def analyzeAFCStudies(log: str, results: str, n0: int, n1: int) -> tuple: """Extracts the times out of the log file generated from DylAFC extracts the x0 and x1 vectors and the ranks from the results file from DylComp""" times = list() with open(log) as f: for line in f: line: list = line.strip().split() times.append(float(line[-1])) data, D0, D1 = continuousScale(n0, n1) comp = Comparator(data, rand=True) comp.learn(results) for arr in treeMergeSort(data[:], comp): pass indeciesAFC: list = [arr.index(i) for i in range(256)] x0, x1 = genX0X1(arr, D1, D0) x0: np.ndarray = np.array([indeciesAFC[i] for i in range(128)]) x1: np.ndarray = np.array([indeciesAFC[i] for i in range(128, 256)]) return times, x0, x1, indeciesAFC
def sort(args) -> list:
"""Performs a sort based on the given args.
Args is of the format (dist, auc, n0, n1) and is one tuple/list.
Throws an error if the array did not sort correctly.
Returns the results."""
dist, auc, n0, n1 = args
results = list()
data, D0, D1 = continuousScale(n0, n1)
comp = Comparator(data, level=0, rand=True)
sep = genSep(dist, auc)
comp.genRand(n0, n1, sep, dist)
for arr, stats in treeMergeSort(data, comp, [(D0, D1), dist, auc], n=2):
stats.extend([len(comp), comp.genSeps(), comp.pc[-1]])
comp.resetPC()
results.append(stats)
if arr != sorted(arr, key=lambda x: comp.getLatentScore(x)[0]):
print(arr)
print(sorted(arr, key=lambda x: comp.getLatentScore(x)[0]))
raise AssertionError("did not sort")
return results
anim = FuncAnimation(fig,
update,
frames=np.arange(0, frames),
interval=100)
anim.save("rocs.gif", writer=PillowWriter(fps=10))
pbar.close()
else:
import matplotlib.pyplot as plt
from apng import APNG
from DylSort import treeMergeSort
from DylComp import Comparator
from DylData import continuousScale
from DylMath import genROC, avROC
seed = 15
data, D0, D1 = continuousScale(128, 128)
comp = Comparator(data, level=0, rand=True, seed=seed)
comp.genRand(len(D0), len(D1), 7.72, 'exponential')
np.random.seed(seed)
im = APNG()
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
fig.suptitle("Pass number, MSE true, MSE empirical")
x = np.linspace(0, 1, num=200)
y = x**(1 / 7.72)
ax1.set_aspect('equal', 'box')
ax2.set_aspect('equal', 'box')
elo = simulation_ELO_targetAUC(True)
merge = treeMergeSort(data,
comp,
statParams=[(D0, D1)],
combGroups=False)
plt.tight_layout()
arrays: list = [[0, 1, 4, 2, 5, 3, 6],
[0, 1, 2, 4, 3, 5, 6],
[0, 1, 2, 4, 3, 5, 6],
[0, 1, 2, 3, 4, 5, 6]]
graphROCs(arrays, D0=[0, 1, 2, 3], D1=[4, 5, 6])
elif test == 5:
graphROC([4, 1, 2, 3], [1, 2], [3, 4])
elif test == 6:
from DylSort import treeMergeSort
from DylComp import Comparator
from DylData import continuousScale
import matplotlib
font: dict = {'size' : 10}
matplotlib.rc('font', **font)
data, D0, D1 = continuousScale(128, 128)
comp: Comparator = Comparator(data, rand=True, level=0, seed=15)
for arr in treeMergeSort(data, comp=comp):
pass
D0.sort(key = comp.getLatentScore)
D1.sort(key = comp.getLatentScore)
roc: dict = ROC1.rocxy(comp.getLatentScore(D1), comp.getLatentScore(D0))
graphROCs([arr], True, True, D0, D1)
elif test == 7:
roc1: list = [[0, 0], [0, 1], [1, 1]]
roc3 = roc2 = roc1
roc4: list = [[0, 0], [0.5, 0], [0.5, 0.5], [1, 1]]
avgROC: tuple = avROC([roc1, roc2, roc3, roc4])
fig = plt.figure(figsize=(4,4))
ax = fig.add_subplot(111)
ax.plot(*zip(*roc1), 'm', label='chunk1', ls='-')
ax.plot(*zip(*roc2), 'b', label='chunk2', ls='--')
for group in groups: arr.extend(group)
else:
arr: list = groups
yield (arr, runStats(groups, statParams + [n, layer, len(mergerss)], comp)) if statParams else arr
if __name__ == "__main__":
test: int = int(argv[1]) if len(argv) > 1 else 1
if test == 1:
if len(argv) > 5 or len(argv) < 4:
print("Usage:")
print(f"{__file__} 1 <n0> <n1> <directory to save file into (optional)>")
else:
import matplotlib.pyplot as plt
plt.rcParams["font.size"]: int = 10
data, D0, D1 = continuousScale(int(argv[2]), int(argv[3]))
comp: Comparator = Comparator(data, rand=True)
for arr in treeMergeSort(data, comp):
pass
arrays: list = [arr]
D0.sort(key=arr.index)
D1.sort(key=arr.index)
plt = graphROCs(arrays, True, D0=D0, D1=D1)
ax = plt.gca()
ax.set_title("")
plt.title("")
plt.gcf().suptitle("")
if len(argv) > 4:
plt.savefig(argv[4] + "/patches.pdf", bbox_inches = 'tight', pad_inches = 0)
else:
plt.show()
elif test == 3:
indeciesRight.pop(i)
if len(groups) == 1:
for i in range(indecies[0], indeciesRight[0] + 1):
output[OIndex] = groups[0][i]
OIndex += 1
yield output
left ^= toggle # toggle left if toggle == True
yield False
if __name__ == '__main__':
from DylComp import Comparator
for test in range(1, 7):
if test == 1:
objs: list = [*range(8)]
comp: Comparator = Comparator(objs, rand=False)
m: MultiMerger = MultiMerger([[0, 4], [1, 5], [2, 6], [3, 7]],
comp,
toggle=True)
while not m.inc():
pass
if m.output != [0, 1, 2, 3, 4, 5, 6, 7]:
raise AssertionError("wasn't right")
comp.clearHistory()
m1: MultiMerger = MultiMerger([[0, 4], [1, 5]], comp, toggle=True)
while not m1.inc():
pass
if m1.output != [0, 1, 4, 5]:
raise AssertionError("wasn't right")
m2: MultiMerger = MultiMerger([[2, 6], [3, 7]], comp, toggle=True)
while not m2.inc():