def lgd(numfolds=5, threshold=0.5, featsfile='featsGBM.txt'):
""""""
# open up file containing the columns we wish to use
f = fio.fileio(usePCA=False)
st = time.time()
# Found these with magic
numcols = pd.read_csv(featsfile).feature.values[:150].tolist()
X = f.loadNumericTrain(usecols=numcols)
y = f.loadLabels().loss.values
print "Training data took %f seconds to load" % (time.time() - st)
rgr = GradientBoostingRegressor(
**ml.INIT_PARAMS['GradientBoostingRegressor'])
# Load the test data
Xtest = f.loadNumericTest(usecols=numcols)
# Open up the train/test files
bTrain = pd.read_csv('../data/subs/train/gbm.csv')
bTest = pd.read_csv('../data/subs/test/gbm.csv')
zp = bTrain.loss.values
yp = bTest.loss.values
if True:
y_ = np.zeros(zp.size)
Z, p = X[zp > threshold, :], y[zp > threshold]
y_[zp > threshold] = ml.stratKFold(Z, p, rgr, nFolds=2, classify=False)
print "CV Error: %f" % mean_absolute_error(y, y_)
# Train on all, transform to log space
yy = y[zp > threshold]
yy = np.log10(yy + 1.)
rgr.fit(X[zp > threshold, :], yy)
# predict and transform
yr = rgr.predict(Xtest)
yr = 10.**(yr) - 1.
yr[yr < 0] = 0
yr[yr > 100] = 100.
print "Training took %f seconds" % (time.time() - st)
sub_ = pd.read_csv('../data/sampleSubmission.csv')
sub_.loss = yr * (yp > threshold)
sub_.loss[sub_.loss < 0] = 0.
# Write to file
sub_.to_csv('../data/subs/testSubmission.csv', index=False)
def lgd(numfolds=5,threshold=0.5,featsfile='featsGBM.txt'):
""""""
# open up file containing the columns we wish to use
f = fio.fileio(usePCA=False)
st = time.time()
# Found these with magic
numcols = pd.read_csv(featsfile).feature.values[:150].tolist()
X = f.loadNumericTrain(usecols=numcols)
y = f.loadLabels().loss.values
print "Training data took %f seconds to load" %(time.time() - st)
rgr = GradientBoostingRegressor(**ml.INIT_PARAMS['GradientBoostingRegressor'])
# Load the test data
Xtest = f.loadNumericTest(usecols=numcols)
# Open up the train/test files
bTrain = pd.read_csv('../data/subs/train/gbm.csv')
bTest = pd.read_csv('../data/subs/test/gbm.csv')
zp = bTrain.loss.values
yp = bTest.loss.values
if True:
y_ = np.zeros(zp.size)
Z, p = X[zp > threshold,:], y[zp > threshold]
y_[zp > threshold] = ml.stratKFold(Z, p, rgr, nFolds=2, classify=False)
print "CV Error: %f"%mean_absolute_error(y, y_)
# Train on all, transform to log space
yy = y[zp > threshold]
yy = np.log10(yy + 1.)
rgr.fit(X[zp > threshold,:],yy)
# predict and transform
yr = rgr.predict(Xtest)
yr = 10.**(yr) - 1.
yr[yr < 0] = 0
yr[yr > 100] = 100.
print "Training took %f seconds"%(time.time() - st)
sub_ = pd.read_csv('../data/sampleSubmission.csv')
sub_.loss = yr*(yp > threshold)
sub_.loss[sub_.loss < 0] = 0.
# Write to file
sub_.to_csv('../data/subs/testSubmission.csv',index=False)
def classifier(filename='',
calctest=True,
traindir='',
testdir='',
numfolds=5):
""""""
outfile = filename + '.csv'
# open up file containing the columns we wish to use
st = time.time()
f = fio.fileio(usePCA=True)
cols = ['f274', 'f727', 'f2', 'f271', 'f527', 'f528']
X = f.loadNumericTrain(usecols=cols)
yf = f.loadLabels()
y = yf.loss.values
print "Training data took %f seconds to load" % (time.time() - st)
# Train the gradient boosting classifier
clf = GradientBoostingClassifier(
**ml.INIT_PARAMS['GradientBoostingClassifier'])
st = time.time()
y_ = ml.stratKFold(X, y, clf, nFolds=numfolds)
fpr, tpr, thresh = roc_curve(y > 0, y_)
# Print the scores
print "AUC: %f" % auc(fpr, tpr)
print "F1 Score: %f" % ml.maxF1(y > 0, y_)
print "%d-Fold CV took %f seconds" % (numfolds, time.time() - st)
yf['loss'] = y_
yf.to_csv(traindir + outfile, index=False)
if calctest:
st = time.time()
# Load the test data
Xtest = f.loadNumericTest(usecols=cols)
# Fit the data
clf.fit(X, y > 0)
sub_ = pd.read_csv('../data/sampleSubmission.csv')
sub_.loss = clf.predict_proba(Xtest)[:, 1]
# Write to file
sub_.to_csv(testdir + outfile, index=False)
print "Test submission took %s seconds" % (time.time() - st)
def classifier(filename='',
calctest=True,
traindir='',
testdir='',
numfolds=5):
""""""
outfile = filename+'.csv'
# open up file containing the columns we wish to use
st = time.time()
f = fio.fileio(usePCA=True)
cols = ['f274','f727', 'f2', 'f271', 'f527', 'f528']
X = f.loadNumericTrain(usecols=cols)
yf = f.loadLabels()
y = yf.loss.values
print "Training data took %f seconds to load" %(time.time() - st)
# Train the gradient boosting classifier
clf = GradientBoostingClassifier(**ml.INIT_PARAMS['GradientBoostingClassifier'])
st = time.time()
y_ = ml.stratKFold(X,y,clf,nFolds=numfolds)
fpr, tpr, thresh = roc_curve(y > 0,y_)
# Print the scores
print "AUC: %f"%auc(fpr,tpr)
print "F1 Score: %f"%ml.maxF1(y > 0,y_)
print "%d-Fold CV took %f seconds"%(numfolds,time.time() - st)
yf['loss'] = y_
yf.to_csv(traindir+outfile,index=False)
if calctest:
st = time.time()
# Load the test data
Xtest = f.loadNumericTest(usecols=cols)
# Fit the data
clf.fit(X,y > 0)
sub_ = pd.read_csv('../data/sampleSubmission.csv')
sub_.loss = clf.predict_proba(Xtest)[:,1]
# Write to file
sub_.to_csv(testdir+outfile,index=False)
print "Test submission took %s seconds" %(time.time() - st)
