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:
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"Model_Type" : {
"Model_Name": ["name1", "name2", "name3"]
} |
Parameter |
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summary | The model type indicates the type of logic to implement, (e.i. the name of the class) |
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name | Model_Type |
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type | json |
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Parameter |
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summary | Holds the names of the actual models to implement. |
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name | model_names |
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type | string array |
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- Each model is stored in a folder with the same name, inside the models_data folder, it makes a path
line ModeLname | model_names |
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type | string 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
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language | text |
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theme | FadeToGrey |
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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 |