Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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In [1]:
import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
In [2]:
h2o.init()
In [3]:
# location of clean file
path = '/Users/phall/workspace/GWU_data_mining/03_regression/data/loan_clean.csv'
In [4]:
# define input variable measurement levels
# strings automatically parsed as enums (nominal)
# numbers automatically parsed as numeric
col_types = {'bad_loan': 'enum'}
In [5]:
frame = h2o.import_file(path=path, col_types=col_types) # multi-threaded import
In [6]:
frame.describe()
In [7]:
# split into training, validation and test
train, test = frame.split_frame([0.7])
In [8]:
# assign target and inputs for linear regression
y = 'bad_loan'
X = [name for name in frame.columns if name not in ['id', '_WARN_', y]]
In [9]:
print(y)
print(X)
In [10]:
# set target to factor - for logisitic regression
train[y] = train[y].asfactor()
test[y] = test[y].asfactor()
In [11]:
# elastic net regularized regression
# - binomial family for logistic regression
# - L1 for variable selection
# - L2 for handling multicollinearity
# - IRLS for handling outliers
# - with lamba parameter tuning for variable selection
# initialize
loan_glm = H2OGeneralizedLinearEstimator(family='binomial',
model_id='loan_glm2',
solver='IRLSM',
standardize=True,
lambda_search=True)
# train
loan_glm.train(X, y, training_frame=train)
# view detailed results at http://host:ip/flow/index.html
In [12]:
# measure train and test AUC
print(loan_glm.auc(train=True))
print(loan_glm.model_performance(test_data=test).auc())
In [13]:
# print non-zero model parameters
for name, val in loan_glm.coef().items():
if val != 0.0:
print(name, ': ', val)
In [14]:
h2o.cluster().shutdown(prompt=False)