def plotFirstTacROC(dataset):
import matplotlib.pylab as plt
from os.path import join
from src.utils import PROJECT_DIR
plt.figure(figsize=(6, 6))
time_sampler = TimeSerieSampler(n_time_points=12)
evaluator = Evaluator()
time_series_idx = 0
methods = {
"cross_correlation": "Cross corr. ",
"kendall": "Kendall ",
"symbol_mutual": "Symbol MI ",
"symbol_similarity": "Symbol sim.",
}
for method in methods:
print method
predictor = SingleSeriesPredictor(good_methods[method], time_sampler)
prediction = predictor.predictAllInstancesCombined(dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
plt.plot(fpr, tpr, label=methods[method] + " (auc = %0.3f)" % roc_auc)
plt.legend(loc="lower right")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.grid()
plt.savefig(join(PROJECT_DIR, "output", "firstTACROC.pdf"))
def plotFirstTacROC(dataset):
import matplotlib.pylab as plt
from os.path import join
from src.utils import PROJECT_DIR
plt.figure(figsize=(6, 6))
time_sampler = TimeSerieSampler(n_time_points=12)
evaluator = Evaluator()
time_series_idx = 0
methods = {
'cross_correlation': 'Cross corr. ',
'kendall': 'Kendall ',
'symbol_mutual': 'Symbol MI ',
'symbol_similarity': 'Symbol sim.'
}
for method in methods:
print method
predictor = SingleSeriesPredictor(good_methods[method], time_sampler)
prediction = predictor.predictAllInstancesCombined(
dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
plt.plot(fpr, tpr, label=methods[method] + ' (auc = %0.3f)' % roc_auc)
plt.legend(loc="lower right")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.grid()
plt.savefig(join(PROJECT_DIR, 'output', 'firstTACROC.pdf'))
def evaluateAllMethods(dataset):
time_sampler = TimeSerieSampler(n_time_points=15)
with open(path.join(OUTPUT_DIR, "evaluation", "presentation_1A.csv"), "w") as pout:
pout.write("," + ",".join(five_pin_setups) + "\n")
for method in good_methods:
print method
pout.write(method)
predictor = SingleSeriesPredictor(good_methods[method], time_sampler)
evaluator = Evaluator()
for time_series_idx in range(5):
prediction = predictor.predictAllInstancesCombined(dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
pout.write(", {0:.3f}".format(roc_auc))
pout.write("\n")
def evaluateNumTimepoints(dataset):
ntp = [e for e in range(4, 15)]
ntp.extend([e for e in range(15, 20, 2)])
methods = ["cross_correlation", "kendall", "symbol_mutual"]
evaluator = Evaluator()
time_series_idx = 0
res = [[] for m in methods]
for n_time_points in ntp:
print n_time_points
time_sampler = TimeSerieSampler(n_time_points=n_time_points)
for i, method in enumerate(methods):
predictor = SingleSeriesPredictor(good_methods[method], time_sampler)
prediction = predictor.predictAllInstancesCombined(dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
res[i].append(roc_auc)
print res
def evaluateAllMethods(dataset):
time_sampler = TimeSerieSampler(n_time_points=15)
with open(path.join(OUTPUT_DIR, "evaluation", "presentation_1A.csv"),
"w") as pout:
pout.write(',' + ','.join(five_pin_setups) + '\n')
for method in good_methods:
print method
pout.write(method)
predictor = SingleSeriesPredictor(good_methods[method],
time_sampler)
evaluator = Evaluator()
for time_series_idx in range(5):
prediction = predictor.predictAllInstancesCombined(
dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
pout.write(', {0:.3f}'.format(roc_auc))
pout.write('\n')
def evaluateNumTimepoints(dataset):
ntp = [e for e in range(4, 15)]
ntp.extend([e for e in range(15, 20, 2)])
methods = ['cross_correlation', 'kendall', 'symbol_mutual']
evaluator = Evaluator()
time_series_idx = 0
res = [[] for m in methods]
for n_time_points in ntp:
print n_time_points
time_sampler = TimeSerieSampler(n_time_points=n_time_points)
for i, method in enumerate(methods):
predictor = SingleSeriesPredictor(good_methods[method],
time_sampler)
prediction = predictor.predictAllInstancesCombined(
dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
res[i].append(roc_auc)
print res