Cryptomice-EonML is the Machine Learning module for the Odyssey Momentum hackathon.
Cryptomice-EonML requires Python3 and Virtualenv installed, execute the script build.sh in order to download requirements and set up the environment.
Create a dataset in a file called training.json with all trainings informations.
{
"append": false,
"dataset":[
{"result": 1, "data": "1,1,0,0.2,0"},
{"result": 1, "data": "0,0,1,0.1,1"},
{"result": 0, "data": "0,1,0,0.4,0"},
{"result": 0, "data": "0,0,1,0.3,0"},
{"result": 0, "data": "0,0,1,0.2,0"},
{"result": 1, "data": "1,0,1,0.4,0"},
{"result": 0, "data": "0,0,1,0.1,0"},
{"result": 0, "data": "0,0,1,0.2,0"},
{"result": 1, "data": "0,0,1,0.1,1"},
{"result": 1, "data": "1,1,0,0.3,0"},
{"result": 1, "data": "1,0,0,0.4,1"},
{"result": 0, "data": "0,1,1,0.5,0"}
]
}
Call the /model/<model>/train API where is the name of your model.
curl http://localhost:5005/model/supplychain123/train -d @training.json -H 'Content-Type: application/json' | jq
{
"results": [
"SVM-light Version V6.02",
"0 # kernel type",
"3 # kernel parameter -d",
"1 # kernel parameter -g",
"1 # kernel parameter -s",
"1 # kernel parameter -r",
"empty# kernel parameter -u",
"5 # highest feature index",
"24 # number of training documents",
"12 # number of support vectors plus 1",
"-0.11942091 # threshold b, each following line is a SV (starting with alpha*y)",
"-0.4684569126832371921409503556788 1:0 2:0 3:1 4:0.2 5:0 #",
"0.14717487910546045437598650096334 1:1 2:1 3:0 4:0.2 5:0 #",
"-0.16659578405527497579896589741111 1:0 2:1 3:0 4:0.40000001 5:0 #",
"-0.53629968728469634697830770164728 1:0 2:1 3:1 4:0.5 5:0 #",
"0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:1 #",
"-0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:0 #",
"0.61398330903942865965916553250281 1:1 2:1 3:0 4:0.30000001 5:0 #",
"-0.61398330903942865965916553250281 1:1 2:0 3:0 4:0.40000001 5:1 #",
"0.61398330903942865965916553250281 1:1 2:0 3:1 4:0.40000001 5:0 #",
"0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:1 #",
"-0.20378911316110831508652267984871 1:0 2:0 3:1 4:0.2 5:0 #"
],
"status": [
"Scanning examples...done",
"Reading examples into memory...OK. (12 examples read)",
"Setting default regularization parameter C=0.6140",
"Optimizing..............done. (15 iterations)",
"Optimization finished (maxdiff=0.00000).",
"Runtime in cpu-seconds: 0.00",
"Number of SV: 11 (including 6 at upper bound)",
"L1 loss: loss=0.76660",
"Norm of weight vector: |w|=0.99891",
"Norm of longest example vector: |x|=1.50000",
"Number of kernel evaluations: 386",
"Writing model file...done"
]
}
Create a dataset in a file called dataset.json with all information for requested predictions.
{
"dataset":[
{"data": "1,0,0,0.2,1"},
{"data": "1,1,0,0.3,0"},
{"data": "0,0,0,0.2,1"},
{"data": "1,0,1,0.2,0"}
]
}
Call the /model/<model>/predict API where is the name of your model.
curl http://localhost:5005/model/supplychain123/predict -d @dataset.json -H 'Content-Type: application/json' | jq
{
"results": [
{
"data": "1 1:1 2:0 3:0 4:0.2 5:1",
"result": "1.4557206"
},
{
"data": "2 1:1 2:1 3:0 4:0.3 5:0",
"result": "0.8805791"
},
{
"data": "3 1:0 2:0 3:0 4:0.2 5:1",
"result": "0.69456241"
},
{
"data": "4 1:1 2:0 3:1 4:0.2 5:0",
"result": "0.86115819"
}
],
"status": [
"Reading model...OK. (11 support vectors read)",
"Classifying test examples..done",
"Runtime (without IO) in cpu-seconds: 0.00"
]
}
If you want call the service using a simple GET call you can encode your request directly in the url (maximum 1 request).
http://localhost:5005/model/supplychain123/predict?data=1,0,1,0.2,0
{
"results": [
"0.86115819"
]
}
Create a dataset in a file called training.json with new trainings informations, and set append to true.
{
"append": true,
"dataset":[
{"result": 1, "data": "1,1,0,0.2,0"},
{"result": 1, "data": "0,0,1,0.1,1"},
{"result": 0, "data": "0,1,0,0.4,0"},
{"result": 0, "data": "0,0,1,0.3,0"},
{"result": 0, "data": "0,0,1,0.2,0"},
{"result": 1, "data": "1,0,1,0.4,0"}
]
}
Call the /model/<model>/train API where is the name of the model you want update.
curl http://localhost:5005/model/supplychain123/train -d @training.json -H 'Content-Type: application/json' | jq
{
"results": [
"SVM-light Version V6.02",
"0 # kernel type",
"3 # kernel parameter -d",
"1 # kernel parameter -g",
"1 # kernel parameter -s",
"1 # kernel parameter -r",
"empty# kernel parameter -u",
"5 # highest feature index",
"24 # number of training documents",
"12 # number of support vectors plus 1",
"-0.11942091 # threshold b, each following line is a SV (starting with alpha*y)",
"-0.4684569126832371921409503556788 1:0 2:0 3:1 4:0.2 5:0 #",
"0.14717487910546045437598650096334 1:1 2:1 3:0 4:0.2 5:0 #",
"-0.16659578405527497579896589741111 1:0 2:1 3:0 4:0.40000001 5:0 #",
"-0.53629968728469634697830770164728 1:0 2:1 3:1 4:0.5 5:0 #",
"0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:1 #",
"-0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:0 #",
"0.61398330903942865965916553250281 1:1 2:1 3:0 4:0.30000001 5:0 #",
"-0.61398330903942865965916553250281 1:1 2:0 3:0 4:0.40000001 5:1 #",
"0.61398330903942865965916553250281 1:1 2:0 3:1 4:0.40000001 5:0 #",
"0.61398330903942865965916553250281 1:0 2:0 3:1 4:0.1 5:1 #",
"-0.20378911316110831508652267984871 1:0 2:0 3:1 4:0.2 5:0 #"
],
"status": [
"Scanning examples...done",
"Reading examples into memory...OK. (12 examples read)",
"Setting default regularization parameter C=0.6140",
"Optimizing..............done. (15 iterations)",
"Optimization finished (maxdiff=0.00000).",
"Runtime in cpu-seconds: 0.00",
"Number of SV: 11 (including 6 at upper bound)",
"L1 loss: loss=0.76660",
"Norm of weight vector: |w|=0.99891",
"Norm of longest example vector: |x|=1.50000",
"Number of kernel evaluations: 386",
"Writing model file...done"
]
}
| Path | Methods | Description |
|---|---|---|
| / | GET | Information about SVM_SERVER commands. |
| /model | GET | Get a list of known models. |
| /model/ | GET | Get informations connected to model . |
| /model//train | POST | Create a new model called . |
| /model//predict | GET/POST | Create a prediction using model and passed dataset. |