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Multivariate Aggregator Module

Build

$ docker build . -t multivariate-aggregator

Run

$ docker run -p 8080:8080 multivariate-aggregator

Unit Tests

Run with python 3.9.12+(>=12)

$ python3.9 -m venv .
$ source bin/activate
$ python -m pip install --upgrade pip
$ python -m pip install -r requirements.txt
$ cd src/tests
$ mkdir -p data
$ pytest test_api.py

Documentation

Use

1. multivariate-lstm-train

Curl

curl -X 'POST' \
  'http://localhost:8080/multivariate-lstm-train' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "train_data": {
    "data": {
		"A1": [601.929 , 587.4339, 590.4059, 596.6575, 582.4339, 585.8266, 597.0676, 584.5786, 583.95  , 595.0085],
		"A2": [650.8372, 650.8372, 650.8372, 650.309 , 650.309 , 649.7221, 649.1147, 649.6167, 649.6167, 649.6167],
		"A3": [636.3697, 636.3697, 636.3697, 636.3697, 636.3697, 636.3697, 636.3697, 636.3697, 636.3697, 636.3697],
		"A4": [ 71.1788,  71.1788,  71.4192,  70.8146,  71.2311,  70.9744, 70.9744,  71.1484,  71.9672,  71.509 ],
		"A5": [ 36.9295,  36.9295,  37.1119,  36.722 ,  36.97  ,  36.8511, 36.8511,  36.9359,  37.4204,  37.1334]
		}
  },
  
  "paths": {
    "model": "keras_mvts_lstm.h5",
    "scaler": "mvts_scaler.gz"
  },
  "activation": "relu",
  "optimizer": "adam",
  "loss": "mae",
  "nb_epochs": 10,
  "batch_size": 64,
  "validation_split": 0.15,
  "initial_embeding_dim": 128,
  "patience": 1
}'

Response Body

{"dump_status": "model is saved successfully"}

2. aggregate-multivariate-lstm-score

Curl

curl -X 'POST' \
  'http://localhost:8080/aggregate-multivariate-lstm-score' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "test_data": {
    "data": {
            "A1": [580.7722, 592.1779, 587.5173, 583.7109, 594.7249, 604.5849, 611.5132, 616.7466, 608.9669, 597.9345],
            "A2": [649.4124, 649.4124, 649.4124, 650.1096, 651.0769, 651.632 , 652.3653, 652.3653, 652.3653, 652.7337],
            "A3": [636.3428, 636.3428, 636.3428, 635.6159, 635.6159, 635.9999, 635.9999, 635.9999, 636.6101, 636.6101],
            "A4": [ 71.9601,  71.9601,  72.342 ,  73.5115,  74.2349,  73.8276, 73.5101,  73.2902,  72.4169,  72.7627],
            "A5": [ 37.4148,  37.4148,  37.5577,  38.3091,  38.7071,  38.4878, 38.3124,  38.1843,  37.69  ,  37.8794]
		}
  },
  "paths": {
    "model": "keras_mvts_lstm.h5",
    "scaler": "mvts_scaler.gz"
  }
}'

Response body

{
  "out": [
    0.449440516570948,
    0.525055252356107,
    0.5844318879905316,
    1.1898242258529614,
    1.6546299190632852,
    1.6099606920840253,
    1.6608597985372362,
    1.6397081285457347,
    1.241337729358707,
    1.28517323311832
  ]
}

2. VAR

2.1 best-multivariate-var-order

Curl

curl -X 'POST' \
  'http://localhost:8080/best-multivariate-var-order' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "train_data": {
    "data": {
	"A1": [581.8548, 593.3871, 606.7692, 617.4495, 607.2034, 611.0798, 608.6863, 612.1547, 616.4299, 613.6036, 612.7401],
	"A2": [650.3324, 653.4324, 656.5324, 660.4851, 664.025 , 667.125 , 670.225 , 673.325 , 675.7723, 678.1468, 670.2468],
	"A3": [636.0783, 639.2783, 642.4783, 645.6783, 648.8783, 652.0783, 655.8941, 659.2941, 662.6941, 666.0941, 657.4941],
	"A4": [ 71.5995,  75.4052,  78.4239,  81.2488,  84.4223,  87.5223, 90.167 ,  94.0031,  96.5254,  99.9436,  91.7232],
	"A5": [ 37.1851,  40.703 ,  39.803 ,  46.7039,  49.8039,  52.9039, 55.7653,  59.2997,  62.0945,  65.3581,  57.2736]
	}
  },
  "low_order": 1,
  "high_order": 5
}'

Response body

{
  "best_order": 3
}

2.2 train-multivariate-var

Curl

curl -X 'POST' \
  'http://localhost:8080/train-multivariate-var' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "train_data": {
    "data": {
	"A1": [581.8548, 593.3871, 606.7692, 617.4495, 607.2034, 611.0798, 608.6863, 612.1547, 616.4299, 613.6036, 612.7401],
	"A2": [650.3324, 653.4324, 656.5324, 660.4851, 664.025 , 667.125 , 670.225 , 673.325 , 675.7723, 678.1468, 670.2468],
	"A3": [636.0783, 639.2783, 642.4783, 645.6783, 648.8783, 652.0783, 655.8941, 659.2941, 662.6941, 666.0941, 657.4941],
	"A4": [ 71.5995,  75.4052,  78.4239,  81.2488,  84.4223,  87.5223, 90.167 ,  94.0031,  96.5254,  99.9436,  91.7232],
	"A5": [ 37.1851,  40.703 ,  43.803 ,  46.7039,  49.8039,  52.9039, 55.7653,  59.2997,  62.0945,  65.3581,  57.2736]
	}
  },
  "paths": {
    "model": "mvts_var.joblib",
    "scaler": ""
  },
  "order": 3
}'

Response body

{
	"dump_status": "model is saved successfully",
	"UCL": 370.6116859955349
}

2.3 aggregate-multivariate-var

Curl

curl -X 'POST' \
  'http://localhost:8080/aggregate-multivariate-var' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "test_data": {
    "data": {
	"A1": [594.1389, 603.7225, 592.7108, 586.646 , 581.9071, 594.399, 603.805 , 610.3627, 594.4159, 585.8747, 593.4889],
	"A2": [660.7306, 661.7306, 662.7306, 663.172, 649.172, 649.172, 649.7999, 649.7999, 649.7999, 650.5692, 651.0472],
	"A3": [648.6941, 649.8941, 651.0941, 652.2941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941],
	"A4": [82.9601,  83.9601,  85.2628,  86.1015,  72.6354,  72.267, 72.6729,  71.5571,  71.9891,  72.7238,  73.0851],
	"A5": [48.4208,  49.4208,  50.5938,  51.4909,  37.7824,  37.5984, 37.8305,  37.1935,  37.4089,  37.8443,  38.0595]
	}
  },
  "paths": {
    "model": "mvts_var.joblib",
    "scaler": ""
  },
  "order": 3
}'

Response body

{
  "out": [
    3.5167876260029486e+28,
    3.192017532950293e+28,
    7.837936344944434e+28,
    2.4636610520425816e+29,
    1.663637488961949e+28,
    1.558962186880391e+28,
    2.952890424647399e+28,
    2.5090219198833513e+28
  ]
}

3. PCA

Curl

curl -X 'POST' \
  'http://localhost:8080/aggregate-multivariate-pca' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "test_data": {
    "data": {
	"A1": [594.1389, 603.7225, 592.7108, 586.646 , 581.9071, 594.399, 603.805 , 610.3627, 594.4159, 585.8747, 593.4889],
	"A2": [660.7306, 661.7306, 662.7306, 663.172, 649.172, 649.172, 649.7999, 649.7999, 649.7999, 650.5692, 651.0472],
	"A3": [648.6941, 649.8941, 651.0941, 652.2941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941, 636.4941],
	"A4": [82.9601,  83.9601,  85.2628,  86.1015,  72.6354,  72.267, 72.6729,  71.5571,  71.9891,  72.7238,  73.0851],
	"A5": [48.4208,  49.4208,  50.5938,  51.4909,  37.7824,  37.5984, 37.8305,  37.1935,  37.4089,  37.8443,  38.0595]
	}
  },
  "principal_component": 1
}'

Response body

{
  "out": [
    0.435516844700798,
    107.34623817837938,
    0.1723554997465683,
    39.66345760703956,
    184.60554329889254,
    1.683081091641335,
    68.56331068453335,
    217.45358186059062,
    1.3691178317147221,
    91.41066434297306,
    4.4618440730474624
  ]
}

New Release

  1. Update __version__ in src/main.py with a new commit.
  2. Tag this commit.