Returning multiple response variables.

[yashkumaratri]

Following sklearn tute (https://github.com/oracle/graphpipe-py/blob/master/examples/sklearn_example/server.py), It returns the response but I need to return the model repsonse as well as probability factor, Can't concat the output of bytearray, So how to do it?

            obj = convert.deserialize_request(inp).input_tensors[0]
            obj = [obj]
            print("printing vectors : ",obj,type(obj))
            outp = convert.serialize_infer_response(
                [model.predict(obj)])
            outp1 = convert.serialize_infer_response(
                [model.predict_proba(obj)])

            print("Probability ",outp,type(outp),outp1,type(outp1))
            jsonapp = {}
            jsonapp['arr'] = outp
            jsonapp['prob'] = outp1
            #some.append(outp)
            #some.append(outp1)

            self.send_response(200)
            self.end_headers()
            self.wfile.write(?????)

I want to send outp and outp1 both , How?

[yashkumaratri]

Don't serialize the results at the time of inference, take the ineference and probability factor and then serialize it later.

[yashkumaratri]

Whole server side code :

import argparse
from http import server

from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

from graphpipe import convert
from sklearn.ensemble import RandomForestClassifier

def create_diabetes_model():
    diabetes = datasets.load_diabetes()
    diabetes_X = diabetes.data
    print(diabetes_X.shape)

    diabetes_X_train = diabetes_X[:-20]
    diabetes_y_train = diabetes.target[:-20]
    clf = RandomForestClassifier(n_estimators=100, max_depth=2,random_state=0)

    #model = linear_model.LinearRegression()

    clf.fit(diabetes_X_train, diabetes_y_train)
    return clf

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--port", default=10000, help="TCP port", type=int)
    args = parser.parse_args()
    server_address = ('', args.port)

    class GPHandler(server.BaseHTTPRequestHandler):

        def do_POST(self):
            inp = self.rfile.read(int(self.headers['Content-Length']))

            obj = convert.deserialize_request(inp).input_tensors[0]
            outp = model.predict(obj)
            outprob = model.predict_proba(obj)
            lis = []
            lis.append(outp)
            lis.append(outprob)
            out = convert.serialize_infer_response(lis)

            self.send_response(200)
            self.end_headers()
            self.wfile.write(out)

    httpd = server.HTTPServer(server_address, GPHandler)

    model = create_diabetes_model()

    print('Starting graphpipe sklearn server on port %d...' % args.port)
    while(True):
        httpd.handle_request()

You can get the response in pred of client and can get the values by pred[0]and pred[1]