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Jan 3, 2019
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making GetCoefficientStatistics public #1979

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9 changes: 3 additions & 6 deletions src/Microsoft.ML.StandardLearners/Standard/LinearModelParameters.cs
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Expand Up @@ -84,10 +84,7 @@ public IEnumerator<float> GetEnumerator()
return _pred.Weight.Items(all: true).Select(iv => iv.Value).GetEnumerator();
}

IEnumerator IEnumerable.GetEnumerator()
{
return GetEnumerator();
}
IEnumerator IEnumerable.GetEnumerator() => GetEnumerator();
}

/// <summary> The predictor's feature weight coefficients.</summary>
Expand Down Expand Up @@ -505,7 +502,7 @@ private protected override void SaveSummary(TextWriter writer, RoleMappedSchema
writer.WriteLine(LinearPredictorUtils.LinearModelAsText("Linear Binary Classification Predictor", null, null,
in weights, Bias, schema));

_stats?.SaveText(writer, this, schema, 20);
_stats?.SaveText(writer, this, schema.Feature.Value, 20);
}

///<inheritdoc/>
Expand All @@ -516,7 +513,7 @@ IList<KeyValuePair<string, object>> ICanGetSummaryInKeyValuePairs.GetSummaryInKe
var weights = Weight;
List<KeyValuePair<string, object>> results = new List<KeyValuePair<string, object>>();
LinearPredictorUtils.SaveLinearModelWeightsInKeyValuePairs(in weights, Bias, schema, results);
_stats?.SaveSummaryInKeyValuePairs(this, schema, int.MaxValue, results);
_stats?.SaveSummaryInKeyValuePairs(this, schema.Feature.Value, int.MaxValue, results);
return results;
}

Expand Down
2 changes: 1 addition & 1 deletion ...Microsoft.ML.StandardLearners/Standard/LogisticRegression/MulticlassLogisticRegression.cs
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Expand Up @@ -783,7 +783,7 @@ void ICanSaveInTextFormat.SaveAsText(TextWriter writer, RoleMappedSchema schema)
}

if (_stats != null)
_stats.SaveText(writer, null, schema, 20);
_stats.SaveText(writer, null, schema.Feature.Value, 20);
}

///<inheritdoc/>
Expand Down
88 changes: 44 additions & 44 deletions src/Microsoft.ML.StandardLearners/Standard/ModelStatistics.cs
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Expand Up @@ -27,12 +27,12 @@ namespace Microsoft.ML.Learners
public readonly struct CoefficientStatistics
{
public readonly string Name;
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@TomFinley TomFinley Dec 28, 2018 •
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public readonly string Name; [](start = 8, length = 28)

As discussed, if this is to be part of our public API we should probably as part of this PR make this entire structure a bit less goofy. If I were to trace back the root of what makes this structure a confusing structure, I think this choice here -- having the name of the slot instead of just the slot index, is the original sin. (There are lots of other really strange choices, but I think much of the evil and confusion stems from here.)

This should have just been The fact that this is a Nameinstead of just aSlotIndex` is the cause for an enormous amount of confusion in this structure. This requires that we link it with the schema column for features, but from an API perspective this is just something that could have easily (in fact, more easily) be done by the user themselves. As it is, the structure is offering "convenience" but in a way that inevitably leads to a more complicated API.

Once you do that, restructuring the odd API below (which I'll comment more on in the test file) should become more approachable.

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@TomFinley TomFinley Dec 28, 2018 •
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This is not to say that it might be convenient to have an internal utility method that makes saving as text more convenient for the LinearModelParameters and whatnot, but confusing the needs of the text exporter (which is not part of our public API) with what we actually do in the public API was a misstep. (Not your misstep to be clear, since this code has existed, I think, more or less forever, but still something we ought to correct now that we are making it part of the publci API.)

In reply to: 244374085 [](ancestors = 244374085)

public readonly Single Estimate;
public readonly Single StandardError;
public readonly Single ZScore;
public readonly Single PValue;
public readonly float Estimate;
public readonly float StandardError;
public readonly float ZScore;
public readonly float PValue;

public CoefficientStatistics(string name, Single estimate, Single stdError, Single zScore, Single pValue)
public CoefficientStatistics(string name, float estimate, float stdError, float zScore, float pValue)
{
Contracts.AssertNonEmpty(name);
Name = name;
Expand Down Expand Up @@ -69,10 +69,10 @@ private static VersionInfo GetVersionInfo()
private readonly long _trainingExampleCount;

// The deviance of this model.
private readonly Single _deviance;
private readonly float _deviance;

// The deviance of the null hypothesis.
private readonly Single _nullDeviance;
private readonly float _nullDeviance;

// Total count of parameters.
private readonly int _paramCount;
Expand All @@ -82,17 +82,17 @@ private static VersionInfo GetVersionInfo()
// It could be null when there are too many non-zero weights so that
// the memory is insufficient to hold the Hessian matrix necessary for the computation
// of the variance-covariance matrix.
private readonly VBuffer<Single>? _coeffStdError;
private readonly VBuffer<float>? _coeffStdError;

public long TrainingExampleCount => _trainingExampleCount;

public Single Deviance => _deviance;
public float Deviance => _deviance;
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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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_deviance [](start = 33, length = 9)

why we need _deviance?

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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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Deviance [](start = 21, length = 8)

need proper comment.


public Single NullDeviance => _nullDeviance;
public float NullDeviance => _nullDeviance;
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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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_nullDeviance [](start = 37, length = 13)

I don't see any reason for _nullDeviance to exist.

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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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NullDeviance [](start = 21, length = 12)

Need proper comment


public int ParametersCount => _paramCount;

internal LinearModelStatistics(IHostEnvironment env, long trainingExampleCount, int paramCount, Single deviance, Single nullDeviance)
internal LinearModelStatistics(IHostEnvironment env, long trainingExampleCount, int paramCount, float deviance, float nullDeviance)
{
Contracts.AssertValue(env);
env.Assert(trainingExampleCount > 0);
Expand All @@ -104,7 +104,7 @@ internal LinearModelStatistics(IHostEnvironment env, long trainingExampleCount,
_nullDeviance = nullDeviance;
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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019 •
edited by sfilipi
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none of _variable from that block should exist.
Just make public readonly version of it, and use it. #Resolved

}

internal LinearModelStatistics(IHostEnvironment env, long trainingExampleCount, int paramCount, Single deviance, Single nullDeviance, in VBuffer<Single> coeffStdError)
internal LinearModelStatistics(IHostEnvironment env, long trainingExampleCount, int paramCount, float deviance, float nullDeviance, in VBuffer<float> coeffStdError)
: this(env, trainingExampleCount, paramCount, deviance, nullDeviance)
{
_env.Assert(coeffStdError.GetValues().Length == _paramCount);
Expand All @@ -120,10 +120,10 @@ internal LinearModelStatistics(IHostEnvironment env, ModelLoadContext ctx)
// *** Binary Format ***
// int: count of parameters
// long: count of training examples
// Single: deviance
// Single: null deviance
// float: deviance
// float: null deviance
// bool: whether standard error is included
// (Conditional) Single[_paramCount]: values of std errors of coefficients
// (Conditional) float[_paramCount]: values of std errors of coefficients
// (Conditional) int: length of std errors of coefficients
// (Conditional) int[_paramCount]: indices of std errors of coefficients

Expand All @@ -143,18 +143,18 @@ internal LinearModelStatistics(IHostEnvironment env, ModelLoadContext ctx)
return;
}

Single[] stdErrorValues = ctx.Reader.ReadFloatArray(_paramCount);
float[] stdErrorValues = ctx.Reader.ReadFloatArray(_paramCount);
int length = ctx.Reader.ReadInt32();
_env.CheckDecode(length >= _paramCount);
if (length == _paramCount)
{
_coeffStdError = new VBuffer<Single>(length, stdErrorValues);
_coeffStdError = new VBuffer<float>(length, stdErrorValues);
return;
}

_env.Assert(length > _paramCount);
int[] stdErrorIndices = ctx.Reader.ReadIntArray(_paramCount);
_coeffStdError = new VBuffer<Single>(length, _paramCount, stdErrorValues, stdErrorIndices);
_coeffStdError = new VBuffer<float>(length, _paramCount, stdErrorValues, stdErrorIndices);
}

internal static LinearModelStatistics Create(IHostEnvironment env, ModelLoadContext ctx)
Expand All @@ -178,10 +178,10 @@ private void SaveCore(ModelSaveContext ctx)
// *** Binary Format ***
// int: count of parameters
// long: count of training examples
// Single: deviance
// Single: null deviance
// float: deviance
// float: null deviance
// bool: whether standard error is included
// (Conditional) Single[_paramCount]: values of std errors of coefficients
// (Conditional) float[_paramCount]: values of std errors of coefficients
// (Conditional) int: length of std errors of coefficients
// (Conditional) int[_paramCount]: indices of std errors of coefficients

Expand Down Expand Up @@ -212,7 +212,7 @@ private void SaveCore(ModelSaveContext ctx)
/// <summary>
/// Computes the standart deviation, Z-Score and p-Value.
/// </summary>
public static bool TryGetBiasStatistics(LinearModelStatistics stats, Single bias, out Single stdError, out Single zScore, out Single pValue)
public static bool TryGetBiasStatistics(LinearModelStatistics stats, float bias, out float stdError, out float zScore, out float pValue)
{
if (!stats._coeffStdError.HasValue)
{
Expand All @@ -226,12 +226,12 @@ public static bool TryGetBiasStatistics(LinearModelStatistics stats, Single bias
stdError = stats._coeffStdError.Value.GetValues()[0];
Contracts.Assert(stdError == stats._coeffStdError.Value.GetItemOrDefault(0));
zScore = bias / stdError;
pValue = 1.0f - (Single)ProbabilityFunctions.Erf(Math.Abs(zScore / sqrt2));
pValue = 1.0f - (float)ProbabilityFunctions.Erf(Math.Abs(zScore / sqrt2));
return true;
}

private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stats, in VBuffer<Single> weights, in VBuffer<ReadOnlyMemory<char>> names,
ref VBuffer<Single> estimate, ref VBuffer<Single> stdErr, ref VBuffer<Single> zScore, ref VBuffer<Single> pValue, out ValueGetter<VBuffer<ReadOnlyMemory<char>>> getSlotNames)
private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stats, in VBuffer<float> weights, in VBuffer<ReadOnlyMemory<char>> names,
ref VBuffer<float> estimate, ref VBuffer<float> stdErr, ref VBuffer<float> zScore, ref VBuffer<float> pValue, out ValueGetter<VBuffer<ReadOnlyMemory<char>>> getSlotNames)
{
if (!stats._coeffStdError.HasValue)
{
Expand Down Expand Up @@ -260,7 +260,7 @@ private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stat
var weight = estimateEditor.Values[i - 1] = weights.GetItemOrDefault(wi);
var stdError = stdErrorEditor.Values[wi] = coeffStdErrorValues[i];
zScoreEditor.Values[i - 1] = weight / stdError;
pValueEditor.Values[i - 1] = 1 - (Single)ProbabilityFunctions.Erf(Math.Abs(zScoreEditor.Values[i - 1] / sqrt2));
pValueEditor.Values[i - 1] = 1 - (float)ProbabilityFunctions.Erf(Math.Abs(zScoreEditor.Values[i - 1] / sqrt2));
}

estimate = estimateEditor.Commit();
Expand All @@ -283,28 +283,30 @@ private static void GetUnorderedCoefficientStatistics(LinearModelStatistics stat
};
}

private List<CoefficientStatistics> GetUnorderedCoefficientStatistics(LinearBinaryModelParameters parent, RoleMappedSchema schema)
private List<CoefficientStatistics> GetUnorderedCoefficientStatistics(LinearBinaryModelParameters parent, Schema.Column featureColumn)
{
Contracts.AssertValue(_env);
_env.CheckValue(parent, nameof(parent));

if (!_coeffStdError.HasValue)
return new List<CoefficientStatistics>();

var weights = parent.Weights as IReadOnlyList<Single>;
var weights = parent.Weights as IReadOnlyList<float>;
_env.Assert(_paramCount == 1 || weights != null);
_env.Assert(_coeffStdError.Value.Length == weights.Count + 1);

var names = default(VBuffer<ReadOnlyMemory<char>>);
MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, weights.Count, ref names);

featureColumn.Metadata.GetValue(MetadataUtils.Kinds.SlotNames, ref names);
_env.Assert(names.Length > 0, "FeatureColumn has no metadata.");

ReadOnlySpan<float> stdErrorValues = _coeffStdError.Value.GetValues();
const Double sqrt2 = 1.41421356237; // Math.Sqrt(2);

List<CoefficientStatistics> result = new List<CoefficientStatistics>(_paramCount - 1);
bool denseStdError = _coeffStdError.Value.IsDense;
ReadOnlySpan<int> stdErrorIndices = _coeffStdError.Value.GetIndices();
Single[] zScores = new Single[_paramCount - 1];
float[] zScores = new float[_paramCount - 1];
for (int i = 1; i < _paramCount; i++)
{
int wi = denseStdError ? i - 1 : stdErrorIndices[i] - 1;
Expand All @@ -315,7 +317,7 @@ private List<CoefficientStatistics> GetUnorderedCoefficientStatistics(LinearBina
var weight = weights[wi];
var stdError = stdErrorValues[i];
var zScore = zScores[i - 1] = weight / stdError;
var pValue = 1 - (Single)ProbabilityFunctions.Erf(Math.Abs(zScore / sqrt2));
var pValue = 1 - (float)ProbabilityFunctions.Erf(Math.Abs(zScore / sqrt2));
result.Add(new CoefficientStatistics(name, weight, stdError, zScore, pValue));
}
return result;
Expand All @@ -324,33 +326,31 @@ private List<CoefficientStatistics> GetUnorderedCoefficientStatistics(LinearBina
/// <summary>
/// Gets the coefficient statistics as an object.
/// </summary>
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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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Need better summary than this one.

internal CoefficientStatistics[] GetCoefficientStatistics(LinearBinaryModelParameters parent, RoleMappedSchema schema, int paramCountCap)
public CoefficientStatistics[] GetCoefficientStatistics(LinearBinaryModelParameters parent, Schema.Column featureColumn, int paramCountCap)
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paramCountCap [](start = 133, length = 13)

this isn't great name. Param is such a generic term, I have no idea what it's capping.

{
Contracts.AssertValue(_env);
_env.CheckValue(parent, nameof(parent));
_env.CheckValue(schema, nameof(schema));
_env.CheckParam(paramCountCap >= 0, nameof(paramCountCap));

if (paramCountCap > _paramCount)
paramCountCap = _paramCount;

Single stdError;
Single zScore;
Single pValue;
float stdError;
float zScore;
float pValue;
var bias = parent.Bias;
if (!TryGetBiasStatistics(parent.Statistics, bias, out stdError, out zScore, out pValue))
return null;

var order = GetUnorderedCoefficientStatistics(parent, schema).OrderByDescending(stat => stat.ZScore).Take(paramCountCap - 1);
var order = GetUnorderedCoefficientStatistics(parent, featureColumn).OrderByDescending(stat => stat.ZScore).Take(paramCountCap - 1);
return order.Prepend(new[] { new CoefficientStatistics("(Bias)", bias, stdError, zScore, pValue) }).ToArray();
}

internal void SaveText(TextWriter writer, LinearBinaryModelParameters parent, RoleMappedSchema schema, int paramCountCap)
internal void SaveText(TextWriter writer, LinearBinaryModelParameters parent, Schema.Column featureColumn, int paramCountCap)
{
Contracts.AssertValue(_env);
_env.CheckValue(writer, nameof(writer));
_env.AssertValueOrNull(parent);
_env.AssertValueOrNull(schema);
writer.WriteLine();
writer.WriteLine("*** MODEL STATISTICS SUMMARY *** ");
writer.WriteLine("Count of training examples:\t{0}", _trainingExampleCount);
Expand All @@ -361,7 +361,7 @@ internal void SaveText(TextWriter writer, LinearBinaryModelParameters parent, Ro
if (parent == null)
return;

var coeffStats = GetCoefficientStatistics(parent, schema, paramCountCap);
var coeffStats = GetCoefficientStatistics(parent, featureColumn, paramCountCap);
if (coeffStats == null)
return;

Expand All @@ -387,7 +387,7 @@ internal void SaveText(TextWriter writer, LinearBinaryModelParameters parent, Ro
/// Support method for linear models and <see cref="ICanGetSummaryInKeyValuePairs"/>.
/// </summary>
internal void SaveSummaryInKeyValuePairs(LinearBinaryModelParameters parent,
RoleMappedSchema schema, int paramCountCap, List<KeyValuePair<string, object>> resultCollection)
Schema.Column featureColumn, int paramCountCap, List<KeyValuePair<string, object>> resultCollection)
{
Contracts.AssertValue(_env);
_env.AssertValue(resultCollection);
Expand All @@ -400,15 +400,15 @@ internal void SaveSummaryInKeyValuePairs(LinearBinaryModelParameters parent,
if (parent == null)
return;

var coeffStats = GetCoefficientStatistics(parent, schema, paramCountCap);
var coeffStats = GetCoefficientStatistics(parent, featureColumn, paramCountCap);
if (coeffStats == null)
return;

foreach (var coeffStat in coeffStats)
{
resultCollection.Add(new KeyValuePair<string, object>(
coeffStat.Name,
new Single[] { coeffStat.Estimate, coeffStat.StandardError, coeffStat.ZScore, coeffStat.PValue }));
new float[] { coeffStat.Estimate, coeffStat.StandardError, coeffStat.ZScore, coeffStat.PValue }));
}
}

Expand Down Expand Up @@ -458,7 +458,7 @@ internal Schema.Metadata MakeStatisticsMetadata(LinearBinaryModelParameters pare
return builder.GetMetadata();
}

private string DecorateProbabilityString(Single probZ)
private string DecorateProbabilityString(float probZ)
{
Contracts.AssertValue(_env);
_env.Assert(0 <= probZ && probZ <= 1);
Expand Down
19 changes: 15 additions & 4 deletions test/Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs
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Expand Up @@ -2,6 +2,7 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

using System.Linq;
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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019

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Why we need Linq here?

using Microsoft.ML.Core.Data;
using Microsoft.ML.Data;
using Microsoft.ML.Internal.Calibration;
Expand Down Expand Up @@ -54,7 +55,7 @@ public void TestEstimatorPoissonRegression()
}

[Fact]
public void TestLogisticRegressionStats()
public void TestLogisticRegressionNoStats()
{
(IEstimator<ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();

Expand All @@ -70,7 +71,7 @@ public void TestLogisticRegressionStats()
}

[Fact]
public void TestLogisticRegressionStats_MKL()
public void TestLogisticRegressionWithStats()
{
(IEstimator<ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();

Expand All @@ -80,14 +81,24 @@ public void TestLogisticRegressionStats_MKL()
s.StdComputer = new ComputeLRTrainingStdThroughHal();
}));

var transformerChain = pipe.Fit(dataView) as TransformerChain<BinaryPredictionTransformer<ParameterMixingCalibratedPredictor>>;
var transformer = pipe.Fit(dataView) as TransformerChain<BinaryPredictionTransformer<ParameterMixingCalibratedPredictor>>;

var linearModel = transformerChain.LastTransformer.Model.SubPredictor as LinearBinaryModelParameters;
var linearModel = transformer.LastTransformer.Model.SubPredictor as LinearBinaryModelParameters;
var stats = linearModel.Statistics;
LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue);
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LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue); [](start = 12, length = 109)

This is the first thing that does not make much sense to me. It seems like linearModel.Statistics should offer similar accessors through an instance method. (Why out parameters? Why not just properties on the original structure?) This is just really complicated.

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+1

As Tom pointed out, the API for getting statistics needs some cleanup. linearModel.Statistics or some other simple 1-liner should be the only code a user needs.

In reply to: 244374354 [](ancestors = 244374354)


CompareNumbersWithTolerance(stdError, 0.250672936);
CompareNumbersWithTolerance(zScore, 7.97852373);

var scoredData = transformer.Transform(dataView);

var coeffcients = stats.GetCoefficientStatistics(linearModel, scoredData.Schema["Features"], 100);
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stats.GetCoefficientStatistics(linearModel, scoredData.Schema["Features"], 100); [](start = 31, length = 80)

This is the second thing that does not make sense to me. Again should just be an instance method. As discussed also, the complication that this code itself is responsible for extracting out the slot names was a mistake. Anticipating some people's reaction, if the reaction from some people would be "well it's more convenient," then the correct remediation is to just make slot names easier to access (somehow), definitely not to "solve" the problem here as is done.

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@Ivanidzo4ka Ivanidzo4ka Jan 3, 2019 •
edited by sfilipi
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GetCoefficientStatistics [](start = 37, length = 24)

I find it strange to have
TryGetBiasStatistics which accept LinearModelStatistics object
and GetCoefficientStatistics which are part of LinearModelStatistics object.
a) relationship to LinearModelStatistics object
b) why one is Try, and other is just Get?
#Resolved

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@sfilipi sfilipi Jan 3, 2019 •
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Yep, ugly. I am fixing all of it now, working on #2010

Markign as resolved since #2010 exists.

In reply to: 245155368 [](ancestors = 245155368)


Assert.Equal(19, coeffcients.Length);
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@TomFinley TomFinley Dec 28, 2018 •
edited by sfilipi
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coeffcients [](start = 29, length = 11)

Not a big deal at all since it's just a test, but while you're at it, this is a typo. coeffcients => coefficients. #Resolved


foreach(var coefficient in coeffcients)
Assert.True(coefficient.StandardError < 1.0);

}
}
}