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Anomoly/codegen #4

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Jan 13, 2022
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Anomoly/codegen #4

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mengaims Jan 13, 2022
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mengaims Jan 13, 2022
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27 changes: 18 additions & 9 deletions ...ydetector/Azure.AI.AnomalyDetector/samples/Sample1_DetectEntireSeriesAnomaly.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -53,24 +53,33 @@ Call the client's `DetectEntireSeriesAsync` method with the `DetectRequest` obje
//detect
Console.WriteLine("Detecting anomalies in the entire time series.");

EntireDetectResponse result = await client.DetectEntireSeriesAsync(request).ConfigureAwait(false);

if (result.IsAnomaly.Contains(true))
try
{
Console.WriteLine("An anomaly was detected at index:");
EntireDetectResponse result = await client.DetectEntireSeriesAsync(request).ConfigureAwait(false);

bool hasAnomaly = false;
for (int i = 0; i < request.Series.Count; ++i)
{
if (result.IsAnomaly[i])
{
Console.Write(i);
Console.Write(" ");
Console.WriteLine("An anomaly was detected at index: {0}.", i);
hasAnomaly = true;
}
}
Console.WriteLine();
if (!hasAnomaly)
{
Console.WriteLine("No anomalies detected in the series.");
}
}
catch (RequestFailedException ex)
{
Console.WriteLine(String.Format("Entire detection failed: {0}", ex.Message));
throw;
}
else
catch (Exception ex)
{
Console.WriteLine(" No anomalies detected in the series.");
Console.WriteLine(String.Format("Detection error. {0}", ex.Message));
throw;
}
```
To see the full example source files, see:
Expand Down
25 changes: 19 additions & 6 deletions ...malydetector/Azure.AI.AnomalyDetector/samples/Sample2_DetectLastPointAnomaly.md
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Original file line number Diff line number Diff line change
Expand Up @@ -53,15 +53,28 @@ Call the client's `DetectLastPointAsync` method with the `DetectRequest` object
//detect
Console.WriteLine("Detecting the anomaly status of the latest point in the series.");

LastDetectResponse result = await client.DetectLastPointAsync(request).ConfigureAwait(false);

if (result.IsAnomaly)
try
{
LastDetectResponse result = await client.DetectLastPointAsync(request).ConfigureAwait(false);

if (result.IsAnomaly)
{
Console.WriteLine("The latest point was detected as an anomaly.");
}
else
{
Console.WriteLine("The latest point was not detected as an anomaly.");
}
}
catch (RequestFailedException ex)
{
Console.WriteLine("The latest point was detected as an anomaly.");
Console.WriteLine(String.Format("Last detection failed: {0}", ex.Message));
throw;
}
else
catch (Exception ex)
{
Console.WriteLine("The latest point was not detected as an anomaly.");
Console.WriteLine(String.Format("Detection error. {0}", ex.Message));
throw;
}
```
To see the full example source files, see:
Expand Down
65 changes: 53 additions & 12 deletions sdk/anomalydetector/Azure.AI.AnomalyDetector/samples/Sample4_MultivariateDetect.md
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Original file line number Diff line number Diff line change
Expand Up @@ -30,13 +30,13 @@ You could add the data source, along with start time and end time to a `ModelInf
Call `TrainMultivariateModel` with the created data feed and extract the model ID from the response header. Afterwards, you can get the model info, including the model status, by calling `GetMultivariateModelAsync` with the model ID . Wait until the model status is ready.

```C# Snippet:TrainMultivariateModel
private async Task<Guid?> trainAsync(AnomalyDetectorClient client, string datasource, DateTimeOffset start_time, DateTimeOffset end_time, int max_tryout = 500)
private async Task<Guid?> TrainAsync(AnomalyDetectorClient client, string datasource, DateTimeOffset start_time, DateTimeOffset end_time, int max_tryout = 500)
{
try
{
Console.WriteLine("Training new model...");

int model_number = await getModelNumberAsync(client, false).ConfigureAwait(false);
int model_number = await GetModelNumberAsync(client, false).ConfigureAwait(false);
Console.WriteLine(String.Format("{0} available models before training.", model_number));

ModelInfo data_feed = new ModelInfo(datasource, start_time, end_time);
Expand Down Expand Up @@ -69,24 +69,24 @@ private async Task<Guid?> trainAsync(AnomalyDetectorClient client, string dataso
Console.WriteLine(String.Format("Request timeout after {0} tryouts", max_tryout));
}

model_number = await getModelNumberAsync(client).ConfigureAwait(false);
model_number = await GetModelNumberAsync(client).ConfigureAwait(false);
Console.WriteLine(String.Format("{0} available models after training.", model_number));
return trained_model_id;
}
catch (Exception e)
{
Console.WriteLine(String.Format("Train error. {0}", e.Message));
throw new Exception(e.Message);
throw;
}
}
```

## Detect anomalies

To detect anomalies using your newly trained model, create a private async Task named `detectAsync`. You will create a new `DetectionRequest`, pass that as a parameter to `DetectAnomalyAsync` and await the response. From the header of the response, you could extract the result ID. With the result ID, you could get the detection content and detection status by `GetDetectionResultAsync`. Return the detection content when the detection status is ready.
To detect anomalies using your newly trained model, create a private async Task named `DetectAsync`. You will create a new `DetectionRequest`, pass that as a parameter to `DetectAnomalyAsync` and await the response. From the header of the response, you could extract the result ID. With the result ID, you could get the detection content and detection status by `GetDetectionResultAsync`. Return the detection content when the detection status is ready.

```C# Snippet:DetectMultivariateAnomaly
private async Task<DetectionResult> detectAsync(AnomalyDetectorClient client, string datasource, Guid model_id,DateTimeOffset start_time, DateTimeOffset end_time, int max_tryout = 500)
private async Task<DetectionResult> DetectAsync(AnomalyDetectorClient client, string datasource, Guid model_id,DateTimeOffset start_time, DateTimeOffset end_time, int max_tryout = 500)
{
try
{
Expand Down Expand Up @@ -118,7 +118,48 @@ private async Task<DetectionResult> detectAsync(AnomalyDetectorClient client, st
catch (Exception e)
{
Console.WriteLine(String.Format("Detection error. {0}", e.Message));
throw new Exception(e.Message);
throw;
}
}
```

## Detect last anomalies

To detect anomalies using your newly trained model, create a private async Task named `DetectLastAsync`. You will create a new `LastDetectionRequest`, you could assign how many last points you want to detect in the request. Pass `LastDetectionRequest` as a parameter to `LastDetectAnomalyAsync` and await the response. Return the detection content when the detection status is ready.

```C# Snippet:DetectLastMultivariateAnomaly
private async Task<LastDetectionResult> DetectLastAsync(AnomalyDetectorClient client, Guid model_id)
{
Console.WriteLine("Start detect...");

List<VariableValues> variables = new List<VariableValues>();
variables.Add(new VariableValues("variables_name1", new[] { "2021-01-01 00:00:00", "2021-01-01 01:00:00", "2021-01-01 02:00:00" }, new[] { 0.0f, 0.0f, 0.0f }));
variables.Add(new VariableValues("variables_name2", new[] { "2021-01-01 00:00:00", "2021-01-01 01:00:00", "2021-01-01 02:00:00" }, new[] { 0.0f, 0.0f, 0.0f }));

LastDetectionRequest lastDetectionRequest = new LastDetectionRequest(variables, 1);

try
{
Response<LastDetectionResult> response = await client.LastDetectAnomalyAsync(model_id, lastDetectionRequest).ConfigureAwait(false);
if (response.GetRawResponse().Status == 200)
{
foreach (AnomalyState state in response.Value.Results)
{
Console.WriteLine(String.Format("timestamp: {}, isAnomaly: {}, score: {}.", state.Timestamp, state.Value.IsAnomaly, state.Value.Score));
}
}

return response;
}
catch (RequestFailedException ex)
{
Console.WriteLine(String.Format("Last detection failed: {0}", ex.Message));
throw;
}
catch (Exception ex)
{
Console.WriteLine(String.Format("Detection error. {0}", ex.Message));
throw;
}
}
```
Expand All @@ -128,7 +169,7 @@ private async Task<DetectionResult> detectAsync(AnomalyDetectorClient client, st
To export the model you trained previously, create a private async Task named `exportAysnc`. You will use `ExportModelAsync` and pass the model ID of the model you wish to export.

```C# Snippet:ExportMultivariateModel
private async Task exportAsync(AnomalyDetectorClient client, Guid model_id, string model_path = "model.zip")
private async Task ExportAsync(AnomalyDetectorClient client, Guid model_id, string model_path = "model.zip")
{
try
{
Expand All @@ -144,7 +185,7 @@ private async Task exportAsync(AnomalyDetectorClient client, Guid model_id, stri
catch (Exception e)
{
Console.WriteLine(String.Format("Export error. {0}", e.Message));
throw new Exception(e.Message);
throw;
}
}
```
Expand All @@ -154,13 +195,13 @@ private async Task exportAsync(AnomalyDetectorClient client, Guid model_id, stri
To delete a model that you have created previously use `DeleteMultivariateModelAsync` and pass the model ID of the model you wish to delete. You can check the number of models after deletion with `getModelNumberAsync`.

```C# Snippet:DeleteMultivariateModel
private async Task deleteAsync(AnomalyDetectorClient client, Guid model_id)
private async Task DeleteAsync(AnomalyDetectorClient client, Guid model_id)
{
await client.DeleteMultivariateModelAsync(model_id).ConfigureAwait(false);
int model_number = await getModelNumberAsync(client).ConfigureAwait(false);
int model_number = await GetModelNumberAsync(client).ConfigureAwait(false);
Console.WriteLine(String.Format("{0} available models after deletion.", model_number));
}
private async Task<int> getModelNumberAsync(AnomalyDetectorClient client, bool delete = false)
private async Task<int> GetModelNumberAsync(AnomalyDetectorClient client, bool delete = false)
{
int count = 0;
AsyncPageable<ModelSnapshot> model_list = client.ListMultivariateModelAsync(0, 10000);
Expand Down
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