In [1]:
# This is a demo of H2O's GLM function
# It imports a data set, parses it, and prints a summary
# Then, it runs GLM with a binomial link function
import h2o
In [2]:
h2o.init()
In [3]:
from h2o.utils.shared_utils import _locate # private function. used to find files within h2o git project directory.
air = h2o.upload_file(path=_locate("smalldata/airlines/AirlinesTrain.csv.zip"))
In [4]:
r = air[0].runif()
air_train = air[r < 0.8]
air_valid = air[r >= 0.8]
In [5]:
myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "fMonth", "fDayofMonth", "fDayOfWeek"]
myY = "IsDepDelayed"
In [6]:
rf_no_bal = h2o.random_forest(x=air_train[myX], y=air_train[myY], validation_x= air_valid[myX],
validation_y=air_valid[myY], seed=12, ntrees=10, max_depth=20, balance_classes=False)
rf_no_bal.show()
In [7]:
rf_bal = h2o.random_forest(x=air_train[myX], y=air_train[myY], validation_x= air_valid[myX],
validation_y=air_valid[myY], seed=12, ntrees=10, max_depth=20, balance_classes=True)
rf_bal.show()
In [8]:
air_test = h2o.import_file(path=_locate("smalldata/airlines/AirlinesTest.csv.zip"))
In [9]:
def model(model_object, test):
#predicting on test file
pred = model_object.predict(test)
pred.head()
#Building confusion matrix for test set
perf = model_object.model_performance(test)
perf.show()
print(perf.confusion_matrix())
print(perf.precision())
print(perf.accuracy())
print(perf.auc())
In [10]:
print("\n\nWITHOUT CLASS BALANCING\n")
model(rf_no_bal, air_test)
In [11]:
print("\n\nWITH CLASS BALANCING\n")
model(rf_bal, air_test)