Prior trainer should accept label column type of boolean ONLY.

src/Microsoft.ML.StandardTrainers/Standard/Simple/SimpleTrainers.cs

@@ -230,7 +230,7 @@ private PriorModelParameters Train(TrainContext context)
             data.CheckBinaryLabel();
             _host.CheckParam(data.Schema.Label.HasValue, nameof(data), "Missing Label column");
             var labelCol = data.Schema.Label.Value;
-            _host.CheckParam(labelCol.Type == NumberDataViewType.Single, nameof(data), "Invalid type for Label column");
+            _host.CheckParam(labelCol.Type == BooleanDataViewType.Instance, nameof(data), "Invalid type for Label column");
 
             double pos = 0;
             double neg = 0;
@@ -243,9 +243,9 @@ private PriorModelParameters Train(TrainContext context)
 
             using (var cursor = data.Data.GetRowCursor(cols))
             {
-                var getLab = cursor.GetLabelFloatGetter(data);
+                var getLab = cursor.GetGetter<bool>(data.Schema.Label.Value);
                 var getWeight = colWeight >= 0 ? cursor.GetGetter<float>(data.Schema.Weight.Value) : null;
-                float lab = default;
+                bool lab = default;
                 float weight = 1;
                 while (cursor.MoveNext())
                 {
@@ -258,9 +258,9 @@ private PriorModelParameters Train(TrainContext context)
                     }
 
                     // Testing both directions effectively ignores NaNs.
-                    if (lab > 0)
+                    if (lab)
                         pos += weight;
-                    else if (lab <= 0)
+                    else
                         neg += weight;
                 }
             }

test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt

@@ -1,4 +1,4 @@
-maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1
+maml.exe CV tr=PriorPredictor threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% seed=1
 Not adding a normalizer.
 Not training a calibrator because it is not needed.
 Not adding a normalizer.

test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt

@@ -1,4 +1,4 @@
 PriorPredictor
 AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.5	0.656163	0	0	0.656163	1	0.935104	-0.00959	NaN	0.418968	PriorPredictor	%Data%		%Output%	99	0	0	maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1		
+0.5	0.656163	0	0	0.656163	1	0.935104	-0.00959	NaN	0.418968	PriorPredictor	%Data%		%Output%	99	0	0	maml.exe CV tr=PriorPredictor threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% seed=1		
 

test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt

@@ -1,4 +1,4 @@
-maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1
+maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% out=%Output% seed=1
 Not adding a normalizer.
 Not training a calibrator because it is not needed.
 TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))

test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt

@@ -1,4 +1,4 @@
 PriorPredictor
 AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.5	0.655222	0	0	0.655222	1	0.929318	0	NaN	0.415719	PriorPredictor	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1		
+0.5	0.655222	0	0	0.655222	1	0.929318	0	NaN	0.415719	PriorPredictor	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% out=%Output% seed=1		
 

test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt

@@ -1,4 +1,4 @@
-maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1
+maml.exe CV tr=PriorPredictor threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% seed=1
 Not adding a normalizer.
 Not training a calibrator because it is not needed.
 Not adding a normalizer.

test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt

@@ -1,4 +1,4 @@
 PriorPredictor
 AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.5	0.656163	0	0	0.656163	1	0.935104	-0.00959	NaN	0.418968	PriorPredictor	%Data%		%Output%	99	0	0	maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1		
+0.5	0.656163	0	0	0.656163	1	0.935104	-0.00959	NaN	0.418968	PriorPredictor	%Data%		%Output%	99	0	0	maml.exe CV tr=PriorPredictor threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% seed=1		
 

test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt

@@ -1,4 +1,4 @@
-maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1
+maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% out=%Output% seed=1
 Not adding a normalizer.
 Not training a calibrator because it is not needed.
 TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))

test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt

@@ -1,4 +1,4 @@
 PriorPredictor
 AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.5	0.655222	0	0	0.655222	1	0.929318	0	NaN	0.415719	PriorPredictor	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1		
+0.5	0.655222	0	0	0.655222	1	0.929318	0	NaN	0.415719	PriorPredictor	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% loader=Text{col=Label:BL:0 col=Features:~} data=%Data% out=%Output% seed=1		
 

test/Microsoft.ML.Predictor.Tests/TestPredictors.cs

@@ -120,8 +120,7 @@ public void BinaryPriorTest()
         {
             var predictors = new[] {
                 TestLearners.binaryPrior};
-            var datasets = GetDatasetsForBinaryClassifierBaseTest();
-            RunAllTests(predictors, datasets);
+            RunAllTests(predictors, new[] { TestDatasets.breastCancerBoolLabel });
             Done();
         }
 

test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs

@@ -18,7 +18,7 @@ private IDataView GetBreastCancerDataviewWithTextColumns()
                         HasHeader = true,
                         Columns = new[]
                         {
-                            new TextLoader.Column("Label", DataKind.Single, 0),
+                            new TextLoader.Column("Label", DataKind.Boolean, 0),
                             new TextLoader.Column("F1", DataKind.String, 1),
                             new TextLoader.Column("F2", DataKind.Int32, 2),
                             new TextLoader.Column("Rest", DataKind.Single, new [] { new TextLoader.Range(3, 9) })