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Fix issue in SRCnnEntireAnomalyDetector #5579

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Feb 9, 2021
Merged

Fix issue in SRCnnEntireAnomalyDetector #5579

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f3fe5ba
update
Jan 7, 2021
71451cc
refine codes
Jan 8, 2021
571fb25
update comments
Jan 8, 2021
5477b57
update for nit
Feb 9, 2021
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33 changes: 30 additions & 3 deletions src/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -362,6 +362,9 @@ internal sealed class SrCnnEntireModeler
private static readonly double _unitForZero = 0.3;
private static readonly double _minimumScore = 0.0;
private static readonly double _maximumScore = 1.0;
// Use this threshold to correct false anomalies
private static readonly double _zscoreThreshold = 1.5;

// If the score window is smaller than this value, the anomaly score is tend to be small.
// Proof: For each point, the SR anomaly score is calculated as (w is average window size):
// (mag - avg_mag) / avg_mag
Expand Down Expand Up @@ -426,6 +429,8 @@ internal sealed class SrCnnEntireModeler
//used in all modes
private double _minimumOriginValue;
private double _maximumOriginValue;
private double _std;
private double _mean;
private readonly double[] _predictArray;
private double[] _backAddArray;
private double[] _fftRe;
Expand Down Expand Up @@ -491,14 +496,23 @@ public void Train(double[] values, ref double[][] results)
_minimumOriginValue = Double.MaxValue;
_maximumOriginValue = Double.MinValue;

var sum = 0.0;
var squareSum = 0.0;

Array.Resize(ref _seriesToDetect, values.Length);
for (int i = 0; i < values.Length; ++i)
{
_seriesToDetect[i] = values[i];
_minimumOriginValue = Math.Min(_minimumOriginValue, values[i]);
_maximumOriginValue = Math.Max(_maximumOriginValue, values[i]);
var value = values[i];
_seriesToDetect[i] = value;
_minimumOriginValue = Math.Min(_minimumOriginValue, value);
_maximumOriginValue = Math.Max(_maximumOriginValue, value);
sum += value;
squareSum += value * value;
}

_mean = sum / values.Length;
_std = Math.Sqrt((squareSum - (sum * sum) / values.Length) / values.Length);

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nit, just a general thoughts that I believe ML.Net has much more efficient way to calculate stddev, min, max and mean. If normalize the value is needed, context.Transforms.NormalizeMeanVariance is much more efficient than 2 loops here.

Also, stddev can be easily calculated via one loop:

double sumSquare = 0;
double sum = 0;
foreach  (int i = 0; i < values.Length; ++i)
                {	        
var value = values[0]; // introduce a var to prevent duplicate so many times
                    _seriesToDetect[i] = value ;	
                    _minimumOriginValue = Math.Min(_minimumOriginValue, value );	                   
                    _maximumOriginValue = Math.Max(_maximumOriginValue, value );	                   
                    _mean += value; // note this should be sum, and it will be cleaner to have a new var called mean.
                  sumSquare += value * value
                }

var variance = (sumSquare - ((sum * sum) / length)) / (length - 1)
var stddev = Math.Sqrt(variance)

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nice suggestion, updated.

In reply to: 553427718 [](ancestors = 553427718)

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Not important, but some notes:

There's also a suitable array normalizer in the FastTree code. But using that would entail an unwanted FastTree NuGet dependency.

There's another version in the GcnTransform, which could work. It operates on a ReadOnlySpan which may let you reduce memory churn. I haven't read the anomaly detector code throughly, though I see various lists and .ToArray() which perhaps could be buffers and spans? Likely not worth the effort unless speed is a concern.

Side note: For speed, most ML algos use floats instead of doubles; except at times for accumulation. You could test if using floats doesn't decrease the algo's utility, and if ok, reduce to floats.

if (_period > 0)
{
_deseasonalityFunction.Deseasonality(ref values, _period, ref _seriesToDetect);
Expand Down Expand Up @@ -612,9 +626,22 @@ private void SpectralResidual(double[] values, double[][] results, double thresh

var detres = score > threshold ? 1 : 0;

// Anomalies correction by zscore
if (detres > 0)
{
// Use zscore to filter out those false anomalies that lie within 1.5 sigma region.
var zscore = Math.Abs(values[i] - _mean) / _std;
if (_std < _eps || zscore < _zscoreThreshold)
{
detres = 0;
score = 0.0;
}
}

results[i][0] = detres;
results[i][1] = score;
results[i][2] = _ifftMagList[i];

}
}

Expand Down
56 changes: 55 additions & 1 deletion test/Microsoft.ML.TimeSeries.Tests/TimeSeriesDirectApi.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@

using System;
using System.Collections.Generic;
using System.Data;
using System.IO;
using System.Linq;
using Microsoft.ML.Data;
Expand Down Expand Up @@ -717,6 +716,61 @@ public void TestSrCnnAnomalyDetectorWithSeasonalData(
}
}

[Theory, CombinatorialData]
public void TestSrCnnAnomalyDetectorBigSpike(
[CombinatorialValues(SrCnnDetectMode.AnomalyOnly, SrCnnDetectMode.AnomalyAndExpectedValue, SrCnnDetectMode.AnomalyOnly)] SrCnnDetectMode mode
)
{
var ml = new MLContext(1);
IDataView dataView;
List<TimeSeriesDataDouble> data;

var dataPath = GetDataPath("Timeseries", "big_spike_data.csv");

// Load data from file into the dataView
dataView = ml.Data.LoadFromTextFile<TimeSeriesDataDouble>(dataPath, hasHeader: true);
data = ml.Data.CreateEnumerable<TimeSeriesDataDouble>(dataView, reuseRowObject: false).ToList();

// Setup the detection arguments
string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
string inputColumnName = nameof(TimeSeriesDataDouble.Value);

// Do batch anomaly detection
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = 0.3,
BatchSize = -1,
Sensitivity = 80.0,
DetectMode = mode,
Period = 0,
DeseasonalityMode = SrCnnDeseasonalityMode.Stl
};

var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);

// Getting the data of the newly created column as an IEnumerable of SrCnnAnomalyDetection.
var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
outputDataView, reuseRowObject: false);

var anomalyIndex = 26;

int k = 0;
foreach (var prediction in predictionColumn)
{
if (anomalyIndex == k)
{
Assert.Equal(1, prediction.Prediction[0]);
}
else
{
Assert.Equal(0, prediction.Prediction[0]);
}

++k;
}

}

[Theory, CombinatorialData]
public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
[CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
Expand Down
136 changes: 136 additions & 0 deletions test/data/Timeseries/big_spike_data.csv
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Original file line number Diff line number Diff line change
@@ -0,0 +1,136 @@
Value
0.333061106
2.198203303
1.705836778
1.861708215
1.085050871
0.548409541
0.365537211
0.433823922
0.450379649
0.485662867
0.59162219
0.678494031
0.735315015
0.780228908
0.779309892
0.71637311
0.783369345
0.829129842
0.769519564
0.74230352
0.914116686
0.970162226
0.964537878
0.983059421
1.009637074
1.054769667
48232.24413
4739.675242
4963.982698
8555.732913
75.25537709
11.2742621
4.388301951
2.584960796
2.273629928
1.972334276
1.811987528
1.854365004
1.581860355
1.478895939
1.447799312
1.406460886
1.333295368
1.282260475
1.345933543
1.264431234
1.235222153
1.204307109
1.133533648
1.110515351
1.017397262
1.103902775
1.099039227
1.061479438
1.063725177
1.072777829
1.044107263
0.981847451
1.038324454
1.033883341
1.004416487
1.017918007
0.345233269
1.092365812
1.078005286
1.033142227
1.024832225
1.098672969
1.092767871
1.095272293
1.139357768
1.0711793
1.119012071
1.11906761
1.131538563
1.113967769
1.141610905
1.14317559
1.108130866
1.083645413
1.147460394
1.177086603
1.153490106
1.145660569
1.132464809
1.106364602
1.003350151
1.099011524
1.109557478
1.065336146
1.081590334
1.075768021
0.986278889
1.001219623
1.080312553
1.075076345
1.057146027
1.106862867
1.084433852
0.975639541
0.944182773
1.088712253
1.067152572
1.107507855
1.069142173
1.036247939
0.995907308
0.932153379
1.074865283
1.065780376
1.05063751
1.077263172
1.033459106
0.985960758
0.981842413
1.032862035
1.005063722
0.862145269
0.491629016
0.473904777
0.777874357
0.945595834
1.020180047
1.025171701
1.031632464
1.02571454
0.950313827
0.935412116
0.991591559
1.013279894
0.991734823
1.007466737
1.019160801
0.919227208
0.977617794