def generate_intervals():
# Script to generate a given number of frequency intervals given
# a frequent items output from Borgelt
intervals = 30
output_folders = []
for interval, res in enumerate(Preprocessor.triple_intervals('../tmp/observed_frequent_items.out', intervals=intervals)):
# Triple set of 1/intervals part of the data
interval_id = 'interval_' + str(intervals) + '_' + str(interval)
output_folder = cross_validate_disc_version(env.BORGELT_ALGORITHM, env.AOL_MERGED_FILE, sample_pct=-100000, iterations=2, restricted_triples=res, extra_id=interval_id, min_support=-30)
output_folders.append(output_folder)
print 'output folders: ', output_folders
def plot_intervals(output_folder):
from parsers import CVOutputParser
from preprocessing import Preprocessor
from utils import avg
import os
import math
"""
Given a cross validation ouput. Certain triple intervals can be plottet
to compare the error for extrapolation, max ent and the heurestic.
The algorithm runs through each triple interval, and then for each sampled estiamte output
the triples in the interval are looked up in each sample and the MAPE error is
recorded and the average errors are added. And the average of these averages
are then plottet for each interval.
"""
if not output_folder[-1] == '/':
output_folder += '/'
intervals = 30
triple_intervals = Preprocessor.triple_intervals(output_folder + 'observed_frequent_items.out', intervals=intervals)
avg_max_ent_errors = []
avg_ext_errors = []
avg_heu_errors = []
pair_triple_ratios = [i/10. for i in range(11)] # binned ratios [0.0 to 1.0]
max_ent_ratio_error = [0 for i in range(11)]
ext_ratio_error = [0 for i in range(11)]
for index, triple_interval in enumerate(triple_intervals):
print 'Triple interval {} of {}'.format(index, intervals)
iteration = 0
MAPE_avg_errors = []
MAPE_avg_errors_ext = []
# MAPE_avg_errors_heu = []
while True:
max_ent_est_file = output_folder + str(iteration) + '_data.tsv'
ext_est_file = output_folder + str(iteration) + '_data_extrapolation.tsv'
# heu_est_file = output_folder + str(iteration) + '_data_heurestic.tsv'
# read baseline also?
# Read until we do not find an output file
if not os.path.exists(max_ent_est_file):
break
max_ent_est = CVOutputParser.read_est_obs_file(max_ent_est_file)
ext_est = CVOutputParser.read_est_obs_file(ext_est_file)
# heu_est = CVOutputParser.read_est_obs_file(heu_est_file)
MAPE_errors = []
MAPE_errors_ext = []
# MAPE_errors_heu = []
for triple in triple_interval:
# Check that the triple has been estimated
if triple in max_ent_est:
# Index 1 should hold the observed value parsed from the file
# is the same mapped to every estimate, so hust read it once.
obs = max_ent_est[triple][1]
# maxent estimate
est = max_ent_est[triple][0]
# extrapolation estimate
est2 = ext_est[triple][0]
# # independence estimat?
# heurestic, use max_ent for 0 triple in sample
# est4 = heu_est[triple][0]
# Index 2 should hold the pair triple ratio.
# is the sam for every estimat
ratio = max_ent_est[triple][2]
# bin the ratio to one decimal
ratio_binned = round(ratio, 1)
# add errors to the ratio
max_ent_ratio_error[pair_triple_ratios.index(ratio_binned)] += abs(est-obs) / float(obs)
ext_ratio_error[pair_triple_ratios.index(ratio_binned)] += abs(est2-obs) / float(obs)
# MAPE error max ent
# error = abs(obs-est) #/ float(obs) * 100
# MAPE_errors.append(error)
# # MAPE error extrapolation
# error2 = abs(obs-est2) #/ float(obs) * 100
# MAPE_errors_ext.append(error2)
# MAPE error independence?
# MAPE error heurestic
# error4 = abs(obs-est4) #/ float(obs) * 100
# MAPE_errors_heu.append(error4)
# MAPE baseline error?
MAPE_avg_errors.append(avg(MAPE_errors))
MAPE_avg_errors_ext.append(avg(MAPE_errors_ext))
# MAPE_avg_errors_heu.append(avg(MAPE_errors_heu))
iteration += 1
avg_max_ent_errors.append(avg(MAPE_avg_errors))
avg_ext_errors.append(avg(MAPE_avg_errors_ext))
# avg_heu_errors.append(avg(MAPE_avg_errors_heu))
plot(range(len(avg_max_ent_errors)), avg_max_ent_errors, color='blue')
plot(range(len(avg_ext_errors)), avg_ext_errors, color='red')