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paper_ploter.py
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paper_ploter.py
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# Jing-Kai Lou (kaeaura@gamil.com)
import os
import sys
sys.path.append('analyzer')
import matplotlib.pylab as plt
from db import LiteDB
def load_data(path, file_dict):
all_dbs = os.listdir(path)
assert(set(all_filenames.values()).issubset(set(all_dbs)))
db = LiteDB()
all_keynames = {}
read_keynames = set()
for read_tracename, read_filename in all_filenames.iteritems():
db.load(os.path.join(path, read_filename))
read_keyname = set(db.keys()).difference(read_keynames).pop()
all_keynames.__setitem__(read_tracename, read_keyname)
read_keynames.add(read_keyname)
return(db, all_keynames)
def plot_frac(db, keynames, save_path):
inFrac_name = 'inFrac_dict'
outFrac_name = 'outFrac_dict'
plt.clf()
plt.figure(figsize = (8, 5))
x_upperbound = .2
plt.subplot(2, 2, 1)
plt.plot(map(lambda x: x / 1000.0, db[keynames['mog']][inFrac_name].keys()), db[keynames['mog']][inFrac_name].values(), 'b-', lw = 5, label = 'fairyland')
plt.plot(map(lambda x: x / 1000.0, db[keynames['mblg']][inFrac_name].keys()), db[keynames['mblg']][inFrac_name].values(), 'r:', lw = 5, label = 'twitter')
plt.plot(map(lambda x: x / 1000.0, db[keynames['im']][inFrac_name].keys()), db[keynames['im']][inFrac_name].values(), 'k--', lw = 5, label = 'yahoo')
plt.xlim(xmax = x_upperbound)
plt.ylabel('wcc fraction (id)')
plt.title('interaction')
plt.grid(True)
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best', prop = {'size':10})
plt.subplot(2, 2, 2)
plt.plot(map(lambda x: x / 1000.0, db[keynames['mogF']][inFrac_name].keys()), db[keynames['mogF']][inFrac_name].values(), 'b-', lw = 5, label = 'fairyland')
plt.plot(map(lambda x: x / 1000.0, db[keynames['mblgF']][inFrac_name].keys()), db[keynames['mblgF']][inFrac_name].values(), 'r:', lw = 5, label = 'twitter')
plt.plot(map(lambda x: x / 1000.0, db[keynames['imF']][inFrac_name].keys()), db[keynames['imF']][inFrac_name].values(), 'k--', lw = 5, label = 'yahoo')
plt.xlim(xmax = x_upperbound)
plt.title('ally')
plt.grid(True)
#plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best')
plt.subplot(2, 2, 3)
plt.plot(map(lambda x: x / 1000.0, db[keynames['mog']][outFrac_name].keys()), db[keynames['mog']][outFrac_name].values(), 'b-', lw = 5, label = 'fairyland')
plt.plot(map(lambda x: x / 1000.0, db[keynames['mblg']][outFrac_name].keys()), db[keynames['mblg']][outFrac_name].values(), 'r:', lw = 5, label = 'twitter')
plt.plot(map(lambda x: x / 1000.0, db[keynames['im']][outFrac_name].keys()), db[keynames['im']][outFrac_name].values(), 'k--', lw = 5, label = 'yahoo')
plt.xlim(xmax = x_upperbound)
plt.xlabel('removed fraction by od (%)')
plt.ylabel('wc fraction (od)')
plt.grid(True)
plt.subplot(2, 2, 4)
plt.plot(map(lambda x: x / 1000.0, db[keynames['mogF']][outFrac_name].keys()), db[keynames['mogF']][outFrac_name].values(), 'b-', lw = 5, label = 'fairyland')
plt.plot(map(lambda x: x / 1000.0, db[keynames['mblgF']][outFrac_name].keys()), db[keynames['mblgF']][outFrac_name].values(), 'r:', lw = 5, label = 'twitter')
plt.plot(map(lambda x: x / 1000.0, db[keynames['imF']][outFrac_name].keys()), db[keynames['imF']][outFrac_name].values(), 'k--', lw = 5, label = 'yahoo')
plt.xlim(xmax = x_upperbound)
plt.xlabel('removed fraction (%)')
plt.grid(True)
plt.savefig(os.path.join(save_dir, save_path))
def plot_degreeRate(db, keynames, save_path):
degRate_x_name = 'degRateDistr_x'
degRate_y_name = 'degRateDistr_y'
plt.clf()
plt.figure(figsize = (8, 5))
plt.subplot(1, 2, 1)
plt.plot(db[keynames['mog']][degRate_x_name], db[keynames['mog']][degRate_y_name], 'b-', lw=5, label = 'fairyland')
plt.plot(db[keynames['mblg']][degRate_x_name], db[keynames['mblg']][degRate_y_name], 'r:', lw=5, label = 'twitter')
plt.plot(db[keynames['im']][degRate_x_name], db[keynames['im']][degRate_y_name], 'k--', lw=5, label = 'yahoo')
plt.xscale('log')
plt.grid(True)
plt.title('interaction')
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 4, prop = {'size': 10})
plt.xlabel('In-degree to Out-degree Ratio')
plt.ylabel('CDF')
plt.subplot(1, 2, 2)
plt.plot(db[keynames['mogF']][degRate_x_name], db[keynames['mogF']][degRate_y_name], 'b-', lw=5, label = 'fairyland')
plt.plot(db[keynames['mblgF']][degRate_x_name], db[keynames['mblgF']][degRate_y_name], 'r:', lw=5, label = 'twitter')
#plt.plot(db[keynames['imF']][degRate_x_name], db[keynames['imF']][degRate_y_name], 'k--', lw=5, label = 'yahoo')
plt.xscale('log')
plt.grid(True)
plt.title('ally')
plt.xlabel('In-degree to Out-degree Ratio')
plt.ylabel('CDF')
plt.savefig(os.path.join(save_dir, save_path))
def plot_degreeOverlap(db, keynames, save_path, attr_name = 'degOverlapRatio'):
plt.clf()
plt.figure(figsize = (8, 5))
x = sorted(db[keynames['mog']][attr_name].keys())
y = [db[keynames['mog']][attr_name][xx] for xx in x]
plt.plot(x, y, 'b-', lw = 5, label = 'fairyland interaction')
x = sorted(db[keynames['mblg']][attr_name].keys())
y = [db[keynames['mblg']][attr_name][xx] for xx in x]
plt.plot(x, y, 'r:', lw = 5, label = 'twitter interaction')
x = sorted(db[keynames['im']][attr_name].keys())
y = [db[keynames['im']][attr_name][xx] for xx in x]
plt.plot(x, y, 'k--', lw = 5, label = 'yahoo interaction')
x = sorted(db[keynames['mogF']][attr_name].keys())
y = [db[keynames['mogF']][attr_name][xx] for xx in x]
plt.plot(x, y, 'b.', label = 'fairyland ally')
x = sorted(db[keynames['mblgF']][attr_name].keys())
y = [db[keynames['mblgF']][attr_name][xx] for xx in x]
plt.plot(x, y, 'k*', label = 'twitter ally')
plt.grid(True)
plt.title('Overlap')
plt.xlabel('Fraction of Users ordered by degree (%)')
plt.ylabel('Overlap (%)')
plt.legend(('fairyland interaction', 'twitter interaction', 'yahoo interaction', 'fairyland ally', 'twitter ally'), loc = 'best')
plt.savefig(os.path.join(save_dir, save_path))
def plot_degreeDistr(db, keynames, save_path):
indeg_x_name = 'inDegDistr_x'
indeg_y_name = 'inDegDistr_y'
outdeg_x_name = 'outDegDistr_x'
outdeg_y_name = 'outDegDistr_y'
deg_x_name = 'degDistr_x'
deg_y_name = 'degDistr_y'
x_upperbound = 10 ** 5
y_lowerbound = 10 ** -6
plt.clf()
plt.figure(figsize = (8, 5))
plt.subplot(2, 3, 1)
plt.loglog(db[keynames['mog']][indeg_x_name], db[keynames['mog']][indeg_y_name], 'b-', linewidth=5, label='id')
plt.loglog(db[keynames['mog']][outdeg_x_name], db[keynames['mog']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['mogF']][indeg_x_name], db[keynames['mogF']][indeg_y_name], 'r--', linewidth=5, label='ally')
plt.ylabel('interaction CCDF')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.title('Fairyland Online')
plt.legend(('id', 'od'), loc = 3, prop = {'size': 7})
plt.subplot(2, 3, 2)
plt.loglog(db[keynames['mblg']][indeg_x_name], db[keynames['mblg']][indeg_y_name], 'b-', linewidth=5, label='id')
plt.loglog(db[keynames['mblg']][outdeg_x_name], db[keynames['mblg']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['mblgF']][indeg_x_name], db[keynames['mblgF']][indeg_y_name], 'r--', linewidth=5, label='ally')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.title('Twitter')
plt.subplot(2, 3, 3)
plt.loglog(db[keynames['im']][indeg_x_name], db[keynames['im']][indeg_y_name], 'b-', linewidth=5, label='id')
plt.loglog(db[keynames['im']][outdeg_x_name], db[keynames['im']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['imF']][indeg_x_name], db[keynames['imF']][indeg_y_name], 'r--', linewidth=5, label='ally')
plt.xlabel('k')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.title('Yahoo! Messenger')
plt.subplot(2, 3, 4)
plt.loglog(db[keynames['mogF']][indeg_x_name], db[keynames['mogF']][indeg_y_name], 'b-', linewidth=5, label='id')
plt.loglog(db[keynames['mogF']][outdeg_x_name], db[keynames['mogF']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['mogF']][outdeg_x_name], db[keynames['mogF']][outdeg_y_name], 'r--', linewidth=5, label='ally')
plt.xlabel('k')
plt.ylabel('ally CCDF')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.subplot(2, 3, 5)
plt.loglog(db[keynames['mblgF']][indeg_x_name], db[keynames['mblgF']][indeg_y_name], 'b-', linewidth=5, label='id')
plt.loglog(db[keynames['mblgF']][outdeg_x_name], db[keynames['mblgF']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['mblgF']][outdeg_x_name], db[keynames['mblgF']][outdeg_y_name], 'r--', linewidth=5, label='ally')
plt.xlabel('k')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.subplot(2, 3, 6)
plt.loglog(db[keynames['imF']][deg_x_name], db[keynames['imF']][deg_y_name], 'k-', linewidth=5, label='d')
#plt.loglog(db[keynames['imF']][indeg_x_name], db[keynames['imF']][indeg_y_name], 'b-', linewidth=5, label='id')
#plt.loglog(db[keynames['imF']][outdeg_x_name], db[keynames['imF']][outdeg_y_name], 'r--', linewidth=5, label='od')
#plt.loglog(db[keynames['imF']][outdeg_x_name], db[keynames['imF']][outdeg_y_name], 'r--', linewidth=5, label='ally')
plt.xlabel('k')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymin = y_lowerbound)
plt.legend(('d'), loc = 'best', prop = {'size':7})
plt.savefig(os.path.join(save_dir, save_path))
def plot_reusage(db, keynames, save_path, attr_name = [ 'linkWeightDistr_x', 'linkWeightDistr_y' ]):
plt.clf()
plt.figure(figsize = (8, 5))
plt.loglog(db[keynames['mog']][attr_name[0]], db[keynames['mog']][attr_name[1]], 'b-', lw = 5, label = 'fariyland')
plt.loglog(db[keynames['mblg']][attr_name[0]], db[keynames['mblg']][attr_name[1]], 'r:', lw = 5, label = 'twitter')
plt.loglog(db[keynames['im']][attr_name[0]], db[keynames['im']][attr_name[1]], 'k--', lw = 5, label = 'yahoo')
plt.xlabel('Usage (days)')
plt.ylabel('CCDF')
plt.title('Usage of Links')
plt.grid(True)
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best')
plt.savefig(os.path.join(save_dir, save_path))
# knn via degree bin ####
def plot_KnnDistr(db, keynames, save_path):
knnMean_x_name = 'knnDistr_avg_x'
knnMean_y_name = 'knnDistr_avg_y'
knnMedian_x_name = 'knnDistr_median_x'
knnMedian_y_name = 'knnDistr_median_y'
inKnnMean_x_name = 'inKnnDistr_avg_x'
inKnnMean_y_name = 'inKnnDistr_avg_y'
inKnnMedian_x_name = 'inKnnDistr_median_x'
inKnnMedian_y_name = 'inKnnDistr_median_y'
outKnnMean_x_name = 'outKnnDistr_avg_x'
outKnnMean_y_name = 'outKnnDistr_avg_y'
outKnnMedian_x_name = 'outKnnDistr_median_x'
outKnnMedian_y_name = 'outKnnDistr_median_y'
x_upperbound = 10 ** 5
x_lowerbound = 10 ** 0
y_upperbound = 10 ** 4
y_lowerbound = 10 ** -1
plt.clf()
plt.figure(figsize = (8, 8))
plt.subplot(2, 2, 1)
plt.loglog(db[keynames['mog']][inKnnMean_x_name], db[keynames['mog']][inKnnMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
plt.loglog(db[keynames['mblg']][inKnnMean_x_name], db[keynames['mblg']][inKnnMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
plt.loglog(db[keynames['im']][inKnnMean_x_name], db[keynames['im']][inKnnMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
plt.xlim((x_lowerbound, x_upperbound))
plt.ylim((y_lowerbound, y_upperbound))
plt.title('interaction')
plt.ylabel('$<k_{nn}>^{in}$')
plt.grid(True)
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best', prop = {'size': 10})
plt.subplot(2, 2, 2)
plt.loglog(db[keynames['mogF']][inKnnMean_x_name], db[keynames['mogF']][inKnnMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
plt.loglog(db[keynames['mblgF']][inKnnMean_x_name], db[keynames['mblgF']][inKnnMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
plt.loglog(db[keynames['imF']][knnMean_x_name], db[keynames['imF']][knnMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
plt.xlim((x_lowerbound, x_upperbound))
plt.ylim((y_lowerbound, y_upperbound))
plt.title('ally')
plt.grid(True)
plt.subplot(2, 2, 3)
plt.loglog(db[keynames['mog']][outKnnMean_x_name], db[keynames['mog']][outKnnMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
plt.loglog(db[keynames['mblg']][outKnnMean_x_name], db[keynames['mblg']][outKnnMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
plt.loglog(db[keynames['im']][outKnnMean_x_name], db[keynames['im']][outKnnMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
plt.xlim((x_lowerbound, x_upperbound))
plt.ylim((y_lowerbound, y_upperbound))
plt.title('interaction')
plt.xlabel('$k$')
plt.ylabel('$<k_{nn}>^{out}$')
plt.grid(True)
plt.subplot(2, 2, 4)
plt.loglog(db[keynames['mogF']][outKnnMean_x_name], db[keynames['mogF']][outKnnMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
#plt.loglog(db[keynames['mogF']][outKnnMedian_x_name], db[keynames['mogF']][outKnnMedian_y_name], 'g--', label='fairyland-median')
plt.loglog(db[keynames['mblgF']][outKnnMean_x_name], db[keynames['mblgF']][outKnnMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
#plt.loglog(db[keynames['mblgF']][outKnnMedian_x_name], db[keynames['mblgF']][outKnnMedian_y_name], 'y--', label='twitter-median')
plt.loglog(db[keynames['imF']][knnMean_x_name], db[keynames['imF']][knnMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
#plt.loglog(db[keynames['imF']][knnMedian_x_name], db[keynames['imF']][knnMedian_y_name], 'c--', ms=5, label='yahoo')
plt.xlim((x_lowerbound, x_upperbound))
plt.ylim((y_lowerbound, y_upperbound))
plt.title('ally')
plt.xlabel('$k$')
plt.grid(True)
plt.savefig(os.path.join(save_dir, save_path))
# clustering ####
def plot_clustDistr(db, keynames, save_path):
cluMean_x_name = 'ccDistr_avg_x'
cluMean_y_name = 'ccDistr_avg_y'
cluMedian_x_name = 'ccDistr_median_x'
cluMedian_y_name = 'ccDistr_median_y'
x_upperbound = 10 ** 4
y_upperbound = .4
y_lowerbound = 10 ** -5
plt.clf()
plt.figure(figsize = (8, 5))
plt.subplot(1, 2, 1)
plt.loglog(db[keynames['mog']][cluMean_x_name], db[keynames['mog']][cluMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
plt.loglog(db[keynames['mblg']][cluMean_x_name], db[keynames['mblg']][cluMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
plt.loglog(db[keynames['im']][cluMean_x_name], db[keynames['im']][cluMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
plt.title('interaction')
plt.xlabel('$k$')
plt.ylabel('cc')
plt.grid(True)
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best', prop = {'size': 10})
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
plt.subplot(1, 2, 2)
plt.loglog(db[keynames['mogF']][cluMean_x_name], db[keynames['mogF']][cluMean_y_name], 'b.', mec='b', ms=5, label='fairyland')
plt.loglog(db[keynames['mblgF']][cluMean_x_name], db[keynames['mblgF']][cluMean_y_name], 'r+', mec='r', mfc='None', ms=5, label='twitter')
plt.loglog(db[keynames['imF']][cluMean_x_name], db[keynames['imF']][cluMean_y_name], 'k>', mec='k', mfc='None', ms=5, label='yahoo')
plt.title('ally')
plt.xlabel('$k$')
plt.grid(True)
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
plt.savefig(os.path.join(save_dir, save_path))
def plot_dist(db, keynames, save_path):
"""plot the sample path distance (in number of hops)"""
diam_name = 'diameter'
dist_name = 'hop_counts'
plt.clf()
plt.figure(figsize = (8, 5))
plt.subplot(1, 2, 1)
x = db[keynames['mog']][dist_name].keys()
y = db[keynames['mog']][dist_name].values()
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'b-', lw = 3, label = 'fairyland')
x = db[keynames['mblg']][dist_name].keys()
y = db[keynames['mblg']][dist_name].values()
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'r:', lw = 3, label = 'twitter')
x = db[keynames['im']][dist_name].keys()
y = db[keynames['im']][dist_name].values()
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'k--', lw = 3, label = 'yahoo')
plt.title('interaction')
plt.grid(True)
plt.xlabel('hop number')
plt.ylabel('probability')
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best')
plt.xlim((0, 15))
plt.ylim((0, .7))
plt.subplot(1, 2, 2)
x = db[keynames['mogF']][dist_name].keys()
y = db[keynames['mogF']][dist_name].values()
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'b-', lw = 3, label = 'fairyland')
plt.title('ally')
plt.grid(True)
plt.xlabel('hop number')
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best')
plt.xlim((0, 15))
plt.ylim((0, .7))
plt.savefig(os.path.join(save_dir, save_path))
def plot_prefAttach(db, keynames, save_path):
"""docstring for plot_pref"""
pa_name = 'pref_attach'
import numpy as np
from scipy.optimize import leastsq
degree = 2
observation_length = 200
fit_length = 200
plt.clf()
plt.figure(figsize = (9, 7))
y_upperbound = 4 * 10 ** 6
y_lowerbound = 0
x_upperbound = observation_length
mSize = 5
x = sorted(db[keynames['mog']][pa_name].keys())
y = []
for k in x:
y.append(db[keynames['mog']][pa_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'b.', label = 'fairyland', ms = mSize)
plt.plot(x, y_est, 'b-', lw = 2, label = 'fairyland-fitness')
x = sorted(db[keynames['mblg']][pa_name].keys())
y = []
for k in x:
y.append(db[keynames['mblg']][pa_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'r+', label = 'twitter', ms = mSize)
plt.plot(x, y_est, 'r-', lw = 2, label = 'twitter-fitness')
x = sorted(db[keynames['im']][pa_name].keys())
y = []
for k in x:
y.append(db[keynames['im']][pa_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'k>', mfc='None', mec='k', label = 'yahoo', ms = mSize)
plt.plot(x, y_est, 'k--', lw = 2, label = 'yahoo-fitness')
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
#plt.grid(True)
plt.xlabel('k')
plt.ylabel('$R_k$')
plt.legend(('fairyland', 'fairyland-fitness', 'twitter', 'twitter-fitness', 'yahoo', 'yahoo-fitness'), loc = 2, ncol = 2, prop = {'size': 12})
plt.savefig(os.path.join(save_dir, save_path))
def plot_clustProb(db, keynames, save_path, attr_name = 'cluster_prob'):
"""docstring for plot_pref"""
import numpy as np
degree = 2
observation_length = 200
fit_length = 200
plt.clf()
plt.figure(figsize = (9, 7))
y_upperbound = 4 * 10 ** 8
y_lowerbound = 0
x_upperbound = observation_length
mSize = 5
x = sorted(db[keynames['mog']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['mog']][attr_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'b.', label = 'fairyland', ms = mSize)
plt.plot(x, y_est, 'b-', lw = 2, label = 'fairyland-fitness')
x = sorted(db[keynames['mblg']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['mblg']][attr_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'r+', label = 'twitter', ms = mSize)
plt.plot(x, y_est, 'r-', lw = 2, label = 'twitter-fitness')
x = sorted(db[keynames['im']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['im']][attr_name][k])
if len(x) >= observation_length:
x = x[:observation_length]
y = y[:observation_length]
A = np.vander(x[:fit_length], degree + 1)
coef, residual, rank, sing_vals = np.linalg.lstsq(A, y[:fit_length])
f = np.poly1d(coef)
y_est = f(x)
plt.plot(x, y, 'k>', mfc='None', mec='k', label = 'yahoo', ms = mSize)
plt.plot(x, y_est, 'k--', lw = 2, label = 'yahoo-fitness')
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
#plt.grid(True)
plt.xlabel('k')
plt.ylabel('$R_k$')
plt.legend(('fairyland', 'fairyland-fitness', 'twitter', 'twitter-fitness', 'yahoo', 'yahoo-fitness'), loc = 1, ncol = 2, prop = {'size': 12})
plt.savefig(os.path.join(save_dir, save_path))
def plot_Knn(db, keynames, save_path, attr_names = ('knn', 'weighted_knn')):
plt.clf()
plt.figure(figsize = (8, 5))
mSize = 7
x_upperbound = 10 ** 4
y_upperbound = 10 ** 4
y_lowerbound = 10 ** 0
plt.subplot(1, 2, 1)
x = sorted(db[keynames['mog']][attr_names[0]].keys())
y = [db[keynames['mog']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'b.', mec = 'b', label = 'fairyland', ms = mSize)
x = sorted(db[keynames['mblg']][attr_names[0]].keys())
y = [db[keynames['mblg']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'r+', label = 'twitter', ms = mSize)
x = sorted(db[keynames['im']][attr_names[0]].keys())
y = [db[keynames['im']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'k>', mfc='None', label = 'yahoo', ms = mSize)
#plt.yscale('log')
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 'best')
plt.title('interaction')
plt.xlabel('$k$')
plt.ylabel('$K_{nn}$')
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
plt.grid(True)
plt.subplot(1, 2, 2)
x = sorted(db[keynames['mogF']][attr_names[0]].keys())
y = [db[keynames['mogF']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'b.', mec = 'b', label = 'fairyland', ms = mSize)
x = sorted(db[keynames['mblgF']][attr_names[0]].keys())
y = [db[keynames['mblgF']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'r+', label = 'twitter', ms = mSize)
x = sorted(db[keynames['imF']][attr_names[0]].keys())
y = [db[keynames['imF']][attr_names[0]][k] for k in x]
plt.loglog(x, y, 'k>', mfc='None', label = 'yahoo', ms = mSize)
plt.title('ally')
#plt.yscale('log')
plt.xlabel('$k$')
plt.xlim(xmax = x_upperbound)
plt.ylim(ymax = y_upperbound, ymin = y_lowerbound)
plt.grid(True)
plt.savefig(os.path.join(save_dir, save_path))
def plot_closureProb(db, keynames, save_path, attr_name):
plt.clf()
plt.figure(figsize = (9, 7))
mSize = 7
x = sorted(db[keynames['mog']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['mog']][attr_name][k])
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'bo-', label = 'fairyland', ms = mSize)
x = sorted(db[keynames['mblg']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['mblg']][attr_name][k])
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'r+:', label = 'twitter', ms = mSize)
x = sorted(db[keynames['im']][attr_name].keys())
y = []
for k in x:
y.append(db[keynames['im']][attr_name][k])
ySum = float(sum(y))
y = map(lambda z: z / ySum, y)
plt.plot(x, y, 'k>--', mfc='None', mec='k', label = 'yahoo', ms = mSize)
plt.grid(True)
plt.xlim(xmax = 15)
plt.xlabel('hop count $d$')
plt.ylabel('$P(d)$')
plt.legend(('fairyland', 'twitter', 'yahoo'), loc = 1)
plt.savefig(os.path.join(save_dir, save_path))
# loading and plot ##########
flag = [1]
save_dir = 'doc/interactions_over_platforms/figures'
print (flag)
if 1 in flag:
db_path = '../exp/db/degree'
all_filenames = {'mog':'anderson_chat_degree.db', 'mogF': 'anderson_friend_degree.db', 'mblg':'twitter_cut_degree.db', 'mblgF':'twitter_follower_degree.db', 'im': 'yahoo_chat_degree.db', 'imF':'yahoo_friend_degree.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_degreeRate(db, all_keynames, 'degree_rate_distribution.pdf')
plot_degreeOverlap(db, all_keynames, 'degree_overlap.pdf')
plot_reusage(db, all_keynames, save_path = 'link_reusage.pdf')
plot_degreeDistr(db, all_keynames, 'degree_distribution.pdf')
if 2 in flag:
db_path = '../exp/db/focus'
all_filenames = {'mog':'anderson_chat.db', 'mogF': 'anderson_friend.db', 'mblg':'twitter_cut.db', 'mblgF':'twitter_follower.db', 'im': 'yahoo_chat.db', 'imF':'yahoo_friend.db'}
db, all_keynames = load_data(db_path, all_filenames)
#plot_degreeDistr(db, all_keynames, 'degree_distribution.pdf')
#plot_KnnDistr(db, all_keynames, 'knn_distribution.pdf')
plot_clustDistr(db, all_keynames, 'clust_distribution.pdf')
if 3 in flag:
db_path = '../exp/db/knn'
all_filenames = {'mog':'anderson_chat_knn.db', 'mogF': 'anderson_friend_knn.db', 'mblg':'twitter_cut_knn.db', 'mblgF':'twitter_follower_knn.db', 'im': 'yahoo_chat_knn.db', 'imF':'yahoo_friend_knn.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_Knn(db, all_keynames, save_path = 'knn_distribution.pdf')
if 4 in flag:
db_path = '../exp/db/frac'
all_filenames = {'mog':'anderson_chat_frac.db', 'mogF': 'anderson_friend_frac.db', 'mblg':'twitter_cut_frac.db', 'mblgF':'twitter_follower_frac.db', 'im': 'yahoo_chat_frac.db', 'imF': 'yahoo_friend_frac.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_frac(db, all_keynames, 'frac.pdf')
if 5 in flag:
db_path = '../exp/db/dist'
all_filenames = {'mog':'anderson_chat_dist.db', 'mogF': 'anderson_friend_dist.db', 'mblg':'twitter_cut_dist.db', 'im': 'yahoo_chat_dist.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_dist(db, all_keynames, 'dist.pdf')
if 6 in flag:
db_path = '../exp/db/pa'
all_filenames = {'mog': 'anderson_chat_pa.db', 'mblg':'twitter_chat_pa.db', 'im': 'yahoo_chat_pa.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_prefAttach(db, all_keynames, 'pref_attachment_rp.pdf')
db_path = '../exp/db/cr'
all_filenames = {'mog': 'anderson_chat_cr.db', 'mblg':'twitter_chat_cr.db', 'im': 'yahoo_chat_cr.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_clustProb(db, all_keynames, 'clust_rp.pdf')
db_path = '../exp/db/cd'
all_filenames = {'mog': 'anderson_chat_cd.db', 'mblg':'twitter_chat_cd.db', 'im': 'yahoo_chat_cd.db'}
db, all_keynames = load_data(db_path, all_filenames)
plot_closureProb(db, all_keynames, 'closure_dist.pdf', attr_name = 'closure_dist')