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exp_addr.py
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exp_addr.py
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#!/usr/bin/env python
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import numpy
import scipy
import csv
import math
import random
import simplejson
from estimator import chao92, qaChao92, fChao92, sChao92,nominal, vNominal, sNominal, wChao92, bChao92, dChao92, goodToulmin, vGoodToulmin, remain_switch, gt_switch, gt_marginal, gt_remaining, extrapolation, extrapolation2, extrapolation3,vRemainSwitch, unseen, twoPhase, vRemainSwitch2, minTasks, minTasks2, minTasks3, sampleCoverage, minTasksToCleanAll, extrapolateFromSample
from datagen import generateDist, generateDataset, generateWeightedDataset, shuffleList
from dataload import simulatedData, loadInstitution, loadCrowdFlowerData, loadRestaurant2, loadProduct, loadRestaurantExtSample, loadAddress
import pickle
from simulation import plotMulti, plotY1Y2, holdoutRealWorkers
##############################################
########### real-world datasets ##############
##############################################
#====total error====
estimators = [lambda x: extrapolateFromSample(x,slist,0.05)+obvious_err,lambda x:vNominal(x)+obvious_err, lambda x: sChao92(x,shift=1)+obvious_err, lambda x: vRemainSwitch2(x)+obvious_err]
legend = ["EXTRAPOL","VOTING","V-CHAO","SWITCH"]
gt_list = [lambda x: gt+obvious_err,lambda x: gt+obvious_err, lambda x: gt+obvious_err, lambda x: gt+obvious_err]
legend_gt=["Ground Truth"]
#====switch====
estimators2 = [lambda x: remain_switch(x) - sNominal(x)]
estimators2a = [lambda x: remain_switch(x,neg_switch=False) - sNominal(x,neg_switch=False)]
estimators2b = [lambda x: remain_switch(x,pos_switch=False) - sNominal(x,pos_switch=False)]
gt_list2 = [lambda x: gt_switch(x,slist)]
gt_list2a = [lambda x: gt_switch(x,slist,neg_switch=False)]
gt_list2b = [lambda x: gt_switch(x,slist,pos_switch=False)]
legend2 = ["REMAIN-SWITCH"]
legend2a = ["REMAIN-SWITCH (+)"]
legend2b = ["REMAIN-SWITCH (-)"]
legend_gt2 = ["Ground Truth"]
#====relative====
#estimators_rel = [lambda x:vNominal(x)+obvious_err, lambda x: extrapolateFromSample(x,slist,0.1)+obvious_err,lambda x: sChao92(x,shift=1)+obvious_err, lambda x: vRemainSwitch2(x)+obvious_err, lambda x: remain_switch(x,pos_switch=False)-sNominal(x,pos_switch=False), lambda x: remain_switch(x,neg_switch=False)-sNominal(x,neg_switch=False)]
estimators_rel = [lambda x:vNominal(x)+obvious_err,lambda x: sChao92(x,shift=1)+obvious_err, lambda x: vRemainSwitch2(x)+obvious_err, lambda x: remain_switch(x)-sNominal(x)]
gt_list_rel = [lambda x: gt + obvious_err, lambda x: gt + obvious_err, lambda x: gt+ obvious_err, lambda x: gt_switch(x,slist)]
#legend_rel = ["VOTING","EXTRAPOL(1%)","V-CHAO","SWITCH","REMAIN SWITCH(+)", "REMAIN SWITCH(-)"]
legend_rel = ["VOTING","V-CHAO","SWITCH","REMAIN-SWITCH"]
#address dataset
d,gt,prec,rec = loadAddress()
task_sol = pickle.load( open('dataset/addr_solution.p','rb') )
slist = task_sol.values()
min_tasks2 = minTasks2(d,0.8)
min_tasks2 = minTasks3(d)
min_tasks5 = minTasksToCleanAll(d)
obvious_err = 0 # no priotization
n_worker = 1000
scale = 100
init = 100
n_rep = 10
priotization = True
logscale = False
show_min_tasks = True
'''
for w in range(1,len(d[0])):
if w%100 == 0:
hist = numpy.sum(d[:,:w]==1,axis=1)
f1 = numpy.sum(hist == 1)
n = numpy.sum(hist)
print 'sample cov %d:'%w, sampleCoverage(d[:,:w]), 'n=%f'%n, 'f1=%f'%f1
'''
#(X,Y,GT) = holdoutRealWorkers(d,gt_list,range(init,n_worker,scale),estimators,rep=n_rep)
#pickle.dump((X,Y,GT),open('dataset/addr_results_1.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/addr_results_1.p','rb'))
plotY1Y2((X,Y,GT),
legend=legend,
legend_gt=legend_gt,
xaxis="Tasks",
yaxis="# Total Error",
ymax=250,
vertical_y=230,
loc='upper right',
title='(a) Total Error',
filename="plot/addr_mostly_hard_all.png",
min_tasks5=min_tasks5,
show_min_tasks = show_min_tasks
)
'''
(X,Y,GT) = holdoutRealWorkers(d,gt_list2,range(init,n_worker,scale),estimators2,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/addr_results_2.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/addr_results_2.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2,
legend_gt = legend_gt2,
xaxis="Tasks", #ymax=100,
yaxis="Estimate (# Remaining Switches)",
#ymax=150,
title='Remaining Switches',
filename="plot/addr_mostly_hard_all_switch.png",
)
'''
#positive switch estimation
#(X,Y,GT) = holdoutRealWorkers(d,gt_list2a,range(init,n_worker,scale),estimators2a,rep=n_rep)
#pickle.dump((X,Y,GT),open('dataset/addr_results_3.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/addr_results_3.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2a,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="# Remaining Switches",
ymax=140,
title='(b) Remaining Positive Switches',
filename="plot/addr_mostly_hard_pos_switch.png",
)
#negative estimation
#(X,Y,GT) = holdoutRealWorkers(d,gt_list2b,range(init,n_worker,scale),estimators2b,rep=n_rep)
#pickle.dump((X,Y,GT),open('dataset/rest2_results_4.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_4.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2b,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="# Remaining Switches",
ymax=140,
title='(c) Remaining Negative Switches',
filename="plot/addr_mostly_hard_neg_switch.png",
)
'''
(X,Y,GT) = holdoutRealWorkers(d,gt_list_rel,range(init,n_worker,scale),estimators_rel,rel_err=True,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/addr_results_5.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/addr_results_5.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend_rel,
xaxis='Tasks',
yaxis='SRMSE',
ymax=1,
title='(c) Relative Error',
loc='upper right',
filename="plot/addr_mse.png",
logscale=logscale,
rel_err=True
)
'''
"""
#restaurant dataset
n_worker = 1400
scale = 100
init = 100
priotization = True
logscale = False
d,gt,prec,rec = loadRestaurant2(['dataset/good_worker/restaurant_additional.csv','dataset/good_worker/restaurant_new.csv'],priotization=priotization)
#['dataset/restaurant_new.csv','dataset/restaurant_new2.csv'],priotization=True)
pair_solution = pickle.load( open('dataset/pair_solution.p','rb') )
# Pair solution ground truth
slist = pair_solution.values()
print 'restaurant data (hueristic pairs):',len(slist),numpy.sum(numpy.array(slist) == 1)
easy_pair_solution = pickle.load( open('dataset/easy_pair_solution.p','rb') )
easy_slist = easy_pair_solution.values()
print 'restaurant data (easy pairs):',len(easy_slist),numpy.sum(numpy.array(easy_slist) == 1)
obvious_err = numpy.sum(numpy.array(easy_slist) == 1)
(X,Y,GT) = holdoutRealWorkers(d,gt_list,range(init,n_worker,scale),estimators,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/rest2_results_1.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_1.p','rb'))
plotY1Y2((X,Y,GT),
legend=legend,
legend_gt=legend_gt,
xaxis="Tasks",
yaxis="Estimate (# Total Error)",
ymax=200,
loc='lower right',
title='(a) Total Error',
filename="plot/rest2_mostly_hard_all.png",
)
(X,Y,GT) = holdoutRealWorkers(d,gt_list2,range(init,n_worker,scale),estimators2,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/rest2_results_2.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_2.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2,
legend_gt = legend_gt2,
xaxis="Tasks", #ymax=100,
yaxis="Estimate (# Remaining Switches)",
ymax=220,
title='Remaining Switches',
filename="plot/rest2_mostly_hard_all_switch.png",
)
#positive switch estimation
print 'switch estimation'
(X,Y,GT) = holdoutRealWorkers(d,gt_list2a,range(init,n_worker,scale),estimators2a,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/rest2_results_3.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_3.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2a,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="Estimate (# Remaining Switches)",
ymax=20,
title='(b) Remaining Positive Switches',
filename="plot/rest2_mostly_hard_pos_switch.png",
)
#negative estimation
(X,Y,GT) = holdoutRealWorkers(d,gt_list2b,range(init,n_worker,scale),estimators2b,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/rest2_results_4.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_4.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2b,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="Estimate (# Remaining Switches)",
ymax=100,
title='(c) Remaining Negative Switches',
filename="plot/rest2_mostly_hard_neg_switch.png",
)
'''
(X,Y,GT) = holdoutRealWorkers(d,gt_list_rel,range(init,n_worker,scale),estimators_rel,rel_err=True,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/rest2_results_5.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_5.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend_rel,
xaxis='Tasks',
yaxis='SRMSE',
ymax=1,
title='(c) Relative Error',
loc='upper right',
filename="plot/rest2_mse.png",
logscale=logscale,
rel_err=True
)
'''
#Product dataset
print 'loading product data'
d,gt,prec,rec = loadProduct(['dataset/jn_heur/jn_heur_products.csv'])#(['dataset/good_worker/product_new.csv','dataset/good_worker/product_additional.csv'])
pair_solution = pickle.load( open('dataset/jn_heur/pair_solution.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
print 'loaded product data'
slist = pair_solution.values()
print 'product data (hueristic pairs):',len(slist),numpy.sum(numpy.array(slist) == 1)
easy_pair_solution = pickle.load( open('dataset/jn_heur/easy_pair_solution.p','rb'))#( open('dataset/products/easy_pair_solution.p','rb') )
easy_slist = easy_pair_solution.values()
print 'product data (easy pairs):',len(easy_slist),numpy.sum(numpy.array(easy_slist) == 1)
obvious_err = numpy.sum(numpy.array(easy_slist) == 1)
n_worker = 3900
scale = 300
init = 100
(X,Y,GT) = holdoutRealWorkers(d,gt_list,range(init,n_worker,scale),estimators,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/product_results_1b.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/product_results_1b.p','rb'))
plotY1Y2((X,Y,GT),
legend=legend,
legend_gt=legend_gt,
xaxis="Tasks",
loc='lower right',
title="(a) Total Error",
yaxis='Estimate (# Total Error)',
ymax=1800,
filename="plot/product_mostly_hard_all.png",
)
(X,Y,GT) = holdoutRealWorkers(d,gt_list2,range(init,n_worker,scale),estimators2,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/product_results_2b.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/product_results_2b.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2,
legend_gt = legend_gt2,
xaxis="Tasks", #ymax=100,
yaxis="Estimate (# Remaining Switches)",
title='Remaining Switches',
filename="plot/product_mostly_hard_all_switch.png",
)
#positive switch estimation
(X,Y,GT) = holdoutRealWorkers(d,gt_list2a,range(init,n_worker,scale),estimators2a,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/product_results_3.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/product_results_3.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2a,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="Estimate (# Remaining Switches)",
title='(b) Remaining Positive Switches',
filename="plot/product_mostly_hard_pos_switch.png",
)
#negative estimation
(X,Y,GT) = holdoutRealWorkers(d,gt_list2b,range(init,n_worker,scale),estimators2b,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/product_results_4.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/product_results_4.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend2b,
legend_gt = legend_gt2,
xaxis="Tasks",
yaxis="Estimate (# Remaining Switches)",
title='(c) Remaining Negative Switches',
filename="plot/product_mostly_hard_neg_switch.png",
)
'''
(X,Y,GT) = holdoutRealWorkers(d,gt_list_rel,range(init,n_worker,scale),estimators_rel,rel_err=True,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/product_results_5b.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/product_results_5b.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend_rel,
xaxis='Tasks',
yaxis='SRMSE',
title='(c) Relative Error',
loc='upper right',
ymax=1,
filename='plot/product_mse.png',
logscale=logscale,
rel_err=True
)
'''
"""
"""
######################################
###########simulated dataset##########
######################################
legend_gt=["Ground Truth"]
print 'Figure 6, Sensitivity of Total Error Estimation'
estimators_sim = [vNominal, chao92]
estimators_sim2 = [vNominal, chao92, lambda x:sChao92(x,shift=1)]
legend_sim = ['VOTING','Chao92']
legend_sim2 = ['VOTING','Chao92','V-CHAO']
logscale = False
rel_err = True
yaxis = 'SRMSE'#'Relative Error %'
priotized = False
err_skew = False
dirty = 0.2
hir = 0.1 #0.1
lowr = 0.01
hiq = 1.0
lowq = 0.85
title = 'Perfect Precision, High Recall'
print title
n_worker = 200
scale = 10
init = 10
n_rep = 5
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=hir,precision=1.0,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt]
gt_list_sim2 = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(init,n_worker,scale),estimators_sim,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_6a.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Perfect Precision, Low Recall'
print title
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=lowr,precision=1.0,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt]
gt_list_sim2 = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(init,n_worker,scale),estimators_sim,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_6b.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Imperfect Precision, Low Recall'
print title
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=lowr,precision=0.75,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt]
gt_list_sim2 = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim2,range(init,n_worker,scale),estimators_sim2,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim2,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_6c.png',
logscale=logscale,
rel_err=rel_err
)
print 'Figure 5, Sensitivity of Total Error Estimation'
title = 'Tradeoff: False Positives'
estimators_sim = [vNominal, chao92, lambda x:sChao92(x,shift=1)]
legend_sim = ['VOTING','Chao92','V-CHAO']
rec(i)/10.
d,gt,pr,re =all = 0.1
Xs = []
Ys = []
GTs = []
for i in range(11):
prec = float(i)/10.
d,gt,pr,re = simulatedData(recall=recall,precision=prec)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt,lambda x:gt,lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,[50],estimators_sim,rel_err=True,rep=n_rep)
Xs.append(prec*100)
Ys.append(Y[0])
GTs.append(GT[0])
plotY1Y2((Xs,Ys,GTs),
legend = legend_sim,
xaxis='Precision %',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_5a.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Tradeoff: False Negatives'
precision = 1.
Xs = []
Ys = []
GTs = []
for i in range(0,11):
rec = float(i)/200.
d,gt,pr,re = simulatedData(recall=rec,precision=precision)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt,lambda x:gt,lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,[50],estimators_sim,rel_err=True,rep=n_rep)
Xs.append((rec)*100)
Ys.append(Y[0])
GTs.append(GT[0])
plotY1Y2((Xs,Ys,GTs),
legend = legend_sim,
xaxis='Coverage %',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_5b.png',
logscale=logscale,
rel_err=rel_err
)
print 'Figure 7: switch estimation'
title = 'Perfect Precision'
d,gt,pr,re = simulatedData(recall=hir,precision=hiq)
slist = pickle.load( open('dataset/sim_label.p','rb') )
estimators_sw = [lambda x: vNominal(x), lambda x: chao92(x), lambda x: remain_switch(x)-sNominal(x)]
gt_list_sw = [lambda x: gt, lambda x: gt, lambda x: gt_switch(x,slist)]
legend_sw = ["VOTING","Chao92","REMAIN-SWITCH"]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sw,range(init,n_worker,scale),estimators_sw,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/sim_7a_results_extp.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/sim_7a_results_extp.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend_sw,
legend_gt = legend_gt,
xaxis="Tasks", #ymax=100,
yaxis="Estimate (# Remaining Switches)",
title=title,
filename="plot/sim_7a.png",
rel_err=True
)
title = 'Imperfect Precision'
d,gt,pr,re = simulatedData(recall=hir,precision=lowq)
slist = pickle.load( open('dataset/sim_label.p','rb') )
estimators_sw = [lambda x: remain_switch(x)-sNominal(x)]
gt_list_sw = [lambda x: gt_switch(x,slist)]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sw,range(init,n_worker,scale),estimators_sw,rep=n_rep)
pickle.dump((X,Y,GT),open('dataset/sim_7b_results_extp.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/sim_7b_results_extp.p','rb'))
plotY1Y2((X,Y,GT),
legend = legend_sw,
legend_gt = legend_gt,
xaxis="Tasks", #ymax=100,
yaxis="Estimate (# Remaining Switches)",
title=title,
filename="plot/sim_7b.png",
rel_err=True
)
print 'Figure 7, Sensitivity of Total Error Estimation'
estimators_sim = [vNominal, chao92, lambda x: sChao92(x,shift=1), lambda x: vRemainSwitch2(x)]
legend_sim = ['VOTING','Chao92','V-CHAO','SWITCH']
title = 'Perfect Precision, High Recall'
print title
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=hir,precision=hiq,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x: gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(init,n_worker,scale),estimators_sim,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_7a2.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Perfect Precision, Low Recall'
print title
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=lowr,precision=hiq,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x: gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(init,n_worker,scale),estimators_sim,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_7b2.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Imperfect Precision, Low Recall'
print title
d,gt,prec,rec = simulatedData(items=1000,workers=n_worker,dirty=dirty,recall=lowr,precision=lowq,err_skew=err_skew,priotized=priotized)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt, lambda x:gt, lambda x:gt, lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(init,n_worker,scale),estimators_sim,rel_err=True,rep=n_rep)
plotY1Y2((X,Y,GT),
legend = legend_sim,
xaxis='Tasks',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_7c2.png',
logscale=logscale,
rel_err=rel_err
)
print 'Figure 10, Heuristics'
estimators_sim = [lambda x: vRemainSwitch2(x)]
legend_sim = ['SWITCH']
n_rep = 1
logscale = False
title = 'Heuristic 10% Error'
print title
n_worker = 50
Xs = []
Ys = []
GTs = []
for i in range(0,11):
eps = float(i)/10.
d,gt,pr,re = simulatedData(recall=0.2,precision=1,priotized=True,eps=eps)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,[50],estimators_sim,rel_err=True,rep=n_rep)
Xs.append(eps)
Ys.append(Y[0])
GTs.append(GT[0])
plotY1Y2((Xs,Ys,GTs),
legend = legend_sim,
xaxis='Epsilon ($\epsilon$)',
yaxis=yaxis,
ymax=1.0,
title=title,
loc='upper right',
filename='plot/sim_10a.png',
logscale=logscale,
rel_err=rel_err
)
title = 'Heuristic 50% Error'
print title
n_worker = 50
Xs = []
Ys = []
GTs = []
for i in range(0,11):
eps = float(i)/10.
d,gt,pr,re = simulatedData(recall=0.2,precision=1,priotized=True,eps=eps,hdirty=0.5)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
gt_list_sim = [lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,[50],estimators_sim,rel_err=True,rep=n_rep)
Xs.append(eps)
Ys.append(Y[0])
GTs.append(GT[0])
plotY1Y2((Xs,Ys,GTs),
legend = legend_sim,
xaxis='Epsilon ($\epsilon$)',
yaxis=yaxis,
ymax=1.0,
title=title,
loc='upper right',
filename='plot/sim_10b.png',
logscale=logscale,
rel_err=rel_err
)
"""
"""
print 'Figure 9, Extrapolation vs. species'
legend_sim = ['OBSERVED','EXTRAPOL','SWITCH']
n_rep=1
title = 'Extrapolation vs. Species'
n_worker = 50
s_size = 25
Xs = []
Ys = []
GTs = []
for rep in range(3):
d,gt,pr,re = simulatedData(workers=n_worker,recall=1,precision=0.7)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
rows = numpy.random.randint(1000,size=s_size)
obs = numpy.sum(slist[rows]>0)
q = float(s_size)/1000.
estimators_sim = [lambda x: nominal(x),lambda x: obs/q,lambda x: vRemainSwitch2(x)]
gt_list_sim = [lambda x: gt, lambda x:gt, lambda x:gt]
(X,Y,GT) = holdoutRealWorkers(d,gt_list_sim,range(10,n_worker+5,5),estimators_sim,rel_err=False,rep=n_rep)
plotHist((X,Y,GT),
legend = legend_sim,
legend_gt = legend_gt,
xaxis='',
yaxis='Estimate (# Total Error)',
title=title,
loc='upper right',
filename='plot/sim_9b.png'
)
print 'Figure 9, # Tasks For Perfect Cleaning'
n_rep = 2
n_worker = 200
title = '# Tasks For Perfect Cleaning'
precisions = [.6, .65, .7, .75, .8, .85, .9, 1.0]
Xs, Ys, GTs = [], [], []
for precision in precisions:
estimators_sim = [lambda x: vNominal(x)]
gt_list_sim = [lambda x: gt]
d,gt,pr,re = simulatedData(items=25,workers=n_worker,recall=1,precision=precision)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
x1s = []
for r in range(n_rep):
for n_w in range(1,n_worker,5):
diff = numpy.sum(numpy.logical_xor(numpy.sum(d[:,:n_w] == 1,axis=1) > numpy.sum(d[:,:n_w] != -1,axis=1)/2, slist))
if diff == 0:
x1s.append(n_w)
break
x1 = numpy.mean(x1s)
#(X,Y1,GT) = holdoutRealWorkers(d,gt_list_sim,range(1,n_worker,1),estimators_sim,rel_err=False,rep=n_rep)
#x1 = [idx for idx, val in enumerate(Y1) if val == GT[0][0]][0]
d,gt,pr,re = simulatedData(items=75,workers=n_worker,recall=1,precision=precision)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
x2s = []
for r in range(n_rep):
for n_w in range(1,n_worker,5):
diff = numpy.sum(numpy.logical_xor(numpy.sum(d[:,:n_w] == 1,axis=1) > numpy.sum(d[:,:n_w] != -1,axis=1)/2, slist))
if diff == 0:
x2s.append(n_w)
break
x2 = numpy.mean(x2s)
#(X,Y2,GT) = holdoutRealWorkers(d,gt_list_sim,range(1,n_worker,1),estimators_sim,rel_err=False,rep=n_rep)
#x2 = [idx for idx, val in enumerate(Y2) if val == GT[0][0]][0]
d,gt,pr,re = simulatedData(items=100,workers=n_worker,recall=1,precision=precision)
slist = pickle.load( open('dataset/sim_label.p','rb'))#( open('dataset/products/pair_solution.p','rb'))
x3s = []
for r in range(n_rep):
for n_w in range(1,n_worker,5):
diff = numpy.sum(numpy.logical_xor(numpy.sum(d[:,:n_w] == 1,axis=1) > numpy.sum(d[:,:n_w] != -1,axis=1)/2, slist))
if diff == 0:
x3s.append(n_w)
break
x3 = numpy.mean(x3s)
#(X,Y3,GT) = holdoutRealWorkers(d,gt_list_sim,range(1,n_worker,1),estimators_sim,rel_err=False,rep=n_rep)
#x3 = [idx for idx, val in enumerate(Y3) if val == GT[0][0]][0]
X = range(1,n_worker,1)
Xs.append(precision)
print x1, x2, x3
Ys.append([X[int(x1)],X[int(x2)],X[int(x3)]])
GTs.append(0)
plotY1Y2((Xs,Ys,GTs),
legend = ['25 records', '75 records', '100 records'],
xaxis='Worker Accuracy',
yaxis=yaxis,
title=title,
loc='upper right',
filename='plot/sim_9a.png',
logscale=False
)
# Extrapolation experiment in Section 2
print 'Extrapolation experiment in Section 2'
estimators_extp = [lambda x:extrapolation(x,pair_solution,len(pair_solution)/50),
lambda x: extrapolation(x,pair_solution,len(pair_solution)/50),
lambda x: extrapolation(x,pair_solution,len(pair_solution)/50),
lambda x: extrapolation(x,pair_solution,len(pair_solution)/50)]
estimators_extp2 = [lambda x:extrapolation2(x,d,sample1)+obvious_err,
lambda x:extrapolation2(x,d,sample2)+obvious_err,
lambda x:extrapolation2(x,d,sample3)+obvious_err,
lambda x:extrapolation2(x,d,sample4)+obvious_err]
estimators_extp3 = [lambda x:max(0,extrapolation3(x,d,sample1)-sNominal(x)),
lambda x:max(0,extrapolation3(x,d,sample2)-sNominal(x)),
lambda x:max(0,extrapolation3(x,d,sample3)-sNominal(x)),
lambda x:max(0,extrapolation3(x,d,sample4)-sNominal(x))]
legend_extp = ['Sample 1','Sample 2','Sample 3','Sample 4']
legend_extp_ = ['Mean','3-Std']
gt_list_extp = [lambda x: gt+obvious_err]
gt_list_extp_ = [lambda x: gt]
gt_list_extp3 = [lambda x: gt_switch(x,slist)]
sample1 = loadRestaurantExtSample(['dataset/extp/restaurant-1.csv'],priotization=priotization)
sample2 = loadRestaurantExtSample(['dataset/extp/restaurant-2.csv'],priotization=priotization)
sample3 = loadRestaurantExtSample(['dataset/extp/restaurant-3.csv'],priotization=priotization)
sample4 = loadRestaurantExtSample(['dataset/extp/restaurant-4.csv'],priotization=priotization)
n_worker = 14
init = 2
scale = 1
n_rep=1
#(X,Y,GT) = holdoutRealWorkers(d,gt_list_extp,range(init,n_worker,scale),estimators_extp2,rep=n_rep)
#pickle.dump((X,Y,GT),open('dataset/rest2_results_extp.p','wb'))
(X,Y,GT) = pickle.load(open('dataset/rest2_results_extp.p','rb'))
plotExtp((X,Y,GT),
legend=legend_extp_,
legend_gt=legend_gt,
yaxis="Estimate (Total # Error)",
xaxis="Workers",
title="Total Error: Real Samples of Size 100",
ymax=-2,
filename="plot/rest2_extp2.png"
)
plotExtp(holdoutRealWorkers(d,gt_list_extp3,range(5,n_worker,scale),estimators_extp3,rel_err=False,rep=n_rep),
legend=legend_extp_,
legend_gt=legend_gt,
yaxis="Remaining Switches",
xaxis="Workers",
title="Switches: Real Samples of Size 100",
filename="plot/rest2_switch_extp2.png"
)
priotization = False
d,gt,prec,rec = loadRestaurant2(['dataset/good_worker/restaurant_additional.csv','dataset/good_worker/restaurant_new.csv'],priotization=priotization)
pair_solution = pickle.load( open('dataset/pair_solution.p','rb') )
slist = pair_solution.values()
sample1 = loadRestaurantExtSample(['dataset/extp/restaurant-1.csv'],priotization=priotization)
sample2 = loadRestaurantExtSample(['dataset/extp/restaurant-2.csv'],priotization=priotization)
sample3 = loadRestaurantExtSample(['dataset/extp/restaurant-3.csv'],priotization=priotization)
sample4 = loadRestaurantExtSample(['dataset/extp/restaurant-4.csv'],priotization=priotization)
plotHist(holdoutRealWorkers(d,gt_list_extp_,range(5,n_worker,scale),estimators_extp,rep=n_rep),
legend=legend_extp,
legend_gt=legend_gt,
yaxis="Estimate (Total # Error)",
xaxis="",
titlt="Total Error: Simulated 2% Samples",
filename="plot/rest2_extp_hist.png",
)
"""