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)
if len(argv) != 4:
print("Usage:")
print(f"{__file__} <log file output> <port> <roc file output>")
else:
from DylData import continuousScale
from DylSort import treeMergeSort
from DylMath import avROC, genROC, calcNLayers
import matplotlib.pyplot as plt
fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2,
ncols=3)
fig.set_size_inches(16, 9)
avgROC = None # this way when it dumps to file from an empty result there's no issues
roc4 = None # ''
with open(argv[1], "w") as f, NetComparator('127.0.0.1', int(argv[2]),
f) as comp:
data, D0, D1 = continuousScale(comp.n0, comp.n1)
comp.genLookup(data)
comp.layers = layers = calcNLayers(comp.n0 + comp.n1)
xVals: list = list(range(1, int(layers) + 1))
xLabels: list = ['' for _ in xVals]
aucs: np.ndarray = np.full((layers, ), np.nan)
varEstimates: np.ndarray = np.full((layers, ), np.nan)
hmnEstimates: np.ndarray = np.full((layers, layers), np.nan)
compLens: np.ndarray = np.full((layers, ), np.nan)
info: List[float] = [np.nan for i in range(layers)]
comp.aucs = aucs
comp.pax = ax1
comp.plt = plt
ax1.set_ylabel("AUC")
ax1.set_xlabel("comparisons")
ax1.set_xticks(xVals)
varEstimate: float = (sum(varOfSM) / (len(varOfSM)**2))
avgROC: tuple = avROC(rocs)
empiricROC: tuple = comp.empiricROC()
sep: float = genSep(dist, float(targetAUC)) # float in case it's a string
stats: list = [avgAUC, varEstimate, sum(hanleyMcNeils) / len(hanleyMcNeils)**2, estimates, *MSE(sep, dist, avgROC, empiricROC)[:2]]
return stats
if __name__ == "__main__":
from DylSort import mergeSort
test: int = 9
if test == 1:
#print(D0, D1)
newData, D0, D1 = continuousScale("sampledata.csv")
print(auc(genROC(newData)))
arrays: list = [newData[:]]
for _ in mergeSort(newData):
arrays.append(newData[:])
print(arrays)
graphROCs(arrays)
elif test == 3:
predicted: list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19]
print(aucSM(successMatrix(predicted, [*range(10)], [*range(10,20)])))
elif test == 4:
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])
arr: list = []
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()