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All Samples(11)  |  Call(11)  |  Derive(0)  |  Import(0)

src/a/m/amazonaccess-HEAD/BSMan/logistic.py   amazonaccess(Download)
relabler = preprocessing.LabelEncoder()
for col in range(len(all_data[0,:])):
    relabler.fit(all_data[:, col])
    all_data[:, col] = relabler.transform(all_data[:, col])
########################## 2nd order features ################################
dp = group_data(all_data, degree=2) 
for col in range(len(dp[0,:])):
    relabler.fit(dp[:, col])
    uniques = len(set(dp[:,col]))
    print uniques
    relabler.fit(dp[:, col])
    dp[:, col] = relabler.transform(dp[:, col])
########################## 3rd order features ################################
dt = group_data(all_data, degree=3)
for col in range(len(dt[0,:])):
    relabler.fit(dt[:, col])
    uniques = len(set(dt[:,col]))
    print uniques
    relabler.fit(dt[:, col])
    dt[:, col] = relabler.transform(dt[:, col])
########################## 1st order features ################################

src/a/m/amazonaccess-HEAD/helpers/feature_extraction.py   amazonaccess(Download)
 
    for j in range(X.shape[1]):
        relabeler.fit(X[:, j])
        X[:, j] = relabeler.transform(X[:, j])
        X_train[:, j] = relabeler.transform(X_train[:, j])

src/g/i/givinggraph-HEAD/givinggraph/companycause/company_cause_svm.py   givinggraph(Download)
	N = len(Y)
	label_enc = LabelEncoder()
	label_enc.fit(list(causes))
	print 'found %d causes' % len(label_enc.classes_)
	Y = [list(label_enc.transform(yi)) for yi in Y]

src/g/i/givinggraph-HEAD/givinggraph/analysis/company_cause_classifier.py   givinggraph(Download)
    N = len(Y)
    label_enc = LabelEncoder()
    label_enc.fit(list(causes))
    print 'found %d causes' % len(label_enc.classes_)
    Y = [list(label_enc.transform(yi)) for yi in Y]

src/d/a/data-HEAD/Assignments/jmankoff-byte5/ml.py   data(Download)
# make a label for empty values too
labels.append(u'')
le.fit(labels)
 
# now transform the array to have only numeric values instead