Rule-based model is an example of usingour human knowledge to solve a problem. You could also use boolean logic, fuzzy logic, or make decision based on statistcs or myriad other ways that humans do to solve problems.
Rule-based model is very useful to solve the cold start problem, where data is not available.
In H1st framework, a human rule model can be implemented by subclassing the h1.Model class and implementing only the predict() function. Basically, it’s a just a model with no training (though training is not forbidden and is sometimes is useful for human models too).
This particular simple model “predicts” if each given value in a stream is an even number or not.
import h1st as h1
class RuleBasedModel(h1.Model):
"""
Simple rule-based model that "predicts" if a given value is an even number
"""
def predict(self, input_data: dict) -> dict:
predictions = [{'prediction': x % 2 == 0, 'value': x} for x in input_data["values"]]
return {"predictions": predictions}
m = RuleBasedModel()
xs = list(range(6))
results = m.predict({"values": xs})
predictions = results["predictions"]
print(f"RuleBasedModel's predictions for {xs} are {predictions}")
RuleBasedModel's predictions for [0, 1, 2, 3, 4, 5] are [{'prediction': True, 'value': 0}, {'prediction': False, 'value': 1}, {'prediction': True, 'value': 2}, {'prediction': False, 'value': 3}, {'prediction': True, 'value': 4}, {'prediction': False, 'value': 5}]
