Remove debugging tests

Author: rgesteve

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

@@ -78,93 +78,5 @@ public void OneDalFastTreeBinaryEstimator()
 
             Assert.True(testingMetrics.Accuracy > 0.8);
         }
-
-
-        //[Fact]
-        //[NativeDependencyFact("MatrixFactorizationNative")]
-        [NativeDependencyFact("OneDalNative")]
-        public void TestDependency()
-        {
-            var currentDir = AppContext.BaseDirectory;
-            Output.WriteLine($"**** Running from directory {currentDir}.");
-
-            var dllDir = AppContext.GetData("NATIVE_DLL_SEARCH_DIRECTORIES").ToString();
-            Output.WriteLine($"**** The search dir is {dllDir}.");
-
-            DependencyContext defaultContext = DependencyContext.Default;
-            var currentRid = RuntimeEnvironment.GetRuntimeIdentifier();
-            Output.WriteLine($"**** the current RID is {currentRid}.");
-
-            var runtimeLibs = defaultContext.RuntimeLibraries;
-            try
-            {
-                // var candidates = runtimeLibs.Where(x => Regex.IsMatch(x.Name, ".*LightG.*", RegexOptions.IgnoreCase));
-                var candidates = runtimeLibs.Where(x => Regex.IsMatch(x.Name, ".*onedal.*", RegexOptions.IgnoreCase));
-                var possible = candidates.First();
-                Output.WriteLine($"**** [{possible.Name}] found of a count of [ {candidates.Count()} ] .");
-                var nativeAssets = possible.GetRuntimeNativeAssets(defaultContext, currentRid);
-                Output.WriteLine($"**** With [ {nativeAssets.Count()}  ] native assets");
-                foreach (var na in nativeAssets)
-                {
-                    Output.WriteLine($"**** -- nativeAsset: [ {na}  ]");
-                }
-            }
-            catch (Exception ex)
-            {
-                Output.WriteLine($"**** Had some trouble retrieving onedal: [ {ex.Message} ] .");
-            }
-
-            Assert.True(true);
-        }
-
-        //[Fact]
-        [NativeDependencyFact("OneDalNative")]
-        public void OneDalFastTreeBinaryEstimatorAtt()
-        {
-            Environment.SetEnvironmentVariable("MLNET_BACKEND", "ONEDAL");
-            var trainDataPath = GetDataPath("binary_synth_data_train.csv");
-            var testDataPath = GetDataPath("binary_synth_data_test.csv");
-
-            var loader = ML.Data.CreateTextLoader(columns: new[] {
-              new TextLoader.Column("f0", DataKind.Single, 0),
-            new TextLoader.Column("f1", DataKind.Single, 1),
-          new TextLoader.Column("f2", DataKind.Single, 2),
-          new TextLoader.Column("f3", DataKind.Single, 3),
-          new TextLoader.Column("f4", DataKind.Single, 4),
-          new TextLoader.Column("f5", DataKind.Single, 5),
-          new TextLoader.Column("f6", DataKind.Single, 6),
-          new TextLoader.Column("f7", DataKind.Single, 7),
-          new TextLoader.Column("target", DataKind.Boolean,8)},
-        separatorChar: ',',
-                hasHeader: true);
-
-            var trainingData = loader.Load(trainDataPath);
-            var testingData = loader.Load(testDataPath);
-
-            var preprocessingPipeline = ML.Transforms.Concatenate("Features", new string[] { "f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7" });
-
-            var preprocessedTrainingData = preprocessingPipeline.Fit(trainingData).Transform(trainingData);
-            var preprocessedTestingData = preprocessingPipeline.Fit(trainingData).Transform(testingData);
-
-            // Output.WriteLine($"**** After preprocessing the data got {preprocessedTrainingData.Schema.Count} columns.");
-
-            FastForestBinaryTrainer.Options options = new FastForestBinaryTrainer.Options();
-            options.LabelColumnName = "target";
-            options.FeatureColumnName = "Features";
-            options.NumberOfTrees = 100;
-            options.NumberOfLeaves = 128;
-            options.MinimumExampleCountPerLeaf = 5;
-            options.FeatureFraction = 1.0;
-
-            var trainer = ML.BinaryClassification.Trainers.FastForest(options);
-            var model = trainer.Fit(preprocessedTrainingData);
-            var trainingPredictions = model.Transform(preprocessedTrainingData);
-            var trainingMetrics = ML.BinaryClassification.EvaluateNonCalibrated(trainingPredictions, labelColumnName: "target");
-            var testingPredictions = model.Transform(preprocessedTestingData);
-            var testingMetrics = ML.BinaryClassification.EvaluateNonCalibrated(testingPredictions, labelColumnName: "target");
-
-            Assert.True(testingMetrics.Accuracy > 0.8);
-        }
-
     }
 }