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Python Bridge can be configure configured using the config.json file in the config folder:

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In Line 4 we can find the logging section, in this section can be specified the logging level for the root logger, and the level for specific loggers

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Each type of model is constructed with an specific structure:

"Model_Type" : {
  "Model_Name": ["name1", "name2", "name3"]
}

• Parameter
  summary The model type indicates the type of logic to implement, (e.i. the name of the class) name Model_Type type json

  • Parameter
    summary Holds the names of the actual models to implement. name model_names type string array

    • Each model is stored in a folder with the same name, inside the models_data folder, it makes a path
    line
    • like "models_data\
    ModeL
    • Model_Name\name1"
    namemodel_namestypestring array

Model Versioning

A model can be retrain, or a new model can be generated using the same algorithm with different parameters, so it could be said every single one of those parameters is a different version, each one of this versions can be stored in folders inside the model_name folder. An example of the directory tree and how each folder is named can be seen below

models_data
│
├───Model_Name
│   ├───name1
│   |   ├───1
│   |   ├───2
│   |   └───3
│   ├───name2
│   |   ├───1
│   |   └───2
│   └───name3
│       └───1
├───LatentSemanticIndexing
│   └───lsi
│       ├───1
│       └───2
├───SentimentAnalysisTextBlob
│   └───textBlob
│       └───1
├───SentimentAnalysisVader
│   └───vader
│       └───1
└───TfidfVectorizer
    └───tfidf
        └───1