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From "Janardhan (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SYSTEMML-1962) Support model-selection via mllearn APIs
Date Sun, 15 Oct 2017 02:50:00 GMT

     [ https://issues.apache.org/jira/browse/SYSTEMML-1962?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Janardhan reassigned SYSTEMML-1962:
-----------------------------------

    Assignee: Janardhan

> Support model-selection via mllearn APIs
> ----------------------------------------
>
>                 Key: SYSTEMML-1962
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1962
>             Project: SystemML
>          Issue Type: New Feature
>            Reporter: Niketan Pansare
>            Assignee: Janardhan
>
> The end goal of this JIRA is to support model selection facility similar to [http://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection].
> Currently, we support model selection using MLPipeline's cross-validator. For example:
please replace `from pyspark.ml.classification import LogisticRegression` with `from systemml.mllearn
import LogisticRegression` in the example http://spark.apache.org/docs/2.1.1/ml-tuning.html#example-model-selection-via-cross-validation.

> However, this invokes k-seperate and independent mlcontext calls. This PR proposes to
add a new class `GridSearchCV`, `RandomizedSearchCV` and possibly bayesian optimization which
like mllearn has methods `fit` and `predict`. These methods internally generate a script that
wraps the external script with a `parfor` when the fit method is called. For example:
> {code}
> from sklearn import datasets
> from systemml.mllearn import GridSearchCV, SVM
> iris = datasets.load_iris()
> parameters = {'C':[1, 10]}
> svm = SVM()
> clf = GridSearchClassifierCV(svm, parameters)
> clf.fit(iris.data, iris.target)
> {code}
> would execute the script:
> {code}
> CVals = matrix("1; 10", rows=2, cols=1)
> parfor(i in seq(1, nrow(CVals))) {
>    C = CVals[i, 1]
>    reg = 1 / C
>     # SVM script
> }
> {code}
> This will require:
> 1. Functionization of the script (for example: L2SVM)
> {code}
> svm = function(matrix[double] X, matrix[double] Y, double icpt, double tol, double reg,
double maxiter) returns (matrix[double] w) {
>    if(nrow(X) < 2)
> 	stop("Stopping due to invalid inputs: Not possible to learn a binary class classifier
without at least 2 rows")
>    check_min = min(Y)
>    ....
>    w = t(cbind(t(w), t(extra_model_params)))
> }
> {code}
>  2. Adding two new java classes in the package `org.apache.sysml.api.ml` called `GridSearchClassifierCV`
which extends `Estimator[GridSearchClassifierCVModel]` and `GridSearchClassifierCVModel` which
`extends Model[GridSearchClassifierCVModel] with BaseSystemMLClassifierModel`. Then you will
have to implement the abstract methods: fit and transform respectively.
> 3. Add a python class GridSearchClassifierCV that invokes the above java classes.
> For more details on step 2 and step 3, please read the design documentation of mllearn
API: https://github.com/apache/systemml/blob/master/src/main/scala/org/apache/sysml/api/ml/BaseSystemMLClassifier.scala#L42
> [~dusenberrymw] may be, this can be part of https://issues.apache.org/jira/browse/SYSTEMML-1159



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