Source code for cornac.metrics.pred_metrics

# -*- coding: utf-8 -*-

"""
@author: Aghiles Salah
"""
import numpy as np
from ..utils.util_functions import which_



[docs]class Mae: """Mean Absolute Error. Parameters ---------- name: string, value: 'MAE' Name of the measure. type: string, value: 'prediction' Type of the metric, e.g., "ranking", "prediction". """ def __init__(self): self.name = 'MAE' self.type = 'prediction' #Compute MAE for a single user def compute(self,data_test,prediction): index_rated = which_(data_test,'>',0.) mae_u = np.sum(abs(data_test[index_rated] - prediction[index_rated]))/len(index_rated) return mae_u
[docs]class Rmse: """Root Mean Squared Error. Parameters ---------- name: string, value: 'RMSE' Name of the measure. type: string, value: 'prediction' Type of the metric, e.g., "ranking", "prediction". """ def __init__(self): self.name = 'RMSE' self.type = 'prediction' #Compute MAE for a single user def compute(self,data_test,prediction): index_rated = which_(data_test,'>',0.) mse_u = np.sum((data_test[index_rated] - prediction[index_rated])**2)/len(index_rated) return np.sqrt(mse_u)