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Samples for FeatureSelection transform estimators - #3184
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@@ Coverage Diff @@## master #3184 +/- ##
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+ Coverage 72.54% 72.6% +0.06%
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Files 807 807 Lines 144774 145077 +303 Branches 16208 16213 +5 ==========================================
+ Hits 105021 105332 +311 + Misses 35339 35326 -13 - Partials 4414 4419 +5
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| /// <format type="text/markdown"> | ||
| /// <] | ||
| /// [!code-csharp[SelectFeaturesBasedOnMutualInformation](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/SelectFeaturesBasedOnMutualInformation.cs)] |
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Dynamic/S [](start = 118, length = 9)
Dynamic/Transforms/ #Resolved
| /// <example> | ||
| /// <format type="text/markdown"> | ||
| /// <] |
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Dynamic [](start = 118, length = 7)
Dynamic/Transforms/ #Resolved
| { | ||
| // Downloading a classification dataset from github.com/dotnet/machinelearning. | ||
| // It will be stored in the same path as the executable | ||
| string dataFilePath = SamplesUtils.DatasetUtils.DownloadBreastCancerDataset(); |
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string dataFilePath = SamplesUtils.DatasetUtils.DownloadBreastCancerDataset(); [](start = 9, length = 81)
can it be done with a small in memory dataset? #Resolved
| pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation( | ||
| new InputOutputColumnPair[] { new InputOutputColumnPair("GroupB"), new InputOutputColumnPair("GroupC") }, | ||
| labelColumnName: "Label", | ||
| slotsInOutput:4); |
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slotsInOutput:4 [](start = 16, length = 15)
one line comment about what this does. #Resolved
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| // 3 7 1 | ||
| // 3 1 1 | ||
| // Second, we define the transformations that we apply on the data. Remember that an Estimator does not transform data |
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Remember [](start = 80, length = 8)
remove #Resolved
| { | ||
| public float[] GroupB { get; set; } | ||
| public float[] GroupC { get; set; } |
| string dataFilePath = SamplesUtils.DatasetUtils.DownloadBreastCancerDataset(); | ||
| // Data Preview | ||
| // 1. Label 0=benign, 1=malignant |
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[](start = 26, length = 7)
He use tabs! Where is my pitchfork! #Resolved
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| // In this example we define a CountFeatureSelectingEstimator, that selects slots in a feature vector that have more non-default | ||
| // values than the specified count. This transformation can be used to remove slots with too many missing values. | ||
| var pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount( | ||
| outputColumnName: "FeaturesSelectedGroupB", inputColumnName: "GroupB", count: 695); |
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695 [](start = 94, length = 3)
Where this number coming from?
#Resolved
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comment in line 29 should clarify this now, also the in-memory example should make it more intuitive
In reply to: 271911491 [](ancestors = 271911491)
| // 5 7 | ||
| // 1 2 | ||
| // 1 3 | ||
| // 3 2 |
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with current data it's not obvious at all. Can we switch to some small in memory sample rather than unknown dataset? #Resolved
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true. moved over to use a small in-memory dataset instead
In reply to: 271912555 [](ancestors = 271912555)
| // We will use the SelectFeaturesBasedOnCount transform estimator, to retain only those slots which have | ||
| // at least 'count' non-default values per slot. | ||
| // Multi column example : This pipeline uses two columns for transformation |
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This pipeline uses two columns for transformation [](start = 38, length = 49)
i think this is clearer: this pipeline transform two columns using the same options.
just want to make sure it's clear that columns are transformed independently and are not mixed #Resolved
| // We define a MutualInformationFeatureSelectingEstimator that selects the top k slots in a feature | ||
| // vector based on highest mutual information between that slot and a specified label. | ||
| var pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation( |
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SelectFeaturesBasedOnMutualInformation [](start = 65, length = 38)
this sample is only for API reference, so small in-memory dataset suffices for this example.
we should have a "tutorial" to show the computation of MI..something along the lines of
| foreach (var item in convertedData) | ||
| Console.WriteLine("{0}\t\t\t{1}", string.Join("\t", item.NumericVector), string.Join("\t", item.StringVector)); | ||
| // 4 NaN 6 A WA Male | ||
| // 4 5 6 A Female |
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[](start = 63, length = 5)
align for just here.. no need to make it match exactly to the output and have it looked tabbing off. #Resolved
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or is it a separate column?
It helps to print the headers.
In reply to: 272391190 [](ancestors = 272391190)
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the alignment here seems good to me... for text this slot is empty or null ..
not sure if u meant something else
In reply to: 272391190 [](ancestors = 272391190)
| // We will use the SelectFeaturesBasedOnCount to retain only those slots which have at least 'count' non-default values per slot. | ||
| // Usage on numeric column. |
| // The pipeline can then be trained, using .Fit(), and the resulting transformer can be used to transform data. | ||
| var transformedData = pipeline.Fit(data).Transform(data); | ||
| Console.WriteLine("Contents of column 'NumericVector'"); |
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Console.WriteLine("Contents of column 'NumericVector'"); [](start = 11, length = 57)
convert to just comment. #Resolved
| Console.Write($"{row[i]}\t"); | ||
| Console.WriteLine(); | ||
| } | ||
| // 4 6 |
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// 4 6 [](start = 12, length = 12)
headers #Resolved
| for (var i = 0; i < row.Length; i++) | ||
| Console.Write($"{row[i]}\t"); | ||
| Console.WriteLine(); | ||
| } |
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would it polish it a bit if you made a little helper for this, since it is being used twice? #Resolved
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one of then is a float[] while the other is string[] .. did not want to over-engineer this...
is there anything specific you had in mind, or can we keep it as is ?
In reply to: 272392068 [](ancestors = 272392068)
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| Console.WriteLine("Contents of two columns 'NumericVector' and 'StringVector'."); | ||
| foreach (var item in convertedData) | ||
| Console.WriteLine("{0}\t\t\t{1}", string.Join("\t", item.NumericVector), string.Join("\t", item.StringVector)); |
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\t [](start = 63, length = 2)
think for vectors we are using comma ',' as the separator. #Resolved
| // Usage on text column. | ||
| pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount( | ||
| outputColumnName: "StringVector", count: 3); |
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I'd probably append to the previous pipeline, and show the prints once. Compacts the sample. #Resolved
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| Console.WriteLine("Contents of two columns 'NumericVector' and 'StringVector'."); | ||
| foreach (var item in rawData) | ||
| Console.WriteLine("{0}\t\t\t{1}", string.Join("\t", item.NumericVector), string.Join("\t", item.StringVector)); |
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\t [](start = 63, length = 2)
same comment, ',' #Resolved
| Console.WriteLine("Contents of column 'NumericVector'"); | ||
| PrintDataColumn(transformedData, "NumericVector"); | ||
| // 4 0 |
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4 [](start = 15, length = 1)
Curious, why is it dropping 6, but keeping 4? It is not obvious to me. Is it because slotsInOutput is 2? A comment about that might help. #Pending
| Console.WriteLine("Contents of columns 'Label', 'NumericVectorA' and 'NumericVectorB'."); | ||
| foreach (var item in rawData) | ||
| Console.WriteLine("{0}\t\t{1}\t\t{2}", item.Label, string.Join(" ", item.NumericVectorA), string.Join(" ", item.NumericVectorB)); |
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[](start = 80, length = 1)
',' #Resolved
| var rawData = GetData(); | ||
| var data = mlContext.Data.LoadFromEnumerable(rawData); | ||
| var convertedData = mlContext.Data.CreateEnumerable<InputData>(data, true); |
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convertedData [](start = 16, length = 13)
Why do you convert that back to Enumerable? rawData is already there. #Resolved
| foreach (var item in convertedData) | ||
| Console.WriteLine("{0}\t\t{1}", string.Join(" ", item.NumericVectorA), string.Join(" ", item.NumericVectorB)); | ||
| // Here, we see SelectFeaturesBasedOnMutualInformation selected 4 slots. |
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4 slot [](start = 76, length = 6)
the 4 slots that had most in common with the respective value in the Label column, maybe? #Resolved
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yes, these 4 slots carried the most MI with Label.... we should have a better tutorial for this though
In reply to: 272398839 [](ancestors = 272398839)
* samples for FeatureSelection transform estimators * fix review comments * fix review comments * review comments * take care of review comments * fix copy paste output error
Towards #1209
The PR makes the following changes
SelectFeaturesBasedOnCounttransform estimator.SelectFeaturesBasedOnMutualInformationtransform estimator.