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
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()
for i in trange(8):
roc, mseTheo, mseEmp, empiricROC = next(elo)
ax1.plot(x, y, linestyle='--', label='true', lw=3)
ax1.plot(empiricROC['x'],
empiricROC['y'],
linestyle=':',
lw=2,
label='empirical')
ax1.plot(roc['x'], roc['y'], label='predicted')
ax1.legend(loc=4)
ax1.set_title(
f"ELO\n{i+1}, {mseTheo[0]*1000:02.3f}E(-3), {mseEmp[0]*1000:02.3f}E(-3)"
)
ax5.set_xticks(range(1, layers + 1))
ax5.set_xlim(left=-0.01, right=1.01)
ax5.set_ylim(bottom=-0.01, top=1.01)
ax5.set_aspect('equal', 'box')
ax5.set_title("avg ROC")
fig.delaxes(ax6)
plt.tight_layout()
plt.savefig("figure.svg")
comp.xVals = xVals
comp.xLabels = xLabels
print(data)
plots = list()
# give dummy 0 vals for dist and target AUC
for currLayer, (groups, stats) in enumerate(
treeMergeSort(data,
comp, [(D0, D1), 0, 0],
combGroups=False)):
print(groups)
rocs = list()
for group in groups:
rocs.append(genROC(group, D0, D1))
avgROC = avROC(rocs)
xLabels[currLayer] = len(comp)
auc, varEstimate, hanleyMcNeil, estimates = stats
f.write(''.join([str(val) + ',' for val in stats]))
f.write('\n')
aucs[currLayer] = auc
varEstimates[currLayer] = varEstimate
hmnEstimates[currLayer] = np.append(
np.full((layers - len(estimates)), np.nan), estimates)
compLens[currLayer] = len(comp)
[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='--')
ax.plot(*zip(*roc3), 'g', label='chunk3', ls=':')