Advanced Python & Machine Learning for Research

Advanced Python · Numerical research systems · Section 1 of 2

Typed interfaces and validated research boundaries

An advanced workflow separates the data contract, fitted model, prediction, and decision policy. A typing.Protocol can describe the methods a component should offer without forcing every implementation into one inheritance tree. Type hints help static tools; they do not validate runtime market data.

Validate shape, finite values, feature order, availability, and output semantics at the relevant boundary. A probability vector and an expected-return vector can share the same numeric shape while requiring different decisions. A clean interface should make that distinction visible.

Prefer small composable components with explicit dependencies. Dependency injection means passing the model or data provider deliberately, which permits a deterministic test substitute. It does not require a large framework or a production service in this section.

from typing import Protocol
import numpy as np

class Predictor(Protocol):
    def predict(self, features: np.ndarray) -> np.ndarray: ...

class FixedPredictor:
    def predict(self, features):
        if features.ndim != 2 or not np.isfinite(features).all():
            raise ValueError("finite 2D features required")
        return np.full(features.shape[0], 0.5)

def forecast(model: Predictor, features):
    return model.predict(features)

print(forecast(FixedPredictor(), np.ones((3, 2))).tolist())

[0.5, 0.5, 0.5]

Keep in mind: Make contracts explicit at data, forecast, and decision boundaries.