def report_time_spent(ga_engine):
global _time_of_prev_gen
now = time.time()
gen = ga_engine.getCurrentGeneration()
print "Time spent on generation %s, total %s." % \
(SimpleTimer.period_to_string(_time_of_prev_gen[1], now),
SimpleTimer.period_to_string(_time_of_prev_gen[0], now))
_time_of_prev_gen[1] = now
def report_time_spent(ga_engine):
global _time_of_prev_gen
now = time.time()
gen = ga_engine.getCurrentGeneration()
print "Time spent on generation %s, total %s." % \
(SimpleTimer.period_to_string(_time_of_prev_gen[1], now),
SimpleTimer.period_to_string(_time_of_prev_gen[0], now))
_time_of_prev_gen[1] = now
# Try smoothing/cleansing different time series lengths
for hindsight_days in [1]:
# Select data
num_hours = 24 * hindsight_days
data = dataset["Load"][-num_hours:].copy()
# Some output and rough timing
print "Cleansing %d hours of data with smoothness %.2f, z-score %.2f..." % (num_hours, smoothness, zscore)
sys.stdout.flush()
start_time = time.time()
# This is the part that takes time
smoother = _get_smoother()(data, smoothness)
cleaner = cln.RegressionCleaner(smoother, zscore)
cleaned, _ = cleaner.get_cleaned_data(method=cln.RegressionCleaner.replace_with_bound)
# Wrap up and plot the result
end_time = time.time()
print "Done in %s." % SimpleTimer.period_to_string(start_time, end_time)
print cleaned
sys.stdout.flush()
plt.figure()
data.plot(style="r", label="Raw load")
spline = pd.TimeSeries(data=smoother.splev(range(len(cleaned))), index=cleaned.index)
spline.plot(style="g", label="Smoothing spline")
# THE SAUSAGE!
lower, upper = cleaner.get_confidence_interval()
ax = plt.gca()
ax.fill_between(cleaned.index, lower, upper, facecolor="g", alpha=0.1)
cleaned.plot(style="b", label="Cleaned load")
# Select data
num_hours = 24 * hindsight_days
data = dataset["Load"][-num_hours:].copy()
# Some output and rough timing
print "Cleansing %d hours of data with smoothness %.2f, z-score %.2f..." % \
(num_hours, smoothness, zscore)
sys.stdout.flush()
start_time = time.time()
# This is the part that takes time
smoother = _get_smoother()(data, smoothness)
cleaner = cln.RegressionCleaner(smoother, zscore)
cleaned, _ = cleaner.get_cleaned_data(
method=cln.RegressionCleaner.replace_with_bound)
# Wrap up and plot the result
end_time = time.time()
print "Done in %s." % SimpleTimer.period_to_string(start_time, end_time)
print cleaned
sys.stdout.flush()
plt.figure()
data.plot(style='r', label='Raw load')
spline = pd.TimeSeries(data=smoother.splev(range(len(cleaned))),
index=cleaned.index)
spline.plot(style='g', label='Smoothing spline')
# THE SAUSAGE!
lower, upper = cleaner.get_confidence_interval()
ax = plt.gca()
ax.fill_between(cleaned.index, lower, upper, facecolor='g', alpha=0.1)
