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activeGitHub
by Gautham Nair
Python wrappers for
the GitHub API
2008 2009 2010 2011 2012 2013
future
activity?
gap
2008 2009 2010 2011 2012 2013
future
activity?
gap
activeGitHub
A quantitative estimator of
future repository activity
2008 2009 2010 2011 2012 2013 2014
Estimated probability of any commits
in the next six months
Present
time
repoA
repoB
repoY..
20,000
repos
birth
Training Data
6 months back
time
repoA
repoB
repoY..
time
repoA
repoB
compute features active or not?
repoY..
time
repoA
repoB
compute features active or not?
Logistic
Regression
repoY..
time
repoA
repoB
compute features active or not?
Logistic
Regression
train
test
AUC = 0.89
repoY..
any Repo
you want
time
any Repo
you want
Present
time
any Repo
you want
compute features
time
any Repo
you want
compute features active ?
predict
p = 0.73
feature!
predicts!
ACTIVE
Regression Coefficient
feature!
predicts!
ACTIVE
Regression Coefficient
2 w
recent commit!
rate
feature!
predicts!
ACTIVE
Regression Coefficient
2 w
recent commit!
rate
days since last commit
feature!
predicts!
ACTIVE
Regression Coefficient
2 w
recent commit!
rate
days since last commit
contributor!
diversity
2 w!
3 m!
6 m!
1 y!
1 m
total commits
recent commit!
rate
contributor!
diversity
days since last commit
age
Regression Coefficient
feature!
predicts!
ACTIVE
by Gautham Nair
R2
R10_9d
R2_48h
R2_9d
R2_On
R10_48h
R10_On
wm983b_0w
0uM_0d
R10
50uM_1d
1uM_1d
1uM_5d
50uM_5d
wm983b_3w
wm983b_4w
wm983b_1w
wm983b_2w
axon guidance
nuclear division
nuclear division
nuclear division
nuclear division
nuclear division
microtubule−based process
gnrt. of a sgnl invl. in cll−cll sgnl.
gnrt. of a sgnl invl. in cll−cll sgnl.
axon guidance
ngtv rgltn of mltclllr orgnsml prcss
actin cytoskeleton organization
by Gautham Nair
Num of cells=27 Num of cells=48
div-1(g19) 25o
C
150015000 0
unc−120 RNAunc−120 RNA
AgesinceEMSdivision(min)
MS E C
D
MS E C
D
div-1(or148) 25o
C
0
50
100
150
MS E C
D
N2 25o
C
N
27
48
15000
unc−120 RNA
C D E
Systems
biology
Physical
Chemistry
2 w!
3 m!
6 m!
1 y!
1 m
total commits
recent commit!
rate
contributor!
diversity
days since last commit
age
predicts!
ACTIVE
Regression Coefficient
0.00
0.25
0.50
0.75
1.00
0 1 2 3
log10(daysSinceLastCommit + 1)
probAlive
250
500
750
1000
1250
count
0.0
0.2
0.4
0.6
0 1 2 3
log10(daysSinceLastCommit + 1)
density
stargazers_count > 1000
FALSE
TRUE
0.00
0.25
0.50
0.75
1.00
0 2 4 6 8 10 12
months
cumulativedistribution
months
cumulative!
distribution
gap length
only gaps > 1 week
Logistic Regression with default regularization
predicted!
aliveOrDead alive dead!
alive 6167 1924!
dead 1673 9036
0
10000
20000
30000
40000
0 1 2 3
log10(gap_length_days + 1)
count
0
250
500
750
0 1 2 3
log10(longest_gap_days + 1)
count
0
500
1000
1500
0 1 2 3
log10(daysSinceLastCommit + 1)
count
All gaps
between
commits
Longest
commits
gap
Time since
last commit
any Repo
you want
time
any Repo
you want
Present
time
any Repo
you want
compute features
time
repoA
repoB
repoZ
compute features active or not?
Finished:
R repos >1 stars
Python > 9 stars
false “Alive”
p_alive > 95%
false “Alive”
false “Alive”
false “Alive”
false “Alive”
p_alive < 5%
false “Dead”
false “Dead”
false “Dead”
false “Dead”

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