codemzs commented 6 years ago

.NET data type system instead of DvTypes

Motivation

Machine Learning datasets often have missing values and to accommodate them along with C# native types without increasing the memory footprint DvType system was created. If we were to use Nullable<T> then we are looking at additional memory for HasValue boolean field plus another 3 bytes for 4 byte alignment. The C# native types that are replaced using DvTypes are bool as DvBool, sbyte as DvInt1, int16 as DvInt2, int32 as DvInt4, int64 as DvInt8, DvDateTime as System.DateTime, DvDateTimeZone as combination of DvDateTime and DvInt2 offset, DvTimeSpan as SysTimeSpan and string as DvText. Float and Double types already have a special value called NaN that can be used for missing value. DvType system achieves a smaller memory footprint by denoting special value for missing value which is usually the smallest number that can be represented by the native type that is encapsulated by DvType, example, DvInt1's missing value indicator would be SByte.MinValue and in the case of types that represent date/time types it is a value that represent maximum ticks.

We plan to remove DvTypes to make IDataView a general commodity that can be used in other products and for this to happen it would be nice if it did not having a dependency on a special type system. If in future we find having DvTypes was useful then we can consider exposing it natively from .NET platform. Once we remove DvTypes then ML.NET platform will be using native non-nullable C# types. Float or double types can be used to represent missing value.

Column Types

Columns in ML.NET make up the dataset and ColumnType defines a column. At high level there are two kinds of column, first is PrimitiveType and that comprises of types such as NumberType, BoolType, TextType, DateTimeType, DateTimeZoneType, KeyType, second is Structured type and it comparises of VectorType. ColumnType is primarily made up of Type and DataKind. Type could refer to any type but it is instantiated with a type referred by DataKind which is an identifer for data types that comprises of DvTypes, native C# types such as float, double and custom big integer UInt128.

Type conversion

DvTypes have implicit and explicit override for assignment operator that handles type conversion. Lets consider DvInt1 for example:

To	From	Current behavior
DvInt1	sbyte	Copy the value as it is
DvInt1	sbyte?	Assign missing value if null otherwise copy the value as it is
sbyte	DvInt1	Copy if not a missing value otherwise throw exception
sbyte?	DvInt1	Assign null for missing values otherwise copy over
DvInt1	DvBool	Assign missing value for a missing value otherwise copy value over	sbyte = bool?
DvInt1	DvInt2	Cast raw value from short to sbyte and compare it with original value if they are not same assign missing value otherwise casted value
DvInt1	DvInt4	Same as above
DvInt1	DvInt8	Same as above
DvInt1	Float
DvInt1	Double	Same as above
Float	DvInt1	Assign NaN for missing value
Double	DvInt1	Same as above

Similar conversion rules exist for DvInt2, DvInt4, DvInt8 and DvBool.

Logical, bitwise and numerical operators

Operations such as ==, !=, !, >, >=, <, <=, +,-,*,pow,|,& take place between same DvTypes only. They also handle missing values and in the case of arithmetic operators overflow is also handled. Most of these overrides are implemented but only few are actively used. Whenever there is an overflow the resulting value is represented as missing value and the same goes when one of the operands is a missing value.

Serialization

DvTypes have their own codecs for efficiently compressing data and writing it to disk, for example, to write DvBool to disk, two bits are used to represent a boolean value, 0x00 is false, 0x01 is true and 0x10 is missing value indicator. Boolean values are written at the level of int32 which has 32 bits that can accommodate 32/2 or 16 boolean values in 4 bytes as opposed to using 1 byte per boolean value using the naive approach that does not even handle missing value. We can reuse this approach to serialize bool by using one bit instead of two. DvInt* codecs need not be changed at all. DateTime and DvText codecs will require some changes.

Intermediate Language(IL) code generation

ML.NET contains a mini compiler that generates IL code at runtime for peak and poke functions that basically perform reflection of objects to set and get values in a more performant manner. Here we can use OpCodes.Stobj to emit IL code for DvTimeSpan,DvDateTime, DvDateTimeZone and ReadOnlyMemory<char> types.

New Behavior

DvInt1, DvInt2, DvInt4, DvInt8 will be replaced with sbyte, short, int and long respectively.
- Conversions will conform to .NET standard conversions.
- Types will be converted using casting and this might cause underflow and overflow and therefore behavior is undefined here, example, casting long to sbyte will result in assigning of low 8 bits from long to sbyte. ML.NET projects by default are unchecked because checked is expensive and hence used in code blocks where it is needed.
```
> unchecked((sbyte)long.MaxValue)
-1
```
- Conversion from Text to Integer type is done by first converting Text to long value in the case of positive number and ulong in the case of negative number and then validating this value is within the legal bounds of the type that it is being converted to from Text type, example, legal bound for sbyte is -128 to 127, so converting "-129" or "128" will result in an exception, also converting a value that is out of legal bounds for a long type will also result in an exception.
```
var c = Convert.ToSByte("129");
Value was either too large or too small for a signed byte.
sbyte.Parse(string, System.Globalization.NumberStyles, System.Globalization.NumberFormatInfo)
System.Convert.ToSByte(string)
```
DvTimeSpan, DvDateTime and DvDateTimeZone will be replaced with TimeSpan, DateTime and DateTimeOffset respectively.
- Offset in DataTimeOffset is represented as long because it records the ticks. Previously this was represented as DvInt2 or short in DvDateTimeZone because it was recorded as minutes and due to this it had a smaller footprint on the disk. With offset being long the footprint will increase, one work around is to convert it to minutes before writing and then converting minutes back to ticks but this might lead to loss in precision. Since DataTime is very rarely used in Machine Learning so I'm not sure if it is worth making an optimization here.
DvText will be replaced with ReadOnlyMemory<char>.
- ReadOnlyMemory<char> does not implement IEquatable<T> and due to this it cannot be be used a type in GroupKeyColumnChecker in Cursor in GroupTransform. The workaround for this is to remove the IEquatable<T> contraint on the type and instead use if else to check if the type implements IEquatable<T> then cast and call Equals method otherwise check if the type is of ReadOnlyMemory<char> then use its utility method for equality otherwise throw an exception.
- ReadOnlyMemory<char> does not implement GetHashCode() and due to this it cannot be used as a key in a dictionary in ReconcileSlotNames<T> in EvaluatorUtils.cs. The workaround for this is to use string representation of ReadOnlyMemory<char> as a key. While this is wastage of memory but its not too bad because this is only used at the end of evaluation phase and the number of strings allocated here will be roughly proportional to the number of classes.
DvBool will be replaced with bool.
- GetPredictedLabel and GetPredictedLabelCore will result in an undefined behavior in the case where score contains a missing value represented as NaN. Here we will default to false.
Backward compatiblity when reading IDV files written with DvTypes.
- Integers are read as they were written to disk, i.e minimum value of the corresponding data type in the case of missing value.
- Boolean is read using the old codec, where two bits are used per value and missing values are converted to false to fit in bool type.
- DateTime, DateTimeSpan, DateTimeZone use long and short type underneath to represent ticks and offset and they are converted using the Integer scheme defined above. In the case where ticks or offset is read and found to contain missing value represented as a minimum of the underlying type then it is converted to default value of that type to prevent an exception from DateTime or TimeSpan or DateTimeOffset class as such minimum values indicate an invalid date.
- DvText is read as it is. Missing values when being converted to Integer types are converted to minimum value of that integer type and empty string is converted to default value of that integer type.
TextLoader
- Will throw an exception if it encounters missing value.
- Will convert empty string to default values of type it is being converted to.
Parquet Loader
- Will throw an exception for nullables or overflow.

Future consideration

Introduce an option in the loader whether to throw an exception in the case of missing value or just replace them with default values. With the current design we will throw an exception in the case of missing for Text Loader and Parquet loader but not IDV(Binary Loader).

Benchmarking the type system changes

(this section was written by @najeeb-kazmi )

ReadOnlyMemory<char> is a data type introduced recently that allows management of strings without unnecessary memory allocation. Strings in C# are immutable. Hence, when we take a string operation such as substring, the resulting string is copied to a new memory location. To prevent unnecessary allocation of memory, ReadOnlyMemory keeps track of the substring via start and end offsets relative to the original string. Hence, for every substring operation, the memory allocated is constant. In ReadOnlyMemory, if one needs to access independent elements, they do it by calling the Span property, which returns a ReadOnlySpan object, which is a stack only concept. It turns out that this Span property is an expensive operation, and our initial benchmarks showed that runtimes of the pipelines regressed by 100%. Upon further performance analysis, we decide to cache the returned ReadOnlySpan as much as we could, and that brought the runtimes on par with DvText.

These benchmarks are intended to compare performance after these optimizations on Span were done, in order to investigate whether we hit parity with DvText or not.

Datasets and pipelines

We chose datasets and pipelines to test to cover a variety of scenarios, including:

numeric data only
numeric + categorical data with categorical transform
numeric + categorical data with categorical and categorical hash transforms
categorical + text data with categorical and text transforms
text transform only on a very large text dataset

The table below shows the datasets and their characteristics, as well as the pipeline that we executed on each dataset. All datasets were ingested in text format, which makes heavy use of DvText / ReadOnlyMemory<char>. Other data types are also involved in the pipelines, although the performance of the pipelines are dominated by DvText / ReadOnlyMemory<char>.

Dataset	Size	Rows	Features	Pipeline	Comments
Criteo	230 MB	1M	13 numeric 26 categorical	Train data={\ct01\data\Criteo\Kaggle\train-1M.txt} loader=TextLoader{ col=Label:R4:0 col=NumFeatures:R4:1-13 col=LowCardCat:TX:19,22,30,33 col=HighCardCat:TX:~ } xf=CategoricalTransform{col=LowCardCat} xf=CategoricalHashTransform{col=HighCardCat bits=16} xf=MissingValueIndicatorTransform{col=NumFeatures} xf=Concat{ col=Features:NumFeatures,LowCardCat,HighCardCat } tr=ap{iter=10} seed=1 cache=-	Numeric + categorical features with categorical and categorical hash transforms
Bing Click Prediction	3 GB	500k	3076 numeric	Train data={\ct01\data\TeamOnly\NumericalDatasets\Ranking\BingClickPrediction\train-500K} loader=TextLoader{col=Label:R4:0 col=Features:R4:8-3083 header=+ quote=-} xf=NAHandleTransform{col=Features ind=-} tr=SDCA seed=1 cache=-	Numeric features only
Flight Delay	227 MB	7M	5 numeric 3 categorical	Train data={\ct01\data\PerformanceAnalysis\Data\Flight\New\FD2007train.csv} loader=TextLoader{ sep=, col=Month:R4:0 col=DayofMonth:R4:1 col=DayofWeek:R4:2 col=DepTime:R4:3 col=Distance:R4:4 col=UniqueCarrier:TX:5 col=Origin:TX:6 col=Dest:TX:7 col=Label:R4:9 header=+ } xf=CategoricalTransform{ col=UniqueCarrier col=Origin col=Dest } xf=Concat{ col=Features:Month,DayofMonth,DayofWeek,DepTime,Distance,UniqueCarrier,Origin,Dest } tr=SDCA seed=1 cache=-	Numeric + categorical features with categorical transform
Wikipedia Detox	74 MB	160k	1 categorical 1 text column	Train data={\ct01\data\SCRATCH_TO_MOVE\BinaryClassification\WikipediaDetox\toxicity_annotated_comments.merged.shuf-75MB,_160k-rows.tsv} loader=TextLoader{ quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=text:TX:2 col=year:TX:3 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 header=+ } xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=NAFilter{col=Label} xf=Term{col=Label:Label} xf=TextTransform{ col=FeaturesText:text wordExtractor=NgramExtractorTransform{ngram=2} charExtractor=NgramExtractorTransform{ngram=3} } xf=Concat{col=Features:logged_in,ns,FeaturesText} tr=OVA {p=AveragedPerceptron{iter=10}} seed=1 cache=-	Categorical transform + text featurization
Amazon Reviews	9 GB	18M	1 text column	Train data={\ct01\users\prroy\dataset\cleandata_VW\Amazon_reviews_cleaned.tsv} loader=TextLoader{col=Label:TX:0 col=text:TX:1 header=+ sparse=-} xf=NAFilter{col=Label} xf=Term{col=Label:Label} xf=TextTransform{ col=Features:text wordExtractor=NgramExtractorTransform{ngram=2} charExtractor=NgramExtractorTransform{ngram=3} } tr=OVA {p=AveragedPerceptron{iter=10}} seed=1 cache=-	Text featurization on a very large dataset

Methodology and experimental setup

The two builds of ML.NET (one using DvTypes and the other using .NET data types) were built to target .NET Core 2.1.
Pipelines were executed from the Microsoft.ML.Console project: dotnet MML.dll <pipeline>
All pipelines were executed on Azure Standard F72s_v2 VMs running Windows Server 2016, which offer an instance isolated to dedicated hardware (Intel Xeon Platinum 8168).
We killed background processes that were not needed to run the experiments, including closing Visual Studio, ensuring that only one console window was open on the VM.
For each pipeline, we discarded the results of the first two runs for each pipeline to control for runtime variability due to a cold start, keeping only the subsequent runs for analysis.

Results

We present the results of the benchmarks here. The deltas indicate performance gap of .NET data types relative to DvTypes: negative values indicate slower performance of .NET data types compared to DvTypes, and percentage deltas are based off the mean runtime for DvTypes. Finally, we did an independent samples t-test with unequal variances for the two builds, and present the p-values for each test. We chose a significance threshold of 0.05, with a smaller p-value indicating significant differences.

We can see that for all the pipelines except the one with Amazon Reviews dataset, the deltas were within 1% of the speed of DvTypes, and were not significant. For Amazon Reviews, the delta was 1.85% of the speed of DvTypes and significant. The statistical significance is not particularly concerning here because the long runtimes on this dataset were bound to return significantly different runtimes even with a small percentage difference. More important thing here is that the performance gap was reduced from ~100% to within 2%. We expect the performance to only improve with further optimizations in future .NET Core runtimes.

Criteo 1M

Run #	.NET data types	DvTypes
1	12.907	12.634
2	12.635	12.847
3	12.989	12.546
4	12.708	12.713
5	12.789	12.463
6	12.565	12.751
7	12.828	12.73
8	12.688	12.425
9	12.791	13.009
10	12.858	12.584

Mean	12.7758	12.6702
S.D.	0.128720887	0.178014232

Delta	-0.1056	-0.83%
p-value	0.073767344	Not significant

Flight Delay 7M

Run #	.NET data types	DvTypes
1	52.536	51.562
2	52.667	52.501
3	52.175	52.475
4	52.076	51.773
5	54.19	51.786
6	51.678	52.698
7	52.647	52.338
8	52.426	52.704
9	51.703	51.214
10	51.742	52.407

Mean	52.384	52.1458
S.D.	0.74152	0.520013632

Delta	-0.2382	-0.46%
p-value	0.208863	Not significant

Bing Click Prediction 500K

Run #	.NET data types	DvTypes
1	222	221
2	222	222
3	220	223
4	221	223
5	220	220
6	223	219
7	222	222
8	223	220
9	223	223
10	222	222

Mean	221.8	221.5
S.D.	1.135292	1.433721

Delta	-0.3	-0.14%
p-value	0.305291	Not significant

Wikipedia Detox

Run #	.NET data types	DvTypes
1	65.992	65.265
2	66.042	65.308
3	65.6	67.457
4	65.146	66.011
5	66.196	65.788
6	65.683	67.611
7	65.498	65.191
8	65.819	66.636
9	65.896	65.412
10	66.564	66.381
11	66.392	66.074
12	65.862	65.155
13	65.958	64.808
14	66.085	65.157
15	66.085	66.116
16	66.116	66.189
17	66.086	65.748
18	66.822	66.066
19	66.227	65.009
20	65.278	65.911

Mean	65.96735	65.86465
S.D.	0.402667	0.758248

Delta	-0.1027	-0.16%
p-value	0.29838	Not significant

Amazon Reviews

Run #	.NET data types	DvTypes
1	5121	4992
2	5121	5016
3	5090	5036
4	5163	4981
5	5112	5003
6	5075	5008
7	5097	5022
8	5093	4991
9	5071	5040
10	5090	5019

Mean	5103.3	5010.8
S.D.	27.10084	19.46393

Delta	-92.5	-1.85%
p-value	7.05E-08	Significant

CC: @eerhardt @Zruty0 @Ivanidzo4ka @TomFinley @shauheen @najeeb-kazmi @markusweimer

casperOne commented 6 years ago

we can use XML serialization though it might increase the footprint on the disk.

You may want to make the part that serializes pluggable (can discover it through DI)/based on a provider model.

The default should probably be JSON (it's the prevalent serialization mechanism currently) and if people are concerned about performance, they can implement the provider for the preferred serialization mechanism (Protobuf comes to mind, but there are other options).

Also, you may want to make sure that the new Memory/Span APIs are used in this area.

Small nitpick; the title should be ".NET types" and not "C# types" as these types are not specific to C#, but are types available throughout the .NET ecosystem.

TomFinley commented 6 years ago

This seems fine on the whole, but what is going to be done about sparsity, implicit values for sparse values, and types like int?, for example? We want sparse vectors of numeric types to have implicit values of 0, for various reasons. We've previously relied on the fact that default of numeric types is 0. Now default of int? is not 0 but is null, we can no longer rely on that mechanism.

casperOne commented 6 years ago

@TomFinley

but what is going to be done about sparsity, implicit values for sparse values, and types like int?, for example?

I could be misreading, but using int? is an option for implicit values and sparse values, it's not mandatory.

The point is to use the .NET type system (and all that it offers) instead of DvTypes, as using DvTypes means transformation in use in many other places (the whole point of a type system is to unify data across operations, not fragments it with other sub type systems).

You could continue to use int with an implicit 0/default/sparse mapping if you wish, or use int? if that suits your needs better.

.NET has this out-of-the box, and other serialization mechanisms map easily to the .NET type system (JSON.Net, Protobuf.NET, etc).

IOW, it's a layer that doesn't need to exist, as it doesn't afford anything that doesn't already exist in the .NET type system.

TomFinley commented 6 years ago

Hello @casperOne, thanks for your response and clarifications. I think perhaps I was not clear -- I'm not actually confused about the proposal, I'm pointing out a serious architectural morass this issue as written engenders. But I'll clarify what I mean a bit more.

Imagine we get rid of this DvInt* and still want NA values by using things like int?. Sparse vectors are a very important part of our architecture for reasons that are probably obvious, and in order to be useful they must have a well defined value for implicit entries. Therefore, logically we must accept one of the two following terrible options for vectors:

We continue to have the implicit values of default in our sparse vector. The sparse vector of length {2:2, 4:5} would then logically be the dense vector {0,0,2,0,5} if it is of int, and {?,?,2,?,5} if it is of int?. That, in addition to making sparse vectors more or less practically useless for int?, makes all conversions from int to int? a densifying operation thereby introducing perf booby traps into the code, and most seriously is pretty confusing.
We change the implicit sparse value to no longer be default for these numeric types, but continue to have it be 0, thereby maintaining the general intuitive expectation people have that implicit values in sparse vectors are 0s. This is perhaps somewhat easier to understand from a "users" perspective, but all general code for VBuffers that might deal with these types will have to find out what the implicit value is, and adapt its code accordingly, inviting considerable code complexity.

Both of these options are awful. Our code and user code in lots of places benefits from the assumption that numeric vectors have a 0 for their implicit values. On the other hand, we also in plenty of places assume that the implicit sparse value for VBuffer<T> is default(T). Breaking either of those now formerly solid assumptions incurs a dizzying amount of engineering cost. This is both in the initial cost of the necessary transition (assuming that it is even possible to reliably do that), and I'd argue going forward makes our code unmaintainable since the issues at play are clearly so non-obvious that I have no faith whatsoever that subtle bugs won't be constantly introduced by misunderstandings about what is correct.

So if we get rid of DvInt* (because, obviously, it serves no useful purpose and exists for no reason, right? 😉), I'd rather simply not allow NA values for our built in integer types at all, and tell people if they want NA values that utility only occurs in float or double (which actually sensibly have a reserved values for NaN, unlike int). Which is probably fine. And if they really, really want it for who knows what reason, since IDV has an extensible type system they are free to do so, just far away from this codebase. It will technically break backcompat here and there in subtle ways, but since people use ints in pipelines sparingly and NA values for them even more sparingly, it's probably practically fine.

Incidentally let me make a secondary point while I'm here. As you say, .NET has a concept for NA values that's close and almost useful, except for one major problem: default(T?) == null, instead of default(T?) == default(T). I'll trust the situation as it stands s a good choice for most .NET applications, but unfortunately that choice compromises its usability for anything dealing with numerics. (By analogy: floats have "NA" (kinda) value with NaN, but certainly I doubt many people would consider default(float) becoming NaN a useful innovation.) It is certainly good to use .NET types where possible, but we have to use sound judgment about the logical implications of using them, even if those implications are not obvious based on casual observation. And sometimes that means not using what already exists in .NET since the implication is, as here, that it is unfit for the purpose.

Ivanidzo4ka commented 6 years ago

This seems fine on the whole, but what is going to be done about sparsity, implicit values for sparse values, and types like int?, for example? We want sparse vectors of numeric types to have implicit values of 0, for various reasons. We've previously relied on the fact that default of numeric types is 0. Now default of int? is not 0 but is null, we can no longer rely on that mechanism.

From what I see in implementation of this issue, and @TomFinley comment we completely remove nullable support for fields and properties. Which is fine if you use Textloader, but in case of IEnumerable -> Dataview conversion looks like really bad decision. Imagine I'm as a user want to train model on top of SQL table. I can fetch data through LINQ2SQL or EF (which provide me drag and drop option to generate classes and methods to get data) as IEnumerable, wrap it in CollectionDataSource and train it. But only if I don't have any nullable fields in my table, as soon as I have at least one nullable field, I don't have no other options than create new class, write conversion from old class to new class, which is can be extremely painful process, especially if in your some relationship with SQL, people can have hundreds of columns (fields).

If only problem which prevents us from nullable support is VBuffer and sparsity, can we change VBuffer code and put check on incoming type and if it's nullable set values to default of inner type?

TomFinley commented 6 years ago

Hi @Ivanidzo4ka . What you are saying I think is that before an SQL user injects their table into our system they will have to be explicit about what null actually means in their case. This strikes me as something good, not bad -- what is meant in a database system by a null column is more often than not incredibly application specific (for evidence of this see please the discussions over the years just among ourselves about how to interpret a null -- if we ourselves could not agree at once, what hope do people that were designing bespoke systems have?). Therefore the fact that we'd appear (in the prior system) to handle that case seamlessly is more misleading than helpful, frankly.

We are writing an API, and that means people are free to (and will) write their own code around us, rather than having our own mechanisms be the only things at people's disposal. Though I understand this requires a shift in perspective, in this new world sometimes the right answer is, we not only don't have to handle this case, but we absolutely should not. I think this is one of those times.

najeeb-kazmi commented 6 years ago

Benchmarking the type system changes

ReadOnlyMemory<char> is a data type introduced recently that allows management of strings without unnecessary memory allocation. Strings in C# are immutable. Hence, when we take a string operation such as substring, the resulting string is copied to a new memory location. To prevent unnecessary allocation of memory, ReadOnlyMemory keeps track of the substring via start and end offsets relative to the original string. Hence, for every substring operation, the memory allocated is constant. In ReadOnlyMemory, if one needs to access independent elements, they do it by calling the Span property, which returns a ReadOnlySpan object, which is a stack only concept. It turns out that this Span property is an expensive operation, and our initial benchmarks showed that runtimes of the pipelines regressed by 100%. Upon further performance analysis, we decide to cache the returned ReadOnlySpan as much as we could, and that brought the runtimes on par with DvText.

These benchmarks are intended to compare performance after these optimizations on Span were done, in order to investigate whether we hit parity with DvText or not.

Datasets and pipelines

We chose datasets and pipelines to test to cover a variety of scenarios, including:

numeric data only
numeric + categorical data with categorical transform
numeric + categorical data with categorical and categorical hash transforms
categorical + text data with categorical and text transforms
text transform only on a very large text dataset

The table below shows the datasets and their characteristics, as well as the pipeline that we executed on each dataset. All datasets were ingested in text format, which makes heavy use of DvText / ReadOnlyMemory<char>. Other data types are also involved in the pipelines, although the performance of the pipelines are dominated by DvText / ReadOnlyMemory<char>.

Dataset	Size	Rows	Features	Pipeline	Comments
Criteo	230 MB	1M	13 numeric 26 categorical	Train data={\ct01\data\Criteo\Kaggle\train-1M.txt} loader=TextLoader{ col=Label:R4:0 col=NumFeatures:R4:1-13 col=LowCardCat:TX:19,22,30,33 col=HighCardCat:TX:~ } xf=CategoricalTransform{col=LowCardCat} xf=CategoricalHashTransform{col=HighCardCat bits=16} xf=MissingValueIndicatorTransform{col=NumFeatures} xf=Concat{ col=Features:NumFeatures,LowCardCat,HighCardCat } tr=ap{iter=10} seed=1 cache=-	Numeric + categorical features with categorical and categorical hash transforms
Bing Click Prediction	3 GB	500k	3076 numeric	Train data={\ct01\data\TeamOnly\NumericalDatasets\Ranking\BingClickPrediction\train-500K} loader=TextLoader{col=Label:R4:0 col=Features:R4:8-3083 header=+ quote=-} xf=NAHandleTransform{col=Features ind=-} tr=SDCA seed=1 cache=-	Numeric features only
Flight Delay	227 MB	7M	5 numeric 3 categorical	Train data={\ct01\data\PerformanceAnalysis\Data\Flight\New\FD2007train.csv} loader=TextLoader{ sep=, col=Month:R4:0 col=DayofMonth:R4:1 col=DayofWeek:R4:2 col=DepTime:R4:3 col=Distance:R4:4 col=UniqueCarrier:TX:5 col=Origin:TX:6 col=Dest:TX:7 col=Label:R4:9 header=+ } xf=CategoricalTransform{ col=UniqueCarrier col=Origin col=Dest } xf=Concat{ col=Features:Month,DayofMonth,DayofWeek,DepTime,Distance,UniqueCarrier,Origin,Dest } tr=SDCA seed=1 cache=-	Numeric + categorical features with categorical transform
Wikipedia Detox	74 MB	160k	1 categorical 1 text column	Train data={\ct01\data\SCRATCH_TO_MOVE\BinaryClassification\WikipediaDetox\toxicity_annotated_comments.merged.shuf-75MB,_160k-rows.tsv} loader=TextLoader{ quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=text:TX:2 col=year:TX:3 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 header=+ } xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=NAFilter{col=Label} xf=Term{col=Label:Label} xf=TextTransform{ col=FeaturesText:text wordExtractor=NgramExtractorTransform{ngram=2} charExtractor=NgramExtractorTransform{ngram=3} } xf=Concat{col=Features:logged_in,ns,FeaturesText} tr=OVA {p=AveragedPerceptron{iter=10}} seed=1 cache=-	Categorical transform + text featurization
Amazon Reviews	9 GB	18M	1 text column	Train data={\ct01\users\prroy\dataset\cleandata_VW\Amazon_reviews_cleaned.tsv} loader=TextLoader{col=Label:TX:0 col=text:TX:1 header=+ sparse=-} xf=NAFilter{col=Label} xf=Term{col=Label:Label} xf=TextTransform{ col=Features:text wordExtractor=NgramExtractorTransform{ngram=2} charExtractor=NgramExtractorTransform{ngram=3} } tr=OVA {p=AveragedPerceptron{iter=10}} seed=1 cache=-	Text featurization on a very large dataset

Methodology and experimental setup

The two builds of ML.NET (one using DvTypes and the other using .NET data types) were built to target .NET Core 2.1.
Pipelines were executed from the Microsoft.ML.Console project: dotnet MML.dll <pipeline>
All pipelines were executed on Azure Standard F72s_v2 VMs running Windows Server 2016, which offer an instance isolated to dedicated hardware (Intel Xeon Platinum 8168).
We killed background processes that were not needed to run the experiments, including closing Visual Studio, ensuring that only one console window was open on the VM.
For each pipeline, we discarded the results of the first two runs for each pipeline to control for runtime variability due to a cold start, keeping only the subsequent runs for analysis.

Results

We present the results of the benchmarks here. The deltas indicate performance gap of .NET data types relative to DvTypes: negative values indicate slower performance of .NET data types compared to DvTypes, and percentage deltas are based off the mean runtime for DvTypes. Finally, we did an independent samples t-test with unequal variances for the two builds, and present the p-values for each test. We chose a significance threshold of 0.05, with a smaller p-value indicating significant differences.

We can see that for all the pipelines except the one with Amazon Reviews dataset, the deltas were within 1% of the speed of DvTypes, and were not significant. For Amazon Reviews, the delta was 1.85% of the speed of DvTypes and significant. The statistical significance is not particularly concerning here because the long runtimes on this dataset were bound to return significantly different runtimes even with a small percentage difference. More important thing here is that the performance gap was reduced from ~100% to within 2%. We expect the performance to only improve with further optimizations in future .NET Core runtimes.