Add modelClass identifier to Elasticsearch index and API response

[vc1492a]

As the software adds functionality for models outside of scikit-learn, it will become necessary to distinguish model implementations in the database as well as in the response from the API. For example, the current API response is as follows:

{
            "_id": "bb9e7a91c256412ca40f57e27e8e90b6",
            "_index": "models",
            "_score": 1.0,
            "_source": {
                "callCount": 30,
                "dependent": "variableTwo",
                "deployed": true,
                "encoderPath": "encoder_variableTwo_1.pickle",
                "encoderType": "CountVectorizer",
                "id": "bb9e7a91c256412ca40f57e27e8e90b6",
                "independent": [
                    {
                        "generator_path": "clean_text.pickle",
                        "inputs": [
                            "title"
                        ],
                        "name": "cleanText"
                    }
                ],
                "lastTestedDate": "2018-01-18T08:29:09",
                "lastTrainedDate": "2018-01-18T08:29:08",
                "modelPath": "model_variableTwo_1.pickle",
                "modelType": "OneVsRestClassifier(estimator=MultinomialNB(alpha=0.001, class_prior=None, fit_prior=True),\n          n_jobs=1)",
                "recommendationThreshold": 0.2,
                "testAccuracy": 0.9019607843137255,
                "testF1": 0.9008452056839288,
                "testPrecision": 0.9044056750340173,
                "testRecall": 0.9019607843137255,
                "testTime": 0.005343914031982422,
                "trainAccuracy": 0.9849376731301939,
                "trainDataBalance": "upsample",
                "trainF1": 0.9849744199555293,
                "trainPrecision": 0.9854426711336097,
                "trainRecall": 0.9849376731301939,
                "trainTime": 0.04866385459899902
            },
            "_type": "model"
        }

The updated response would include a modelClass field that indicates whether the model is one implemented in scikit-learn or another library, e.g. something along the lines of the following (see the modelClass field):

{
            "_id": "bb9e7a91c256412ca40f57e27e8e90b6",
            "_index": "models",
            "_score": 1.0,
            "_source": {
                "callCount": 30,
                "dependent": "variableTwo",
                "deployed": true,
                "encoderPath": "encoder_variableTwo_1.pickle",
                "encoderType": "CountVectorizer",
                "id": "bb9e7a91c256412ca40f57e27e8e90b6",
                "independent": [
                    {
                        "generator_path": "clean_text.pickle",
                        "inputs": [
                            "title"
                        ],
                        "name": "cleanText"
                    }
                ],
                "lastTestedDate": "2018-01-18T08:29:09",
                "lastTrainedDate": "2018-01-18T08:29:08",
                "modelPath": "model_variableTwo_1.pickle",
                "modelClass": "SKModel()",
                "modelType": "OneVsRestClassifier(estimator=MultinomialNB(alpha=0.001, class_prior=None, fit_prior=True),\n          n_jobs=1)",
                "recommendationThreshold": 0.2,
                "testAccuracy": 0.9019607843137255,
                "testF1": 0.9008452056839288,
                "testPrecision": 0.9044056750340173,
                "testRecall": 0.9019607843137255,
                "testTime": 0.005343914031982422,
                "trainAccuracy": 0.9849376731301939,
                "trainDataBalance": "upsample",
                "trainF1": 0.9849744199555293,
                "trainPrecision": 0.9854426711336097,
                "trainRecall": 0.9849376731301939,
                "trainTime": 0.04866385459899902
            },
            "_type": "model"
        }