pyunit_smallcatRF.py

import sys
sys.path.insert(1, "../../../")
import h2o

import numpy as np
from sklearn import ensemble
from sklearn.metrics import roc_auc_score

def smallcatRF(ip,port):

    # Training set has 26 categories from A to Z
    # Categories A, C, E, G, ... are perfect predictors of y = 1
    # Categories B, D, F, H, ... are perfect predictors of y = 0

    # Connect to h2o
    h2o.init(ip,port)

    #Log.info("Importing alphabet_cattest.csv data...\n")
    alphabet = h2o.import_frame(path=h2o.locate("smalldata/gbm_test/alphabet_cattest.csv"))
    alphabet["y"] = alphabet["y"].asfactor()
    #Log.info("Summary of alphabet_cattest.csv from H2O:\n")
    #alphabet.summary()

    # Prepare data for scikit use
    trainData = np.loadtxt(h2o.locate("smalldata/gbm_test/alphabet_cattest.csv"), delimiter=',', skiprows=1,
                           converters={0:lambda s: ord(s.split("\"")[1])})
    trainDataResponse = trainData[:,1]
    trainDataFeatures = trainData[:,0]

    # Train H2O GBM Model:
    #Log.info("H2O GBM (Naive Split) with parameters:\nntrees = 1, max_depth = 1, nbins = 100\n")
    rf_h2o = h2o.random_forest(x=alphabet[['X']], y=alphabet["y"], ntrees=1, max_depth=1, nbins=100)

    # Train scikit GBM Model:
    # Log.info("scikit GBM with same parameters:")
    rf_sci = ensemble.RandomForestClassifier(n_estimators=1, criterion='entropy', max_depth=1)
    rf_sci.fit(trainDataFeatures[:,np.newaxis],trainDataResponse)

    # h2o
    rf_perf = rf_h2o.model_performance(alphabet)
    auc_h2o = rf_perf.auc()

    # scikit
    auc_sci = roc_auc_score(trainDataResponse, rf_sci.predict_proba(trainDataFeatures[:,np.newaxis])[:,1])

    #Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o))
    assert auc_h2o >= auc_sci, "h2o (auc) performance degradation, with respect to scikit"

if __name__ == "__main__":
  h2o.run_test(sys.argv, smallcatRF)