1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
#!/usr/bin/env python3
import pickle
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
def unpickle(pkl):
with open(pkl, "rb") as f:
data = pickle.load(f)
return data
def fix_vp(data, procedures):
procs = deepcopy(procedures)
if data["train_0"]["procedure_order"] == data["test_0"]["procedure_order"]:
keys = list(data["train_0"].keys())
keys.remove("procedure_order")
keys.remove("water_sample")
for key in keys:
procs.remove(key)
proc_from = keys[2]
proc_to = procs[0]
for train in [x for x in data.keys() if x.startswith("train")]:
vp = deepcopy(data[train])
vp[proc_to] = vp.pop(proc_from)
data[train] = vp
return data
def block_vps(data, condition):
blocked_vps = {}
for vp in data[condition].keys():
blocked_vps[vp] = blocked_time(data[condition][vp])
return blocked_vps
def blocked_time(vp):
key_stem = list(vp.keys())[0].split("_")[0]
trial_count = len(vp.keys())
block_size = 5
block_count = trial_count / block_size
result = {}
sum_time = 0
block_i = 1
for trial in range(1, trial_count):
if trial % 5 == 0:
sum_time = 0
block_i += 1
sum_time += sum_time_over_trial(vp[f"{key_stem}_{trial}"])
if block_i == 1:
avg = sum_time / 4
else:
avg = sum_time / 5
result[block_i] = avg
return result
def sum_time_over_trial(trial):
total_time = 0
for proc in trial.keys():
if proc != "procedure_order" and proc != "water_sample":
total_time += trial[proc]["time"]
return total_time
def plot_vp(ax, data_dict):
x = data_dict.keys()
y = data_dict.values()
ax.scatter(x, y)
def plot_average_vps(ax, label, blocked_vps):
xlist = [list(blocked_vps[x].keys()) for x in blocked_vps]
ylist = [list(blocked_vps[x].values()) for x in blocked_vps]
x = xlist[0]
yarray = np.array(ylist)
y = np.average(yarray, axis=0)
ax.scatter(x, y, label=label)
def count_correct(vp, trials, procedures):
trials_correct = {}
for proc in procedures:
trials_correct[proc] = 0
for sample in trials:
for proc in vp[sample]["procedure_order"]:
vp_ans = vp[sample][proc]["answer"]
for c in vp_ans:
if not c.isdigit():
vp_ans = vp_ans.replace(c, "")
vp_ans = int(vp_ans)
if vp_ans == vp[sample]["water_sample"][proc][0]:
trials_correct[proc] += 1
return trials_correct
def total_accuracy(vp, procedures):
train = [x for x in vp.keys() if "train" in x]
test = [x for x in vp.keys() if "test" in x]
train_total = len(train) * len(vp[train[0]]["procedure_order"])
test_total = len(test) * len(vp[test[0]]["procedure_order"])
acc_train = count_correct(vp, train, procedures)
acc_test = count_correct(vp, test, procedures)
acc_train = sum([acc_train[x] for x in acc_train.keys()]) / train_total
acc_test = sum([acc_test[x] for x in acc_test.keys()]) / test_total
return acc_train, acc_test
def train_test_split(data):
def delete_trials(data, string):
new_dict = {}
for cond in data.keys():
new_dict[cond] = {}
for vp in data[cond].keys():
new_dict[cond][vp] = {}
for trial in data[cond][vp].keys():
if string in trial and trial != "train_0":
new_dict[cond][vp][trial] = data[cond][vp][trial]
return new_dict
data_train = delete_trials(data, "train")
data_test = delete_trials(data, "test")
return data_train, data_test
print("imported tools")
|
