internal static ClassificationMetrics FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
var enumerator = metricsEnumerable.GetEnumerator();
if (!enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
SerializationClass metrics = enumerator.Current;
if (enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics contained more than 1 row.");
}
return(new ClassificationMetrics()
{
AccuracyMicro = metrics.AccuracyMicro,
AccuracyMacro = metrics.AccuracyMacro,
LogLoss = metrics.LogLoss,
LogLossReduction = metrics.LogLossReduction,
TopKAccuracy = metrics.TopKAccuracy,
PerClassLogLoss = metrics.PerClassLogLoss,
ConfusionMatrix = ConfusionMatrix.Create(env, confusionMatrix)
});
}
internal static List <BinaryClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix, int confusionMatriceStartIndex = 0)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
if (!metricsEnumerable.GetEnumerator().MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
List <BinaryClassificationMetrics> metrics = new List <BinaryClassificationMetrics>();
var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();
int index = 0;
foreach (var metric in metricsEnumerable)
{
if (index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())
{
throw env.Except("Confusion matrices didn't have enough matrices.");
}
metrics.Add(
new BinaryClassificationMetrics()
{
Auc = metric.Auc,
Accuracy = metric.Accuracy,
PositivePrecision = metric.PositivePrecision,
PositiveRecall = metric.PositiveRecall,
NegativePrecision = metric.NegativePrecision,
NegativeRecall = metric.NegativeRecall,
LogLoss = metric.LogLoss,
LogLossReduction = metric.LogLossReduction,
Entropy = metric.Entropy,
F1Score = metric.F1Score,
Auprc = metric.Auprc,
RowTag = metric.RowTag,
ConfusionMatrix = confusionMatrices.Current,
});
}
return(metrics);
}
internal static List <ClassificationMetrics> FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix,
int confusionMatriceStartIndex = 0)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
if (!metricsEnumerable.GetEnumerator().MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
List <ClassificationMetrics> metrics = new List <ClassificationMetrics>();
var confusionMatrices = ConfusionMatrix.Create(env, confusionMatrix).GetEnumerator();
int index = 0;
foreach (var metric in metricsEnumerable)
{
if (index++ >= confusionMatriceStartIndex && !confusionMatrices.MoveNext())
{
throw env.Except("Confusion matrices didn't have enough matrices.");
}
metrics.Add(
new ClassificationMetrics()
{
AccuracyMicro = metric.AccuracyMicro,
AccuracyMacro = metric.AccuracyMacro,
LogLoss = metric.LogLoss,
LogLossReduction = metric.LogLossReduction,
TopKAccuracy = metric.TopKAccuracy,
PerClassLogLoss = metric.PerClassLogLoss,
ConfusionMatrix = confusionMatrices.Current,
RowTag = metric.RowTag,
});
}
return(metrics);
}
internal static BinaryClassificationMetrics FromMetrics(IHostEnvironment env, IDataView overallMetrics, IDataView confusionMatrix)
{
Contracts.AssertValue(env);
env.AssertValue(overallMetrics);
env.AssertValue(confusionMatrix);
var metricsEnumerable = overallMetrics.AsEnumerable <SerializationClass>(env, true, ignoreMissingColumns: true);
var enumerator = metricsEnumerable.GetEnumerator();
if (!enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics didn't have any rows.");
}
SerializationClass metrics = enumerator.Current;
if (enumerator.MoveNext())
{
throw env.Except("The overall RegressionMetrics contained more than 1 row.");
}
return(new BinaryClassificationMetrics()
{
Auc = metrics.Auc,
Accuracy = metrics.Accuracy,
PositivePrecision = metrics.PositivePrecision,
PositiveRecall = metrics.PositiveRecall,
NegativePrecision = metrics.NegativePrecision,
NegativeRecall = metrics.NegativeRecall,
LogLoss = metrics.LogLoss,
LogLossReduction = metrics.LogLossReduction,
Entropy = metrics.Entropy,
F1Score = metrics.F1Score,
Auprc = metrics.Auprc,
ConfusionMatrix = ConfusionMatrix.Create(env, confusionMatrix),
});
}