IRIS and DEEPaaS integration

install-and-run

$ virtualenv env --python=python3
    (...)
$ source env/bin/activate
(env) $ pip3 install .
    (...)
(env) $ pip3 install deepaas
    (...)
(env) $ deepaas-run

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    ##   ## ## //   ##  //  ##  ##   ##
    ##. .##  ###  ###   // ###  ##   ##
      ## ##    ####     ####    #####.
              Hybrid-DataCloud  ##


Welcome to the DEEPaaS API API endpoint. You can directly browse to the
API documentation endpoint to check the API using the builtint Swagger UI
or you can use any of our endpoints.

    API documentation: http://127.0.0.1:5000/ui
    API specification: http://127.0.0.1:5000/swagger.json
          V2 endpoint: http://127.0.0.1:5000/v2

-------------------------------------------------------------------------

2020-02-04 13:10:50.027 21186 INFO deepaas [-] Starting DEEPaaS version 1.0.0
2020-02-04 13:10:50.231 21186 INFO deepaas.api [-] Serving loaded V2 models: ['iris-deepaas']

iris-deepaas.py

from joblib import dump, load                                                      
import numpy                                                                       
from sklearn import svm                                                            
from sklearn import datasets                                                       
from webargs import fields, validate                                               
                                                                                   
                                                                                   
def train():                                                                       
    clf = svm.SVC()                                                                
    X, y = datasets.load_iris(return_X_y=True)                                     
    clf.fit(X, y)                                                                  
    dump(clf, 'iris.joblib')                                                       
                                                                                   
                                                                                   
def predict(data):                                                              
    clf = load('iris.joblib')                                                   
    data = numpy.array(data).reshape(1, -1)                                     
    prediction = clf.predict(data)                                              
    return {"labels": prediction.tolist()}                                      
                                                                                
                                                                                
def get_predict_args():                                                         
    args = {                                                                    
        "data": fields.List(                                                    
            fields.Float(),                                                     
            required=True,                                                      
            description="Data to make a prediction. The IRIS dataset expects "  
                        "four values containing the Sepal Length, Sepal Width, " 
                        "Petal Length and Petal Width.",                        
            validate=validate.Length(equal=4),                                  
        ),                                                                      
    }                                                                           
    return args   

iris.py

from joblib import dump, load                                                      
import numpy                                                                       
from sklearn import svm                                                            
from sklearn import datasets                                                       
                                                                                   
                                                                                   
def train():                                                                       
    clf = svm.SVC()                                                                
    X, y = datasets.load_iris(return_X_y=True)                                     
    clf.fit(X, y)                                                                  
    dump(clf, 'iris.joblib')                                                       
                                                                                   
                                                                                   
def predict(data):                                                                 
    clf = load('iris.joblib')                                                      
    data = numpy.array(data).reshape(1, -1)                                        
    prediction = clf.predict(data)                                                 
    return {"labels": prediction.tolist()}                                             

setup.py

from distutils.core import setup                                                   
                                                                                   
setup(                                                                             
    name='test-iris-with-deepaas',                                                 
    version='1.0',                                                                 
    description='This is an SVM trained with the IRIS dataset',                    
    author='Álvaro López',                                                         
    author_email='aloga@ifca.unican.es',                                           
    py_modules="iris-deepaas.py",                                                          
    dependencies=['joblib', 'scikit-learn'],                                       
    entry_points={                                                                 
        'deepaas.v2.model': ['iris=iris-deepaas'],                                 
    }                                                                              
)