static void DetectAnomaly(MLContext mlContext, IDataView phoneCalls, int period)
{
Console.WriteLine("Detect anomaly points in the series");
//STEP 2: Setup the parameters
// <SnippetSetupSrCnnParameters>
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = 0.3,
Sensitivity = 64.0,
DetectMode = SrCnnDetectMode.AnomalyAndMargin,
Period = period,
};
// </SnippetSetupSrCnnParameters>
//STEP 3: Detect anomaly by SR-CNN algorithm
// <SnippetDetectAnomaly>
IDataView outputDataView =
mlContext
.AnomalyDetection.DetectEntireAnomalyBySrCnn(
phoneCalls,
nameof(PhoneCallsPrediction.Prediction),
nameof(PhoneCallsData.value),
options);
// </SnippetDetectAnomaly>
// <SnippetCreateEnumerableForResult>
IEnumerable <PhoneCallsPrediction> predictions = mlContext.Data.CreateEnumerable <PhoneCallsPrediction>(
outputDataView, reuseRowObject: false);
// </SnippetCreateEnumerableForResult>
// <SnippetDisplayHeader>
Console.WriteLine("Index\tAnomaly\tExpectedValue\tUpperBoundary\tLowerBoundary");
// </SnippetDisplayHeader>
// <SnippetDisplayAnomalyDetectionResults>
var index = 0;
foreach (var p in predictions)
{
if (p.Prediction[0] == 1)
{
Console.WriteLine("{0},{1},{2},{3},{4} <-- alert is on, detected anomaly", index,
p.Prediction[0], p.Prediction[3], p.Prediction[5], p.Prediction[6]);
}
else
{
Console.WriteLine("{0},{1},{2},{3},{4}", index,
p.Prediction[0], p.Prediction[3], p.Prediction[5], p.Prediction[6]);
}
++index;
}
Console.WriteLine("");
// </SnippetDisplayAnomalyDetectionResults>
}
public void TestSrCnnAnomalyDetectorWithSeasonalAnomalyData(
[CombinatorialValues(SrCnnDeseasonalityMode.Stl, SrCnnDeseasonalityMode.Mean, SrCnnDeseasonalityMode.Median)] SrCnnDeseasonalityMode mode
)
{
var ml = new MLContext(1);
IDataView dataView;
var dataPath = GetDataPath("Timeseries", "period_anomaly.csv");
// Load data from file into the dataView
dataView = ml.Data.LoadFromTextFile <TimeSeriesDataDouble>(dataPath, hasHeader: true);
// Setup the detection arguments
string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
string inputColumnName = nameof(TimeSeriesDataDouble.Value);
// Do batch anomaly detection
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = 0.23,
BatchSize = -1,
Sensitivity = 53.0,
DetectMode = SrCnnDetectMode.AnomalyAndMargin,
Period = 288,
DeseasonalityMode = mode
};
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 anomalyStartIndex = 2988;
var anomalyEndIndex = 3095;
int k = 0;
foreach (var prediction in predictionColumn)
{
Assert.Equal(7, prediction.Prediction.Length);
if (anomalyStartIndex <= k && k <= anomalyEndIndex)
{
Assert.Equal(1, prediction.Prediction[0]);
}
else
{
Assert.Equal(0, prediction.Prediction[0]);
}
++k;
}
}
/// <summary>
/// Create <see cref="SrCnnEntireAnomalyDetector"/>, which detects timeseries anomalies for entire input using SRCNN algorithm.
/// </summary>
/// <param name="catalog">The AnomalyDetectionCatalog.</param>
/// <param name="input">Input DataView.</param>
/// <param name="outputColumnName">Name of the column resulting from data processing of <paramref name="inputColumnName"/>.
/// The column data is a vector of <see cref="System.Double"/>. The length of this vector varies depending on <paramref name="detectMode"/>.</param>
/// <param name="inputColumnName">Name of column to process. The column data must be <see cref="System.Double"/>.</param>
/// <param name="threshold">The threshold to determine an anomaly. An anomaly is detected when the calculated SR raw score for a given point is more than the set threshold. This threshold must fall between [0,1], and its default value is 0.3.</param>
/// <param name="batchSize">Divide the input data into batches to fit srcnn model.
/// When set to -1, use the whole input to fit model instead of batch by batch, when set to a positive integer, use this number as batch size.
/// Must be -1 or a positive integer no less than 12. Default value is 1024.</param>
/// <param name="sensitivity">Sensitivity of boundaries, only useful when srCnnDetectMode is AnomalyAndMargin. Must be in [0,100]. Default value is 99.</param>
/// <param name="detectMode">An enum type of <see cref="SrCnnDetectMode"/>.
/// When set to AnomalyOnly, the output vector would be a 3-element Double vector of (IsAnomaly, RawScore, Mag).
/// When set to AnomalyAndExpectedValue, the output vector would be a 4-element Double vector of (IsAnomaly, RawScore, Mag, ExpectedValue).
/// When set to AnomalyAndMargin, the output vector would be a 7-element Double vector of (IsAnomaly, AnomalyScore, Mag, ExpectedValue, BoundaryUnit, UpperBoundary, LowerBoundary).
/// The RawScore is output by SR to determine whether a point is an anomaly or not, under AnomalyAndMargin mode, when a point is an anomaly, an AnomalyScore will be calculated according to sensitivity setting.
/// Default value is AnomalyOnly.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static IDataView DetectEntireAnomalyBySrCnn(this AnomalyDetectionCatalog catalog, IDataView input, string outputColumnName, string inputColumnName,
double threshold = 0.3, int batchSize = 1024, double sensitivity = 99, SrCnnDetectMode detectMode = SrCnnDetectMode.AnomalyOnly)
{
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = threshold,
BatchSize = batchSize,
Sensitivity = sensitivity,
DetectMode = detectMode,
};
return(DetectEntireAnomalyBySrCnn(catalog, input, outputColumnName, inputColumnName, options));
}
// funcion que detecta las anomalias
static void DetectAnomaly(MLContext mlContext, IDataView Llamadas, int period, Boolean imprimir, string textFileName)
{
Console.WriteLine("Detectando las anomalías");
// Inicializando los valores limites.
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = 0.3, // Valor considerado como anormal o umbral
Sensitivity = 64.0, // Determina el rango del limite
DetectMode = SrCnnDetectMode.AnomalyAndMargin, // Tipo de Anomalía a detectar
Period = period, // Periodo o frencuencia que se presentan los datos
};
// Datos de Salida luego de ser procesados los datos y verificados
IDataView outputDataView =
mlContext
.AnomalyDetection.DetectEntireAnomalyBySrCnn( // Detecta una anomalia usando SRCNN
Llamadas,
nameof(PrediccionDeLlamada.Prediccion),
nameof(Llamada.tiempo),
options);
// Crea la lista de Peedicciones
IEnumerable <PrediccionDeLlamada> predictions = mlContext.Data.CreateEnumerable <PrediccionDeLlamada>(
outputDataView, reuseRowObject: false);
if (imprimir)
{
using (var stream = File.Create(textFileName))
mlContext.Data.SaveAsText(outputDataView, stream, ';');
}
// Encabezado
Console.WriteLine("Indice\t Anomalia\t Valor_esperado\t\t Limite_Superior\t Limite_inferior");
var index = 0;
foreach (var p in predictions)
{
if (p.Prediccion[0] == 1)
{
Console.WriteLine("{0}\t {1}\t\t {2}\t {3}\t {4} <-- alerta detectada un anomalia", index,
p.Prediccion[0], p.Prediccion[3], p.Prediccion[5], p.Prediccion[6]);
}
else
{
Console.WriteLine("{0}\t {1}\t\t {2}\t {3}\t {4}", index,
p.Prediccion[0], p.Prediccion[3], p.Prediccion[5], p.Prediccion[6]);
}
++index;
}
Console.WriteLine("");
}
/// <summary> /// Create <see cref="SrCnnEntireAnomalyDetector"/>, which detects timeseries anomalies for entire input using SRCNN algorithm. /// </summary> /// <param name="catalog">The AnomalyDetectionCatalog.</param> /// <param name="input">Input DataView.</param> /// <param name="outputColumnName">Name of the column resulting from data processing of <paramref name="inputColumnName"/>. /// The column data is a vector of <see cref="System.Double"/>. The length of this vector varies depending on <paramref name="options.DetectMode"/>.</param> /// <param name="inputColumnName">Name of column to process. The column data must be <see cref="System.Double"/>.</param> /// <param name="options">Defines the settings of the load operation.</param> /// <example> /// <format type="text/markdown"> /// <] /// ]]> /// </format> /// </example> public static IDataView DetectEntireAnomalyBySrCnn(this AnomalyDetectionCatalog catalog, IDataView input, string outputColumnName, string inputColumnName, SrCnnEntireAnomalyDetectorOptions options) => new SrCnnEntireAnomalyDetector(CatalogUtils.GetEnvironment(catalog), input, outputColumnName, inputColumnName, options);
static void DetectAnomaly(IDataView dataView)
{
Console.WriteLine("===============Detect anomalies in pattern===============");
//STEP 1: Specify the input column and output column names.
string inputColumnName = nameof(PhoneCallsData.value);
string outputColumnName = nameof(PhoneCallsPrediction.Prediction);
//STEP 2: Detect period on the given series.
int period = mlContext.AnomalyDetection.DetectSeasonality(dataView, inputColumnName);
Console.WriteLine("Period of the series is: {0}.", period);
//STEP 3: Setup the parameters
var options = new SrCnnEntireAnomalyDetectorOptions()
{
Threshold = 0.3,
Sensitivity = 64.0,
DetectMode = SrCnnDetectMode.AnomalyAndMargin,
Period = period,
};
//STEP 4: Invoke SrCnn algorithm to detect anomaly on the entire series.
var outputDataView = mlContext.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName, options);
//STEP 5: Get the detection results as an IEnumerable
var predictions = mlContext.Data.CreateEnumerable <PhoneCallsPrediction>(
outputDataView, reuseRowObject: false);
Console.WriteLine("The anomaly detection results obtained.");
var index = 0;
Console.WriteLine("Index\tData\tAnomaly\tAnomalyScore\tMag\tExpectedValue\tBoundaryUnit\tUpperBoundary\tLowerBoundary");
foreach (var p in predictions)
{
if (p.Prediction[0] == 1)
{
Console.WriteLine("{0},{1},{2},{3},{4},{5},{6},{7} <-- alert is on, detecte anomaly", index,
p.Prediction[0], p.Prediction[1], p.Prediction[2], p.Prediction[3], p.Prediction[4], p.Prediction[5], p.Prediction[6]);
}
else
{
Console.WriteLine("{0},{1},{2},{3},{4},{5},{6},{7}", index,
p.Prediction[0], p.Prediction[1], p.Prediction[2], p.Prediction[3], p.Prediction[4], p.Prediction[5], p.Prediction[6]);
}
++index;
}
Console.WriteLine("");
//Index Data Anomaly AnomalyScore Mag ExpectedValue BoundaryUnit UpperBoundary LowerBoundary
//0,0,0,0.012431224740909462,36.841787256739266,32.92296779138513,41.14206982401966,32.541504689458876
//1,0,0,0.06732467206114204,35.67303618137362,32.92296779138513,39.97331874865401,31.372753614093227
//2,0,0,0.053027383620274836,34.710132999891826,33.06901172138514,39.029491313022824,30.390774686760828
//3,0,0,0.027326808903921952,33.44765248883495,33.215055651385136,37.786086547816545,29.10921842985335
//4,0,0,0.0074169435448767015,28.937110922276364,33.06901172138514,33.25646923540736,24.61775260914537
//5,0,0,0.01068288760963436,5.143895892785781,32.92296779138513,9.444178460066171,0.843613325505391
//6,0,0,0.02901575691006479,5.163325228419392,32.92296779138513,9.463607795699783,0.8630426611390014
//7,0,0,0.015220262187074987,36.76414836240396,32.92296779138513,41.06443092968435,32.46386579512357
//8,0,0,0.029223955855920452,35.77908590657007,32.92296779138513,40.07936847385046,31.478803339289676
//9,0,0,0.05014588266429284,34.547259536635245,32.92296779138513,38.847542103915636,30.246976969354854
//10,0,0,0.006478629327524482,33.55193524820608,33.06901172138514,37.871293561337076,29.23257693507508
//11,0,0,0.0144699438892775,29.091800129624648,32.92296779138513,33.392082696905035,24.79151756234426
//12,0,0,0.00941397738418861,5.154836630338823,32.92296779138513,9.455119197619213,0.8545540630584334
//13,0,0,0.01012680059746895,5.234332502492464,32.92296779138513,9.534615069772855,0.934049935212073
//14,0,0,0.0391359937506989,36.54992549471526,32.92296779138513,40.85020806199565,32.24964292743487
//15,0,0,0.01879091709088552,35.79526470980883,32.92296779138513,40.095547277089224,31.494982142528443
//16,0,0,0.04275209137629126,34.34099013096804,32.92296779138513,38.64127269824843,30.040707563687647
//17,0,0,0.024479312458949517,33.61201516582131,32.92296779138513,37.9122977331017,29.31173259854092
//18,0,0,0.010781906482188448,29.223563320561812,32.92296779138513,33.5238458878422,24.923280753281425
//19,0,0,0.006907498717766534,5.170512168851533,32.92296779138513,9.470794736131923,0.8702296015711433
//20,0,0,0.003183991678813579,5.2614938889462834,32.92296779138513,9.561776456226674,0.9612113216658926
//21,0,0,0.04256581040333137,36.37103858487317,32.92296779138513,40.67132115215356,32.07075601759278
//22,0,0,0.022860533704528126,35.813544599026855,32.92296779138513,40.113827166307246,31.513262031746464
//23,0,0,0.019266922707912835,34.05600492733225,32.92296779138513,38.356287494612644,29.755722360051863
//24,0,0,0.008008656062259012,33.65828319077884,32.92296779138513,37.95856575805923,29.358000623498448
//25,0,0,0.018746201354033914,29.381125690882463,32.92296779138513,33.681408258162854,25.080843123602072
//26,0,0,0.0141022037992637,5.261543539820418,32.92296779138513,9.561826107100808,0.9612609725400283
//27,0,0,0.013396001938040617,5.4873712582971805,32.92296779138513,9.787653825577571,1.1870886910167897
//28,1,0.4971326063712256,0.3521692757832201,36.504694001629254,32.92296779138513,40.804976568909645,32.20441143434886 < --alert is on, detecte anomaly
}
