Fix ResultProcessor bug, LogisticRegression bug and missing value conversion bug

src/Microsoft.ML.Data/Data/Conversion.cs

@@ -1170,7 +1170,7 @@ private bool IsStdMissing(ref ReadOnlySpan<char> span)
         public bool TryParseKey(ref TX src, U8 min, U8 max, out U8 dst)
         {
             var span = src.Span;
-            Contracts.Check(!IsStdMissing(ref span), "Missing text value cannot be converted to unsigned integer type.");
+            Contracts.Check(span.IsEmpty || !IsStdMissing(ref span), "Missing text value cannot be converted to unsigned integer type.");
             Contracts.Assert(min <= max);
 
             // This simply ensures we don't have min == 0 and max == U8.MaxValue. This is illegal since
@@ -1530,7 +1530,7 @@ public bool TryParse(ref TX src, out BL dst)
         {
             var span = src.Span;
 
-            Contracts.Check(!IsStdMissing(ref span), "Missing text values cannot be converted to bool value.");
+            Contracts.Check(span.IsEmpty || !IsStdMissing(ref span), "Missing text value cannot be converted to bool type.");
 
             char ch;
             switch (src.Length)

src/Microsoft.ML.ResultProcessor/ResultProcessor.cs

@@ -1063,10 +1063,10 @@ private static Experiment CreateVisualizationExperiment(ExperimentItemResult res
             var experiment = new ML.Runtime.ExperimentVisualization.Experiment
             {
                 Key = index.ToString(),
-                CompareGroup = string.IsNullOrEmpty(result.CustomizedTag) ? result.Trainer.Kind : result.CustomizedTag,
+                CompareGroup = string.IsNullOrEmpty(result.CustomizedTag) ? result.TrainerKind : result.CustomizedTag,
                 Trainer = new ML.Runtime.ExperimentVisualization.Trainer
                 {
-                    Name = result.Trainer.Kind,
+                    Name = result.TrainerKind,
                     ParameterSets = new List<ML.Runtime.ExperimentVisualization.Item>()
                 },
                 DataSet = new ML.Runtime.ExperimentVisualization.DataSet { File = result.Datafile },
@@ -1152,7 +1152,10 @@ private static object Load(Stream stream)
 
         public static int Main(string[] args)
         {
-            return Main(new ConsoleEnvironment(42), args);
+            string currentDirectory = Path.GetDirectoryName(typeof(ResultProcessor).Module.FullyQualifiedName);
+            using (var env = new ConsoleEnvironment(42))
+            using (AssemblyLoadingUtils.CreateAssemblyRegistrar(env, currentDirectory))
+                return Main(env, args);
         }
 
         public static int Main(IHostEnvironment env, string[] args)

src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs

@@ -373,7 +373,7 @@ protected override ParameterMixingCalibratedPredictor CreatePredictor()
             CurrentWeights.GetItemOrDefault(0, ref bias);
             CurrentWeights.CopyTo(ref weights, 1, CurrentWeights.Length - 1);
             return new ParameterMixingCalibratedPredictor(Host,
-                new LinearBinaryPredictor(Host, ref weights, bias),
+                new LinearBinaryPredictor(Host, ref weights, bias, _stats),
                 new PlattCalibrator(Host, -1, 0));
         }
 

src/Microsoft.ML.Sweeper/ConfigRunner.cs

@@ -107,7 +107,10 @@ public virtual void Finish()
             if (Exe == null || Exe.EndsWith("maml", StringComparison.OrdinalIgnoreCase) ||
                 Exe.EndsWith("maml.exe", StringComparison.OrdinalIgnoreCase))
             {
+                string currentDirectory = Path.GetDirectoryName(typeof(ExeConfigRunnerBase).Module.FullyQualifiedName);
+
                 using (var ch = Host.Start("Finish"))
+                using (AssemblyLoadingUtils.CreateAssemblyRegistrar(Host, currentDirectory))
                 {
                     var runs = RunNums.ToArray();
                     var args = Utils.BuildArray(RunNums.Count + 2,
@@ -120,7 +123,7 @@ public virtual void Finish()
                             return string.Format("{{{0}}}", GetFilePath(runs[i], "out"));
                         });
 
-                    ResultProcessorInternal.ResultProcessor.Main (args);
+                    ResultProcessorInternal.ResultProcessor.Main(args);
 
                     ch.Info(@"The summary of the run results has been saved to the file {0}\{1}.summary.txt", OutputFolder, Prefix);
                 }

src/Microsoft.ML.Sweeper/Microsoft.ML.Sweeper.csproj

@@ -16,4 +16,8 @@
 
   </ItemGroup>
 
+  <ItemGroup>
+    <Compile Include="..\Common\AssemblyLoadingUtils.cs" Link="Common\AssemblyLoadingUtils.cs" />
+  </ItemGroup>
+
 </Project>

test/BaselineOutput/SingleDebug/Command/CommandTrainMlrWithStats-1-out.txt

@@ -0,0 +1 @@
+Saving predictor summary

test/BaselineOutput/SingleDebug/Command/CommandTrainMlrWithStats-out.txt

@@ -0,0 +1,15 @@
+maml.exe Train tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,} data=%Data% out=%Output% seed=1 xf=Term{col=Label}
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 11 of 15 weights.
+Model trained with 150 training examples.
+Residual Deviance: 	132.0122
+Null Deviance:    	329.5837
+AIC:              	154.0122
+Not training a calibrator because it is not needed.
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+

test/BaselineOutput/SingleDebug/Command/CommandTrainMlrWithStats-summary.txt

@@ -0,0 +1,18 @@
+MulticlassLogisticRegression bias and non-zero weights
+	Iris-setosa+(Bias)	2.265129
+	Iris-versicolor+(Bias)	0.7695086
+	Iris-virginica+(Bias)	-3.034663
+	Iris-setosa+f3	-3.180634
+	Iris-setosa+f2	-2.88663
+	Iris-setosa+f1	0.5392878
+	Iris-setosa+f0	-0.03958065
+	Iris-versicolor+f1	-0.7073272
+	Iris-virginica+f3	3.158146
+	Iris-virginica+f2	1.907791
+	Iris-virginica+f0	0.01793481
+
+*** MODEL STATISTICS SUMMARY ***   
+Count of training examples:	150
+Residual Deviance:         	132.0122
+Null Deviance:             	329.5837
+AIC:                       	154.0122

test/BaselineOutput/SingleDebug/Command/CommandTrainingLrWithStats-1-out.txt

@@ -0,0 +1 @@
+Saving predictor summary

test/BaselineOutput/SingleDebug/Command/CommandTrainingLrWithStats-out.txt

@@ -0,0 +1,15 @@
+maml.exe Train feat=Num lab=Lab tr=lr{t=- stat=+} loader=text{header+ sep=comma col=Lab:14 col=Num:0,2,4,10-12} data=%Data% out=%Output%
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Beginning optimization
+num vars: 7
+improvement criterion: Mean Improvement
+L1 regularization selected 7 of 7 weights.
+Model trained with 32561 training examples.
+Residual Deviance: 	26705.74 (on 32554 degrees of freedom)
+Null Deviance:     	35948.08 (on 32560 degrees of freedom)
+AIC:               	26719.74
+Not training a calibrator because it is not needed.
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+

test/BaselineOutput/SingleDebug/Command/CommandTrainingLrWithStats-summary.txt

@@ -0,0 +1,15 @@
+Linear Binary Classification Predictor non-zero weights
+
+(Bias)	-8.228298
+capital-gain	18.58347
+education-num	5.066041
+hours-per-week	3.946534
+age	3.86064
+capital-loss	2.81616
+fnlwgt	0.7489593
+
+*** MODEL STATISTICS SUMMARY ***   
+Count of training examples:	32561
+Residual Deviance:         	26705.74
+Null Deviance:             	35948.08
+AIC:                       	26719.74

test/BaselineOutput/SingleDebug/FastRank/FastRank-CV-MSM-sparse-sample-out.txt

@@ -0,0 +1,8 @@
+maml.exe CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} threads=- dout=%Output% loader=Text{col=Name:TX:0 col=Label:Num:1 col=Features:Num:~} data=%Data% seed=1 xf=Expr{col=Name expr={x=>right(x, 1)}}
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+Could not find file '%Data%
+Error log has been saved to '%Temp%\%ErrorLog%'. Please refer to https://aka.ms/MLNetIssue if you need assistance.
+--- Progress log ---

test/BaselineOutput/SingleDebug/FastRank/FastRank-CV-breast-cancer-out.txt

@@ -0,0 +1,83 @@
+maml.exe CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} threads=- dout=%Output% data=%Data% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Warning: Skipped 8 instances with missing features during training
+Processed 329 instances
+Binning and forming Feature objects
+Reserved memory for tree learner: 3852 bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Warning: Skipped 8 instances with missing features during training
+Processed 354 instances
+Binning and forming Feature objects
+Reserved memory for tree learner: 3816 bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO:	0.3702 (134.0/(134.0+228.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      131 |        3 | 0.9776
+ negative ||       10 |      218 | 0.9561
+          ||======================
+Precision ||   0.9291 |   0.9864 |
+OVERALL 0/1 ACCURACY: 0.964088
+LOG LOSS/instance:  0.211336
+Test-set entropy (prior Log-Loss/instance): 0.950799
+LOG-LOSS REDUCTION (RIG): 77.772765
+AUC:                0.983225
+TEST POSITIVE RATIO:	0.3175 (107.0/(107.0+230.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       98 |        9 | 0.9159
+ negative ||        5 |      225 | 0.9783
+          ||======================
+Precision ||   0.9515 |   0.9615 |
+OVERALL 0/1 ACCURACY: 0.958457
+LOG LOSS/instance:  0.137700
+Test-set entropy (prior Log-Loss/instance): 0.901650
+LOG-LOSS REDUCTION (RIG): 84.727964
+AUC:                0.993681
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.988453 (0.0052)
+Accuracy:           0.961273 (0.0028)
+Positive precision: 0.940267 (0.0112)
+Positive recall:    0.946750 (0.0309)
+Negative precision: 0.973982 (0.0124)
+Negative recall:    0.967201 (0.0111)
+Log-loss:           0.174518 (0.0368)
+Log-loss reduction: 81.250364 (3.4776)
+F1 Score:           0.943030 (0.0097)
+AUPRC:              0.962986 (0.0211)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree data preparation' started.
+[1] 'FastTree data preparation' finished in %Time%.
+[2] 'FastTree in-memory bins initialization' started.
+[2] 'FastTree in-memory bins initialization' finished in %Time%.
+[3] 'FastTree feature conversion' started.
+[3] 'FastTree feature conversion' finished in %Time%.
+[4] 'FastTree training' started.
+[4] 'FastTree training' finished in %Time%.
+[5] 'FastTree data preparation #2' started.
+[5] 'FastTree data preparation #2' finished in %Time%.
+[6] 'FastTree in-memory bins initialization #2' started.
+[6] 'FastTree in-memory bins initialization #2' finished in %Time%.
+[7] 'FastTree feature conversion #2' started.
+[7] 'FastTree feature conversion #2' finished in %Time%.
+[8] 'FastTree training #2' started.
+[8] 'FastTree training #2' finished in %Time%.

test/BaselineOutput/SingleDebug/FastRank/FastRank-CV-breast-cancer-rp.txt

@@ -0,0 +1,4 @@
+FastRank
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.988453	0.961273	0.940267	0.94675	0.973982	0.967201	0.174518	81.25037	0.94303	0.962986	0.25	5	5	20	FastRank	%Data%		%Output%	99	0	0	maml.exe CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} threads=- dout=%Output% data=%Data% seed=1	/lr:0.25;/nl:5;/mil:5;/iter:20	
+