Source code for chap_core.predictor.poisson

import sklearn.linear_model as lm




class Poisson:
    def __init__(self, alpha=1, fit_intercept=True):
        self.model = lm.PoissonRegressor(alpha=alpha, fit_intercept=fit_intercept)



    def predict(self, x):
        """X has shape (n_samples, n_features)"""
        return self.model.predict(x)




    def train(self, x, y):
        """Train the model on a dataframe that has the column Disease, plus other features. The feature order is
         expected to be the same between training and prediction without explicit feature names.

        sample_data = pd.DataFrame({
            "Disease": [1, 2, 3, 4, 5],
            "Disease1": [1, 2, 3, 4, 5],
            "Disease2": [1, 2, 3, 4, 5],
            "Rain": [1, 2, 3, 4, 5],
            "Temperature": [1, 2, 3, 4, 5],
        })
        """
        # Ensure y is a 1D array to avoid sklearn warnings
        if hasattr(y, "values"):
            # If y is a DataFrame or Series, get the values and flatten
            y = y.values.ravel()
        elif hasattr(y, "ravel"):
            # If y is a numpy array, ensure it's 1D
            y = y.ravel()
        self.model.fit(x, y)