Time Series samples for stateful prediction engine.#3213

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Time Series samples for stateful prediction engine. #3213

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Add time series samples for stateful prediction engine.
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
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Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
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Time Series samples for stateful prediction engine.#3213

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Merged
codemzs merged 7 commits into from
Apr 11, 2019
Merged

Time Series samples for stateful prediction engine. #3213

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6e6b4e6
Add time series samples for stateful prediction engine.
codemzs Apr 5, 2019
28de467
PR feedback.
codemzs Apr 5, 2019
c668e07
PR feedback.
codemzs Apr 9, 2019
f81d115
PR feedback.
codemzs Apr 10, 2019
cab1b81
cleanup.
codemzs Apr 10, 2019
a98751a
PR feedback.
codemzs Apr 10, 2019
45acb31
cleanup.
codemzs Apr 10, 2019
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
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Time Series samples for stateful prediction engine.#3213

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Time Series samples for stateful prediction engine. #3213

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Add time series samples for stateful prediction engine.
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Time Series samples for stateful prediction engine.#3213

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Merged
codemzs merged 7 commits into from
Apr 11, 2019
Merged

Time Series samples for stateful prediction engine. #3213

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6e6b4e6
Add time series samples for stateful prediction engine.
codemzs Apr 5, 2019
28de467
PR feedback.
codemzs Apr 5, 2019
c668e07
PR feedback.
codemzs Apr 9, 2019
f81d115
PR feedback.
codemzs Apr 10, 2019
cab1b81
cleanup.
codemzs Apr 10, 2019
a98751a
PR feedback.
codemzs Apr 10, 2019
45acb31
cleanup.
codemzs Apr 10, 2019
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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codemzs merged 7 commits into from
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Time Series samples for stateful prediction engine. #3213

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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Time Series samples for stateful prediction engine.#3213

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Merged
codemzs merged 7 commits into from
Apr 11, 2019
Merged

Time Series samples for stateful prediction engine. #3213

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6e6b4e6
Add time series samples for stateful prediction engine.
codemzs Apr 5, 2019
28de467
PR feedback.
codemzs Apr 5, 2019
c668e07
PR feedback.
codemzs Apr 9, 2019
f81d115
PR feedback.
codemzs Apr 10, 2019
cab1b81
cleanup.
codemzs Apr 10, 2019
a98751a
PR feedback.
codemzs Apr 10, 2019
45acb31
cleanup.
codemzs Apr 10, 2019
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Time Series samples for stateful prediction engine.#3213

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Merged
codemzs merged 7 commits into from
Apr 11, 2019
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Time Series samples for stateful prediction engine. #3213

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6e6b4e6
Add time series samples for stateful prediction engine.
codemzs Apr 5, 2019
28de467
PR feedback.
codemzs Apr 5, 2019
c668e07
PR feedback.
codemzs Apr 9, 2019
f81d115
PR feedback.
codemzs Apr 10, 2019
cab1b81
cleanup.
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a98751a
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codemzs Apr 10, 2019
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,114 @@
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
Comment thread
codemzs marked this conversation as resolved.
{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Time Series samples for stateful prediction engine.#3213

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Merged
codemzs merged 7 commits into from
Apr 11, 2019
Merged

Time Series samples for stateful prediction engine. #3213

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6e6b4e6
Add time series samples for stateful prediction engine.
codemzs Apr 5, 2019
28de467
PR feedback.
codemzs Apr 5, 2019
c668e07
PR feedback.
codemzs Apr 9, 2019
f81d115
PR feedback.
codemzs Apr 10, 2019
cab1b81
cleanup.
codemzs Apr 10, 2019
a98751a
PR feedback.
codemzs Apr 10, 2019
45acb31
cleanup.
codemzs Apr 10, 2019
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160 changes: 103 additions & 57 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,29 +1,16 @@
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsa
{
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class SsaChangePointData
{
public float Value;

public SsaChangePointData(float value)
{
Value = value;
}
}

// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
Expand All@@ -32,60 +19,119 @@ public static void Example()
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
// Generate sample series data with a recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<SsaChangePointData>();
for (int i = 0; i < TrainingSeasons; i++)
for (int j = 0; j < SeasonalitySize; j++)
data.Add(new SsaChangePointData(j));
// This is a change point
for (int i = 0; i < SeasonalitySize; i++)
data.Add(new SsaChangePointData(i * 100));
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(SsaChangePointData.Value);
// Setup SsaChangePointDetector arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Train the change point detector.
ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);

// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from ChangePoint predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
Console.WriteLine("");

// Prediction column obtained post-transformation.

// Output from ChangePoint predictions on new data:
// Data Alert Score P-Value Martingale value
// 0 0 - 2.53 0.50 0.00
// 1 0 - 0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 - 1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 - 1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 - 1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86

for (int i = 0; i < 5; i++)
PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

// 0 0 -1.01 0.50 0.00
// 1 0 -0.24 0.22 0.00
// 2 0 -0.31 0.30 0.00
// 3 0 0.44 0.01 0.00
// 4 0 2.16 0.00 0.24

// Now stream data points that reflect a change in trend.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}
// 100 0 86.23 0.00 2076098.24
// 200 0 171.38 0.00 809668524.21
// 300 1 256.83 0.01 22130423541.93 <-- alert is on, note that delay is expected
// 400 0 326.55 0.04 241162710263.29
// 500 0 364.82 0.08 597660527041.45 <-- saved to disk

// Now we demonstrate saving and loading the model.

// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);

// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);

// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);

// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
int value = (i + 1) * 100;
PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
}

// 100 0 -58.58 0.15 1096021098844.34 <-- loaded from disk and running new predictions
// 200 0 -41.24 0.20 97579154688.98
// 300 0 -30.61 0.24 95319753.87
// 400 0 58.87 0.38 14.24
// 500 0 219.28 0.36 0.05

}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
114 changes: 114 additions & 0 deletions ...crosoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
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using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
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{
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// The estimator is applied then to identify points where data distribution changed.
// This estimator can account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging,
// as well as the source of randomness.
var ml = new MLContext();

// Generate sample series data with a recurring pattern and then a change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),

//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};

// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);

// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);

// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);

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

Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);

// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}

private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);

class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}

class TimeSeriesData
{
public float Value;

public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
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