ann03-train#39027/v6

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ann03-train[SERVICE-39027]

Tracker:	Services	Priority:	Normal	Status:	New
Submitted by:	sidereusJan 06 2018 10:47	Modified by:	sidereusJan 10 2018 10:44	Type:	Model
Path:	m/train/1.0	Context:	csip-ann	Version:	1.0
Endpoints:	--
Maintainers:	sidereus
Website:	--	Apps:	--
Test Status:	--	Last Tested:	--	Test Count:	--
Test Success Rate:	--	Failed Tests:	--

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Description

Train a NEAT-based ANN

This service trains and validates a neural network after splitting the data set into training and validation.

Procedure

1. Scaling mechanism

Normalized data set from db-name/normalized collection is retrieved and split into training and validation.
For this step, user is asked to set 2 parameters in the payload (defaults provided):

training_perc (default 0.9): percentage of training data. Currently no automated procedure is available.
scale_mechanism (default "samedistribution"): algorithm that actually splits training and validation data set. Available algorithms:
1. "random": shuffles permutations and splits them;
2. "samedistribution": identifies permutations that contain min and max values of output nodes. Then it shuffles the permutations, splits them, makes sure that min and max values of output nodes are in the training set and runs a Kolmogorov-Smirnov Test to check if training and validation sets have the same distribution. It repeats the entire procedure until the two sets have the same distribution.

2. Training algorithm

Standard NEAT algorithm from encog machine learning framework trains the ANN.
For this step, user is asked to set 4 parameters in the payload (defaults provided):

training_error (default 0.1):
max_epochs (default 49999):
population (default 1000):
connection_density (default 1):

{
    "metainfo": {},
    "parameter": [
        {
            "name": "annName",
            "value": "test_ann"
        },
        {
            "name": "annName_out",
            "value": "test_ann"
        },
        {
            "name": "training_error",
            "value": 0.01
        },
        {
            "name": "max_epochs",
            "value": 50000
        },
        {
            "name": "training_perc",
            "value": 0.8
        },
        {
            "name": "scale_mechanism",
            "value": "SameDistribution"
        },
        {
            "name": "population",
            "value": 3000
        },
        {
            "name": "connection_density",
            "value": 1
        }
    ]
}

{
    "metainfo": {
        "status": "Finished",
        "suid": "ace4ac6f-f5c1-11e7-b48e-1d3102711e59",
        "cloud_node": "10.1.27.11",
        "request_ip": "129.82.23.199",
        "service_url": "http://csip.engr.colostate.edu:8088/csip-ann/m/train/1.0",
        "csip.version": "$version: 2.2.4 3c276d66bde6 2017-12-15 od, built at 2018-01-09 15:35 by jenkins$",
        "tstamp": "2018-01-09 21:49:43",
        "cpu_time": 5540,
        "expiration_date": "2018-01-09 21:50:19"
    },
    "parameter": [
        {
            "name": "annName",
            "value": "test_ann"
        },
        {
            "name": "annName_out",
            "value": "test_ann"
        },
        {
            "name": "training_error",
            "value": 0.01
        },
        {
            "name": "max_epochs",
            "value": 50000
        },
        {
            "name": "training_perc",
            "value": 0.8
        },
        {
            "name": "scale_mechanism",
            "value": "SameDistribution"
        },
        {
            "name": "population",
            "value": 3000
        },
        {
            "name": "connection_density",
            "value": 1
        }
    ],
    "result": [
        {
            "name": "status",
            "value": "ok"
        },
        {
            "name": "exit_reason",
            "value": "threshold_error reached: 0.01"
        }
    ]
}

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