public void TestEstimatorSymSgdInitPredictor()
{
(var pipe, var dataView) = GetBinaryClassificationPipeline();
var transformedData = pipe.Fit(dataView).Transform(dataView);
var args = new LinearClassificationTrainer.Arguments();
var initPredictor = new LinearClassificationTrainer(Env, args, "Features", "Label").Fit(transformedData);
var data = initPredictor.Transform(transformedData);
var withInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData, initialPredictor: initPredictor.Model);
var outInitData = withInitPredictor.Transform(transformedData);
var notInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData);
var outNoInitData = notInitPredictor.Transform(transformedData);
int numExamples = 10;
var col1 = data.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
var col2 = outInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
var col3 = outNoInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
bool col12Diff = default;
bool col23Diff = default;
bool col13Diff = default;
for (int i = 0; i < numExamples; i++)
{
col12Diff = col12Diff || (col1[i] != col2[i]);
col23Diff = col23Diff || (col2[i] != col3[i]);
col13Diff = col13Diff || (col1[i] != col3[i]);
}
Contracts.Assert(col12Diff && col23Diff && col13Diff);
Done();
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and a custom loss.
/// Note that because we cannot be sure that all loss functions will produce naturally calibrated outputs, setting
/// a custom loss function will not produce a calibrated probability column.
/// </summary>
/// <param name="ctx">The binary classification context trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="loss">The custom loss.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l2Const">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained, as well as the calibrator on top of that model. Note that this action cannot change the
/// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), and the predicted label.</returns>
/// <seealso cref="Sdca(BinaryClassificationContext.BinaryClassificationTrainers, Scalar{bool}, Vector{float}, Scalar{float}, float?, float?, int?, Action{LinearBinaryPredictor, ParameterMixingCalibratedPredictor})"/>
public static (Scalar <float> score, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float> features,
ISupportSdcaClassificationLoss loss,
Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <LinearBinaryPredictor> onFit = null
)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValue(loss, nameof(loss));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(onFit);
bool hasProbs = loss is LogLoss;
var args = new LinearClassificationTrainer.Arguments()
{
L2Const = l2Const,
L1Threshold = l1Threshold,
MaxIterations = maxIterations,
LossFunction = new TrivialSdcaClassificationLossFactory(loss)
};
var rec = new TrainerEstimatorReconciler.BinaryClassifierNoCalibration(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LinearClassificationTrainer(env, args, featuresName, labelName, weightsName);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
var model = trans.Model;
if (model is ParameterMixingCalibratedPredictor cali)
{
onFit((LinearBinaryPredictor)cali.SubPredictor);
}
else
{
onFit((LinearBinaryPredictor)model);
}
}));
}
return(trainer);
}, label, features, weights, hasProbs);
return(rec.Output);
}
/// <summary>
/// Predict a target using a linear binary classification model trained with the SDCA trainer, and log-loss.
/// </summary>
/// <param name="ctx">The binary classification context trainer object.</param>
/// <param name="label">The label, or dependent variable.</param>
/// <param name="features">The features, or independent variables.</param>
/// <param name="weights">The optional example weights.</param>
/// <param name="l2Const">The L2 regularization hyperparameter.</param>
/// <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
/// <param name="maxIterations">The maximum number of passes to perform over the data.</param>
/// <param name="onFit">A delegate that is called every time the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}.Fit(DataView{TTupleInShape})"/> method is called on the
/// <see cref="Estimator{TTupleInShape, TTupleOutShape, TTransformer}"/> instance created out of this. This delegate will receive
/// the linear model that was trained, as well as the calibrator on top of that model. Note that this action cannot change the
/// result in any way; it is only a way for the caller to be informed about what was learnt.</param>
/// <returns>The set of output columns including in order the predicted binary classification score (which will range
/// from negative to positive infinity), the calibrated prediction (from 0 to 1), and the predicted label.</returns>
public static (Scalar <float> score, Scalar <float> probability, Scalar <bool> predictedLabel) Sdca(
this BinaryClassificationContext.BinaryClassificationTrainers ctx,
Scalar <bool> label, Vector <float> features, Scalar <float> weights = null,
float?l2Const = null,
float?l1Threshold = null,
int?maxIterations = null,
Action <LinearBinaryPredictor, ParameterMixingCalibratedPredictor> onFit = null)
{
Contracts.CheckValue(label, nameof(label));
Contracts.CheckValue(features, nameof(features));
Contracts.CheckValueOrNull(weights);
Contracts.CheckParam(!(l2Const < 0), nameof(l2Const), "Must not be negative, if specified.");
Contracts.CheckParam(!(l1Threshold < 0), nameof(l1Threshold), "Must not be negative, if specified.");
Contracts.CheckParam(!(maxIterations < 1), nameof(maxIterations), "Must be positive if specified");
Contracts.CheckValueOrNull(onFit);
var args = new LinearClassificationTrainer.Arguments()
{
L2Const = l2Const,
L1Threshold = l1Threshold,
MaxIterations = maxIterations,
};
var rec = new TrainerEstimatorReconciler.BinaryClassifier(
(env, labelName, featuresName, weightsName) =>
{
var trainer = new LinearClassificationTrainer(env, args, featuresName, labelName, weightsName);
if (onFit != null)
{
return(trainer.WithOnFitDelegate(trans =>
{
// Under the default log-loss we assume a calibrated predictor.
var model = trans.Model;
var cali = (ParameterMixingCalibratedPredictor)model;
var pred = (LinearBinaryPredictor)cali.SubPredictor;
onFit(pred, cali);
}));
}
return(trainer);
}, label, features, weights);
return(rec.Output);
}
public MySdca(IHostEnvironment env, LinearClassificationTrainer.Arguments args, string featureCol, string labelCol)
: base(env, new TrainerInfo(), featureCol, labelCol)
{
_args = args;
}