Auto Select with SymetryML

Auto Select Introduction

The auto-select build on top of the Select Model functionality and allows to automate finding the best model for a given task (binary classification or regression model). This functionality will try various combinations of input attributes on various types of models and select the best one automatically. Auto Select perform the following:

1. Using a heuristic - see Select Heuristic - it will try various combination of the input attributes to build a model

   • When using a regression Task, there is also another parameter that controls the grid search size. Please see the Select Grid table for details

2. It uses a provided out-of-sample datasets to compute a score for the model. Example of scoring are AUC or RMSE.

3. It repeats step (1) and (2) to build many models - possibly thousands - and then select the best model.

Depending on how many attributes your project has it can take anywhere a few seconds to several hours. Be sure to understand the Select Heuristic section as well as the Select Grid.

Auto Select Rest API

Allows to invoke the auto select functionality by specifying an external data source id as the out of sample data to use for model assessment.

URL

POST /symetry/rest/{cid}/projects/{pid}/autoSelect/{dsid} [body=MLContext]

Query Parameters

Parameter	Required / Optional	Description
modelid	Required	ID to assign to the new model.
task	Required	Task to perform binary_classification or regression or multiclass_classifier.

MLContext Build Parameters

Parameter	Required / Optional	Type	Description
rnd_seed	Optional	Integer	Set the seed of the randomizer
selector_type	Optional	String	Default is selector_type_fw_bw. Please see Selector Heuristic and Selector Types sections for details.
autoselect_grid_type	Optional	String	Default is autoselect_grid_type_tiny. Please see Select Grid for details.
autoselect_thread_pool_size	Optional	Integer	Default is 8.
sml_model_assessment_type	Optional	String	Defaults are auc for binary classifier rmse for regression mf1_w for multi-class classifier, that is multi class F1 weighted scoring. Possible value are: classifier tasks auc recall precision mcc Matthews correlation coefficient early_detection Rewards models that return a positive prediction earlier in a sequence of observations. Designed for binary classification models used in anomaly or malfunction detection on equipment, where earlier detection of a true positive yields a better score. regression tasks rmse r2 rmslogp1e Root mean square of the sum of the log of the error multi-class classifier tasks: mf1_w mkappa, multi-class Kappa

HTTP Responses

|HTTP Status Code |HTTP Status Message |Description |202 | OK | Job accepted. |400 | BAD REQUEST | Unknown SymetryML project. {"statusCode":"BAD_REQUEST","statusString":" + Cannot Find SYMETRYML id[r2] for Customer id [c1]","values":{}}

Sample Request Response Classifier

Request:
POST url="/symetry/rest/c1/projects/p1/autoSelect/Iris_rtlm.csv?task=binary_classifier&modelid=autoSelectModel1

Body:
{"targets":[],"inputAttributes":[],"inputAttributeNames":["sepal_length","sepal_width","petal_length","petal_width","sepal_lengt_b1","sepal_lengt_b2","sepal_width_b1","sepal_width_b2","petal_length_b1","petal_length_b2","petal_width_b1","petal_width_b2"],"targetAttributeNames":["Iris_virginica"],"extraParameters":{"rnd_seed":"1"}}

Sample Request Response Regression

This example uses:

• regression task: ?task=regression

• specify the heuristic: "selector_type":"selector_type_fw_bw"

• specify the grid search: "autoselect_grid_type":"autoselect_grid_type_small"

Request:
POST url="/symetry/rest/c1/projects/p1/autoSelect/Iris_rtlm.csv?task=binary_classifier&modelid=autoSelectModel1

Body:
{"targets":["13"],"inputAttributes":["44","88","45","89","46","47","48","49","90","91","50","51","52","53","10","54","11","55","12","56","57","14","58","15","59","16","17","18","19","0","1","2","3","4","5","6","7","8","9","60","61","62","63","20","64","21","65","22","66","23","67","24","68","25","69","26","27","28","29","70","71","72","73","30","74","31","75","32","76","33","77","34","78","35","79","36","37","38","39","80","81","82","83","40","84","41","85","42","86","43","87"],"inputAttributeNames":[],"targetAttributeNames":[],"extraParameters":{"rnd_seed":"1","selector_type":"selector_type_fw_bw","autoselect_grid_type":"autoselect_grid_type_small"}}

Auto Select DataFrame Rest API

Allows to invoke the auto select model functionality by using a DataFrame passed in the request body.

URL

POST /symetry/rest/{cid}/projects/{pid}/autoSelect [body=Map{"dataframe"=DataFrame, "mlcontext"=MLContext}]

Query Parameters

Parameter	Required / Optional	Description
modelid	Required	ID to assign to the new model.
task	Required	Task to perform binary_classification or regression or multiclass_classifier

MLContext Build Parameters

Same as Auto Select Rest API. Please consult the MLContext Build Parameters section for details.

HTTP Responses

HTTP Status Code	HTTP Status Message	Description
202	OK	Job accepted.
400	BAD REQUEST	Unknown SymetryML project. {"statusCode":"BAD_REQUEST","statusString":" + Cannot Find SYMETRYML id[r2] for Customer id [c1]","values":{}}

Sample Request Response Classifier

Request:
POST url="/symetry/rest/c1/projects/p1/autoSelect?task=binary_classifier&modelid=autoSelectModel1

Body:
{
    "mlcontext" : {
        "targets":[],
        "inputAttributes":[],
        "inputAttributeNames":["sepal_length","sepal_width","petal_length","petal_width","sepal_lengt_b1","sepal_lengt_b2","sepal_width_b1","sepal_width_b2","petal_length_b1","petal_length_b2","petal_width_b1","petal_width_b2","Iris_setosa","Iris_versicolor","Iris_virginica"],
        "targetAttributeNames":["Iris_virginica"],
        "extraParameters":{"rnd_seed":"1"}}
    "dataframe" : {
        "attributeNames":["sepal_length","sepal_width","petal_length","petal_width","sepal_lengt_b1","sepal_lengt_b2","sepal_width_b1","sepal_width_b2","petal_length_b1","petal_length_b2","petal_width_b1","petal_width_b2","Iris_setosa","Iris_versicolor","Iris_virginica"],
        "attributeTypes":["C","C","C","C","B","B","B","B","B","B","B","B","B","B","B"]
        "data":[
            ["4.3","3","1.1","0.1","1","0","0","1","1","0","1","0","1","0","0"],
            ["4.8","3","1.4","0.1","1","0","0","1","1","0","1","0","1","0","0"],
            ["4.9","3.1","1.5","0.1","1","0","0","1","1","0","1","0","1","0","0"]
            (...)
            ["7.2","3.6","6.1","2.5","0","1","0","1","0","1","0","1","0","0","1"]
        ],

    }

}

Sample Request Response Regression

This example uses:

• regression task: ?task=regression

• specify the heuristic: "selector_type":"selector_type_fw_bw"

• specify the grid search: "autoselect_grid_type":"autoselect_grid_type_small"

Request:
POST url="/symetry/rest/c1/projects/p1/autoSelect?task=regression&modelid=autoSelectModelReg1

Body:
{
    "mlcontext" : {
        "targets":[],
        "inputAttributes":[],
        "inputAttributeNames":["sepal_width","petal_length","petal_width","sepal_lengt_b1","sepal_lengt_b2","sepal_width_b1","sepal_width_b2","petal_length_b1","petal_length_b2","petal_width_b1","petal_width_b2","Iris_setosa","Iris_versicolor","Iris_virginica"],
        "targetAttributeNames":["sepal_length"],
        "extraParameters":{"rnd_seed":"1","selector_type":"selector_type_fw_bw","autoselect_grid_type":"autoselect_grid_type_small"}}
    "dataframe" : {
        "attributeNames":["sepal_length","sepal_width","petal_length","petal_width","sepal_lengt_b1","sepal_lengt_b2","sepal_width_b1","sepal_width_b2","petal_length_b1","petal_length_b2","petal_width_b1","petal_width_b2","Iris_setosa","Iris_versicolor","Iris_virginica"],
        "attributeTypes":["C","C","C","C","B","B","B","B","B","B","B","B","B","B","B"]
        "data":[
            ["4.3","3","1.1","0.1","1","0","0","1","1","0","1","0","1","0","0"],
            ["4.8","3","1.4","0.1","1","0","0","1","1","0","1","0","1","0","0"],
            ["4.9","3.1","1.5","0.1","1","0","0","1","1","0","1","0","1","0","0"]
            (...)
            ["7.2","3.6","6.1","2.5","0","1","0","1","0","1","0","1","0","0","1"]
        ],

    }

}