$ docker build . -t multivariate-aggregator$ docker run -p 8080:8080 multivariate-aggregatorRun 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- Swagger: http://localhost:8080/documentation
- ReDoc: http://localhost:8080/redoc
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
}'{"dump_status": "model is saved successfully"}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"
}
}'{
"out": [
0.449440516570948,
0.525055252356107,
0.5844318879905316,
1.1898242258529614,
1.6546299190632852,
1.6099606920840253,
1.6608597985372362,
1.6397081285457347,
1.241337729358707,
1.28517323311832
]
}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
}'{
"best_order": 3
}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
}'{
"dump_status": "model is saved successfully",
"UCL": 370.6116859955349
}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
}'{
"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
]
}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
}'{
"out": [
0.435516844700798,
107.34623817837938,
0.1723554997465683,
39.66345760703956,
184.60554329889254,
1.683081091641335,
68.56331068453335,
217.45358186059062,
1.3691178317147221,
91.41066434297306,
4.4618440730474624
]
}- Update
__version__insrc/main.pywith a new commit. - Tag this commit.