def test_networks(datasetname, statesonly, expanded, name, batchsize=20000):
"""
Measure the accuracy and the legality of a triplet of networks
Loads three networks and a dataset and than measure how accurate are the
networks predictions with respect to the dataset and if their choices are
legal. If the dataset is made only by states (therefore no moves), the
accuracy test is not performed.
Parameters
----------
datasetname : string
The name of the dataset file.
statesonly : boolean
True if the dataset has doesn't contains informations about the moves.
expanded : boolean
True if the dataset has been already expanded through symmetries.
name : string
The name of the network configuration to load
"""
print("Testing " + name)
print("Loading networks...")
print("\tloading " + name + "_TO")
TOnet = load_net(name + "_TO")
print("\tloading " + name + "_FROM")
FROMnet = load_net(name + "_FROM")
print("\tloading " + name + "_REMOVE")
REMOVEnet = load_net(name + "_REMOVE")
print("\tNetworks loaded!")
data_format = TOnet[3]
print(str(data_format))
orderc = ['X', 'X', 'X']
orderc[TOnet[2]] = 'T'
orderc[FROMnet[2]] = 'F'
orderc[REMOVEnet[2]] = 'R'
order = "" + orderc[0] + orderc[1] + orderc[2]
print("\tOrder: " + order)
print("Loading data...")
if statesonly:
if (expanded):
A, B = load_expanded_states_dataset(datasetname)
else:
A, B = load_states_dataset(datasetname)
else:
if expanded:
A, B = load_expanded_dataset(datasetname)
else:
A, B = load_dataset(datasetname)
print("\tData loaded! Loaded: " + str(len(B)) + " data")
print("Processing data and getting choices")
X_TO_set = []
X_FROM_set = []
X_REMOVE_set = []
TO_choice_set = []
FROM_choice_set = []
REMOVE_choice_set = []
y_TO_set = []
y_FROM_set = []
y_REMOVE_set = []
numbatch = int(len(A) / batchsize) + 1
for i in range(1, numbatch + 1):
print("\t" + str(i * batchsize * 100.0 / len(A)) + "%")
if (i * batchsize <= len(A)):
Ai = A[(i - 1) * batchsize:i * batchsize]
Bi = B[(i - 1) * batchsize:i * batchsize]
else:
Ai = A[(i - 1) * batchsize:]
Bi = B[(i - 1) * batchsize:]
y_TO = process_move_onlyTO(Bi)
y_FROM = process_move_onlyFROM(Bi)
y_REMOVE = process_move_onlyREMOVE(Bi)
init_state = process_state_binary(Ai, data_format)
if order == "FTR":
X_FROM = init_state
FROM_choice = get_choices(FROMnet, X_FROM)
X_TO = add_CHOICE_binary_raw(X_FROM, FROM_choice)
TO_choice = get_choices(TOnet, X_TO)
X_REMOVE = add_CHOICE_binary_raw(X_TO, TO_choice)
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
elif order == "RFT":
X_REMOVE = init_state
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
X_FROM = add_CHOICE_binary_raw(X_REMOVE, REMOVE_choice)
FROM_choice = get_choices(FROMnet, X_FROM)
X_TO = add_CHOICE_binary_raw(X_FROM, FROM_choice)
TO_choice = get_choices(TOnet, X_TO)
elif order == "FRT":
X_FROM = init_state
FROM_choice = get_choices(FROMnet, X_FROM)
X_REMOVE = add_CHOICE_binary_raw(X_FROM, FROM_choice)
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
X_TO = add_CHOICE_binary_raw(X_REMOVE, REMOVE_choice)
TO_choice = get_choices(TOnet, X_TO)
elif order == "RTF":
X_REMOVE = init_state
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
X_TO = add_CHOICE_binary_raw(X_REMOVE, REMOVE_choice)
TO_choice = get_choices(TOnet, X_TO)
X_FROM = add_CHOICE_binary_raw(X_TO, TO_choice)
FROM_choice = get_choices(FROMnet, X_FROM)
elif order == "TRF":
X_TO = init_state
TO_choice = get_choices(TOnet, X_TO)
X_REMOVE = add_CHOICE_binary_raw(X_TO, TO_choice)
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
X_FROM = add_CHOICE_binary_raw(X_REMOVE, REMOVE_choice)
FROM_choice = get_choices(FROMnet, X_FROM)
elif order == "TFR":
X_TO = init_state
TO_choice = get_choices(TOnet, X_TO)
#print("\tGot TO choices")
X_FROM = add_CHOICE_binary_raw(X_TO, TO_choice)
FROM_choice = get_choices(FROMnet, X_FROM)
#print("\tGot FROM choices")
X_REMOVE = add_CHOICE_binary_raw(X_FROM, FROM_choice)
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
else:
print("Unknown configuration. Using TFR")
X_TO = init_state
TO_choice = get_choices(TOnet, X_TO)
#print("\tGot TO choices")
X_FROM = add_CHOICE_binary_raw(X_TO, TO_choice)
FROM_choice = get_choices(FROMnet, X_FROM)
#print("\tGot FROM choices")
X_REMOVE = add_CHOICE_binary_raw(X_FROM, FROM_choice)
REMOVE_choice = get_choices(REMOVEnet, X_REMOVE)
for j in range(len(X_TO)):
X_TO_set.append(X_TO[j])
X_FROM_set.append(X_FROM[j])
X_REMOVE_set.append(X_REMOVE[j])
TO_choice_set.append(TO_choice[j])
FROM_choice_set.append(FROM_choice[j])
REMOVE_choice_set.append(REMOVE_choice[j])
y_TO_set.append(y_TO[j])
y_FROM_set.append(y_FROM[j])
y_REMOVE_set.append(y_REMOVE[j])
#print("\tGot REMOVE choices")
# variable renaming
X_TO = X_TO_set
X_FROM = X_FROM_set
X_REMOVE = X_REMOVE_set
TO_choice = TO_choice_set
FROM_choice = FROM_choice_set
REMOVE_choice = REMOVE_choice_set
y_TO = y_TO_set
y_FROM = y_FROM_set
y_REMOVE = y_REMOVE_set
print("Testing legality")
legalities = get_legalities(TO_choice, FROM_choice, REMOVE_choice,
X_REMOVE, data_format)
print("\ttesting the legality of " + str(len(legalities[0])) + " data\n")
TO_self_leg = legalities[0]
FROM_self_leg = legalities[1]
REMOVE_self_leg = legalities[2]
FROM_leg = legalities[3]
REMOVE_leg = legalities[4]
wholeFROM = legalities[5]
wholeREMOVE = legalities[6]
wholeMOVE = legalities[7]
fileT = open(name + "_testing.txt", 'a')
leg = ("Legality response on " + datasetname + ":" + "\n\tOnly TO leg:\t" +
str(numpy.mean(TO_self_leg) * 100) + "\n\tOnly FROM leg:\t" +
str(numpy.mean(FROM_self_leg) * 100) + "\n\tOnly REMOVE leg:\t" +
str(numpy.mean(REMOVE_self_leg) * 100) + "\n\tOnly FROM-TO leg:\t" +
str(numpy.mean(FROM_leg) * 100) + "\n\tOnly REMOVE-FROM-TO leg:\t" +
str(numpy.mean(REMOVE_leg) * 100) + "\n\tWhole FROM leg:\t" +
str(numpy.mean(wholeFROM) * 100) + "\n\tWhole REMOVE leg:\t" +
str(numpy.mean(wholeREMOVE) * 100) + "\n\tWhole MOVE leg:\t" +
str(numpy.mean(wholeMOVE) * 100))
print(leg)
fileT.write(leg)
print("\nTesting accuracy and special cases\n")
# accuracy valutation and legality evaluation for some particular cases:
# the FROM move in phase 2 and the REMOVE when is not 0
correctTO = 0
correctFROM = 0
correctREMOVE = 0
correctWHOLE = 0
# legality for FROM in phase 2
legalFROM2 = 0
f2 = 0
# legality for REMOVE != 0 for the network
legalREMOVEeat = 0
re = 0
# accuracy when REMOVE != 0 in dataset
correctREMOVEyes = 0
ry = 0
# accuracy when FROM != 0 in dataset
correctFROMyes = 0
fy = 0
# accuracy for the different phases
correctWHOLE1 = 0
p1 = 0
correctWHOLE2 = 0
p2 = 0
correctWHOLE3 = 0
p3 = 0
size = len(TO_choice)
print("\tTesting the accuracy of " + str(size) + " data")
for i in range(size):
# TO DECISION
if TO_choice[i] == y_TO[i]:
correctTO += 1
# legal FROM decision in phase 2
if (is_phase_2(X_REMOVE[i], data_format)):
f2 += 1
# legality
l = get_legalities(TO_choice[i:i + 1], FROM_choice[i:i + 1],
REMOVE_choice[i:i + 1], X_REMOVE[i:i + 1],
data_format)
if (l[5] == 1):
legalFROM2 += 1
# FROM decision is different from 0
if y_FROM[i] != 0:
fy += 1
# FROM decision
if FROM_choice[i] == y_FROM[i]:
correctFROM += 1
# correct FROM decision if different from 0
if y_FROM[i] != 0:
correctFROMyes += 1
# REMOVE decision when present
if y_REMOVE[i] != 0:
ry += 1
if REMOVE_choice[i] != 0:
re += 1
# legality
l = get_legalities(TO_choice[i:i + 1], FROM_choice[i:i + 1],
REMOVE_choice[i:i + 1], X_REMOVE[i:i + 1],
data_format)
if (l[6] == 1):
legalREMOVEeat += 1
# REMOVE decision
if REMOVE_choice[i] == y_REMOVE[i]:
correctREMOVE += 1
if (y_REMOVE[i] != 0):
correctREMOVEyes += 1
if is_phase_1(X_REMOVE[i], data_format):
p1 += 1
elif is_phase_3(X_REMOVE[i], data_format):
p3 += 1
else:
p2 += 1
# WHOLE move (in different phases of game)
if (TO_choice[i] == y_TO[i] and FROM_choice[i] == y_FROM[i]
and REMOVE_choice[i] == y_REMOVE[i]):
correctWHOLE += 1
if is_phase_1(X_REMOVE[i], data_format):
correctWHOLE1 += 1
elif is_phase_3(X_REMOVE[i], data_format):
correctWHOLE3 += 1
else:
correctWHOLE2 += 1
if (size > 0):
correctTO = correctTO * 100.0 / size
correctFROM = correctFROM * 100.0 / size
correctREMOVE = correctREMOVE * 100.0 / size
correctWHOLE = correctWHOLE * 100.0 / size
else:
correctTO = -1
correctFROM = -1
correctREMOVE = -1
correctWHOLE = -1
if (f2 > 0):
legalFROM2 = legalFROM2 * 100.0 / f2
else:
legalFROM2 = -1
if (re > 0):
legalREMOVEeat = (legalREMOVEeat * 100.0) / re
else:
legalREMOVEeat = -1
if (ry > 0):
correctREMOVEyes = correctREMOVEyes * 100.0 / ry
else:
correctREMOVEyes = -1
if (fy > 0):
correctFROMyes = correctFROMyes * 100.0 / fy
else:
correctFROMyes = -1
if (p1 > 0):
correctWHOLE1 = correctWHOLE1 * 100.0 / p1
else:
correctWHOLE1 = -1
if (p2 > 0):
correctWHOLE2 = correctWHOLE2 * 100.0 / p2
else:
correctWHOLE2 = -1
if (p3 > 0):
correctWHOLE3 = correctWHOLE3 * 100.0 / p3
else:
correctWHOLE3 = -1
leg = ("\n\tFROM phase 2 leg:\t" + str(legalFROM2) +
"\n\tREMOVE when chosen leg:\t" + str(legalREMOVEeat))
if (not statesonly):
leg += ("\n--------------------\n\nAccuracy response on " +
datasetname + ":" + "\n\tTO accuracy:\t" + str(correctTO) +
"\n\tFROM accuracy:\t" + str(correctFROM) +
"\n\tREMOVE accuracy:\t" + str(correctREMOVE) +
"\n\tWhole MOVE accuracy:\t" + str(correctWHOLE) +
"\n\n\tWhole MOVE accuracy in phase 1:\t" +
str(correctWHOLE1) + "\n\tWhole MOVE accuracy in phase 2:\t" +
str(correctWHOLE2) + "\n\tWhole MOVE accuracy in phase 3:\t" +
str(correctWHOLE3) + "\n\n\tFROM not 0 accuracy:\t" +
str(correctFROMyes) + "\n\tREMOVE not 0 accuracy:\t" +
str(correctREMOVEyes) + "\n\n\n")
print(leg)
fileT.write(leg)
fileT.close()
def test_networks_reliability(datasetname, expanded, name, batchsize=10000):
"""
Loads three existent networks and evaluate their mean reliability.
The reliability is similar to a precision-recall curve, with the aim to
measure the ability of a network to assign highest probability to legal
moves.
Defined the task to retrieve all the legal moves, the possible choices of
each network are ranked accordingly their probability score.
For defined percentages of recall, the precision percentage is computed.
These values are saved in a realtive file.
If the previous networks have taken an illegal choice for a state, that
state it is not taken into account to compute the mean values.
Also the cases in which the only legal choice is 0 are not considered.
Parameters
----------
datasetname : string
The name of the dataset file.
expanded : boolean
True if the dataset has been already expanded through symmetries.
name : string
The name of the network configuration to load
"""
start_time = time.time()
print("Testing " + name)
print("Loading networks...")
print("\tloading " + name + "_TO")
TOnet = load_net(name + "_TO")
print("\tloading " + name + "_FROM")
FROMnet = load_net(name + "_FROM")
print("\tloading " + name + "_REMOVE")
REMOVEnet = load_net(name + "_REMOVE")
print("\tNetworks loaded!")
data_format = TOnet[3]
# an array in which is loaded the configuration: TFR/RFT/FTR ecc.
orderc = ['X', 'X', 'X']
orderc[TOnet[2]] = 'T'
orderc[FROMnet[2]] = 'F'
orderc[REMOVEnet[2]] = 'R'
order = "" + orderc[0] + orderc[1] + orderc[2]
nets = ['X', 'X', 'X']
nets[TOnet[2]] = TOnet
nets[FROMnet[2]] = FROMnet
nets[REMOVEnet[2]] = REMOVEnet
ordernames = ['X', 'X', 'X']
ordernames[TOnet[2]] = "TO"
ordernames[FROMnet[2]] = "FROM"
ordernames[REMOVEnet[2]] = "REMOVE"
print("\tOrder: " + order)
print("Loading data from \"" + datasetname + "\" ...")
if expanded:
A, B = load_expanded_states_dataset(datasetname)
else:
A, B = load_states_dataset(datasetname)
print("\tData loaded! Loaded: " + str(len(A)) + " data")
numbatch = int(len(A) / batchsize) + 1
T_p = []
O_p = []
Z_p = []
num_data = len(A)
perc = 0
for i in range(1, numbatch + 1):
if (i * batchsize <= len(A)):
Ai = A[(i - 1) * batchsize:i * batchsize]
percentage = i * batchsize * 100 / (num_data * 1.0)
else:
Ai = A[(i - 1) * batchsize:]
percentage = 100.0
X_0 = process_state_binary(Ai, data_format)
bin_state = process_state_binary(Ai, "binary raw")
ZERO_choice = get_choices(nets[0], X_0)
ZERO_predictions = get_predictions(nets[0], X_0)
X_1 = add_CHOICE_binary_raw(X_0, ZERO_choice)
ONE_choice = get_choices(nets[1], X_1)
ONE_predictions = get_predictions(nets[1], X_1)
X_2 = add_CHOICE_binary_raw(X_1, ONE_choice)
# remove scorings
TWO_predictions = get_predictions(nets[2], X_2)
# for each state
for j in range(len(TWO_predictions)):
moves = find_legal_moves(Ai[j], bin_state[j], "binary raw")
# approximated precision values
casep = []
# real precision values
prec = []
# choices of the first and second networks
ZC = ZERO_choice[j]
OC = ONE_choice[j]
choices = [ZC, OC, -1]
Tsp = TWO_predictions[j]
num_pred = len(Tsp)
# se la seconda rete pensa che sia il caso di rimuovere
if (numpy.argmax(Tsp) != 0):
l = 0
# SPEED UP: exclude this state if the previous networks have
# already made illegal decisions
TOchoice = choices[TOnet[2]]
FROMchoice = choices[FROMnet[2]]
REMOVEchoice = choices[REMOVEnet[2]]
if orderc[2] == 'T':
move = ("F" + str(FROMchoice) + "R" + str(REMOVEchoice))
elif orderc[2] == 'F':
move = ("T" + str(TOchoice) + "R" + str(REMOVEchoice))
elif orderc[2] == 'R':
move = ("T" + str(TOchoice) + "F" + str(FROMchoice))
if move in moves:
# per ogni possibile terza scelta
for k in range(num_pred):
TWO_choice = numpy.argmax(Tsp)
Tsp[TWO_choice] = -1
choices[2] = TWO_choice
TOchoice = choices[TOnet[2]]
FROMchoice = choices[FROMnet[2]]
REMOVEchoice = choices[REMOVEnet[2]]
move = ("T" + str(TOchoice) + "F" + str(FROMchoice) +
"R" + str(REMOVEchoice))
# each time a legal move is found,
# compute the precision
if move in moves:
l += 1
prec.append(l * 1.0 / (k + 1.0) * 100.0)
# compute the approximated values
if (l > 0):
rec = 0
for k in range(num_pred):
# if the approximated recal il less than the
# real recall
if ((k + 1.0) / num_pred) <= ((rec + 1.0) / l):
# assign the next value of precision
casep.append(prec[rec])
else:
# look at the next recall value and
# assign its precision value
rec += 1
casep.append(prec[rec])
T_p.append(casep)
# probabilities of this state for the second network
casep = []
prec = []
choices = [ZC, -1, -1]
Osp = ONE_predictions[j]
num_pred = len(Osp)
l = 0
# exclude this state if
if (numpy.argmax(Osp) != 0):
# SPEED UP: exclude this state if the previous networks have
# already made illegal decisions
TOchoice = choices[TOnet[2]]
FROMchoice = choices[FROMnet[2]]
REMOVEchoice = choices[REMOVEnet[2]]
if orderc[0] == 'T':
move = ("T" + str(TOchoice))
elif orderc[0] == 'F':
move = ("F" + str(FROMchoice))
elif orderc[0] == 'R':
move = ("R" + str(REMOVEchoice))
if move in moves:
# per ogni possibile seconda scelta
for k in range(num_pred):
OC = numpy.argmax(Osp)
Osp[OC] = -1
choices[1] = OC
#print("Choices: " + str(choices))
TOchoice = choices[TOnet[2]]
FROMchoice = choices[FROMnet[2]]
REMOVEchoice = choices[REMOVEnet[2]]
move = "ERROR"
# create the move according to the lacking network (the 3)
if orderc[2] == 'T':
move = ("F" + str(FROMchoice) + "R" +
str(REMOVEchoice))
elif orderc[2] == 'F':
move = ("T" + str(TOchoice) + "R" +
str(REMOVEchoice))
elif orderc[2] == 'R':
move = ("T" + str(TOchoice) + "F" +
str(FROMchoice))
if move in moves:
l += 1
prec.append(l * 1.0 / (k + 1.0) * 100.0)
if (l > 0):
rec = 0
for k in range(num_pred):
#print (rec)
if ((k + 1.0) / num_pred) <= ((rec + 1.0) / l):
casep.append(prec[rec])
else:
rec += 1
casep.append(prec[rec])
#print(str(casep))
O_p.append(casep)
# probabilities of this state for the first network
casep = []
prec = []
choices = [-1, -1, -1]
Zsp = ZERO_predictions[j]
num_pred = len(Zsp)
# se la prima rete pensa che sia il caso di rimuovere
if (numpy.argmax(Zsp) != 0):
l = 0.0
# per ogni possibile rimozione
for k in range(num_pred):
ZC = numpy.argmax(Zsp)
Zsp[ZC] = -1
choices[0] = ZC
TOchoice = choices[TOnet[2]]
FROMchoice = choices[FROMnet[2]]
REMOVEchoice = choices[REMOVEnet[2]]
move = "ERROR"
# create the move according to the only network
if orderc[0] == 'T':
move = ("T" + str(TOchoice))
elif orderc[0] == 'F':
move = ("F" + str(FROMchoice))
elif orderc[0] == 'R':
move = ("R" + str(REMOVEchoice))
if move in moves:
l += 1
prec.append(l * 1.0 / (k + 1.0) * 100.0)
if (l > 0):
rec = 0
for k in range(num_pred):
#print (rec)
if ((k + 1.0) / num_pred) <= ((rec + 1.0) / l):
casep.append(prec[rec])
else:
rec += 1
casep.append(prec[rec])
#print(str(casep))
Z_p.append(casep)
act_time = time.time()
# gives feedback about the amount of data processed
while (percentage > perc):
perc += 1
if perc != 100:
print(
str(round(percentage, 2)) + "%\t" + str(i * batchsize) +
" data processed\ttime passed: " + str(act_time - start_time))
print("100%\t" + str(num_data) + " data processed\ttime passed: " +
str(act_time - start_time))
filerel = open(name + "_reliability.txt", 'a')
filerel.write("Test on: " + datasetname)
report = ""
report += ("\n\n" + ordernames[2] + ":\n")
if len(T_p) > 0:
m = numpy.mean(T_p, 0)
for num in m:
report += (str(round(num, 2)) + "\t")
report += ("\n\n" + ordernames[1] + ":\n")
if len(O_p) > 0:
m = numpy.mean(O_p, 0)
for num in m:
report += (str(round(num, 2)) + "\t")
report += ("\n\n" + ordernames[0] + ":\n")
m = numpy.mean(Z_p, 0)
for num in m:
report += (str(round(num, 2)) + "\t")
report += ("\n\n\n\n\n")
filerel.write(report)
print(report)
filerel.close()
act_time = time.time()
print("Time occurred: " + str(act_time - start_time))