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Naive application of Machine Learning to Software Development

Naive application of Machine Learning to Software Developmentor... what developers don't tell :)

What and why42 Coffee Cups:

completely distributed development team



Hard facts about how software is done



Hard facts about how software is done

LOTS OF THEM

What and why

Facts

What and why

Facts Profit

What and why

Facts Profit???

What and why

???Toy problem:

get ticket and predict how long it will take to close it

What and why

???Toy problem:

get ticket and predict how long it will take to close it

Bonus: learn scikit-learn :)

Install scikit-learn● sudo apt-get install python-

dev


dev python-numpy python-numpy-dev


dev python-numpy python-numpy-dev python-scipy


dev python-numpy python-numpy-dev python-scipy python-setuptools libatlas-dev g++


dev python-numpy python-numpy-dev python-scipy python-setuptools libatlas-dev g++

● pip install -U scikit-learn

Data: closed ticketsimport urllib2

url = \

'https://code.djangoproject.com/query?format=csv' +\

'&col=id&col=time&col=changetime&col=reporter' + \

'&col=summary&col=status&col=owner&col=type' + \'&col=component&order=priority'

tickets = urllib2.urlopen(url).read()

open('2012-10-09.csv','w').write(tickets)

Data: closed ticketsid,time,changetime,reporter,summary,status,owner,type,component1,2005-07-13 12:03:27,2012-05-20 08:12:37,adrian,Create architecture for anonymous sessions,closed,jacob,enhancement,Core (Other)2,2005-07-13 12:04:45,2007-07-03 16:04:18,anonymous,Calendar popup - next/previous month links close the popup in Safari,closed,jacob,defect,contrib.admin

Data: closed date and description

def get_data(ticket):

url = 'https://code.djangoproject.com/ticket/%s'\

% ticket

ticket_html = urllib2.urlopen(url)

bs = BeautifulSoup(ticket_html)


# get closing date

d = bs.find_all('div','date')[0]

p = list(d.children)[3]

href = p.find('a')['href']

close_time_str = urlparse.parse_qs(href)

['/timeline?from'][0]

close_time = datetime.datetime.strptime

(close_time_str[:-6],

'%Y-%m-%dT%H:%M:%S')

# ... more black magic, see code


def get_data(ticket):

[...]

# get description and return

de = bs.find_all('div', 'description')[0]

return close_time, de.text


tickets_file = csv.reader(open('2012-10-09.csv'))

output = \

csv.writer(open('2012-10-09.close.csv','w'))

for id, time, changetime, reporter, summary, \

status, owner, type, component in tickets_file:

closetime, descr = get_data(id)

row = [id, time, changetime, closetime, reporter,

summary, status, owner, type, component,

descr.encode('utf-8'), ],)

output.writerow(row)

Scoring: Train/Test set split

cross_validation.train_test_split

(tickets_train, tickets_test, times_train,

times_test) =

cross_validation.train_test_split(

tickets, times,

test_size=0.2,

random_state=0)

Scoring: Mean squared error

sklearn.metrics.mean_squared_error

train_error = metrics.mean_squared_error(

times_train, times_train_predict)

test_error = metrics.mean_squared_error(

times_test, times_test_predict)

Fun #1: just ticket number?for number, created, ... in tickets_file:

row = []

created = dt.datetime.strptime(created,

time_format)

closetime = dt.datetime.strptime(closetime,

time_format)

time_to_fix = closetime - created

row.append(float(number))

tickets.append(row)

times.append(total_seconds(time_to_fix))

Fun #1: just ticket number?import numpy as np

from sklearn import preprocessing

scaler = preprocessing.Scaler().fit(

np.array(tickets))

tickets = scaler.transform(tickets)

Fun #1: just ticket number?clf = SVR()

clf.fit(tickets_train, times_train)

times_train_predict = clf.predict(tickets_train)

times_test_predict = clf.predict(tickets_test)

Fun #1: just ticket number?train_error = metrics.mean_squared_error

(times_train, times_train_predict)

test_error = metrics.mean_squared_error(times_test,

times_test_predict)

print 'Train error: %.1f\n Test error: %.2f' % (

math.sqrt(train_error)/(24*3600),

math.sqrt(test_error)/(24*3600))

# .. in days

Fun #1: just ticket number?

Train error: 363.4

Test error: 361.41

Finding best parametersSVM C controls regularization:

larger C leads to ● closer fit to the train data ● with the risk of overfitting

Finding best parametersCs = np.logspace(-1, 10, 10)

for c in Cs:

learn(c)

Finding best parameters0.1: Train error: 363.4 Test error: 361.41

1.71: Train error: 363.4 Test error: 361.41








10000000000.0: Train error: 361.1 Test error:

358.91

Finding best parameterssklearn.grid_search.GridSearchCV

bonus: it can run in parallel

clf = GridSearchCV(estimator=SVR(

param_grid=dict(C=np.logspace(-1,10,10)),

n_jobs=-1)


Finding best parameterssklearn.grid_search.GridSearchCV

bonus: it can run in parallel

clf = GridSearchCV(estimator=SVR(

param_grid=dict(C=np.logspace(-1,10,10)),

n_jobs=-1)


Train error: 361.1 Test error: 358.91

Best C: 1.0e+10

Fun #2: creation date?

row = []

row.append(float(number))

row.append(float(time.mktime(

created.timetuple())))

tickets.append(row)

Fun #2: creation date?


Best C: 1.0e+10

String vectorizer and Tfidf transform

from sklearn.feature_extraction.text \

import CountVectorizer, \

TfidfTransformer

String vectorizer and Tfidf transformreporters = []

for number, ... in tickets_file:

[...]

reporters.append(reporter)

String vectorizer and Tfidf transformCountVectorizer().fit_transform(reporters) ->

TfidfTransformer().fit_transform( … ) ->

hstack((tickets, …)

note: TF-IDF matrix is sparse!

String vectorizer and Tfidf transformimport scipy.sparse as sp

tickets = sp.hstack((

tickets,

TfidfTransformer().fit_transform(

CountVectorizer().fit_transform(reporters))))

# remember to re-scale!

scaler = preprocessing.Scaler(with_mean=False

).fit(tickets)

tickets = scaler.transform(tickets)

Fun #3: reporter


Best C: 1.8e+07

subjects = []

for number, created, ... in tickets_file:

[...]

subjects.append(summary)

[...]

tickets = sp.hstack((tickets,

TfidfTransformer().fit_transform(

CountVectorizer(ngram_range=(1,3)

).fit_transform(subjects))))

Fun #3: subject

Train error: 21.0 Test error: XXXX

Best C: 1.0e+10

Fun #3: subject


Best C: 1.0e+10

Fun #3: subject

def learn(kernel='rbf', param_grid=None,

verbose=False):

[...]

clf = GridSearchCV(

estimator=SVR(kernel=kernel,

verbose=verbose),

param_grid=param_grid,

n_jobs=-1)

[...]

Different SVM kernels

RBF


Best C: 1.0e+10

Linear


Best C: 1.0e+02

Different SVM kernels

components = []

for number, .. component, ... in tickets_file:

[...]

components.append(component)

[...]

tickets = sp.hstack((tickets, TfidfTransformer().

fit_transform(

CountVectorizer().fit_transform(components))))

Fun #5: account for the Component

RBF


Best C: 1.0e+10

Linear:


Best C: 1.0e+02

Fun #5: account for the Component

descriptions = []

for number, ... description in tickets_file:

[...]

descriptions.append(description)

[...]

tickets = sp.hstack((tickets, TfidfTransformer().

fit_transform( CountVectorizer(ngram_range=

(1,3)).fit_transform(

descriptions))))

Fun #6: ticket Description

RBF


Best C: 1.0e+10

Linear


Best C: 3.2e+03

Fun #6: ticket Description

● All steps of a simple machine learning algo

Conclusions


● scikit-learn

Conclusions


● scikit-learn

● data, explicitly available in tickets is NOT ENOUGH to predict closing date

Conclusions

Developers, what are you hiding?

:)

Questions?Source code and dataset available at

https://github.com/42/django-trac-learning.git

Contacts:● @akhavr● http://42coffeecups.com/

Download - Naive application of Machine Learning to Software Development

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