我有一个具有35个功能的模型。将字符串值转换为向量后，我最终得到了具有54个特征的转换模型。
训练后，我想评估特征权重，但是最终我得到了比可用特征多得多的特征（104次，所以两次）。找到相应要素列的结果是“索引超出范围”。
PFI
var featureImportanceMetrics =
permutationFeatureImportance
.Select((metric, index) => new { index, metric.RSquared })
.OrderByDescending(myFeatures => Math.Abs(myFeatures.RSquared.Mean));
任何人都可以告诉我我做错了什么，还是我刚刚发现ML.Net库中的缺陷？
（请注意，我注释了一些算法，因为我想使用BinaryClassification进行播放，但无法使其正常运行且没有错误）
代码
public class Employee
{
[LoadColumn(0)]
public float Age { get; set; }
[LoadColumn(1)]
[ColumnName("Label")]
public float Attrition { get; set; }
[LoadColumn(2)]
public string BusinessTravel { get; set; }
[LoadColumn(3)]
public float DailyRate { get; set; }
[LoadColumn(4)]
public string Department { get; set; }
[LoadColumn(5)]
public float DistanceFromHome { get; set; }
[LoadColumn(6)]
public float Education { get; set; }
[LoadColumn(7)]
public string EducationField { get; set; }
[LoadColumn(8)]
public float EmployeeCount { get; set; }
[LoadColumn(9)]
public float EmployeeNumber { get; set; }
[LoadColumn(10)]
public float EnvironmentSatisfaction { get; set; }
[LoadColumn(11)]
public string Gender { get; set; }
[LoadColumn(12)]
public float HourlyRate { get; set; }
[LoadColumn(13)]
public float JobInvolvement { get; set; }
[LoadColumn(14)]
public float JobLevel { get; set; }
[LoadColumn(15)]
public string JobRole { get; set; }
[LoadColumn(16)]
public float JobSatisfaction { get; set; }
[LoadColumn(17)]
public string MaritalStatus { get; set; }
[LoadColumn(18)]
public float MonthlyIncome { get; set; }
[LoadColumn(19)]
public float MonthlyRate { get; set; }
[LoadColumn(20)]
public float NumCompaniesWorked { get; set; }
[LoadColumn(21)]
public string Over18 { get; set; }
[LoadColumn(22)]
public string OverTime { get; set; }
[LoadColumn(23)]
public float PercentSalaryHike { get; set; }
[LoadColumn(24)]
public float PerformanceRating { get; set; }
[LoadColumn(25)]
public float RelationshipSatisfaction { get; set; }
[LoadColumn(26)]
public float StandardHours { get; set; }
[LoadColumn(27)]
public float StockOptionLevel { get; set; }
[LoadColumn(28)]
public float TotalWorkingYears { get; set; }
[LoadColumn(29)]
public float TrainingTimesLastYear { get; set; }
[LoadColumn(30)]
public float WorkLifeBalance { get; set; }
[LoadColumn(31)]
public float YearsAtCompany { get; set; }
[LoadColumn(32)]
public float YearsInCurrentRole { get; set; }
[LoadColumn(33)]
public float YearsSinceLastPromotion { get; set; }
[LoadColumn(34)]
public float YearsWithCurrManager { get; set; }
}
public class EmployeeTransformed
{
public float Age { get; set; }
[ColumnName("Label")]
public float Attrition { get; set; }
public float[] BusinessTravel { get; set; }
public float DailyRate { get; set; }
public float[] Department { get; set; }
public float DistanceFromHome { get; set; }
public float Education { get; set; }
public float[] EducationField { get; set; }
public float EmployeeCount { get; set; }
public float EmployeeNumber { get; set; }
public float EnvironmentSatisfaction { get; set; }
public float[] Gender { get; set; }
public float HourlyRate { get; set; }
public float JobInvolvement { get; set; }
public float JobLevel { get; set; }
public float[] JobRole { get; set; }
public float JobSatisfaction { get; set; }
public float[] MaritalStatus { get; set; }
public float MonthlyIncome { get; set; }
public float MonthlyRate { get; set; }
public float NumCompaniesWorked { get; set; }
public float[] Over18 { get; set; }
public float[] OverTime { get; set; }
public float PercentSalaryHike { get; set; }
public float PerformanceRating { get; set; }
public float RelationshipSatisfaction { get; set; }
public float StandardHours { get; set; }
public float StockOptionLevel { get; set; }
public float TotalWorkingYears { get; set; }
public float TrainingTimesLastYear { get; set; }
public float WorkLifeBalance { get; set; }
public float YearsAtCompany { get; set; }
public float YearsInCurrentRole { get; set; }
public float YearsSinceLastPromotion { get; set; }
public float YearsWithCurrManager { get; set; }
}
public ActionResult Turnover()
{
MLContext mlContext = new MLContext();
var _appPath = AppDomain.CurrentDomain.BaseDirectory;
//var _dataPath = Path.Combine(_appPath, "Datasets", "WA_Fn-UseC_-HR-Employee-Attrition.csv");
var _dataPath = Path.Combine(_appPath, "Datasets", "attrition_small_dataset.csv");
// Load data from file
IDataView dataView = mlContext.Data.LoadFromTextFile<Employee>(_dataPath, separatorChar: ',', hasHeader: true);
var a = mlContext.Data.CreateEnumerable<Employee>(dataView, true).ToList();
// Define categorical transform estimator
var categoricalEstimator = mlContext.Transforms.Categorical.OneHotEncoding("BusinessTravel")
.Append(mlContext.Transforms.Categorical.OneHotEncoding("Department"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("EducationField"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("Gender"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("JobRole"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("MaritalStatus"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("Over18"))
.Append(mlContext.Transforms.Categorical.OneHotEncoding("OverTime"));
IDataView transformedData = categoricalEstimator.Fit(dataView).Transform(dataView);
// Split into train and test dataset
DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(transformedData, testFraction: 0.2);
IDataView trainData = dataSplit.TrainSet;
IDataView testData = dataSplit.TestSet;
// Get the column names of input features.
string[] featureColumnNames =
trainData.Schema
.Select(column => column.Name)
.Where(columnName => columnName != "Label").ToArray();
// Define estimator with data pre-processing steps
IEstimator<ITransformer> dataPrepEstimator =
mlContext.Transforms.Concatenate("Features", featureColumnNames)
.Append(mlContext.Transforms.NormalizeMinMax("Features"));
IDataView preprocessedTrainData = dataPrepEstimator.Fit(trainData).Transform(trainData);
var e = mlContext.Data.CreateEnumerable<EmployeeTransformed>(preprocessedTrainData, true).ToList();
/*
// Define Stochastic Dual Coordinate Ascent machine learning estimator
//var sdcaEstimator = mlContext.Regression.Trainers.Sdca(labelColumnName: "Age", featureColumnName: "Features");
//var sdcaEstimator = mlContext.Regression.Trainers.Sdca(labelColumnName: "Attrition", maximumNumberOfIterations: 100);
//var sdcaEstimator = mlContext.BinaryClassification.Trainers.FastTree(labelColumnName : "Attrition", featureColumnName : "Features", numberOfLeaves: 50, numberOfTrees: 50, minimumExampleCountPerLeaf: 20);
*/
var sdcaEstimator = mlContext.Regression.Trainers.Sdca();
// Train machine learning model
var sdcaModel = sdcaEstimator.Fit(preprocessedTrainData);
// Explain the model with Permutation Feature Importance (PFI)
ImmutableArray<RegressionMetricsStatistics> permutationFeatureImportance =
mlContext
.Regression
.PermutationFeatureImportance(sdcaModel, preprocessedTrainData, permutationCount: 3);
// Order features by importance
var featureImportanceMetrics =
permutationFeatureImportance
.Select((metric, index) => new { index, metric.RSquared })
.OrderByDescending(myFeatures => Math.Abs(myFeatures.RSquared.Mean));
var line = "Feature\tPFI <br>";
var z = featureColumnNames;
foreach (var feature in featureImportanceMetrics)
{
line += $"{featureColumnNames[feature.index],-20}|\t{feature.RSquared.Mean:F6} <br>";
}
return Content(line);
}
答案 0 :(得分：0)
我解决了我的问题，希望这对其他人也有帮助。 ML.net文档主要侧重于快乐的流程，对框架的各种更改使各种代码示例不再更新。
转换内容后，我使用转换后的架构而不是原始架构。我必须过滤掉标签，功能列。对于每个字符串属性，ML.net都会将其转换为Int32和一个Vector（float []），但具有属性“ IsHidden”，因此您也可以过滤掉它们，并留下所需的属性。
var categoricalEstimator = mlContext.Transforms.Categorical.OneHotEncoding("BusinessTravel");
dataView = categoricalEstimator.Fit(dataView).Transform(dataView);
DataOperationsCatalog.TrainTestData dataSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
IDataView trainData = dataSplit.TrainSet;
IDataView testData = dataSplit.TestSet;
var pipeline = mlContext.Transforms.Concatenate("Features", featureColumns)
.Append(mlContext.Transforms.NormalizeMinMax("Features")) .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression());
var model = pipeline.Fit(trainData);
var transformedData = model.Transform(trainData);
var linearPredictor = model.LastTransformer;
var permutationMetrics = mlContext.BinaryClassification.PermutationFeatureImportance(linearPredictor, transformedData, permutationCount: 30);
var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.AreaUnderRocCurve })
.OrderByDescending(feature => Math.Abs(feature.AreaUnderRocCurve.Mean))
.Select(feature => feature.index);
var sb = new System.Text.StringBuilder();
// Calculate metrics of the model on the test data.
var trainedModelMetrics = mlContext.BinaryClassification.Evaluate(model.Transform(testData), labelColumnName: "Label");
sb.Append("<h1>Binary Classification Model, Predicting Employee Turnover</h1>");
sb.Append(String.Format("<h3>Accuracy:{0}</h3>",trainedModelMetrics.Accuracy));
sb.Append(String.Format("<h3>F1Score:{0}</h3>", trainedModelMetrics.F1Score));
sb.Append("<table border=1><thead><tr><th>Feature</th><th>Model Weight</th><th>Change in AUC</th><th>95% Confidence in the Mean Change in AUC</th></tr></thead><tbody>");
var auc = permutationMetrics.Select(x => x.AreaUnderRocCurve).ToArray();
foreach (int i in sortedIndices)
{
if (transformedData.Schema[i].IsHidden || transformedData.Schema[i].Name == "Label" || transformedData.Schema[i].Name == "Features")
{
continue;
}
var s = String.Format("<tr><td>{0}</td><td>{1:0.00}</td><td>{2:G4}</td><td>{3:G4}</td></tr>",
transformedData.Schema[i].Name,
linearPredictor.Model.SubModel.Weights[i],
auc[i].Mean,
1.96 * auc[i].StandardError);
sb.Append(s);
}
sb.Append("</tbody></table>");
return Content(sb.ToString());