Add metrics correlation algorithm (#3877)
* Add metrics correlation algorithm Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Update algorithms.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update algorithms.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update algorithms.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/algorithms.md Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/algorithms.md Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/algorithms.md Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/algorithms.md Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> --------- Signed-off-by: Naarcha-AWS <naarcha@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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@ -629,4 +629,79 @@ POST _plugins/_ml/_predict/logistic_regression/SsfQaoIBEoC4g4joZiyD
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### Limitations
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Convergence metrics are not built into Tribuo's trainers. Therefore, ML Commons cannot indicate the convergence status through the ML Commons API.
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Convergence metrics are not built into Tribuo's trainers. Therefore, ML Commons cannot indicate the convergence status through the ML Commons API.
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## Metrics correlation
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The metrics correlation feature is an experimental feature released in OpenSearch 2.7. It can't be used in a production environment. To leave feedback on improving the feature, create an issue in the [ML Commons repository](https://github.com/opensearch-project/ml-commons).
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{: .warning }
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The metrics correlation algorithm finds events in a set of metrics data. The algorithm defines events as a window in time in which multiple metrics simultaneously display anomalous behavior. When given a set of metrics, the algorithm counts the number of events that occurred, when each event occurred, and determines which metrics were involved in each event.
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To enable the metrics correlation algorithm, update the following cluster setting:
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```
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PUT /_cluster/settings
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{
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"persistent" : {
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"plugins.ml_commons.enable_inhouse_python_model": true
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}
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}
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```
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### Parameters
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To use the metrics correlation algorthim, include the following parameters.
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| Parameter | Type | Description | Default value |
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|---|---|---|---|
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metrics | Array | A list of metrics within the time series that can be correlated to anomalous behavior | N/A
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### Input
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The metrics correlation input is an $M$ x $T$ array of metrics data, where M is the number of metrics and T is the length of each individual sequence of metric values.
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When inputting metrics into the algorthim, assume the following:
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1. For each metric, the input sequence has the same length, $T$.
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2. All input metrics should have the same corresponding set of timestamps.
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3. The total number of data points are $M$ * $T$ <= 10000.
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### Example: Simple metrics correlation
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The following example inputs the number of metrics ($M$) as 3 and the number of timesteps ($T$) as 128:
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```
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POST /_plugins/_ml/_execute/METRICS_CORRELATION
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{"metrics": [[-1.1635416, -1.5003631, 0.46138194, 0.5308311, -0.83149344, -3.7009873, -3.5463789, 0.22571462, -5.0380244, 0.76588845, 1.236113, 1.8460795, 1.7576948, 0.44893077, 0.7363948, 0.70440894, 0.89451003, 4.2006273, 0.3697659, 2.2458954, -2.302939, -1.7706926, 1.7445002, -1.5246059, 0.07985192, -2.7756078, 1.0002468, 1.5977372, 2.9152713, 1.4172368, -0.26551363, -2.2883027, 1.5882446, 2.0145164, 3.4862874, -1.2486862, -2.4811826, -0.17609037, -2.1095612, -1.2184235, 0.63118523, -1.8909532, 2.039797, -0.5317177, -2.2922578, -2.0179775, -0.07992507, -0.12554549, -0.2553092, 1.1450123, -0.4640453, -2.190223, -4.671612, -1.5076426, 1.635445, -1.1394824, -0.7503817, 0.98424894, -0.38896716, 1.0328646, 1.9543738, -0.5236269, 0.14298044, 3.2963762, 8.1641035, 5.717064, 7.4869685, 2.5987444, 11.018798, 9.151356, 5.7354255, 6.862203, 3.0524514, 4.431755, 5.1481285, 7.9548607, 7.4519925, 6.09533, 7.634116, 8.898271, 3.898491, 9.447067, 8.197385, 5.8284273, 5.804283, 7.7688456, 10.574343, 7.5679493, 7.1888094, 7.1107903, 8.454468, 8.066334, 8.83665, 7.11204, 4.4898267, 8.614764, 6.336754, 11.577503, 3.3998494, 9.501525, 13.17289, 6.1116023, 5.143777, 2.7813284, 3.7917604, 7.1683135, 7.627272, 7.290255, 3.1299121, 7.089733, 9.140584, 8.844729, 9.403275, 10.220029, 8.039719, 8.85549, 4.034555, 4.412663, 7.54451, 7.2116737, 4.6346946, 7.0044127, 9.7557, 10.982841, 5.897937, 6.870126, 3.5638695, 5.7872133], [1.3037996, 2.7976995, -0.12042701, 1.3688855, 1.6955005, -2.2575269, 0.080582514, 3.011721, -0.4320283, 3.2440786, -1.0321085, 1.2346085, -2.3152106, -0.9783513, 0.6837618, 1.5320586, -1.6148578, -0.94538075, 0.55978125, -4.7430468, 3.466028, 2.3792691, 1.3269067, -0.35359794, -1.5547276, 0.5202475, 1.0269136, -1.7531714, 0.43987304, -0.18845831, 2.3086758, 2.519588, 2.0116413, 0.019745048, -0.010070452, 2.496933, 1.1557871, 0.08433053, 1.375894, -1.2135965, -1.2588277, -0.31454003, 0.045949124, -1.7518936, -2.3533764, -2.0125146, 0.10255043, 1.1782314, 2.4579153, -0.8780899, -4.1442213, 3.8300152, 2.772975, 2.6803262, 0.9867382, 0.77618766, 0.46541777, 3.8959959, -2.1713195, 0.10609512, -0.26438138, -2.145317, 3.6734529, 1.4830295, -5.3445525, -10.6427765, -8.300354, -1.9608921, -6.6779685, -10.019544, -8.341513, -9.607174, -7.2441607, -3.411102, -6.180552, -8.318714, -6.060591, -7.790343, -5.9695, -7.9429936, -3.775652, -5.2827606, -3.7168224, -6.729588, -9.761094, -7.4683576, -7.2595067, -6.6790915, -9.832726, -8.352172, -6.936336, -8.252518, -6.787475, -9.091013, -11.465944, -6.712504, -8.987438, -6.946672, -8.877166, -6.7854185, -3.6417139, -6.1036086, -5.360772, -4.0435786, -4.5864973, -6.971063, -10.522461, -6.3692527, -4.387658, -9.723745, -4.7020173, -5.097396, -9.903703, -4.882414, -4.1999683, -6.7829437, -6.2555966, -8.121125, -5.334131, -9.174302, -3.9752126, -4.179469, -8.335524, -9.359406, -6.4938803, -6.794677, -8.382997, -9.879416], [1.8792984, -3.1561708, -0.8443318, -1.998743, -0.6319316, 2.4614046, -0.44511616, 0.82785237, 1.7911717, -1.8172283, 0.46574894, -1.8691323, 3.9586513, 0.8078605, 0.9049874, 5.4086914, -0.7425967, -0.20115769, -1.197923, 2.741789, 0.85432875, -1.1688408, -1.7771784, 1.615249, -4.1103697, 0.4721327, -2.75669, -0.38393462, -3.1137516, -2.2572582, 0.9580673, -3.7139492, -0.68303126, 1.6007807, 0.6313973, -2.5115106, 0.703251, 2.4844077, -1.7405633, -3.007687, 2.372802, 2.4684637, 0.6443977, -3.1433117, 0.05976736, -1.9809214, 3.514713, 2.1880944, 1.242541, 1.8236228, 0.8642841, -0.17313614, 1.7042321, 0.8298376, 4.2443194, 0.13983983, 1.1940852, 2.5076652, 39.285202, 82.73858, 44.707516, -4.267148, 0.25930226, 0.20799652, -3.7213502, 1.475217, -1.2394199, -0.0034497892, 1.1413965, 55.18923, -2.2969518, -4.1400924, -2.4707043, 43.193188, -0.19258368, 3.471275, 1.1374166, 1.2147579, 4.13017, -2.0576499, 2.1529694, -0.28360432, 0.8477302, -0.63012695, 1.2569811, 1.943168, 0.17070436, 3.2358394, -2.3737662, 0.77060974, 4.99065, 3.1079204, 3.6347675, 0.6801177, -2.2205186, 1.0961101, -2.4445753, -2.0919478, -2.895031, 2.5458927, 0.38599384, 1.0492333, -0.081834644, -7.4079595, -2.1785216, -0.7277175, -2.7413428, -3.2083786, 3.2958643, -1.1839997, 5.4849496, 2.0259023, 5.607272, -1.0125756, 3.721461, 2.5715313, 0.7741753, -0.55034757, 0.7526307, -2.6758716, -2.964664, -0.57379586, -0.28817406, -3.2334063, -0.22387607, -2.0793931, -6.4562697, 0.80134094]]}
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```
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**Response**
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The API returns the following information:
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- `event_window`: The event interval
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- `event_pattern`: The intensity score across the time window and the overall severity of the event
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- `suspected_metrics`: The set of metrics involved
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In the following example response, each item corresponds to an event discovered in the metrics data. The algorithm finds one event in the input data of the request, as indicated by the output in `event_pattern` having a length of `1`. `event_window` shows that the event occurred between time point $t$ = 52 and $t$ = 72. Lastly, `suspected_metrics` shows that the event involved all three metrics.
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```json
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{
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"function_name": "METRICS_CORRELATION",
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"output": {
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"inference_results": [
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{
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"event_window": [
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52,
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72
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],
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"event_pattern": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.99625e-05, 0.0001052875, 0.0002605894, 0.00064648513, 0.0014303402, 0.002980127, 0.005871893, 0.010885878, 0.01904726, 0.031481907, 0.04920215, 0.07283493, 0.10219432, 0.1361888, 0.17257516, 0.20853643, 0.24082609, 0.26901975, 0.28376183, 0.29364157, 0.29541212, 0.2832976, 0.29041746, 0.2574534, 0.2610143, 0.22938538, 0.19999361, 0.18074994, 0.15539801, 0.13064545, 0.10544432, 0.081248805, 0.05965102, 0.041305058, 0.027082501, 0.01676033, 0.009760197, 0.005362286, 0.0027713624, 0.0013381141, 0.0006126331, 0.0002634901, 0.000106459476, 4.0407333e-05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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"suspected_metrics": [0,1,2]
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}
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]
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}
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}
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```
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