Online-Go.Com Player Statistics¶

# Setup, connect to database, fetch data
%pylab inline
from pylab import *
# from prettyplotlib import *
import numpy as np
import datetime

import matplotlib.pyplot as plt
plt.style.use('ggplot')



figsize(8,6)

# connect to the player database
import sqlite3
db = sqlite3.connect('playerinfo_dump-2016-05-06.db')
cursor = db.cursor()

# registrations are strings in YYYY-MM-DD HH:MM:SS... format
# last_active are in scientific notation in milliseconds since the epoch, but sometimes "None"
registered_dates, last_active, rating, games_count = \
    zip(*cursor.execute('SELECT registered, last_active, rating, wins+losses+draws FROM pony').fetchall())

# Convert to numpy arrays
registered_dates = array([c[:10] for c in registered_dates], dtype='datetime64')
# convert to second since the epoch
last_active = array([(float(x) if x != "None" else -inf) for x in last_active]) / 1000
rating = array(rating)
games_count = array(games_count)

# Helper function for plotting time series nicely
import time
import datetime
import matplotlib.dates as mdates

def plot_time_series(dates, values):
    # setup - see http://matplotlib.org/examples/api/date_demo.html
    years    = mdates.YearLocator()   # every year
    months   = mdates.MonthLocator()  # every month
    yearsFmt = mdates.DateFormatter('%Y')

    dates = dates.astype(datetime.datetime)
    
    # plot user count vs time
    plot_date(dates, values, '-k')
    ax = gca()

    ax.xaxis.set_major_locator(years)
    ax.xaxis.set_major_formatter(yearsFmt)
    ax.xaxis.set_minor_locator(months)

    gcf().autofmt_xdate()
    
# useful for some plots where aliasing hides actual data
def jitter(data, lo=-1, hi=1):
    return data + np.random.uniform(lo, hi, len(data))

# registration dates are not sorted, due to the Nova/OGS merger
sorted_registered_dates = sort(registered_dates).astype(datetime.datetime)
plot_time_series(sorted_registered_dates, arange(len(sorted_registered_dates)))
title('Total registered users by date')
ylabel('# of registered users')
show()

# Registration trends since 2013.  Nova started in January (black line).
# Merge was in October of 2013 (green line).
# The cliff in the curve is a DB-udpate data artifact.
recent = sorted_registered_dates[sorted_registered_dates > datetime64('2012-11')]
base = len(sorted_registered_dates) - len(recent)
plot_time_series(recent, base + arange(len(recent)))
nova_start = datetime64('2013-01-01').astype(datetime.datetime)
ogs_merge = datetime64('2013-10-01').astype(datetime.datetime)
alphago_paper = datetime64('2016-01-28').astype(datetime.datetime)
leesedol_alphago = datetime64('2016-03-09').astype(datetime.datetime)
axvline(nova_start, c='k', lw=0.5)
axvline(ogs_merge, c='g', lw=0.5)
axvline(alphago_paper, c='g', lw=0.5)
axvline(leesedol_alphago, c='g', lw=0.5)

text(nova_start, 90000, " Nova")
text(ogs_merge, 80000, " Merge with OGS")
text(alphago_paper, 80000, "AlphaGo Paper", ha='right')
text(leesedol_alphago, 60000, "Lee Sedol vs. Alphago", ha='right')

ylabel('# of registered users')
title('Same graph as above, since October of 2013')

figure()

recent = sorted_registered_dates[sorted_registered_dates > datetime64('2015-01-01')]
base = len(sorted_registered_dates) - len(recent)
plot_time_series(recent, base + arange(len(recent)))

axvline(alphago_paper, c='g', lw=0.5)
axvline(leesedol_alphago, c='g', lw=0.5)

text(alphago_paper, 150000, "AlphaGo Paper", ha='right')
text(leesedol_alphago, 125000, "Lee Sedol vs. Alphago", ha='right')

ylabel('# of registered users')
title('... and since 2015')
show()

days_since_registered = ((max(registered_dates) - registered_dates) / timedelta64(1,'D')).astype(int)
hist(days_since_registered[days_since_registered < 120], bins=arange(121))
ylabel('registrations per day')
xlabel('days since {}'.format(max(registered_dates)))
show()

np.array([1, 2, np.inf]).astype('datetime64[s]')

# last_active is in seconds since the epoch.  Some values are "None", represented as infinity, or other weird values
last_day_active = last_active.astype('datetime64[s]')
days_since_active = (max(last_day_active) - last_day_active) / np.timedelta64(1, 'D')
keep = isfinite(last_active) & (days_since_active < 365)
days_since_active = days_since_active[keep]
last_day_active = last_day_active[keep]

print("Number of users: {}".format(len(last_active)))
print("Number without a last_active entry in the last 365 days: {}".format(sum(~keep)))

hist(days_since_active, 200)
ylabel('# of users last seen N days ago')
xlabel('number of days (N)')
axis('tight')


figure()
# January 1 1970 was a Thursday - map this to 4 so Sunday is 0
day_of_week = (((last_day_active - np.datetime64(0, 's')) / np.timedelta64(1, 'D')).astype(int) + 4) % 7

hist(day_of_week[np.logical_and(days_since_active < 365, days_since_active > 27)],
     bins=np.linspace(-0.5, 6.5, 8),
     normed=True)
title('Day of week')
ylabel('fraction of users last active on this day of the week')
xticks(np.arange(7),
       'Sun Mon Tue Wed Thu Fri Sat'.split(' '),
       rotation=45)
axis('tight')
show()

days_since_active = (max(last_active) - last_active) / (24 * 60 * 60)
days_since_registered = max(registered_dates) - registered_dates

DATA_BREAKPOINT = 365
keep = days_since_active < DATA_BREAKPOINT
days_since_active = days_since_active[keep]
days_since_registered = days_since_registered[keep] / timedelta64(1,'D')
days_since_registered += np.random.uniform(-0.5, 0.5, days_since_registered.shape)

scatter(days_since_registered, days_since_active, alpha=0.2, lw=0)
xlabel('days since registering')
ylabel('days since last active')
axis('tight')

figure()
mask2 = days_since_registered < DATA_BREAKPOINT
scatter(days_since_registered[mask2], days_since_active[mask2], alpha=0.2, lw=0)
xlabel('days since registering')
ylabel('days since last active')
title('Same as above, for last {} days'.format(DATA_BREAKPOINT))
axis('tight')

figure()
DATA_BREAKPOINT = 60
mask2 = days_since_registered < DATA_BREAKPOINT
scatter(days_since_registered[mask2], days_since_active[mask2], alpha=0.2, lw=0)
xlabel('days since registering')
ylabel('days since last active')
title('Same as above, for last {} days'.format(DATA_BREAKPOINT))
axis('tight')