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exp_figures.py
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exp_figures.py
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import pandas as pd
import numpy as np
import datetime
import matplotlib.dates as md
from lib.plot import *
import matplotlib.pyplot as plt
# plt.switch_backend('agg')
from matplotlib import rc, font_manager
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
rcParams['xtick.major.pad'] = '8'
rcParams['xtick.minor.pad'] = '20'
rcParams['ytick.major.pad'] = '8'
rcParams['lines.linewidth'] = 2
rcParams['font.serif'] = 'Times New Roman'
rcParams['lines.markersize'] = 15
rcParams['legend.numpoints'] = 1
rcParams['lines.color'] = 'black'
rcParams["patch.force_edgecolor"] = True
sizeOfFont =22
fontProperties = {'family' : 'serif', 'serif':['Times New Roman'],
'weight' : 'normal', 'size' : sizeOfFont}
rc('font',**fontProperties)
legend_font = {'family': 'serif',
'weight': 'normal',
'size': 20,
}
bar_legend_font = {'family': 'serif',
'weight': 'normal',
'size': sizeOfFont,
}
label_font = {'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': sizeOfFont,
}
sec_label_font = {'family': 'serif',
'color': 'green',
'weight': 'normal',
'size': sizeOfFont,
}
color_list = ['slategrey', 'darkviolet', 'red', 'k', 'b', 'r', 'c',
'm', 'y', '#6ACC65',
'#988ED5', '#fdb462',
'#feffb3', '#8b8b8b']
marker_list = ['.', 'v', 's', 'x', 'd']
line_style = ['ko-', 'r^-', 'bd-', 'rh-', 'm*-']
patterns = [ "ko", "\\" , " ", "\/", "-" , "*" , "x", "o", ".", "*" ]
line_width = 2.5
mark_size = 2
fig_dpi = 100
label_space = 0.1
def hist_figure():
# zone = 'polygon'
zone = 'taxi_zone'
# df_file = './result/GODS_{}_S15_s6h3.csv'.format(zone)
# df_fcd_file = './result/od_{}_S15_s6h3.csv'.format(zone)
# df_fods_file = './result/direct_{}_S15_s6h3.csv'.format(zone)
df_file = './result/GODS_{}_S15_s6h3_add_sigma_S3H3.csv'.format(zone)
df_fcd_file = './result/od_{}_S15_s6h3_S3H3.csv'.format(zone)
df_fods_file = './result/direct_{}_S15_s6h3_S3H3.csv'.format(zone)
#
df_fods = pd.read_csv(df_fcd_file, sep=',')
df_fcd = pd.read_csv(df_fods_file, sep=',')
df_data = pd.read_csv(df_file, sep=',')
df_data['Interval'] = df_data['TI'] % 96
df_fcd['Interval'] = df_fcd['TI'] % 96
df_fods['Interval'] = df_fods['TI'] % 96
df_mura_file = './result/MURA_OD_nyc_15.csv'
df_mura = pd.read_csv(df_mura_file, sep=',')
df_mura['Interval'] = df_fods['TI'] % 96
print(df_data.head())
df_dict = {
'MR': df_mura,
'RNN': df_fcd,
'BF': df_fods,
'AF': df_data[df_data.Hopk == 2]
}
for i, keyf in enumerate(['FC', 'BF', 'AF']):
print(keyf)
for ki, key in enumerate(['KL', 'JS', 'EMD']):
x = []
y = []
for label, data in df_dict[keyf].groupby('Horizon'):
x.append(label)
y.append(data[key].mean())
print(x)
print(y)
print("_________________________")
# fig, ax = plt.subplots(1)
# for label, data in df_data.groupby('Horizon'):
# for ki, key in enumerate(['KL', 'JS', 'EMD']):
# x = []
# y = []
# std = []
# for interval, data_int in data.groupby('Hopk'):
# x.append(interval)
# y.append(data_int[key].mean())
# std.append(data_int[key].std())
# plt.plot(x, y, label=key, color=color_list[ki],
# marker=marker_list[ki], markersize=10)
# plt.legend(labelspacing=label_space, frameon=False)
# plt.ylim([0.0, 0.5])
# plt.xticks(range(2, 11, 2))
# plt.xlabel(r'$\alpha$', fontdict=label_font)
# plt.savefig('D:\\Dropbox\\OD_Prediction\\WWW19\\figure'
# '\\Diff_metrics_hopk_{}_{}.pdf'.format(zone, label), dpi=1000)
# plt.show()
#
# # # # Plot the Performance over time
# ylims = [0.4, 0.6, 0.4]
# leg_loc = [(0.4, 0.6), (0.0, 0.15), (0., 0.25)]
# for mi, method in enumerate(['EMD', 'KL', 'JS']):
# for label, data in df_data.groupby('Horizon'):
# fig, ax = plt.subplots(1)
# ax2 = ax.twinx()
# for i, key in enumerate(['FC', 'BF', 'AF']):
# df_i = df_dict[key]
# df_i = df_i[df_i.Horizon == label]
# total_amount = df_i.shape[0]
# x = []
# y = []
# time_x = []
# pers = []
# for interval in range(8):
# df_i_j = df_i[(df_i.Interval >= interval* 12) & (df_i.Interval < (interval + 1)*12)]
# if interval < 7:
# time_x.append(datetime.datetime(
# year=2013, month=8, day=3,
# hour=int((interval+1)*3), minute=0))
# else:
# time_x.append(datetime.datetime(
# year=2013, month=8, day=4,
# hour=0, minute=0))
# x.append(interval)
# y.append(df_i_j[method].mean())
# pers.append(df_i_j.shape[0] / total_amount * 100)
#
# line_x = np.array(x)*3 + 1.5
# bar_x = np.array(x)*3
# ax.plot(line_x, y, label=key, color=color_list[i],
# marker=marker_list[i], markersize=10)
# ax2.bar(bar_x, pers, align='edge', width=3, hatch='//')
#
# ax2.set_ylim([0.0, 50.0])
# ax2.set_ylabel('Percentage (%)', fontdict=sec_label_font)
# ax2.tick_params('y', colors='g')
#
# ax.legend(loc=leg_loc[mi], labelspacing=label_space, frameon=False)
# ax.set_ylim([0.0, ylims[mi]])
# ax.set_ylabel(method, fontdict=label_font)
# ax.set_xlabel('Hour in a Day', fontdict=label_font)
#
# xlocator = np.arange(0, 25, 3)
# ax.set_xticks(xlocator)
# ax.set_xlim([0.0, 24])
# # xformatter = md.DateFormatter('%H:%M')
# # xlocator = md.HourLocator(byhour=range(0, 24, 1), interval=6)
# # ax.xaxis.set_major_locator(xlocator)
# # ax.xaxis.set_major_formatter(xformatter)
# # datemin = datetime.datetime(year=2013, month=8, day=3, hour=2, minute=30)
# # ax.set_xlim(datemin)
#
# plt.savefig('D:\\Dropbox\\OD_Prediction\\WWW19\\figure'
# '\\Diff_methods_time_{}_{}_{}.pdf'.format(zone, method, label), dpi=1000)
# plt.show()
#
# # Plot the Performance over Distance
# ylims = [0.4, 0.6, 0.5]
# leg_loc = [(0.4, 0.6), (0.5, 0.2), (0.5, 0.2)]
# for mi, method in enumerate(['EMD', 'KL', 'JS']):
# for label, data in df_data.groupby('Horizon'):
# fig, ax = plt.subplots(1)
# ax2 = ax.twinx()
# for i, key in enumerate(['FC', 'BF', 'AF']):
# df_i = df_dict[key]
# df_i = df_i[df_i.Horizon == label]
# dist_range = np.arange(0., 3.01, 0.5)
# df_i = df_i[df_i.Dist <= 3.1]
# total_amount = df_i.shape[0]
# x = []
# y = []
# pers = []
# for j, dist_i in enumerate(dist_range[:-1]):
# df_i_j = df_i[(df_i.Dist >= dist_i) & (df_i.Dist < dist_range[j + 1])]
# x.append(dist_i)
# y.append(df_i_j[method].mean())
# pers.append(df_i_j.shape[0] / total_amount * 100)
#
# line_x = np.array(x) + 0.25
# bar_x = np.array(x)
# ax.plot(line_x, y, label=key, color=color_list[i],
# marker=marker_list[i], markersize=10)
# ax2.bar(bar_x, pers, align='edge', width=0.5, hatch='//')
#
# ax2.set_ylim([0.0, 90.0])
# ax2.set_ylabel('Percentage (%)', fontdict=sec_label_font)
# ax2.tick_params('y', colors='g')
#
# xlocator = np.arange(0., 3.01, 0.5)
# ax.set_xticks(xlocator)
# ax.set_xlim([0.0, 3.0])
#
# ax.legend(loc=leg_loc[mi], labelspacing=label_space, frameon=False)
# ax.set_ylim([0.0, ylims[mi]])
# ax.set_ylabel(method, fontdict=label_font)
# ax.set_xlabel('Distance (km)', fontdict=label_font)
# plt.savefig('D:\\Dropbox\\OD_Prediction\\WWW19\\figure'
# '\\Diff_methods_dist_{}_{}_{}.pdf'.format(zone, method, label), dpi=1000)
# plt.show()
# # Plot the Data Distribution
# label_font = {'family': 'serif',
# 'color': 'black',
# 'weight': 'normal',
# 'size': 18,
# }
# data_file = './data/chengdu/SecRing/2014-08-03_2014-08-31.csv'
# df_data = pd.read_csv(data_file)
# fig, ax = plt.subplots()
# ax2 = ax.twinx()
# n, bins, patches = ax.hist(df_data.speed, bins=10, density=True)
# n, bins, patches = ax2.hist(
# df_data.speed, cumulative=1, histtype='step', bins=10,
# color='tab:orange', density=True)
# plt.xlim([0.0, 18.0])
# plt.xticks(range(0, 19, 2))
# ax.set_ylabel('PDF', fontdict=label_font)
# ax2.set_ylabel('CDF', fontdict=label_font)
# plt.xlabel('Speed (m/s)', fontdict=label_font)
# plt.savefig('CD_Speed_Hist.pdf', dpi=1000)
#
# data_file = './data/nyc/Manhattan/2013-11-01_2014-01-01.csv'
# df_data = pd.read_csv(data_file)
# fig, ax = plt.subplots()
# ax2 = ax.twinx()
# data_speed = df_data.speed * 1607
# n, bins, patches = ax.hist(data_speed, bins=10, density=True)
# n, bins, patches = ax2.hist(
# data_speed, cumulative=1, histtype='step', bins=10,
# color='tab:orange', density=True)
# plt.xlim([0.0, 21.0])
# plt.xticks(range(0, 22, 4))
# ax.set_ylabel('PDF', fontdict=label_font)
# ax2.set_ylabel('CDF', fontdict=label_font)
# plt.xlabel('Speed (m/s)', fontdict=label_font)
# plt.savefig('NYC_Speed_Hist.pdf', dpi=1000)
if __name__ == '__main__':
hist_figure()