print(data[["Label", "f1", "f2", data.columns[-1]]].head())
###################################################
# Let's train a logistic regression.
formula = "Label ~ {0}".format(" + ".join(data.columns[1:]))
print(formula[:50] + " + ...")
from microsoftml import rx_logistic_regression
try:
logregml = rx_logistic_regression(formula, data=data)
except Exception as e:
# The error is expected because patsy cannot handle
# so many features.
print(e)
#########################################
# Let's skip patsy's parser to manually define the formula
# with object `ModelDesc <http://patsy.readthedocs.io/en/latest/API-reference.html?highlight=lookupfactor#patsy.ModelDesc>`_.
from patsy.desc import ModelDesc, Term
from patsy.user_util import LookupFactor
patsy_features = [Term([LookupFactor(n)]) for n in data.columns[1:]][:10]
model_formula = ModelDesc([Term([LookupFactor("Label")])], [Term([])] + patsy_features)
print(model_formula.describe() + " + ...")
logregml = rx_logistic_regression(model_formula, data=data)
