The implementation between each model can be entire different, we are not going to go into details about each particular model, each model implementation in Python Bridge is a set of expected functions focus in functionality
Every model class must be under the package models, inside the Python Bridge folder, and must extended from the class ModelWrapper, which can be import from models.model_wrapper. An example of a model class with the bare minimum, can be found below
from models.model_wrapper import ModelWrapper class Test(ModelWrapper): def __init__(self): super().__init__() def initialize(self, model_dir, **kwargs): pass def load(self, model_dir, name, version): pass def save(self): pass def clear(self): pass def feed(self, data): pass def train(self, **kwargs): pass def predict(self, data: list): pass def regress(self, data): pass def classify(self, data) -> (str, float): pass
Every class extending from ModelWrapper must implements the following methods, but if by any reason you don't need one of them, you can leave it as pass
def initialize(self, model_dir, **kwargs):
Receive the path to the model type and the configuration for the path
def load(self, model_dir, name, version):
Receive the path to the model type, the name of the model and the version of it, in this section the loading of the model is expected
def save(self):
Save the current loaded model
def clear(self):
Remove the current loaded model, and any training data in memory
def feed(self, data):
Receives a list of string tokens tokens to be added to the training data
def train(self, **kwargs):
Trains model with the fed documents, the model can be either kept in memory or saved
def predict(self, data: list):
Retrieves a vector or an array of vectors, from processing the data with the loaded mode, which is return inside a JSON { 'vector': [ ] }, the value of the vector key must be always be an array.
def regress(self, data):
Implements regression. We yet haven't implement a model for this particular method, at the moment it exists for future implementation
def classify(self, data) -> (str, float):
Retrieves a label or multiple labels, using the loaded model, from the data. We recommend returning the label along with its confidence.
Now that the class is ready, we need to make it available to be use, for this we need to add a reference in 2 files
models/__init__.py
Inside the models package there is a __init__.py file which exposes the Model Classes to the server. Any new class needs to be added to this files, and example can be seen below with the Test class
from .latent_semantic_indexing import LatentSemanticIndexing from .bert import Bert from .sentiment_analysis_vader import SentimentAnalysisVader from .sentiment_analysis_text_blob import SentimentAnalysisTextBlob from .model_wrapper import ModelWrapper from .test import Test
config/config.json
The other reference lies inside the config.json file in the config folder, in this file there is a section called "model_types", which refers to the classes available. As in the __init__.py file, any new class needs to be reference in this files
"model_names" does reference to the actual model data, each name in the model_names, refers to a folder, which also contains folders representing the version of the model
"model_types": { "LatentSemanticIndexing" : { "model_names": ["lsi"] }, "Bert": { "model_names": ["bert-base-uncased"], "default_model": "bert-base-uncased" }, "SentimentAnalysisVader": { "model_names": ["vader"] }, "SentimentAnalysisTextBlob": { "model_names": ["textBlob"] }, "Test"" { "model_names": ["test"] } }
Every model to be used by its implementation needs to be stored in a specific path, compoused by the Name of model type, a representative name of the model and a folder representing the version (the version doesn't have to be a number, it can be a name). As it can be seen below, the model for the Test Class was added following this structure
models_data │ ├───Bert │ ├───bert-base-uncased │ | └───1 ├───LatentSemanticIndexing │ └───lsi │ ├───1 │ └───2 ├───SentimentAnalysisTextBlob │ └───textBlob │ └───1 ├───SentimentAnalysisVader │ └───vader │ └───1 ├───TfidfVectorizer │ └───tfidf │ └───1 └───Test └───test └───1