def train_models(columns, rf=True, svm=False, logit=False):
# Prepare the data
dataCols = dict(
[(colName,
read_data_col('%s.train.dat' % fileName, valCol=valCol))
for colName, fileName, valCol in COLUMNS
if colName in columns or colName == 'TF']
)
data, colnames = create_matrix(dataCols)
cimt = colnames.index('TF')
y = data[:, cimt]
x = data[:, [c for c in range(len(colnames)) if c != cimt]]
print >> sys.stderr, '\n>> Training model with cols:', colnames
print >> sys.stderr, x
# Fit the models
models = {}
if rf:
rfModel = ensemble.RandomForestClassifier(
n_estimators=100,
## criterion='entropy',
verbose=1,
n_jobs=-1,
## oob_score=True,
## min_samples_leaf=1,
)
rfModel.fit(x, y)
models['rf'] = rfModel
if svm:
svmModel = svm.SVC(kernel='rbf')
svmModel.fit(x, y)
models['svm'] = svmModel
if logit:
logitModel = linear_model.LogisticRegression()
logitModel.fit(x, y)
models['logit'] = logitModel
return {'models' : models, 'colnames' : [c for c in colnames if c!='TF']}
def cross_validation(logit=False):
# Split authors in train/validation
confirmed, deleted = get_train()
aids = confirmed.keys()
shuffle(aids)
cutPoint = int(len(aids) * F)
aidsTrain = aids[:cutPoint]
aidsValid = aids[cutPoint:]
# Prepare the training data
dataCols = dict(
[(colName,
read_data_col('%s.train.dat' % fileName, valCol=valCol))
for colName, fileName, valCol in COLUMNS]
)
data, colnames = create_matrix(dataCols, exclude_aids=aidsValid)
cimt = colnames.index('TF')
ytrain = data[:, cimt]
xtrain = data[:, [c for c in range(len(colnames)) if c != cimt]]
# validation
data, colnames2, pairs = create_matrix(dataCols,
exclude_aids=aidsTrain,
return_pairs=True)
if colnames2 != colnames:
raise WTF
yvalid = data[:, cimt]
xvalid = data[:, [c for c in range(len(colnames)) if c != cimt]]
print >> sys.stderr, '==> %s train; %s valid <==' % (str(xtrain.shape),
str(xvalid.shape))
# Train the model
models = train_models(xtrain, ytrain, logit=logit)
# Calculate performance of each algorithm
performance = {}
for algorithm in models:
print >> sys.stderr, '\n\n--> %s <--\n' % algorithm
print >> sys.stderr, models[algorithm]
# make the predictions
predBin = models[algorithm].predict(xvalid)
pred = models[algorithm].predict_proba(xvalid)
print >> sys.stderr, ''
print >> sys.stderr, yvalid
print >> sys.stderr, predBin
print >> sys.stderr, pred
# extract author-paper scores
decorated = {}
for n in range(len(pairs)):
aid, pid = pairs[n]
score = pred[n, 0]
try:
decorated[aid].append((score, pid, yvalid[n]))
except KeyError:
decorated[aid] = [(score, pid, yvalid[n])]
# get the MAP
MAPs = []
for aid in decorated:
sortedPapers = decorated[aid]
sortedPapers.sort()
ntrue, ntot, MAPTerms = 0, 0, []
for s, p, tf in sortedPapers:
ntot += 1
if tf == 1:
ntrue += 1
MAPTerms.append(float(ntrue)/float(ntot))
MAPs.append(np.mean(MAPTerms))
# output results
performance[algorithm] = np.mean(MAPs)
print >> sys.stderr, '\n>> %s: %f' % (algorithm, np.mean(MAPs))
# Done
print >> sys.stderr, '\n'
return performance
## ('nameinit', '../name.train.dat', 4),
# ('nname', '../nname.train.dat', 3),
('npapers', '../npapers.train.dat', 3),
('nauthors', '../nauthors.train.dat', 3),
# ('coauthors', '../coauthors_diff.train.dat', 3),
## ('zcoauthors', '../coauthors_diff.train.dat', 4),
('affiliation', '../affiliation.train.dat', 3),
('year', '../year.train.dat', 3),
('nvalidated', '../nvalidated.train.dat', 3),
('sumcoauthors', '../sumcoauthors.train.dat', 3),
)
if __name__ == '__main__':
dataCols = dict([(colName, read_data_col(fileName, valCol=valCol))
for colName, fileName, valCol in COLUMNS])
data, colnames = create_matrix(dataCols)
print colnames
cimt = colnames.index('TF')
y = data[:, cimt]
x = data[:, [c for c in range(len(colnames)) if c != cimt]]
N = len(y)
ntrain = int(N * F)
ttindices = range(N)
shuffle(ttindices)
yTrain = y[ttindices[:ntrain], :]
xTrain = x[ttindices[:ntrain], :]
yTest = y[ttindices[ntrain+1:], :]
xTest = x[ttindices[ntrain+1:], :]
