Model/etl/battery_model/train.py

import pandas as pd
from sklearn.linear_model import Ridge


class SAPUpliftTrainer:
    """
    Offline training class — discovers SAP uplift model coefficients.
    """

    def __init__(self, alpha=1.0):
        self.alpha = alpha
        self.model = Ridge(alpha=self.alpha)
        self.feature_names = ["starting SAP", "PV Array size"]

    def prepare_data(self, df):
        df = df.copy()
        # df["is_electric"] = df["heating"].str.contains(
        #     "Electric", case=False, na=False
        # ).astype(int)
        X = df[self.feature_names]
        y = df["SAP points"]
        return X, y

    def fit(self, df):
        X, y = self.prepare_data(df)
        self.model.fit(X, y)

    def coefficients(self):
        return {
            "intercept": float(self.model.intercept_),
            **{
                name: float(coef)
                for name, coef in zip(self.feature_names, self.model.coef_)
            }
        }

    def export_runtime_config(self):
        """
        Returns a dict suitable for copy-pasting into the runtime scoring class.
        """
        coefs = self.coefficients()
        return {
            "intercept": coefs["intercept"],
            "coef_starting_sap": coefs["starting SAP"],
            "coef_pv_size": coefs["PV Array size"],
            # "coef_is_electric": coefs["is_electric"],
        }


# The training data can be found in the Domna sharepoint in Product Development > Solar Battery Recommendations
df = pd.read_csv("/Users/khalimconn-kowlessar/Downloads/SAP Movement data(Sheet1).csv")

trainer = SAPUpliftTrainer(alpha=1.0)
trainer.fit(df)

print(trainer.coefficients())
print(trainer.export_runtime_config())

# Last updated: 9th December 2025
# Coefficients:
# {'intercept': 10.310168559226678, 'starting SAP': -0.16120648633993315, 'PV Array size': 1.0500492005420736}
# The code for scoring with this model can be found in backend/app/BatterySapScorer.py