Time-Dependent Metrics to Assess Performance Prediction Systems

Installation

pip install assess_pps

Get started

# Importing our library

from assess_pps import metrics as m



# for working with data we need pandas

import pandas as pd



# implementing csv files

ectel = pd.read_csv("D:/path")  # put your link here

ectelsys2 = pd.read_csv("D:/path")



# We use the file ectel.csv for testing.

prediction_time = 35  # till which week we should search the overall stability value

ectel_temp = ectel.loc[ectel.weeknumber <= prediction_time]  # dataframe with all data till predicted time

S = ectel_temp['idUser'].unique()  # list with student indexes

Y = [0, 1, 2]  # list of classes

x = 10  # x  earliest times of correct predictions



# Stability 

stability = m.Stability(ectel_temp, S)

print("Stability: ", stability)



# Accuracy

accuracy = m.Accuracy(ectel_temp, S)

print("Accuracy: ", accuracy)



# Earliness 

earliness = m.Earliness_Total(S, Y, x, ectel_temp)

print("Earliness: ", earliness)



# ESS 

# for ESS we need to know stability and earliness

ESS = m.ESS(stability, earliness)

print("ESS of average earliness: ", ESS)



# EAS 

# for EAS we need to know accuracy and earliness

EAS = m.EAS(accuracy, earliness)

print("EAS of average earliness: ", EAS)