1, conc: 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.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
new SdcaMultiClassTrainer.Options {
NumberOfThreads = 1, ConvergenceTolerance = 1e-2f,
}));
var model = pipeline.Fit(data);
_irisModel = model.CreatePredictionEngine <IrisData, IrisPrediction>(env);
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = Program.GetInvariantCultureDataPath("iris.txt");
using (var env = new ConsoleEnvironment(seed: 1, conc: 1, verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
var reader = new TextLoader(env,
new TextLoader.Arguments()
{
Separator = "\t",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
}
});
IDataView data = reader.Read(new MultiFileSource(_irisDataPath));
var pipeline = new ConcatEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
NumThreads = 1, ConvergenceTolerance = 1e-2f
}, "Features", "Label"));
var model = pipeline.Fit(data);
_irisModel = model.MakePredictionFunction <IrisData, IrisPrediction>(env);
}
}
public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_consumer.Consume(_trainedModel.Predict(_example));
var testData = new Legacy.Data.TextLoader(_dataPath).CreateFrom <IrisData>(useHeader: true);
var evaluator = new ClassificationEvaluator();
_metrics = evaluator.Evaluate(_trainedModel, testData);
_batches = new IrisData[_batchSizes.Length][];
for (int i = 0; i < _batches.Length; i++)
{
var batch = new IrisData[_batchSizes[i]];
_batches[i] = batch;
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = _example;
}
}
}
public void Setup()
{
s_dataPath = Program.GetDataPath("iris.txt");
s_trainedModel = TrainCore();
IrisPrediction prediction = s_trainedModel.Predict(s_example);
var testData = new TextLoader(s_dataPath).CreateFrom <IrisData>(useHeader: true);
var evaluator = new ClassificationEvaluator();
s_metrics = evaluator.Evaluate(s_trainedModel, testData);
s_batches = new IrisData[s_batchSizes.Length][];
for (int i = 0; i < s_batches.Length; i++)
{
var batch = new IrisData[s_batchSizes[i]];
s_batches[i] = batch;
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = s_example;
}
}
}
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);
IDataView scoredTestData = _trainedModel.Transform(testData);
var evaluator = new MulticlassClassificationEvaluator(_mlContext, new MulticlassClassificationEvaluator.Arguments());
_metrics = evaluator.Evaluate(scoredTestData, 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;
}
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = Program.GetInvariantCultureDataPath("iris.txt");
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader(_irisDataPath).CreateFrom <IrisData>(useHeader: true, separator: '\t'));
pipeline.Add(new ColumnConcatenator("Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" }));
pipeline.Add(new StochasticDualCoordinateAscentClassifier()
{
NumThreads = 1, ConvergenceTolerance = 1e-2f
});
_irisModel = pipeline.Train <IrisData, IrisPrediction>();
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = Program.GetInvariantCultureDataPath("iris.txt");
var env = new MLContext(seed: 1, conc: 1);
var reader = new TextLoader(env,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
},
hasHeader: true
);
IDataView data = reader.Read(_irisDataPath);
var pipeline = env.Transforms.Concatenate("Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
new SdcaMultiClassTrainer.Options {
NumThreads = 1, ConvergenceTolerance = 1e-2f
}));
var model = pipeline.Fit(data);
_irisModel = model.CreatePredictionEngine <IrisData, IrisPrediction>(env);
}