Updates ml.net reference of LightGBM to version 2.2

build/Dependencies.props

@@ -14,7 +14,7 @@
   <!-- Other/Non-Core Product Dependencies -->
   <PropertyGroup>
     <GoogleProtobufPackageVersion>3.5.1</GoogleProtobufPackageVersion>
-    <LightGBMPackageVersion>2.2.1.1</LightGBMPackageVersion>
+    <LightGBMPackageVersion>2.2.3</LightGBMPackageVersion>
     <MicrosoftMLOnnxRuntimePackageVersion>0.2.1</MicrosoftMLOnnxRuntimePackageVersion>
     <MlNetMklDepsPackageVersion>0.0.0.7</MlNetMklDepsPackageVersion>
     <ParquetDotNetPackageVersion>2.1.3</ParquetDotNetPackageVersion>

src/Microsoft.ML.LightGBM/LightGbmArguments.cs

@@ -371,6 +371,9 @@ public enum EvalMetricType
         [TlcModule.SweepableDiscreteParam("CatL2", new object[] { 0.1, 0.5, 1, 5, 10 })]
         public double CatL2 = 10;
 
+        [Argument(ArgumentType.AtMostOnce, HelpText = "Sets the random seed for LightGBM to use.")]
+        public int? Seed;
+
         [Argument(ArgumentType.Multiple, HelpText = "Parallel LightGBM Learning Algorithm", ShortName = "parag")]
         public ISupportParallel ParallelTrainer = new SingleTrainerFactory();
 
@@ -390,7 +393,7 @@ internal Dictionary<string, object> ToDictionary(IHost host)
             if (NThread.HasValue)
                 res["nthread"] = NThread.Value;
 
-            res["seed"] = host.Rand.Next();
+            res["seed"] = (Seed.HasValue) ? Seed : host.Rand.Next();
 
             string metric = null;
             switch (EvalMetric)

src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs

@@ -36,7 +36,7 @@ public sealed class LightGbmMulticlassTrainer : LightGbmTrainerBase<VBuffer<floa
         public override PredictionKind PredictionKind => PredictionKind.MultiClassClassification;
 
         internal LightGbmMulticlassTrainer(IHostEnvironment env, Options options)
-             : base(env, LoadNameValue, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumn))
+             : base(env, LoadNameValue, options, TrainerUtils.MakeU4ScalarColumn(options.LabelColumn))
         {
             _numClass = -1;
         }

src/Microsoft.ML.LightGBM/WrappedLightGbmBooster.cs

@@ -100,12 +100,12 @@ private static double[] Str2DoubleArray(string str, char delimiter)
             var values = new List<double>();
             foreach (var token in str.Split(delimiter))
             {
-                var trimmed = token.Trim();
+                var trimmed = token.Trim().ToLowerInvariant();
 
-                if (trimmed.Equals("inf", StringComparison.OrdinalIgnoreCase))
-                    values.Add(double.PositiveInfinity);
-                else if (trimmed.Equals("-inf", StringComparison.OrdinalIgnoreCase))
+                if (trimmed.Contains("-inf"))
                     values.Add(double.NegativeInfinity);
+                else if (trimmed.Contains("inf"))
+                    values.Add(double.PositiveInfinity);
                 else if (trimmed.Contains("nan"))
                     values.Add(double.NaN);
                 else

src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

@@ -607,6 +607,9 @@ private void NormalizeSumToOne(float[] output, int count)
                 for (int i = 0; i < count; i++)
                 {
                     var value = output[i];
+                    if (float.IsNaN(value))
+                        continue;
+
                     if (value >= 0)
                         sum += value;
                     else

test/BaselineOutput/Common/EntryPoints/core_manifest.json

@@ -11606,6 +11606,15 @@
             ]
           }
         },
+        {
+          "Name": "Seed",
+          "Type": "Int",
+          "Desc": "Sets the random seed for LightGBM to use.",
+          "Required": false,
+          "SortOrder": 150.0,
+          "IsNullable": true,
+          "Default": null
+        },
         {
           "Name": "ParallelTrainer",
           "Type": {
@@ -12101,6 +12110,15 @@
             ]
           }
         },
+        {
+          "Name": "Seed",
+          "Type": "Int",
+          "Desc": "Sets the random seed for LightGBM to use.",
+          "Required": false,
+          "SortOrder": 150.0,
+          "IsNullable": true,
+          "Default": null
+        },
         {
           "Name": "ParallelTrainer",
           "Type": {
@@ -12596,6 +12614,15 @@
             ]
           }
         },
+        {
+          "Name": "Seed",
+          "Type": "Int",
+          "Desc": "Sets the random seed for LightGBM to use.",
+          "Required": false,
+          "SortOrder": 150.0,
+          "IsNullable": true,
+          "Default": null
+        },
         {
           "Name": "ParallelTrainer",
           "Type": {
@@ -13091,6 +13118,15 @@
             ]
           }
         },
+        {
+          "Name": "Seed",
+          "Type": "Int",
+          "Desc": "Sets the random seed for LightGBM to use.",
+          "Required": false,
+          "SortOrder": 150.0,
+          "IsNullable": true,
+          "Default": null
+        },
         {
           "Name": "ParallelTrainer",
           "Type": {

test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt → test/BaselineOutput/Common/LightGBMMC/LightGBMMC-CV-iris.key-out.txt

@@ -21,8 +21,8 @@ TRUTH     ||========================
 Precision ||1.0000 |0.9310 |0.8966 |
 Accuracy(micro-avg): 0.936709
 Accuracy(macro-avg): 0.942857
-Log-loss:           0.312759
-Log-loss reduction: 71.240938
+Log-loss:           0.285741
+Log-loss reduction: 73.725386
 
 Confusion table
           ||========================
@@ -35,15 +35,15 @@ TRUTH     ||========================
 Precision ||1.0000 |0.9048 |0.9524 |
 Accuracy(micro-avg): 0.957746
 Accuracy(macro-avg): 0.953030
-Log-loss:           0.193389
-Log-loss reduction: 82.186751
+Log-loss:           0.160970
+Log-loss reduction: 85.172898
 
 OVERALL RESULTS
 ---------------------------------------
 Accuracy(micro-avg): 0.947228 (0.0105)
 Accuracy(macro-avg): 0.947944 (0.0051)
-Log-loss:           0.253074 (0.0597)
-Log-loss reduction: 76.713844 (5.4729)
+Log-loss:           0.223355 (0.0624)
+Log-loss reduction: 79.449142 (5.7238)
 
 ---------------------------------------
 Physical memory usage(MB): %Number%

test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt → test/BaselineOutput/Common/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt

@@ -1,4 +1,4 @@
 LightGBMMC
 Accuracy(micro-avg)	Accuracy(macro-avg)	Log-loss	Log-loss reduction	/iter	/lr	/nl	/mil	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.947228	0.947944	0.253074	76.71384	10	0.2	20	10	1	LightGBMMC	%Data%		%Output%	99	0	0	maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label}	/iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1	
+0.947228	0.947944	0.223355	79.44914	10	0.2	20	10	1	LightGBMMC	%Data%		%Output%	99	0	0	maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label}	/iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1	
 

test/BaselineOutput/Common/LightGBMMC/LightGBMMC-CV-iris.key.txt

@@ -0,0 +1,151 @@
+Instance	Label	Assigned	Log-loss	#1 Score	#2 Score	#3 Score	#1 Class	#2 Class	#3 Class
+5	0	0	0.18795057786518246	0.82865566	0.13240473	0.03893963	0	1	2
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+87	1	1	0.10970840394213197	0.8960954	0.0690198541	0.0348847173	1	2	0
+89	1	1	0.094191382978362242	0.910108566	0.0542792119	0.0356122442	1	2	0
+94	1	1	0.094191382978362242	0.910108566	0.0542792119	0.0356122442	1	2	0
+101	2	2	0.16029335824881216	0.851893842	0.115021013	0.0330851451	2	1	0
+103	2	2	0.10737741038979709	0.8981866	0.0664602146	0.03535317	2	1	0
+107	2	2	0.10254983204222122	0.9025332	0.06078896	0.03667784	2	1	0
+110	2	2	0.22186873543222146	0.8010205	0.167042211	0.03193731	2	1	0
+114	2	2	0.16029335824881216	0.851893842	0.115021013	0.0330851451	2	1	0
+116	2	2	0.10737741038979709	0.8981866	0.0664602146	0.03535317	2	1	0
+118	2	2	0.085412022670855015	0.9181339	0.0462341458	0.0356319249	2	1	0
+119	2	1	0.90674956791950001	0.558204055	0.40383473	0.03796125	1	2	0
+124	2	2	0.11442077120766009	0.8918826	0.07259029	0.0355271064	2	1	0
+126	2	2	0.40083567848156304	0.6697601	0.293806285	0.0364336222	2	1	0
+127	2	2	0.36452230942262392	0.694528341	0.2717706	0.0337010734	2	1	0
+130	2	2	0.085412022670855015	0.9181339	0.0462341458	0.0356319249	2	1	0
+134	2	1	0.87385387392584235	0.537953556	0.41734007	0.04470639	1	2	0
+135	2	2	0.085412022670855015	0.9181339	0.0462341458	0.0356319249	2	1	0
+136	2	2	0.12080648753504018	0.886205435	0.0794470161	0.0343475267	2	1	0
+139	2	2	0.11557084397877333	0.890857458	0.07233209	0.0368104242	2	1	0
+140	2	2	0.10625640378340648	0.899194062	0.0649875849	0.03581835	2	1	0
+142	2	2	0.16029335824881216	0.851893842	0.115021013	0.0330851451	2	1	0
+143	2	2	0.11442077120766009	0.8918826	0.07259029	0.0355271064	2	1	0
+146	2	2	0.16029335824881216	0.851893842	0.115021013	0.0330851451	2	1	0
+148	2	2	0.1306706508046877	0.877506733	0.08750639	0.034986876	2	1	0
+149	2	2	0.19668370574494914	0.8214504	0.145130783	0.03341879	2	1	0

test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-out.txt → test/BaselineOutput/Common/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt

@@ -1,4 +1,4 @@
-maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label}
+maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-2]:0 col=Features:1-4} data=%Data% seed=1
 Not adding a normalizer.
 Auto-tuning parameters: UseCat = False
 Auto-tuning parameters: UseSoftmax = False
@@ -21,8 +21,8 @@ TRUTH     ||========================
 Precision ||1.0000 |0.9310 |0.8966 |
 Accuracy(micro-avg): 0.936709
 Accuracy(macro-avg): 0.942857
-Log-loss:           0.312759
-Log-loss reduction: 71.240938
+Log-loss:           0.285741
+Log-loss reduction: 73.725386
 
 Confusion table
           ||========================
@@ -35,33 +35,27 @@ TRUTH     ||========================
 Precision ||1.0000 |0.9048 |0.9524 |
 Accuracy(micro-avg): 0.957746
 Accuracy(macro-avg): 0.953030
-Log-loss:           0.193389
-Log-loss reduction: 82.186751
+Log-loss:           0.160970
+Log-loss reduction: 85.172898
 
 OVERALL RESULTS
 ---------------------------------------
 Accuracy(micro-avg): 0.947228 (0.0105)
 Accuracy(macro-avg): 0.947944 (0.0051)
-Log-loss:           0.253074 (0.0597)
-Log-loss reduction: 76.713844 (5.4729)
+Log-loss:           0.223355 (0.0624)
+Log-loss reduction: 79.449142 (5.7238)
 
 ---------------------------------------
 Physical memory usage(MB): %Number%
 Virtual memory usage(MB): %Number%
 %DateTime%	 Time elapsed(s): %Number%
 
 --- Progress log ---
-[1] 'Building term dictionary' started.
-[1] (%Time%)	71 examples	Total Terms: 3
-[1] 'Building term dictionary' finished in %Time%.
-[2] 'Loading data for LightGBM' started.
-[2] 'Loading data for LightGBM' finished in %Time%.
-[3] 'Training with LightGBM' started.
-[3] 'Training with LightGBM' finished in %Time%.
-[4] 'Building term dictionary #2' started.
-[4] (%Time%)	79 examples	Total Terms: 3
-[4] 'Building term dictionary #2' finished in %Time%.
-[5] 'Loading data for LightGBM #2' started.
-[5] 'Loading data for LightGBM #2' finished in %Time%.
-[6] 'Training with LightGBM #2' started.
-[6] 'Training with LightGBM #2' finished in %Time%.
+[1] 'Loading data for LightGBM' started.
+[1] 'Loading data for LightGBM' finished in %Time%.
+[2] 'Training with LightGBM' started.
+[2] 'Training with LightGBM' finished in %Time%.
+[3] 'Loading data for LightGBM #2' started.
+[3] 'Loading data for LightGBM #2' finished in %Time%.
+[4] 'Training with LightGBM #2' started.
+[4] 'Training with LightGBM #2' finished in %Time%.

test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt → test/BaselineOutput/Common/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt

@@ -1,4 +1,4 @@
 LightGBMMC
 Accuracy(micro-avg)	Accuracy(macro-avg)	Log-loss	Log-loss reduction	/iter	/lr	/nl	/mil	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
-0.947228	0.947944	0.253074	76.71384	10	0.2	20	10	1	LightGBMMC	%Data%		%Output%	99	0	0	maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% seed=1	/iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1	
+0.947228	0.947944	0.223355	79.44914	10	0.2	20	10	1	LightGBMMC	%Data%		%Output%	99	0	0	maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-2]:0 col=Features:1-4} data=%Data% seed=1	/iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1