regressor.py

import os
from tensorflow.keras.models import load_model
from .services import 

class PriceEstimator:
    """
        PriceEstimator object to collect prediction methods to be accessed
        by API services.
    """

    def __init__(self, model):
        self.model = model

    @classmethod
    def from_pretrained(cls, pretrained_model_path):
        model = load_model(pretrained_model_path)

        return cls(model)

    def predict(self, X):
        X = feature_normalisation(X)
        preds = self.model.predict(X)

        return [{f'Prediction price {i}': float(prediction)} for i, prediction in enumerate(preds)]
    
    @staticmethod
    def feature_normalisation(data):
        """
            feature_normalisation function.
            It takes data array and returns it with feature normalised.
    
            Arguments:
                data np.array of shape (n_training_example, n_features)
      
            Returns:
                data_normalised np.array of shape (n_training_example, n_features)
        """
  
        data_normalised = data
        mean = data_normalised.mean(axis=0)
        data_normalised -= mean
        std = data_normalised.std(axis=0)
        data_normalised /= std
  
        assert data_normalised.shape == data.shape, "Data leaking!"
        return data_normalised