Update TestAutoInference to use correct Environment.

Author: eerhardt

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

@@ -74,7 +74,8 @@ public void TestLearn()
         [Fact(Skip = "Need CoreTLC specific baseline update")]
         public void TestTextDatasetLearn()
         {
-            using (var env = new ConsoleEnvironment())
+            using (var env = new ConsoleEnvironment()
+                .AddStandardComponents()) // AutoInference uses ComponentCatalog to find all learners
             {
                 string pathData = GetDataPath(@"../UnitTest/tweets_labeled_10k_test_validation.tsv");
                 int batchSize = 5;
@@ -133,29 +134,43 @@ public void TestPipelineNodeCloning()
             }
         }
 
-        /*[Fact]
+        [Fact]
         public void TestHyperparameterFreezing()
         {
             string pathData = GetDataPath("adult.train");
             int numOfSampleRows = 1000;
             int batchSize = 1;
             int numIterations = 10;
             int numTransformLevels = 3;
-            using (var env = new ConsoleEnvironment())
+            using (var env = new ConsoleEnvironment()
+                .AddStandardComponents()) // AutoInference uses ComponentCatalog to find all learners
             {
                 SupportedMetric metric = PipelineSweeperSupportedMetrics.GetSupportedMetric(PipelineSweeperSupportedMetrics.Metrics.Auc);
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 1");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 1");
+                Log("ERIC: " + FullTestName + " 1");
+
                 // Using the simple, uniform random sampling (with replacement) brain
-                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(Env);
+                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(env);
 
                 // Run initial experiments
-                var amls = AutoInference.InferPipelines(Env, autoMlBrain, pathData, "", out var _, numTransformLevels, batchSize,
+                var amls = AutoInference.InferPipelines(env, autoMlBrain, pathData, "", out var _, numTransformLevels, batchSize,
                     metric, out var bestPipeline, numOfSampleRows, new IterationTerminator(numIterations),
                     MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer);
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 2");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 2");
+                Log("ERIC: " + FullTestName + " 2");
+
                 // Clear results
                 amls.ClearEvaluatedPipelines();
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + "3");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 3");
+                Log("ERIC: " + FullTestName + " 3");
+
+
                 // Get space, remove transforms and all but one learner, freeze hyperparameters on learner.
                 var space = amls.GetSearchSpace();
                 var transforms = space.Item1.Where(t =>
@@ -164,22 +179,40 @@ public void TestHyperparameterFreezing()
                 var hyperParam = learners[0].PipelineNode.SweepParams.First();
                 var frozenParamValue = hyperParam.RawValue;
                 hyperParam.Frozen = true;
+
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 4");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 4");
+                Log("ERIC: " + FullTestName + " 4");
+
                 amls.UpdateSearchSpace(learners, transforms);
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 5");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 5");
+                Log("ERIC: " + FullTestName + " 5");
+
+
                 // Allow for one more iteration
                 amls.UpdateTerminator(new IterationTerminator(numIterations + 1));
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 6");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 6");
+                Log("ERIC: " + FullTestName + " 6");
+
                 // Do learning. Only retained learner should be left in all pipelines.
                 bestPipeline = amls.InferPipelines(numTransformLevels, batchSize, numOfSampleRows);
 
+                System.Diagnostics.Debug.WriteLine("ERIC: " + FullTestName + " 7");
+                System.Console.WriteLine("ERIC: " + FullTestName + " 7");
+                Log("ERIC: " + FullTestName + " 7");
+
                 // Make sure all pipelines have retained learner
                 Assert.True(amls.GetAllEvaluatedPipelines().All(p => p.Learner.LearnerName == learners[0].LearnerName));
 
                 // Make sure hyperparameter value did not change
                 Assert.NotNull(bestPipeline);
                 Assert.Equal(bestPipeline.Learner.PipelineNode.SweepParams.First().RawValue, frozenParamValue);
             }
-        }*/
+        }
 
         [Fact(Skip = "Dataset not available.")]
         public void TestRegressionPipelineWithMinimizingMetric()
@@ -189,15 +222,16 @@ public void TestRegressionPipelineWithMinimizingMetric()
             int batchSize = 5;
             int numIterations = 10;
             int numTransformLevels = 1;
-            using (var env = new ConsoleEnvironment())
+            using (var env = new ConsoleEnvironment()
+                .AddStandardComponents()) // AutoInference uses ComponentCatalog to find all learners
             {
                 SupportedMetric metric = PipelineSweeperSupportedMetrics.GetSupportedMetric(PipelineSweeperSupportedMetrics.Metrics.AccuracyMicro);
 
                 // Using the simple, uniform random sampling (with replacement) brain
-                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(Env);
+                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(env);
 
                 // Run initial experiments
-                var amls = AutoInference.InferPipelines(Env, autoMlBrain, pathData, "", out var _, numTransformLevels, batchSize,
+                var amls = AutoInference.InferPipelines(env, autoMlBrain, pathData, "", out var _, numTransformLevels, batchSize,
                 metric, out var bestPipeline, numOfSampleRows, new IterationTerminator(numIterations),
                 MacroUtils.TrainerKinds.SignatureRegressorTrainer);
 
@@ -223,15 +257,16 @@ public void TestLearnerConstrainingByName()
             int numIterations = 1;
             int numTransformLevels = 2;
             var retainedLearnerNames = new[] { $"LogisticRegressionBinaryClassifier", $"FastTreeBinaryClassifier" };
-            using (var env = new ConsoleEnvironment())
+            using (var env = new ConsoleEnvironment()
+                .AddStandardComponents()) // AutoInference uses ComponentCatalog to find all learners
             {
                 SupportedMetric metric = PipelineSweeperSupportedMetrics.GetSupportedMetric(PipelineSweeperSupportedMetrics.Metrics.Auc);
 
                 // Using the simple, uniform random sampling (with replacement) brain.
-                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(Env);
+                PipelineOptimizerBase autoMlBrain = new UniformRandomEngine(env);
 
                 // Run initial experiment.
-                var amls = AutoInference.InferPipelines(Env, autoMlBrain, pathData, "", out var _,
+                var amls = AutoInference.InferPipelines(env, autoMlBrain, pathData, "", out var _,
                 numTransformLevels, batchSize, metric, out var _, numOfSampleRows,
                 new IterationTerminator(numIterations), MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer);