def encodeNumericalData(interp_params, keyday, bkd, auth, avail, cap):
start, stop, num_points = interp_params
keyday, bkd, auth, avail = Utils.sortByIndex(keyday, bkd, auth, avail)
keyday, bkd, auth, avail, cap = filterDataForKeyDay(
start, keyday, bkd, auth, avail, cap)
keyday, bkd, auth, avail, cap = interpolateFlight(
interp_params, keyday, bkd, auth, avail, cap)
# Create any other features
delta_bkd = np.diff(bkd)
# Stack the numerical data into a feature matrix
nums = [each[:-1] for each in [keyday, bkd, auth, avail, cap]]
nums = np.column_stack(nums)
return delta_bkd, nums
def bookingClassTicketFrequencies(f, data, cabin):
print "Grouping into unique flight/booking class combinations"
flight_data = f.getUniqueFlightsAndBookings(data)
bcs = Utils.mapCabinToBookingClasses(cabin)
bcs = {bc: 0 for (bc, r) in bcs}
print "Iterating through all booking classes"
for flight, flight_df in flight_data:
bc = flight[-1]
keyday = -1 * flight_df["KEYDAY"]
bkd = flight_df["BKD"]
keyday, bkd = Utils.sortByIndex(keyday, bkd)
bcs[bc] += bkd[-1]
total_bkd = 0.0
for bc, num_bkd in bcs.items():
total_bkd += num_bkd
for bc in bcs:
bcs[bc] /= total_bkd
ks, vs = zip(*bcs.items())
ks, vs = zip(*sorted(zip(ks, vs), key=lambda tup: Utils.compareBCs(tup[0])))
indices = np.arange(len(ks))
width = 0.75
fig, ax = plt.subplots()
rects = ax.bar(indices, vs, width)
ax.set_ylabel("Percent of Total Booked")
ax.set_title("Booking Class Ticketing Distribution - Economy Cabin")
ax.set_xticks(indices + width / 2.0)
ax.set_xticklabels(ks)
plt.grid()
plt.show()
