Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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yaeldekel merged 6 commits into from
May 16, 2018
Merged

Fix a bug in Tree leaf featurizer entry point, and add a test for it. #131

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96703e3
Fix a bug in Tree leaf featurizer entry point, and add a test for it.
yaeldMS May 11, 2018
13de95d
Merge branch 'master' of https://github.com/dotnet/machinelearning in…
yaeldMS May 15, 2018
86d2a15
Improve unit test
yaeldMS May 15, 2018
431ca8e
Update unit test
yaeldMS May 15, 2018
fb7e5e5
Decrease number of trees and leaves in unit test
yaeldMS May 15, 2018
eec515e
Trigger build
yaeldMS May 15, 2018
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14 changes: 8 additions & 6 deletions src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
using (var ch = host.Start("Create Tree Ensemble Scorer"))
{
var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
var predictor = args.PredictorModel?.Predictor;
var predictor = args.PredictorModel.Predictor;
ch.Trace("Prepare data");
RoleMappedData data = null;
args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
ch.AssertValue(data);
ch.Assert(predictor == predictor2);

// Make sure that the given predictor has the correct number of input features.
if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
// be non-null.
var vm = predictor as IValueMapper;
ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)

@TomFinleyTomFinleyMay 11, 2018•
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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)

If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?

@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.

@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.

In reply to: 187707032 [](ancestors = 187707032)

{
throw ch.ExceptUserArg(nameof(args.PredictorModel),
"Predictor expects {0} features, but data has {1} features",
vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
}

var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
var bound = bindable.Bind(env, data?.Schema);
xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
var bound = bindable.Bind(env, data.Schema);
xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
ch.Done();
}
return xf;
Expand Down
1 change: 1 addition & 0 deletions test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

<ItemGroup>
<NativeAssemblyReference Include="CpuMathNative" />
<NativeAssemblyReference Include="FastTreeNative" />
</ItemGroup>

</Project>
67 changes: 66 additions & 1 deletion test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
}
}
}

[Fact]
public void EntryPointTreeLeafFeaturizer()
{
var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()

@glebukglebukMay 15, 2018•
edited by codemzs
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CatTransformDict [](start = 34, length = 16)

Would it be easier to use the LearningPipeline for the test here?

@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.

I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.

In reply to: 188444049 [](ancestors = 188444049)

{
Data = dataView,
Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
});
var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
{
Data = cat.OutputData,
Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
});

var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
{
FeatureColumn = "Features",
NumTrees = 5,
NumLeaves = 4,
LabelColumn = DefaultColumnNames.Label,
TrainingData = concat.OutputData
});

var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
{
PredictorModel = fastTree.PredictorModel,
TransformModels = new[] { cat.Model, concat.Model }
});

var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
{
Data = dataView,
PredictorModel = combine.PredictorModel
});

var view = treeLeaf.OutputData;
Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
VBuffer<float> treeValues = default(VBuffer<float>);
VBuffer<float> leafIndicators = default(VBuffer<float>);
VBuffer<float> pathIndicators = default(VBuffer<float>);
using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
{
var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
while (curs.MoveNext())
{
treesGetter(ref treeValues);
leavesGetter(ref leafIndicators);
pathsGetter(ref pathIndicators);

Assert.Equal(5, treeValues.Length);
Assert.Equal(5, treeValues.Count);
Assert.Equal(20, leafIndicators.Length);
Assert.Equal(5, leafIndicators.Count);
Assert.Equal(15, pathIndicators.Length);
}
}
}
}
}
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

from

May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

from

May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

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May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

edited

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

from

May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

edited

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

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May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

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May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
Expand Down

1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
Fix a bug in Tree leaf featurizer entry point, and add a test for it.#131

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 Merged

yaeldekel
merged 6 commits into

from

May 16, 2018
 Merged


Fix a bug in Tree leaf featurizer entry point, and add a test for it.
#131
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96703e3

Fix a bug in Tree leaf featurizer entry point, and add a test for it.


yaeldMS May 11, 2018
13de95d

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


yaeldMS May 15, 2018
86d2a15

Improve unit test


yaeldMS May 15, 2018
431ca8e

Update unit test


yaeldMS May 15, 2018
fb7e5e5

Decrease number of trees and leaves in unit test


yaeldMS May 15, 2018
eec515e

Trigger build


yaeldMS May 15, 2018

File filter

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14 changes: 8 additions & 6 deletions
src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -703,10 +703,12 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 using (var ch = host.Start("Create Tree Ensemble Scorer"))
 {
 var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments() { Suffix = args.Suffix };
 var predictor = args.PredictorModel?.Predictor;
 var predictor = args.PredictorModel.Predictor;
 ch.Trace("Prepare data");
 RoleMappedData data = null;
 args.PredictorModel?.PrepareData(env, input, out data, out var predictor2);
 args.PredictorModel.PrepareData(env, input, out data, out var predictor2);
 ch.AssertValue(data);
 ch.Assert(predictor == predictor2);

 // Make sure that the given predictor has the correct number of input features.
 if (predictor is CalibratedPredictorBase)
Expand All@@ -715,16 +717,16 @@ public static IDataTransform CreateForEntryPoint(IHostEnvironment env, Arguments
 // be non-null.
 var vm = predictor as IValueMapper;
 ch.CheckUserArg(vm != null, nameof(args.PredictorModel), "Predictor does not have compatible type");
 if (data != null && vm?.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
 if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize)
@TomFinleyTomFinleyMay 11, 2018•

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if (data != null && vm.InputType.VectorSize != data.Schema.Feature.Type.VectorSize) [](start = 16, length = 83)
If someone were to fit in a predictor that did implement IValueMapper (which most do), but that is nevertheless not a tree, what do we expect would happen? From my reading of the code it would just apply a generic scorer. Should we at least verify, in some fashion, that it is the type of predictor we expect beyond being merely a IValueMapper?
@TomFinleyTomFinleyMay 11, 2018

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Though mayhap, even if I'm right about that it should be addressed as a separate issue.
@yaeldekelyaeldekelMay 15, 2018

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Hi Tom, thanks for reviewing this change. If the predictor is an IValueMapper but is not a tree, then the constructor of TreeEnsembleFeaturizerBindableMapper below will fail.
In reply to: 187707032 [](ancestors = 187707032)
 {
 throw ch.ExceptUserArg(nameof(args.PredictorModel),
 "Predictor expects {0} features, but data has {1} features",
 vm?.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 vm.InputType.VectorSize, data.Schema.Feature.Type.VectorSize);
 }

 var bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
 var bound = bindable.Bind(env, data?.Schema);
 xf = new GenericScorer(env, scorerArgs, input, bound, data?.Schema);
 var bound = bindable.Bind(env, data.Schema);
 xf = new GenericScorer(env, scorerArgs, data.Data, bound, data.Schema);
 ch.Done();
 }
 return xf;
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1 change: 1 addition & 0 deletions
test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,6 +18,7 @@

 <ItemGroup>
 <NativeAssemblyReference Include="CpuMathNative" />
 <NativeAssemblyReference Include="FastTreeNative" />
 </ItemGroup>

</Project>

67 changes: 66 additions & 1 deletion
test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,13 +6,13 @@
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Core.Tests.UnitTests;
using Microsoft.ML.Runtime.Data;
using Microsoft.ML.Runtime.Data.IO;
using Microsoft.ML.Runtime.EntryPoints;
using Microsoft.ML.Runtime.EntryPoints.JsonUtils;
using Microsoft.ML.Runtime.FastTree;
using Microsoft.ML.Runtime.Internal.Utilities;
using Microsoft.ML.Runtime.Learners;
using Newtonsoft.Json;
Expand DownExpand Up@@ -2521,5 +2521,70 @@ public void EntryPointPrepareLabelConvertPredictedLabel()
 }
 }
 }

 [Fact]
 public void EntryPointTreeLeafFeaturizer()
 {
 var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
 var inputFile = new SimpleFileHandle(Env, dataPath, false, false);
 var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data;
 var cat = Categorical.CatTransformDict(Env, new CategoricalTransform.Arguments()
@glebukglebukMay 15, 2018•

edited by codemzs

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CatTransformDict [](start = 34, length = 16)
Would it be easier to use the LearningPipeline for the test here?
@codemzscodemzsMay 16, 2018

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This file contains tests for entrypoints. Learning pipeline has its own file with its own test cases. I would not create a learning pipeline test here for this scenario, instead I would create this scenario in learning pipeline test file.
I believe this pipeline can be created using learning pipeline with much fewer lines. We should try to add this scenario using learning pipeline under "Scenario" folder in the test folder. This way the users will see how tree leaf featurizer is used.
In reply to: 188444049 [](ancestors = 188444049)
 {
 Data = dataView,
 Column = new[] { new CategoricalTransform.Column { Name = "Categories", Source = "Categories" } }
 });
 var concat = SchemaManipulation.ConcatColumns(Env, new ConcatTransform.Arguments()
 {
 Data = cat.OutputData,
 Column = new[] { new ConcatTransform.Column { Name = "Features", Source = new[] { "Categories", "NumericFeatures" } } }
 });

 var fastTree = FastTree.FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments
 {
 FeatureColumn = "Features",
 NumTrees = 5,
 NumLeaves = 4,
 LabelColumn = DefaultColumnNames.Label,
 TrainingData = concat.OutputData
 });

 var combine = ModelOperations.CombineModels(Env, new ModelOperations.PredictorModelInput()
 {
 PredictorModel = fastTree.PredictorModel,
 TransformModels = new[] { cat.Model, concat.Model }
 });

 var treeLeaf = TreeFeaturize.Featurizer(Env, new TreeEnsembleFeaturizerTransform.ArgumentsForEntryPoint
 {
 Data = dataView,
 PredictorModel = combine.PredictorModel
 });

 var view = treeLeaf.OutputData;
 Assert.True(view.Schema.TryGetColumnIndex("Trees", out int treesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Leaves", out int leavesCol));
 Assert.True(view.Schema.TryGetColumnIndex("Paths", out int pathsCol));
 VBuffer<float> treeValues = default(VBuffer<float>);
 VBuffer<float> leafIndicators = default(VBuffer<float>);
 VBuffer<float> pathIndicators = default(VBuffer<float>);
 using (var curs = view.GetRowCursor(c => c == treesCol || c == leavesCol || c == pathsCol))
 {
 var treesGetter = curs.GetGetter<VBuffer<float>>(treesCol);
 var leavesGetter = curs.GetGetter<VBuffer<float>>(leavesCol);
 var pathsGetter = curs.GetGetter<VBuffer<float>>(pathsCol);
 while (curs.MoveNext())
 {
 treesGetter(ref treeValues);
 leavesGetter(ref leafIndicators);
 pathsGetter(ref pathIndicators);

 Assert.Equal(5, treeValues.Length);
 Assert.Equal(5, treeValues.Count);
 Assert.Equal(20, leafIndicators.Length);
 Assert.Equal(5, leafIndicators.Count);
 Assert.Equal(15, pathIndicators.Length);
 }
 }
 }
 }
}