def group_by():
# Connect to a pre-existing cluster
h2o_iris = h2o.import_file(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
pd_iris = pd.read_csv(h2o.locate("smalldata/iris/iris_wheader.csv"))
na_handling = ["ignore","rm","all"]
col_names = h2o_iris.col_names[0:4]
print "Running smoke test"
# smoke test
for na in na_handling:
grouped = h2o_iris.group_by("class")
grouped \
.count(na=na) \
.min( na=na) \
.max( na=na) \
.mean( na=na) \
.var( na=na) \
.sd( na=na) \
.ss( na=na) \
.sum( na=na)
print grouped.get_frame()
def getModelKmeans(ip,port):
# Connect to a pre-existing cluster
h2o.init(ip,port) # connect to localhost:54321
#Log.info("Importing benign.csv data...\n")
benign_h2o = h2o.import_frame(path=h2o.locate("smalldata/logreg/benign.csv"))
#benign_h2o.summary()
benign_sci = np.genfromtxt(h2o.locate("smalldata/logreg/benign.csv"), delimiter=",")
# Impute missing values with column mean
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
benign_sci = imp.fit_transform(benign_sci)
for i in range(2,7):
# Log.info("H2O K-Means")
km_h2o = h2o.kmeans(x=benign_h2o, k=i)
km_h2o.show()
#TODO: impement h2o.getModel()
model = h2o.getModel(km_h2o._key)
model.show()
km_sci = KMeans(n_clusters=i, init='k-means++', n_init=1)
km_sci.fit(benign_sci)
print "sckit centers"
print km_sci.cluster_centers_
def frame_slicing(ip,port):
iris = h2o.import_file(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
prostate = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate.csv.zip"))
airlines = h2o.import_file(path=h2o.locate("smalldata/airlines/allyears2k.zip"))
iris.show()
prostate.show()
airlines.show()
###################################################################
# H2OFrame[int] (column slice)
res1 = iris[0]
assert abs(res1[8,:] - 4.4) < 1e-10, "incorrect values"
# H2OFrame[int,int]
res2 = prostate[13, 3]
assert abs(res2 - 1) < 1e-10, "incorrect values"
# H2OFrame[int, slice]
res3 = airlines[12, 0:3]
assert abs(res3[0,0] - 1987) < 1e-10 and abs(res3[0,1] - 10) < 1e-10 and abs(res3[0,2] - 29) < 1e-10, \
"incorrect values"
# H2OFrame[slice, int]
res4 = iris[5:8, 1]
assert abs(res4[0,:] - 3.9) < 1e-10 and abs(res4[1,:] - 3.4) < 1e-10 and abs(res4[2,:] - 3.4) < 1e-10, "incorrect values"
# H2OFrame[slice, slice]
res5 = prostate[5:8, 0:3]
assert abs(res5[0,0] - 6) < 1e-10 and abs(res5[1,1] - 0) < 1e-10 and abs(res5[2,2] - 61) < 1e-10, "incorrect values"
def plot_test():
kwargs = {}
kwargs['server'] = True
air = h2o.import_file(h2o.locate("smalldata/airlines/AirlinesTrain.csv.zip"))
# Constructing test and train sets by sampling (20/80)
s = air[0].runif()
air_train = air[s <= 0.8]
air_valid = air[s > 0.8]
myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "fMonth", "fDayofMonth", "fDayOfWeek"]
myY = "IsDepDelayed"
air_gbm = h2o.gbm(x=air_train[myX], y=air_train[myY], validation_x=air_valid[myX], validation_y=air_valid[myY],
distribution="bernoulli", ntrees=100, max_depth=3, learn_rate=0.01)
# Plot ROC for training and validation sets
air_gbm.plot(type="roc", train=True, **kwargs)
air_gbm.plot(type="roc", valid=True, **kwargs)
air_test = h2o.import_file(h2o.locate("smalldata/airlines/AirlinesTest.csv.zip"))
perf = air_gbm.model_performance(air_test)
#Plot ROC for test set
perf.plot(type="roc", **kwargs)
def fiftycatGBM(ip,port):
# Training set has only 45 categories cat1 through cat45
#Log.info("Importing 50_cattest_train.csv data...\n")
train = h2o.import_file(path=h2o.locate("smalldata/gbm_test/50_cattest_train.csv"))
train["y"] = train["y"].asfactor()
#Log.info("Summary of 50_cattest_train.csv from H2O:\n")
#train.summary()
# Train H2O GBM Model:
#Log.info(paste("H2O GBM with parameters:\nntrees = 10, max_depth = 20, nbins = 20\n", sep = ""))
model = h2o.gbm(x=train[["x1","x2"]], y=train["y"], distribution="bernoulli", ntrees=10, max_depth=5, nbins=20)
model.show()
# Test dataset has all 50 categories cat1 through cat50
#Log.info("Importing 50_cattest_test.csv data...\n")
test = h2o.import_file(path=h2o.locate("smalldata/gbm_test/50_cattest_test.csv"))
#Log.info("Summary of 50_cattest_test.csv from H2O:\n")
#test.summary()
# Predict on test dataset with GBM model:
#Log.info("Performing predictions on test dataset...\n")
predictions = model.predict(test)
predictions.show()
# Get the confusion matrix and AUC
#Log.info("Confusion matrix of predictions (max accuracy):\n")
performance = model.model_performance(test)
test_cm = performance.confusion_matrix()
test_auc = performance.auc()
def anomaly(ip, port):
h2o.init(ip, port)
print "Deep Learning Anomaly Detection MNIST"
train = h2o.import_frame(h2o.locate("bigdata/laptop/mnist/train.csv.gz"))
test = h2o.import_frame(h2o.locate("bigdata/laptop/mnist/test.csv.gz"))
predictors = range(0,784)
resp = 784
# unsupervised -> drop the response column (digit: 0-9)
train = train[predictors]
test = test[predictors]
# 1) LEARN WHAT'S NORMAL
# train unsupervised Deep Learning autoencoder model on train_hex
ae_model = h2o.deeplearning(x=train[predictors], training_frame=train, activation="Tanh", autoencoder=True,
hidden=[50], l1=1e-5, ignore_const_cols=False, epochs=1)
# 2) DETECT OUTLIERS
# anomaly app computes the per-row reconstruction error for the test data set
# (passing it through the autoencoder model and computing mean square error (MSE) for each row)
test_rec_error = ae_model.anomaly(test)
# 3) VISUALIZE OUTLIERS
# Let's look at the test set points with low/median/high reconstruction errors.
# We will now visualize the original test set points and their reconstructions obtained
# by propagating them through the narrow neural net.
# Convert the test data into its autoencoded representation (pass through narrow neural net)
test_recon = ae_model.predict(test)
def iris_h2o_vs_sciKmeans(ip,port):
# Connect to a pre-existing cluster
h2o.init(ip,port) # connect to localhost:54321
iris_h2o = h2o.import_frame(path=h2o.locate("smalldata/iris/iris.csv"))
iris_sci = np.genfromtxt(h2o.locate("smalldata/iris/iris.csv"), delimiter=',')
iris_sci = iris_sci[:,0:4]
s =[[4.9,3.0,1.4,0.2],
[5.6,2.5,3.9,1.1],
[6.5,3.0,5.2,2.0]]
start = h2o.H2OFrame(s)
start_key = start.send_frame()
h2o_km = h2o.kmeans(x=iris_h2o[0:4], k=3, user_points=start_key, standardize=False)
sci_km = KMeans(n_clusters=3, init=np.asarray(s), n_init=1)
sci_km.fit(iris_sci)
# Log.info("Cluster centers from H2O:")
print "Cluster centers from H2O:"
h2o_centers = h2o_km.centers()
print h2o_centers
# Log.info("Cluster centers from scikit:")
print "Cluster centers from scikit:"
sci_centers = sci_km.cluster_centers_.tolist()
print sci_centers
for hcenter, scenter in zip(h2o_centers, sci_centers):
for hpoint, spoint in zip(hcenter,scenter):
assert (hpoint- spoint) < 1e-10, "expected centers to be the same"
def offsets_and_distributions(ip,port):
# cars
cars = h2o.upload_file(h2o.locate("smalldata/junit/cars_20mpg.csv"))
cars = cars[cars["economy_20mpg"].isna() == 0]
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
offset = h2o.H2OFrame(python_obj=[[.5] for x in range(398)])
offset.setNames(["x1"])
cars = cars.cbind(offset)
# insurance
insurance = h2o.import_file(h2o.locate("smalldata/glm_test/insurance.csv"))
insurance["offset"] = insurance["Holders"].log()
# bernoulli - offset not supported
#dl = h2o.deeplearning(x=cars[2:8], y=cars["economy_20mpg"], distribution="bernoulli", offset_column="x1",
# training_frame=cars)
#predictions = dl.predict(cars)
# gamma
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="gamma", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# gaussian
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="gaussian", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# poisson
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="poisson", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# tweedie
dl = h2o.deeplearning(x=insurance.names[0:3], y="Claims", distribution="tweedie", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
def fiftycatRF(ip, port):
# Training set has only 45 categories cat1 through cat45
# Log.info("Importing 50_cattest_train.csv data...\n")
train = h2o.import_file(path=h2o.locate("smalldata/gbm_test/50_cattest_train.csv"))
train["y"] = train["y"].asfactor()
# Log.info("Summary of 50_cattest_train.csv from H2O:\n")
# train.summary()
# Train H2O DRF Model:
# Log.info(paste("H2O DRF with parameters:\nclassification = TRUE, ntree = 50, depth = 20, nbins = 500\n", sep = ""))
model = h2o.random_forest(x=train[["x1", "x2"]], y=train["y"], ntrees=50, max_depth=20, nbins=500)
# Test dataset has all 50 categories cat1 through cat50
# Log.info("Importing 50_cattest_test.csv data...\n")
test = h2o.import_file(path=h2o.locate("smalldata/gbm_test/50_cattest_test.csv"))
# Log.info("Summary of 50_cattest_test.csv from H2O:\n")
# test.summary()
# Predict on test dataset with DRF model:
# Log.info("Performing predictions on test dataset...\n")
preds = model.predict(test)
preds.head()
# Get the confusion matrix and AUC
# Log.info("Confusion matrix of predictions (max accuracy):\n")
perf = model.model_performance(test)
perf.show()
cm = perf.confusion_matrix()
print(cm)
def link_functions_binomial(ip,port):
# Connect to h2o
h2o.init(ip,port)
print("Read in prostate data.")
h2o_data = h2o.import_frame(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.read_csv(zipfile.ZipFile(h2o.locate("smalldata/prostate/prostate_complete.csv.zip")).open("prostate_complete.csv")).as_matrix()
sm_data_response = sm_data[:,2]
sm_data_features = sm_data[:,[1,3,4,5,6,7,8,9]]
print("Testing for family: BINOMIAL")
print("Set variables for h2o.")
myY = "CAPSULE"
myX = ["ID","AGE","RACE","GLEASON","DCAPS","PSA","VOL","DPROS"]
print("Create models with canonical link: LOGIT")
h2o_model = h2o.glm(x=h2o_data[myX], y=h2o_data[myY].asfactor(), family="binomial", link="logit",alpha=[0.5], Lambda=[0])
sm_model = sm.GLM(endog=sm_data_response, exog=sm_data_features, family=sm.families.Binomial(sm.families.links.logit)).fit()
print("Compare model deviances for link function logit")
h2o_deviance = h2o_model._model_json['output']['residual_deviance'] / h2o_model._model_json['output']['null_deviance']
sm_deviance = sm_model.deviance / sm_model.null_deviance
assert h2o_deviance - sm_deviance < 0.01, "expected h2o to have an equivalent or better deviance measures"
def wide_dataset_large(ip,port):
# Connect to h2o
h2o.init(ip,port)
print("Reading in Arcene training data for binomial modeling.")
trainDataResponse = np.genfromtxt(h2o.locate("smalldata/arcene/arcene_train_labels.labels"), delimiter=' ')
trainDataResponse = np.where(trainDataResponse == -1, 0, 1)
trainDataFeatures = np.genfromtxt(h2o.locate("smalldata/arcene/arcene_train.data"), delimiter=' ')
trainData = h2o.H2OFrame(np.column_stack((trainDataResponse, trainDataFeatures)).tolist())
print("Run model on 3250 columns of Arcene with strong rules off.")
model = h2o.glm(x=trainData[1:3250], y=trainData[0].asfactor(), family="binomial", lambda_search=False, alpha=[1])
print("Test model on validation set.")
validDataResponse = np.genfromtxt(h2o.locate("smalldata/arcene/arcene_valid_labels.labels"), delimiter=' ')
validDataResponse = np.where(validDataResponse == -1, 0, 1)
validDataFeatures = np.genfromtxt(h2o.locate("smalldata/arcene/arcene_valid.data"), delimiter=' ')
validData = h2o.H2OFrame(np.column_stack((validDataResponse, validDataFeatures)).tolist())
prediction = model.predict(validData)
print("Check performance of predictions.")
performance = model.model_performance(validData)
print("Check that prediction AUC better than guessing (0.5).")
assert performance.auc() > 0.5, "predictions should be better then pure chance"
def link_functions_gamma(ip,port):
# Connect to h2o
h2o.init(ip,port)
print("Read in prostate data.")
h2o_data = h2o.import_frame(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.read_csv(zipfile.ZipFile(h2o.locate("smalldata/prostate/prostate_complete.csv.zip")).open("prostate_complete.csv")).as_matrix()
sm_data_response = sm_data[:,5]
sm_data_features = sm_data[:,[1,2,3,4,6,7,8,9]]
print("Testing for family: GAMMA")
print("Set variables for h2o.")
myY = "DPROS"
myX = ["ID","AGE","RACE","GLEASON","DCAPS","PSA","VOL","CAPSULE"]
print("Create models with canonical link: INVERSE")
h2o_model_in = h2o.glm(x=h2o_data[myX], y=h2o_data[myY], family="gamma", link="inverse",alpha=[0.5], Lambda=[0])
sm_model_in = sm.GLM(endog=sm_data_response, exog=sm_data_features, family=sm.families.Gamma(sm.families.links.inverse_power)).fit()
print("Compare model deviances for link function inverse")
h2o_deviance_in = h2o_model_in._model_json['output']['residual_deviance'] / h2o_model_in._model_json['output']['null_deviance']
sm_deviance_in = sm_model_in.deviance / sm_model_in.null_deviance
assert h2o_deviance_in - sm_deviance_in < 0.01, "expected h2o to have an equivalent or better deviance measures"
print("Create models with canonical link: LOG")
h2o_model_log = h2o.glm(x=h2o_data[myX], y=h2o_data[myY], family="gamma", link="log",alpha=[0.5], Lambda=[0])
sm_model_log = sm.GLM(endog=sm_data_response, exog=sm_data_features, family=sm.families.Gamma(sm.families.links.log)).fit()
print("Compare model deviances for link function log")
h2o_deviance_log = h2o_model_log._model_json['output']['residual_deviance'] / h2o_model_log._model_json['output']['null_deviance']
sm_deviance_log = sm_model_log.deviance / sm_model_log.null_deviance
assert h2o_deviance_log - sm_deviance_log < 0.01, "expected h2o to have an equivalent or better deviance measures"
def link_functions_gaussian(ip,port):
print("Read in prostate data.")
h2o_data = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.read_csv(zipfile.ZipFile(h2o.locate("smalldata/prostate/prostate_complete.csv.zip")).
open("prostate_complete.csv")).as_matrix()
sm_data_response = sm_data[:,9]
sm_data_features = sm_data[:,1:9]
print("Testing for family: GAUSSIAN")
print("Set variables for h2o.")
myY = "GLEASON"
myX = ["ID","AGE","RACE","CAPSULE","DCAPS","PSA","VOL","DPROS"]
print("Create models with canonical link: IDENTITY")
h2o_model = h2o.glm(x=h2o_data[myX], y=h2o_data[myY], family="gaussian", link="identity",alpha=[0.5], Lambda=[0])
sm_model = sm.GLM(endog=sm_data_response, exog=sm_data_features,
family=sm.families.Gaussian(sm.families.links.identity)).fit()
print("Compare model deviances for link function identity")
h2o_deviance = h2o_model.residual_deviance() / h2o_model.null_deviance()
sm_deviance = sm_model.deviance / sm_model.null_deviance
assert h2o_deviance - sm_deviance < 0.01, "expected h2o to have an equivalent or better deviance measures"
def pubdev_1953():
# small_test = [h2o.locate("bigdata/laptop/citibike-nyc/2013-10.csv")]
# data = h2o.import_file(path=small_test)
# startime = data["starttime"]
# secsPerDay=1000*60*60*24
# data["Days"] = (startime/secsPerDay).floor()
# grouped = data.group_by(["Days","start station name"])
# bpd = grouped.count(name="bikes").get_frame()
# secs = bpd["Days"]*secsPerDay
# bpd["Month"] = secs.month().asfactor()
# bpd["DayOfWeek"] = secs.dayOfWeek()
# wthr1 = h2o.import_file(path=[h2o.locate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2013.csv"), h2o.locate("bigdata/laptop/citibike-nyc/31081_New_York_City__Hourly_2014.csv")])
# wthr2 = wthr1[["Year Local","Month Local","Day Local","Hour Local","Dew Point (C)","Humidity Fraction","Precipitation One Hour (mm)","Temperature (C)","Weather Code 1/ Description"]]
# wthr2.set_name(wthr2.index("Precipitation One Hour (mm)"), "Rain (mm)")
# wthr2.set_name(wthr2.index("Weather Code 1/ Description"), "WC1")
# wthr3 = wthr2[ wthr2["Hour Local"]==12 ]
# wthr3["msec"] = h2o.H2OFrame.mktime(year=wthr3["Year Local"], month=wthr3["Month Local"]-1, day=wthr3["Day Local"]-1, hour=wthr3["Hour Local"])
# secsPerDay=1000*60*60*24
# wthr3["Days"] = (wthr3["msec"]/secsPerDay).floor()
# wthr4 = wthr3.drop("Year Local").drop("Month Local").drop("Day Local").drop("Hour Local").drop("msec")
# rain = wthr4["Rain (mm)"]
# rain[ rain.isna() ] = 0
# bpd_with_weather = bpd.merge(wthr4,allLeft=True,allRite=False)
# r = bpd_with_weather['Days'].runif(seed=356964763)
# train = bpd_with_weather[ r < 0.6]
# test = bpd_with_weather[(0.6 <= r) & (r < 0.9)]
predictors = ['DayOfWeek', 'WC1', 'start station name', 'Temperature (C)', 'Days', 'Month', 'Humidity Fraction', 'Rain (mm)', 'Dew Point (C)']
train = h2o.import_file(h2o.locate("smalldata/glm_test/citibike_small_train.csv"))
test = h2o.import_file(h2o.locate("smalldata/glm_test/citibike_small_test.csv"))
glm0 = h2o.glm(x=train[predictors], y=train["bikes"], validation_x=test[predictors], validation_y=test["bikes"], family="poisson")
def smallcatGBM(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
#Log.info("Importing alphabet_cattest.csv data...\n")
alphabet = h2o.import_file(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")
gbm_h2o = h2o.gbm(x=alphabet[['X']], y=alphabet["y"], distribution="bernoulli", ntrees=1, max_depth=1, nbins=100)
gbm_h2o.show()
# Train scikit GBM Model:
# Log.info("scikit GBM with same parameters:")
gbm_sci = ensemble.GradientBoostingClassifier(n_estimators=1, max_depth=1, max_features=None)
gbm_sci.fit(trainDataFeatures[:,np.newaxis],trainDataResponse)
def dim_checks():
# Log.info("Uploading logreg/princeton/cuse.dat")
h2o_data = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate.csv"))
np_data = np.loadtxt(h2o.locate("smalldata/logreg/prostate.csv"), delimiter=',', skiprows=1)
h2o_rows, h2o_cols = h2o_data.dim
np_rows, np_cols = list(np_data.shape)
print 'The dimensions of h2o frame is: {0} x {1}'.format(h2o_rows, h2o_cols)
print 'The dimensions of numpy array is: {0} x {1}'.format(np_rows, np_cols)
assert [h2o_rows, h2o_cols] == [np_rows, np_cols], "expected equal number of columns and rows"
# Log.info("Slice out a column and data frame it, try dim on it...")
h2o_slice = h2o_data[4]
np_slice = np_data[:,4]
h2o_rows, h2o_cols = h2o_slice.dim
np_rows = np_slice.shape[0]
print 'The dimensions of h2o column slice is: {0} x {1}'.format(h2o_rows, h2o_cols)
print 'The dimensions of numpy array column slice is: {0} x 1'.format(np_rows)
assert [h2o_rows, h2o_cols] == [np_rows, 1], "expected equal number of columns and rows"
# Log.info("OK, now try an operator, e.g. '&', and then check dimensions agao...")
h2oColAmpFive = h2o_slice & 5
assert h2oColAmpFive.nrow == h2o_rows, "expected the number of rows to remain unchanged"
def sdev(ip,port):
# Connect to h2o
h2o.init(ip,port)
iris_h2o = h2o.import_frame(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
iris_np = np.genfromtxt(h2o.locate("smalldata/iris/iris_wheader.csv"),
delimiter=',',
skip_header=1,
usecols=(0, 1, 2, 3))
sd_np = np.std(iris_np, axis=0, ddof=1)
for i in range(4):
sd_h2o = iris_h2o[i].sd()
assert abs(sd_np[i] - sd_h2o) < 1e-10, "expected standard deviations to be the same"
try:
iris_h2o[4].sd()
assert False, "expected an error. column is categorical."
except EnvironmentError:
assert True
try:
iris_h2o[0:2].sd()
assert False, "expected an error. more than one column."
except EnvironmentError:
assert True
def asnumeric(ip,port):
# Connect to h2o
h2o.init(ip,port)
h2oframe = h2o.import_frame(path=h2o.locate("smalldata/junit/cars.csv"))
rows = h2oframe.nrow()
h2oframe['cylinders'] = h2oframe['cylinders'].ascharacter()
assert h2oframe["cylinders"].isfactor(), "expected the column to be a factor"
# H2OFrame case
h2oframe = h2o.asnumeric(h2oframe)
h2oframe['cylinders'] = h2oframe['cylinders'] - h2oframe['cylinders']
h2oframe = h2oframe[h2oframe['cylinders'] == 0]
assert h2oframe.nrow() == rows, "expected the same number of rows as before {0}, but got {1}".format(rows, h2oframe.nrow())
h2oframe = h2o.import_frame(path=h2o.locate("smalldata/junit/cars.csv"))
h2oframe['cylinders'] = h2oframe['cylinders'].ascharacter()
assert h2oframe["cylinders"].isfactor(), "expected the column to be a factor"
# H2OVec case
h2oframe['cylinders'] = h2o.asnumeric(h2oframe['cylinders'])
h2oframe['cylinders'] = h2oframe['cylinders'] - h2oframe['cylinders']
h2oframe = h2oframe[h2oframe['cylinders'] == 0]
assert h2oframe.nrow() == rows, "expected the same number of rows as before {0}, but got {1}".format(rows, h2oframe.nrow())
def group_by(ip,port):
# Connect to a pre-existing cluster
h2o.init(ip,port)
h2o_iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
pd_iris = pd.read_csv(h2o.locate("smalldata/iris/iris_wheader.csv"))
h2o_agg_funcs = ["count","count_unique","first","last","min","max","mean","avg","sd","stdev","var","sum","ss"]
na_handling = ["ignore","rm","all"]
col_names = h2o_iris.col_names()[0:4]
print "Running smoke test"
# smoke test
for a in h2o_agg_funcs:
for n in na_handling:
for c in col_names:
print "group by : " + str(a) + "; " + str(n) + "; " + str(c)
h2o.group_by(h2o_iris, ["class"], {"foo":[a,c,n]})
# h2o/pandas/numpy comparison test
h2o_np_agg_dict = {"min":np.min, "max":np.max, "mean":np.mean, "sum":np.sum}
for k in h2o_np_agg_dict.keys():
for c in col_names:
print "group by comparison: " + str(k) + "; " + str(c)
h2o_res = h2o.group_by(h2o_iris, ["class"], {"foo":[k,c,"all"]})
pd_res = pd_iris.groupby("class")[c].aggregate(h2o_np_agg_dict[k])
for i in range(3):
h2o_val = h2o_res[i,1]
pd_val = pd_res[h2o_res[i,0]]
assert abs(h2o_val - pd_val) < 1e-06, \
"check unsuccessful! h2o computed {0} and pandas computed {1}. expected equal aggregate {2} " \
"values between h2o and pandas on column {3}".format(h2o_val,pd_val,k,c)
def milsong_checkpoint(ip,port):
milsong_train = h2o.upload_file(h2o.locate("bigdata/laptop/milsongs/milsongs-train.csv.gz"))
milsong_valid = h2o.upload_file(h2o.locate("bigdata/laptop/milsongs/milsongs-test.csv.gz"))
distribution = "gaussian"
# build first model
ntrees1 = random.sample(range(50,100),1)[0]
max_depth1 = random.sample(range(2,6),1)[0]
min_rows1 = random.sample(range(10,16),1)[0]
print "ntrees model 1: {0}".format(ntrees1)
print "max_depth model 1: {0}".format(max_depth1)
print "min_rows model 1: {0}".format(min_rows1)
model1 = h2o.gbm(x=milsong_train[1:],y=milsong_train[0],ntrees=ntrees1,max_depth=max_depth1, min_rows=min_rows1,
distribution=distribution,validation_x=milsong_valid[1:],validation_y=milsong_valid[0])
# save the model, then load the model
model_path = h2o.save_model(model1, name="delete_model", force=True)
restored_model = h2o.load_model(model_path)
shutil.rmtree("delete_model")
# continue building the model
ntrees2 = ntrees1 + 50
max_depth2 = max_depth1
min_rows2 = min_rows1
print "ntrees model 2: {0}".format(ntrees2)
print "max_depth model 2: {0}".format(max_depth2)
print "min_rows model 2: {0}".format(min_rows2)
model2 = h2o.gbm(x=milsong_train[1:],y=milsong_train[0],ntrees=ntrees2,max_depth=max_depth2, min_rows=min_rows2,
distribution=distribution,validation_x=milsong_valid[1:],validation_y=milsong_valid[0],
checkpoint=restored_model._id)
# build the equivalent of model 2 in one shot
model3 = h2o.gbm(x=milsong_train[1:],y=milsong_train[0],ntrees=ntrees2,max_depth=max_depth2, min_rows=min_rows2,
distribution=distribution,validation_x=milsong_valid[1:],validation_y=milsong_valid[0])
def shuffling_large(ip,port):
# Connect to h2o
h2o.init(ip,port)
print("Reading in Arcene training data for binomial modeling.")
train_data = h2o.upload_file(path=h2o.locate("smalldata/arcene/shuffle_test_version/arcene.csv"))
train_data_shuffled = h2o.upload_file(path=h2o.locate("smalldata/arcene/shuffle_test_version/arcene_shuffled.csv"))
print("Create model on original Arcene dataset.")
h2o_model = h2o.glm(x=train_data[0:1000], y=train_data[1000], family="binomial", lambda_search=True, alpha=[0.5], use_all_factor_levels=True)
print("Create second model on original Arcene dataset.")
h2o_model_2 = h2o.glm(x=train_data[0:1000], y=train_data[1000], family="binomial", lambda_search=True, alpha=[0.5], use_all_factor_levels=True)
print("Create model on shuffled Arcene dataset.")
h2o_model_s = h2o.glm(x=train_data_shuffled[0:1000], y=train_data_shuffled[1000], family="binomial", lambda_search=True, alpha=[0.5], use_all_factor_levels=True)
print("Assert that number of predictors remaining and their respective coefficients are equal.")
for x, y in zip(h2o_model._model_json['output']['coefficients_table'].cell_values,h2o_model_2._model_json['output']['coefficients_table'].cell_values):
assert (type(x[1]) == type(y[1])) and (type(x[2]) == type(y[2])), "coefficients should be the same type"
if isinstance(x[1],float):
assert abs(x[1] - y[1]) < 5e-10, "coefficients should be equal"
if isinstance(x[2],float):
assert abs(x[2] - y[2]) < 5e-10, "coefficients should be equal"
for x, y in zip(h2o_model._model_json['output']['coefficients_table'].cell_values,h2o_model_s._model_json['output']['coefficients_table'].cell_values):
assert (type(x[1]) == type(y[1])) and (type(x[2]) == type(y[2])), "coefficients should be the same type"
if isinstance(x[1],float):
assert abs(x[1] - y[1]) < 5e-10, "coefficients should be equal"
if isinstance(x[2],float):
assert abs(x[2] - y[2]) < 5e-10, "coefficients should be equal"
def get_model_test(ip,port):
# Connect to h2o
h2o.init(ip,port)
prostate = h2o.import_frame(path=h2o.locate("smalldata/logreg/prostate.csv"))
r = prostate[0].runif()
train = prostate[r < 0.70]
test = prostate[r >= 0.30]
# Regression
regression_gbm1 = h2o.gbm(y=train[1], x=train[2:9], distribution="gaussian")
predictions1 = regression_gbm1.predict(test)
regression_gbm2 = h2o.get_model(regression_gbm1._key)
assert regression_gbm2._model_json['output']['model_category'] == "Regression"
predictions2 = regression_gbm2.predict(test)
for r in range(predictions1.nrow()):
p1 = predictions1[r,0]
p2 = predictions2[r,0]
assert p1 == p2, "expected regression predictions to be the same for row {0}, but got {1} and {2}" \
"".format(r, p1, p2)
# Binomial
train[1] = train[1].asfactor()
bernoulli_gbm1 = h2o.gbm(y=train[1], x=train[2:9], distribution="bernoulli")
predictions1 = bernoulli_gbm1.predict(test)
bernoulli_gbm2 = h2o.get_model(bernoulli_gbm1._key)
assert bernoulli_gbm2._model_json['output']['model_category'] == "Binomial"
predictions2 = bernoulli_gbm2.predict(test)
for r in range(predictions1.nrow()):
p1 = predictions1[r,0]
p2 = predictions2[r,0]
assert p1 == p2, "expected binomial predictions to be the same for row {0}, but got {1} and {2}" \
"".format(r, p1, p2)
# Clustering
benign_h2o = h2o.import_frame(path=h2o.locate("smalldata/logreg/benign.csv"))
km_h2o = h2o.kmeans(x=benign_h2o, k=3)
benign_km = h2o.get_model(km_h2o._key)
assert benign_km._model_json['output']['model_category'] == "Clustering"
# Multinomial
train[4] = train[4].asfactor()
multinomial_dl1 = h2o.deeplearning(x=train[0:2], y=train[4], loss='CrossEntropy')
predictions1 = multinomial_dl1.predict(test)
multinomial_dl2 = h2o.get_model(multinomial_dl1._key)
assert multinomial_dl2._model_json['output']['model_category'] == "Multinomial"
predictions2 = multinomial_dl2.predict(test)
for r in range(predictions1.nrow()):
p1 = predictions1[r,0]
p2 = predictions2[r,0]
assert p1 == p2, "expected multinomial predictions to be the same for row {0}, but got {1} and {2}" \
"".format(r, p1, p2)
def test_locate():
iris_path = h2o.locate("smalldata/iris/iris.csv")
try:
h2o.locate("smalldata/iris/afilethatdoesnotexist.csv")
assert False, "Expected h2o.locate to raise a ValueError"
except ValueError:
assert True
def frame_show(ip, port):
iris = h2o.import_file(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
prostate = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate.csv.zip"))
airlines = h2o.import_file(path=h2o.locate("smalldata/airlines/allyears2k.zip"))
iris.show()
prostate.show()
airlines.show()
def bernoulliGBM(ip,port):
#Log.info("Importing prostate.csv data...\n")
prostate_train = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_train.csv"))
#Log.info("Converting CAPSULE and RACE columns to factors...\n")
prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor()
#Log.info("H2O Summary of prostate frame:\n")
#prostate.summary()
# Import prostate_train.csv as numpy array for scikit comparison
trainData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_train.csv"), delimiter=',', skiprows=1)
trainDataResponse = trainData[:,0]
trainDataFeatures = trainData[:,1:]
ntrees = 100
learning_rate = 0.1
depth = 5
min_rows = 10
# Build H2O GBM classification model:
#Log.info(paste("H2O GBM with parameters:\ndistribution = 'bernoulli', ntrees = ", ntrees, ", max_depth = 5,
# min_rows = 10, learn_rate = 0.1\n", sep = ""))
gbm_h2o = h2o.gbm(x=prostate_train[1:], y=prostate_train["CAPSULE"], ntrees=ntrees, learn_rate=learning_rate,
max_depth=depth, min_rows=min_rows, distribution="bernoulli")
# Build scikit GBM classification model
#Log.info("scikit GBM with same parameters\n")
gbm_sci = ensemble.GradientBoostingClassifier(learning_rate=learning_rate, n_estimators=ntrees, max_depth=depth,
min_samples_leaf=min_rows, max_features=None)
gbm_sci.fit(trainDataFeatures,trainDataResponse)
#Log.info("Importing prostate_test.csv data...\n")
prostate_test = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_test.csv"))
#Log.info("Converting CAPSULE and RACE columns to factors...\n")
prostate_test["CAPSULE"] = prostate_test["CAPSULE"].asfactor()
# Import prostate_test.csv as numpy array for scikit comparison
testData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_test.csv"), delimiter=',', skiprows=1)
testDataResponse = testData[:,0]
testDataFeatures = testData[:,1:]
# Score on the test data and compare results
# scikit
auc_sci = roc_auc_score(testDataResponse, gbm_sci.predict_proba(testDataFeatures)[:,1])
# h2o
gbm_perf = gbm_h2o.model_performance(prostate_test)
auc_h2o = gbm_perf.auc()
#Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o))
assert auc_h2o >= auc_sci, "h2o (auc) performance degradation, with respect to scikit"
def headers(ip,port):
# Connect to h2o
h2o.init(ip,port)
headers = h2o.import_frame(h2o.locate("smalldata/airlines/allyears2k_headers_only.csv"))
headers_and = h2o.import_frame(h2o.locate("smalldata/airlines/allyears2k.zip"), col_names=headers)
print headers.names()
print headers_and.names()
assert headers.names() == headers_and.names(), "Expected the same column names but got {0} and {1}". \
format(headers.names(), headers_and.names())
def headers():
headers = h2o.import_file(h2o.locate("smalldata/airlines/allyears2k_headers_only.csv"))
headers_and = h2o.import_file(h2o.locate("smalldata/airlines/allyears2k.zip"))
headers_and.set_names(headers.names)
print headers.names
print headers_and.names
assert headers.names == headers_and.names, "Expected the same column names but got {0} and {1}".format(
headers.names, headers_and.names
)
def pubdev_1839(ip, port):
train = h2o.import_file(h2o.locate("smalldata/jira/pubdev_1839_repro_train.csv"))
test = h2o.import_file(h2o.locate("smalldata/jira/pubdev_1839_repro_test.csv"))
glm0 = h2o.glm(x =train.drop("bikes"),
y =train ["bikes"],
validation_x=test .drop("bikes"),
validation_y=test ["bikes"],
family="poisson")
def deeplearning_autoencoder(ip, port):
resp = 784
nfeatures = 20 # number of features (smallest hidden layer)
train_hex = h2o.upload_file(h2o.locate("bigdata/laptop/mnist/train.csv.gz"))
train_hex[resp] = train_hex[resp].asfactor()
test_hex = h2o.upload_file(h2o.locate("bigdata/laptop/mnist/test.csv.gz"))
test_hex[resp] = test_hex[resp].asfactor()
# split data into two parts
sid = train_hex[0].runif(1234)
# unsupervised data for autoencoder
train_unsupervised = train_hex[sid >= 0.5]
train_unsupervised.drop(resp)
train_unsupervised.describe()
# supervised data for drf
train_supervised = train_hex[sid < 0.5]
train_supervised.describe()
# train autoencoder
ae_model = h2o.deeplearning(x=train_unsupervised[0:resp],
activation="Tanh",
autoencoder=True,
hidden=[nfeatures],
epochs=1,
reproducible=True, #slow, turn off for real problems
seed=1234)
# conver train_supervised with autoencoder to lower-dimensional space
train_supervised_features = ae_model.deepfeatures(train_supervised[0:resp]._frame(), 0)
assert train_supervised_features.ncol() == nfeatures, "Dimensionality of reconstruction is wrong!"
# Train DRF on extracted feature space
drf_model = h2o.random_forest(x=train_supervised_features[0:20],
y=train_supervised[resp],
ntrees=10,
min_rows=10,
seed=1234)
# Test the DRF model on the test set (processed through deep features)
test_features = ae_model.deepfeatures(test_hex[0:resp]._frame(), 0)
test_features = test_features.cbind(test_hex[resp])._frame()
# Confusion Matrix and assertion
cm = drf_model.confusion_matrix(test_features)
cm.show()
# 10% error +/- 0.001
assert abs(cm.cell_values[10][10] - 0.1078) < 0.001, "Error. Expected 0.1078, but got {0}".format(cm.cell_values[10][10])
def pub_445_long_request_uri(ip,port):
# Connect to h2o
h2o.init(ip,port)
mnistTrain = h2o.import_frame(path=h2o.locate("bigdata/laptop/mnist/train.csv.gz"))
mnistTest = h2o.import_frame(path=h2o.locate("bigdata/laptop/mnist/train.csv.gz"))
mnistTrain[784]._name = "label"
mnistTest[784]._name = "label"
mnistModel = h2o.gbm(x=mnistTrain.drop("label"), y=mnistTrain["label"], validation_x=mnistTest.drop("label"), validation_y=mnistTest["label"], ntrees=100, max_depth=10)
def swpredsRF(ip, port):
# Training set has two predictor columns
# X1: 10 categorical levels, 100 observations per level; X2: Unif(0,1) noise
# Ratio of y = 1 per Level: cat01 = 1.0 (strong predictor), cat02 to cat10 = 0.5 (weak predictors)
#Log.info("Importing swpreds_1000x3.csv data...\n")
swpreds = h2o.import_file(
path=h2o.locate("smalldata/gbm_test/swpreds_1000x3.csv"))
swpreds["y"] = swpreds["y"].asfactor()
#Log.info("Summary of swpreds_1000x3.csv from H2O:\n")
#swpreds.summary()
# Train H2O DRF without Noise Column
#Log.info("Distributed Random Forest with only Predictor Column")
model1 = h2o.random_forest(x=swpreds[["X1"]],
y=swpreds["y"],
ntrees=50,
max_depth=20,
nbins=500)
model1.show()
perf1 = model1.model_performance(swpreds)
print(perf1.auc())
# Train H2O DRF Model including Noise Column:
#Log.info("Distributed Random Forest including Noise Column")
model2 = h2o.random_forest(x=swpreds[["X1", "X2"]],
y=swpreds["y"],
ntrees=50,
max_depth=20,
nbins=500)
model2.show()
perf2 = model2.model_performance(swpreds)
print(perf2.auc())
def var_test(ip, port):
iris_h2o = h2o.import_file(
path=h2o.locate("smalldata/iris/iris_wheader.csv"))
iris_np = np.genfromtxt(h2o.locate("smalldata/iris/iris_wheader.csv"),
delimiter=',',
skip_header=1,
usecols=(0, 1, 2, 3))
var_np = np.var(iris_np, axis=0, ddof=1)
for i in range(4):
var_h2o = iris_h2o[i].var()
assert abs(var_np[i] - var_h2o) < 1e-10, "expected equal variances"
var_cov_h2o = iris_h2o[0:4].var()
var_cov_np = np.cov(iris_np, rowvar=0, ddof=1)
def nfold_predict(ip,port):
fr = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_train.csv"))
m = h2o.gbm(x=fr[2:], y=fr[1], nfolds=10, ntrees=10)
xval_models = m.get_xval_models()
fr["weights"]=1
preds = [model.predict(fr) for model in xval_models]
(sum(preds)/10).show()
def multi_dim_slicing(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
prostate = h2o.import_frame(
path=h2o.locate("smalldata/logreg/prostate.csv"))
# prostate[int,int] case
# 48,0,68,1,2,1,12.3,16.3,8
pros = prostate[47:51, 7]
assert pros[0] == 16.3, "Incorrect slicing result"
pros = prostate[172, 8]
assert pros == 7, "Incorrect slicing result"
# prostate[slice,int] case
# rows:
# 171,1,74,1,3,1,7,0,6
# 172,1,71,1,3,1,3.3,0,6
# 173,1,60,1,4,1,7.3,0,7
# 174,1,62,1,2,1,17.2,0,7
# 175,0,71,1,2,1,3.8,19,6
# 176,0,67,1,3,1,5.7,15.4,6
pros = prostate[170:176, 2]
assert pros[0] == 74, "Incorrect slicing result"
assert pros[1] == 71, "Incorrect slicing result"
assert pros[2] == 60, "Incorrect slicing result"
assert pros[3] == 62, "Incorrect slicing result"
assert pros[4] == 71, "Incorrect slicing result"
assert pros[5] == 67, "Incorrect slicing result"
# # prostate[int,list] case
# # 353,0,54,1,3,1,21.6,25,7
# # 226,0,68,1,2,1,12.7,0,7
# # 238,0,66,1,2,1,11,36.6,6
# pros = prostate[6,[352,225,237]]
# assert (pros[0,0] - 12.7) < 1e-10, "Incorrect slicing result"
# assert (pros[0,1] - 11) < 1e-10, "Incorrect slicing result"
# assert (pros[0,2] - 21.6) < 1e-10, "Incorrect slicing result"
# prostate [int,slice] case
# 189,1,69,1,3,2,8,31.2,6
pros = prostate[188, 0:3]
assert pros[0, 0] == 189, "Incorrect slicing result"
assert pros[0, 1] + 1 == 2, "Incorrect slicing result"
assert pros[0, 2] == 69, "Incorrect slicing result"
# prostate [slice,slice] case
# 84,0,75,1,2,1,11,35,7
# 85,0,75,1,1,1,9.9,15.4,7
# 86,1,75,1,3,1,3.7,0,6
pros = prostate[83:86, 1:4]
assert pros[0, 0] == 0, "Incorrect slicing result"
assert pros[0, 1] == 75, "Incorrect slicing result"
assert pros[0, 2] - 1 == 0, "Incorrect slicing result"
assert pros[1, 0] == 0, "Incorrect slicing result"
assert pros[1, 1] + 75 == 150, "Incorrect slicing result"
assert pros[1, 2] == 1, "Incorrect slicing result"
assert pros[2, 0] + 1 == 2, "Incorrect slicing result"
assert pros[2, 1] == 75, "Incorrect slicing result"
assert pros[2, 2] == 1, "Incorrect slicing result"
def sub_gsub_check(ip, port):
# Connect to a pre-existing cluster
frame = h2o.import_file(path=h2o.locate("smalldata/iris/iris.csv"))
# single column (frame)
frame["C5"] = frame["C5"].gsub("s", "z")
assert frame[
0, 4] == "Iriz-zetoza", "Expected 'Iriz-zetoza', but got {0}".format(
frame[0, 4])
frame["C5"] = frame["C5"].sub("z", "s")
assert frame[
1, 4] == "Iris-zetoza", "Expected 'Iris-zetoza', but got {0}".format(
frame[1, 4])
# single column (vec)
vec = frame["C5"]
vec = vec.sub("z", "s")
assert vec[
2, 0] == "Iris-setoza", "Expected 'Iris-setoza', but got {0}".format(
vec[2, 0])
vec = vec.gsub("s", "z")
assert vec[
3, 0] == "Iriz-zetoza", "Expected 'Iriz-zetoza', but got {0}".format(
vec[3, 0])
def demo_prostateGBM(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/prostate_gbm.ipynb"),
save_and_norun=False)
def rf_balance_classes(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/rf_balance_classes.ipynb"),
save_and_norun=False)
def link_functions_tweedie_basic(ip, port):
print "Read in prostate data."
hdf = h2o.upload_file(
h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
print "Testing for family: TWEEDIE"
print "Set variables for h2o."
y = "CAPSULE"
x = ["AGE", "RACE", "DCAPS", "PSA", "VOL", "DPROS", "GLEASON"]
print "Create models with canonical link: TWEEDIE"
model_h2o_tweedie = h2o.glm(x=hdf[x],
y=hdf[y],
family="tweedie",
link="tweedie",
alpha=[0.5],
Lambda=[0])
print "Compare model deviances for link function tweedie (using precomputed values from R)"
deviance_h2o_tweedie = model_h2o_tweedie.residual_deviance(
) / model_h2o_tweedie.null_deviance()
assert 0.721452 - deviance_h2o_tweedie <= 0.01, "h2o's residual/null deviance is more than 0.01 lower than R's. h2o: " \
"{0}, r: {1}".format(deviance_h2o_tweedie, 0.721452)
def expr_show(ip, port):
# Connect to h2o
h2o.init(ip, port)
iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
print "iris:"
iris.show()
###################################################################
# expr[int], expr._data is pending
res = 2 - iris
res2 = res[0]
print "res2:"
res2.show()
# expr[int], expr._data is remote
res3 = res[0]
print "res3:"
res3.show()
# expr[int], expr._data is local
expr = Expr([1, 2, 3])
print "expr:"
expr.show()
# expr[tuple], expr._data is local
expr = Expr([[1, 2, 3], [4, 5, 6]])
print "expr:"
expr.show()
def center_scale(ip, port):
# Connect to h2o
h2o.init(ip, port)
iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris.csv"))[0:4]
# frame (default args)
foo = iris.scale()
# TODO: the below assertion fails. Should it?
#assert abs(foo[0,0] - -0.8976739) < 1e-6 and abs(foo[0,1] - 1.01560199) < 1e-6 and abs(foo[0,2] - -1.335752) < 1e-6 \
# and abs(foo[0,3] - -1.311052) < 1e-6, "h2o differed from r. h2o got {0}, {1}, {2}, and {3}" \
# "".format(foo[0,0],foo[0,1],foo[0,2],foo[0,3])
# frame (centers=True, scale=False)
foo = iris.scale(center=True, scale=False)
# frame (centers=False, scale=True)
foo = iris.scale(center=False, scale=True)
# frame (centers=False, scale=False)
foo = iris.scale(center=False, scale=False)
# vec (default args)
foo = iris[0].scale()
# vec (centers=True, scale=False)
foo = iris[1].scale(center=True, scale=False)
# vec (centers=False, scale=True)
foo = iris[2].scale(center=False, scale=True)
# vec (centers=False, scale=False)
foo = iris[3].scale(center=False, scale=False)
def demo_citibike_small(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/citi_bike_small.ipynb"),
save_and_norun=False)
def offset_gaussian(ip,port):
# Connect to a pre-existing cluster
insurance = h2o.import_file(h2o.locate("smalldata/glm_test/insurance.csv"))
insurance["offset"] = insurance["Holders"].log()
gbm = h2o.gbm(x=insurance[0:3], y=insurance["Claims"], distribution="gaussian", ntrees=600, max_depth=1, min_rows=1,
learn_rate=.1, offset_column="offset", training_frame=insurance)
predictions = gbm.predict(insurance)
# Comparison result generated from R's gbm:
# fit2 <- gbm(Claims ~ District + Group + Age+ offset(log(Holders)) , interaction.depth = 1,n.minobsinnode = 1,
# shrinkage = .1,bag.fraction = 1,train.fraction = 1,
# data = Insurance, distribution ="gaussian", n.trees = 600)
# pg = predict(fit2, newdata = Insurance, type = "response", n.trees=600)
# pr = pg - - log(Insurance$Holders)
assert abs(44.33016 - gbm._model_json['output']['init_f']) < 1e-5, "expected init_f to be {0}, but got {1}". \
format(44.33016, gbm._model_json['output']['init_f'])
assert abs(1491.135 - gbm.mse()) < 1e-2, "expected mse to be {0}, but got {1}".format(1491.135, gbm.mse())
assert abs(49.23438 - predictions.mean()) < 1e-2, "expected prediction mean to be {0}, but got {1}". \
format(49.23438, predictions.mean())
assert abs(-45.5720659304 - predictions.min()) < 1e-2, "expected prediction min to be {0}, but got {1}". \
format(-45.5720659304, predictions.min())
assert abs(207.387 - predictions.max()) < 1e-2, "expected prediction max to be {0}, but got {1}". \
format(207.387, predictions.max())
def benign(ip, port):
# Connect to h2o
h2o.init(ip, port)
training_data = h2o.import_frame(h2o.locate("smalldata/logreg/benign.csv"))
Y = 3
X = [x for x in range(2, 11) if x != Y]
#Log.info("Build the model")
model = h2o.glm(y=training_data[Y].asfactor(),
x=training_data[X],
family="binomial",
alpha=[0],
Lambda=[1e-5])
#Log.info("Check that the columns used in the model are the ones we passed in.")
#Log.info("===================Columns passed in: ================")
in_names = [training_data.names()[i] for i in X]
#Log.info("===================Columns passed out: ================")
out_names = [
model._model_json['output']['coefficients_table'].cell_values[c][0]
for c in range(len(X))
]
assert in_names == out_names
def deep_learning_metrics_test(ip, port):
h2o.init(ip, port) # connect to existing cluster
df = h2o.import_frame(path=h2o.locate("smalldata/logreg/prostate.csv"))
del df['ID'] # remove ID
df['CAPSULE'] = df['CAPSULE'].asfactor() # make CAPSULE categorical
vol = df['VOL']
vol[vol == 0] = None # 0 VOL means 'missing'
r = vol.runif() # random train/test split
train = df[r < 0.8]
test = df[r >= 0.8]
# See that the data is ready
train.describe()
train.head()
test.describe()
test.head()
# Run DeepLearning
print "Train a Deeplearning model: "
dl = h2o.deeplearning(x = train[1:],
y = train['CAPSULE'],
epochs = 100,
hidden = [10, 10, 10],
loss = 'CrossEntropy')
print "Binomial Model Metrics: "
print
dl.show()
# print dl._model_json
dl.model_performance(test).show()
def prep_airlines(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
air = h2o.import_frame(
h2o.locate("smalldata/airlines/allyears2k_headers.zip"))
numRows, numCols = air.dim()
x_cols = [
"Month", "DayofMonth", "DayOfWeek", "CRSDepTime", "CRSArrTime",
"UniqueCarrier", "CRSElapsedTime", "Origin", "Dest", "Distance"
]
y_col = "SynthDepDelayed"
noDepDelayedNAs = air[air["DepDelay"].isna() == 0]
print "Dimensions of new dataset: {0}".format(noDepDelayedNAs.dim())
minutesOfDelayWeTolerate = 15
noDepDelayedNAs.cbind(
noDepDelayedNAs["DepDelay"] > minutesOfDelayWeTolerate)
noDepDelayedNAs[numCols] = noDepDelayedNAs[numCols].asfactor()
noDepDelayedNAs._vecs[numCols].setName(y_col)
gbm = h2o.gbm(x=noDepDelayedNAs[x_cols],
y=noDepDelayedNAs[y_col],
loss="bernoulli")
gbm.show()
def link_incompatible_error(ip, port):
print("Reading in original prostate data.")
prostate = h2o.import_file(
path=h2o.locate("smalldata/prostate/prostate.csv.zip"))
print(
"Throw error when trying to create model with incompatible logit link."
)
try:
h2o.model = h2o.glm(x=prostate[1:8],
y=prostate[8],
family="gaussian",
link="logit")
assert False, "expected an error"
except EnvironmentError:
assert True
try:
h2o.model = h2o.glm(x=prostate[1:8],
y=prostate[8],
family="tweedie",
link="log")
assert False, "expected an error"
except EnvironmentError:
assert True
try:
h2o.model = h2o.glm(x=prostate[2:9],
y=prostate[1],
family="binomial",
link="inverse")
assert False, "expected an error"
except EnvironmentError:
assert True
def offset_gamma(ip, port):
# Connect to a pre-existing cluster
insurance = h2o.import_file(h2o.locate("smalldata/glm_test/insurance.csv"))
insurance["offset"] = insurance["Holders"].log()
gbm = h2o.gbm(x=insurance[0:3],
y=insurance["Claims"],
distribution="gamma",
ntrees=600,
max_depth=1,
min_rows=1,
learn_rate=.1,
offset_column="offset",
training_frame=insurance)
predictions = gbm.predict(insurance)
# Comparison result generated from harrysouthworth's gbm:
# fit2 = gbm(Claims ~ District + Group + Age+ offset(log(Holders)) , interaction.depth = 1,n.minobsinnode = 1,shrinkage = .1,bag.fraction = 1,train.fraction = 1,
# data = Insurance, distribution ="gamma", n.trees = 600)
# pr = predict(fit2, Insurance)
# pr = exp(pr+log(Insurance$Holders))
assert abs(-1.714958 - gbm._model_json['output']['init_f']) < 1e-5, "expected init_f to be {0}, but got {1}". \
format(-1.714958, gbm._model_json['output']['init_f'])
assert abs(50.1087 - predictions.mean()) < 1e-2, "expected prediction mean to be {0}, but got {1}". \
format(50.1087, predictions.mean())
assert abs(0.9133843 - predictions.min()) < 1e-4, "expected prediction min to be {0}, but got {1}". \
format(0.9133843, predictions.min())
assert abs(392.6667 - predictions.max()) < 0.1, "expected prediction max to be {0}, but got {1}". \
format(392.6667, predictions.max())
def expr_slicing(ip,port):
# Connect to h2o
h2o.init(ip,port)
iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris_wheader.csv"))
iris.show()
###################################################################
# expr[int] (column slice), expr is pending
res = 2 - iris
res2 = res[0]
assert abs(res2[3,0] - -2.6) < 1e-10 and abs(res2[17,0] - -3.1) < 1e-10 and abs(res2[24,0] - -2.8) < 1e-10, \
"incorrect values"
# expr[int,int], expr is remote
assert abs(res[13, 3] - 1.9) < 1e-10, "incorrect values"
# expr[int, slice], expr is remote
res4 = res[12, 0:3]
assert abs(res4[0,0] - -2.8) < 1e-10 and abs(res4[0,1] - -1.0) < 1e-10 and abs(res4[0,2] - 0.6) < 1e-10 and \
abs(res4[0,3] - 1.9) < 1e-10, "incorrect values"
# expr[slice, int], expr is remote
res5 = res[5:8, 1]
assert abs(res5[0,0] - -1.9) < 1e-10 and abs(res5[1,0] - -1.4) < 1e-10 and abs(res5[2,0] - -1.4) < 1e-10 and \
abs(res5[3,0] - -0.9) < 1e-10, "incorrect values"
# expr[slice, slice], expr is pending
res = iris * 2
res6 = res[5:8, 0:3]
assert abs(res6[0,0] - 10.8) < 1e-10 and abs(res6[1,1] - 6.8) < 1e-10 and abs(res6[2,2] - 3.0) < 1e-10 and \
abs(res6[3,3] - 0.4) < 1e-10, "incorrect values"
def demo_imputation(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/imputation.ipynb"),
save_and_norun=False)
def weights_gamma(ip, port):
htable = h2o.upload_file(h2o.locate("smalldata/gbm_test/moppe.csv"))
htable["premiekl"] = htable["premiekl"].asfactor()
htable["moptva"] = htable["moptva"].asfactor()
htable["zon"] = htable["zon"]
#gg = gbm(formula = medskad ~ premiekl + moptva + zon,data = table.1.2,distribution = "gamma", weights = table.1.2$antskad ,
# n.trees = 20,interaction.depth = 1,n.minobsinnode = 1,shrinkage = 1,bag.fraction = 1,train.fraction = 1)
#pr = predict(gg,newdata = table.1.2,type = "response")
#htable= as.h2o(table.1.2,destination_frame = "htable")
hh = h2o.gbm(x=htable[0:3],
y=htable["medskad"],
training_frame=htable,
distribution="gamma",
weights_column="antskad",
ntrees=20,
max_depth=1,
min_rows=1,
learn_rate=1)
ph = hh.predict(htable)
assert abs(8.804447 - hh._model_json['output']['init_f']) < 1e-6 * 8.804447
assert abs(3751.01 - ph[0].min()) < 1e-4 * 3751.01
assert abs(15298.87 - ph[0].max()) < 1e-4 * 15298.87
assert abs(8121.98 - ph[0].mean()) < 1e-4 * 8121.98
def ls_test(ip, port):
# Connect to h2o
h2o.init(ip, port)
iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris.csv"))
h2o.ls()
def perfectSeparation_balanced(ip, port):
# Connect to h2o
h2o.init(ip, port)
print("Read in synthetic balanced dataset")
data = h2o.import_frame(
path=h2o.locate("smalldata/synthetic_perfect_separation/balanced.csv"))
print("Fit model on dataset")
model = h2o.glm(x=data[["x1", "x2"]],
y=data["y"],
family="binomial",
lambda_search=True,
use_all_factor_levels=True,
alpha=[0.5],
Lambda=[0])
print(
"Extract models' coefficients and assert reasonable values (ie. no greater than 50)"
)
print("Balanced dataset")
coef = [
c[1]
for c in model._model_json['output']['coefficients_table'].cell_values
if c[0] != "Intercept"
]
for c in coef:
assert c < 50, "coefficient is too large"
def covtype(ip,port):
# Connect to h2o
h2o.init(ip,port)
# Log.info("Importing covtype.20k.data...\n")
covtype = h2o.import_frame(path=h2o.locate("smalldata/covtype/covtype.20k.data"))
#
myY = 54
myX = [x for x in range(0,54) if x not in [20,28]]
# Set response to be indicator of a particular class
res_class = random.randint(1,4)
# Log.info(paste("Setting response column", myY, "to be indicator of class", res_class, "\n"))
covtype[54] = (covtype[54] == res_class)
#covtype.summary()
# L2: alpha = 0, lambda = 0
covtype_mod1 = h2o.glm(y=covtype[myY], x=covtype[myX], family="binomial", n_folds=0, alpha=[0], Lambda=[0])
covtype_mod1.show()
# Elastic: alpha = 0.5, lambda = 1e-4
covtype_mod2 = h2o.glm(y=covtype[myY], x=covtype[myX], family="binomial", n_folds=0, alpha=[0.5], Lambda=[1e-4])
covtype_mod2.show()
# L1: alpha = 1, lambda = 1e-4
covtype_mod3 = h2o.glm(y=covtype[myY], x=covtype[myX], family="binomial", n_folds=0, alpha=[1], Lambda=[1e-4])
covtype_mod3.show()
def link_functions_tweedie_vpow(ip,port):
# Load example data from HDtweedie, y = aggregate claim loss
hdf = h2o.upload_file(h2o.locate("smalldata/glm_test/auto.csv"))
y = "y"
x = list(set(hdf.names) - set(["y"]))
print "Testing for family: TWEEDIE"
print "Create models with canonical link: TWEEDIE"
# Iterate over different variance powers for tweedie
vpower = [0, 1, 1.5]
r_dev = [0.7516627, 0.6708826, 0.7733762]
r_null = [221051.88369951, 32296.29783702, 20229.47425307]
for ridx, vpow in enumerate(vpower):
print "Fit h2o.glm:"
h2ofit = h2o.glm(x=hdf[x], y=hdf[y], family="tweedie", link="tweedie", tweedie_variance_power=vpow, tweedie_link_power=1-vpow,
alpha=[0.5], Lambda=[0])
print "Testing Tweedie variance power: {0}".format(vpow)
print "Compare model deviances for link function tweedie"
deviance_h2o_tweedie = h2ofit.residual_deviance() / h2ofit.null_deviance()
assert r_dev[ridx] - deviance_h2o_tweedie <= 0.01, "h2o's residual/null deviance is more than 0.01 lower than " \
"R's. h2o: {0}, r: {1}".format(deviance_h2o_tweedie, r_dev[ridx])
print "compare null and residual deviance between R glm and h2o.glm for tweedie"
assert abs(r_null[ridx] - h2ofit.null_deviance()) < 1e-6, "h2o's null deviance is not equal to R's. h2o: {0}, r: " \
"{1}".format(h2ofit.null_deviance(), r_null[ridx])
def vi_toy_test(ip, port):
# Connect to h2o
h2o.init(ip, port)
toy_data = h2o.import_frame(
path=h2o.locate("smalldata/gbm_test/toy_data_RF.csv"))
#toy_data.summary()
toy_data[6] = toy_data[6].asfactor()
toy_data.show()
rf = h2o.random_forest(x=toy_data[[0, 1, 2, 3, 4, 5]],
y=toy_data[6],
ntrees=500,
max_depth=20,
nbins=100,
seed=0)
ranking = [
rf._model_json['output']['variable_importances'].cell_values[v][0]
for v in range(toy_data.ncol() - 1)
]
print(ranking)
assert tuple(ranking) == tuple(
["V3", "V2", "V6", "V5", "V1",
"V4"]), "expected specific variable importance ranking"
def bigcatRF(ip, port):
# Connect to h2o
h2o.init(ip, port)
# Training set has 100 categories from cat001 to cat100
# Categories cat001, cat003, ... are perfect predictors of y = 1
# Categories cat002, cat004, ... are perfect predictors of y = 0
#Log.info("Importing bigcat_5000x2.csv data...\n")
bigcat = h2o.import_frame(
path=h2o.locate("smalldata/gbm_test/bigcat_5000x2.csv"))
bigcat["y"] = bigcat["y"].asfactor()
#Log.info("Summary of bigcat_5000x2.csv from H2O:\n")
#bigcat.summary()
# Train H2O DRF Model:
#Log.info("H2O DRF (Naive Split) with parameters:\nclassification = TRUE, ntree = 1, depth = 1, nbins = 100, nbins_cats=10\n")
model = h2o.random_forest(x=bigcat[["X"]],
y=bigcat["y"],
ntrees=1,
max_depth=1,
nbins=100,
nbins_cats=10)
model.show()
def parametersKmeans(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port) # connect to localhost:54321
#Log.info("Getting data...")
iris = h2o.import_frame(path=h2o.locate("smalldata/iris/iris.csv"))
#Log.info("Create and and duplicate...")
iris_km = h2o.kmeans(x=iris[0:4], k=3, seed=1234)
parameters = iris_km._model_json['parameters']
param_dict = {}
for p in range(len(parameters)):
param_dict[parameters[p]['label']] = parameters[p]['actual_value']
iris_km_again = h2o.kmeans(x=iris[0:4], **param_dict)
#Log.info("wss")
wss = iris_km.withinss().sort()
wss_again = iris_km_again.withinss().sort()
assert wss == wss_again, "expected wss to be equal"
#Log.info("centers")
centers = iris_km.centers()
centers_again = iris_km_again.centers()
assert centers == centers_again, "expected centers to be the same"
def link_correct_default(ip,port):
print("Reading in original prostate data.")
h2o_data = h2o.upload_file(path=h2o.locate("smalldata/prostate/prostate.csv.zip"))
print("Compare models with link unspecified and canonical link specified.")
print("GAUSSIAN: ")
h2o_model_unspecified = h2o.glm(x=h2o_data[1:8], y=h2o_data[8], family="gaussian")
h2o_model_specified = h2o.glm(x=h2o_data[1:8], y=h2o_data[8], family="gaussian", link="identity")
assert h2o_model_specified._model_json['output']['coefficients_table'].cell_values == \
h2o_model_unspecified._model_json['output']['coefficients_table'].cell_values, "coefficient should be equal"
print("BINOMIAL: ")
h2o_model_unspecified = h2o.glm(x=h2o_data[2:9], y=h2o_data[1], family="binomial")
h2o_model_specified = h2o.glm(x=h2o_data[2:9], y=h2o_data[1], family="binomial", link="logit")
assert h2o_model_specified._model_json['output']['coefficients_table'].cell_values == \
h2o_model_unspecified._model_json['output']['coefficients_table'].cell_values, "coefficient should be equal"
print("POISSON: ")
h2o_model_unspecified = h2o.glm(x=h2o_data[2:9], y=h2o_data[1], family="poisson")
h2o_model_specified = h2o.glm(x=h2o_data[2:9], y=h2o_data[1], family="poisson", link="log")
assert h2o_model_specified._model_json['output']['coefficients_table'].cell_values == \
h2o_model_unspecified._model_json['output']['coefficients_table'].cell_values, "coefficient should be equal"
print("GAMMA: ")
h2o_model_unspecified = h2o.glm(x=h2o_data[3:9], y=h2o_data[2], family="gamma")
h2o_model_specified = h2o.glm(x=h2o_data[3:9], y=h2o_data[2], family="gamma", link="inverse")
assert h2o_model_specified._model_json['output']['coefficients_table'].cell_values == \
h2o_model_unspecified._model_json['output']['coefficients_table'].cell_values, "coefficient should be equal"
def demo_chicago_crimes(ip, port):
# Connect to a pre-existing cluster
h2o.init(ip, port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/chicago_crimes.ipynb"),
save_and_norun=False)
def asfactor_basic(ip,port):
#Log.info("Printing out the head of the cars datasets")
h2oframe = h2o.import_file(path=h2o.locate("smalldata/junit/cars.csv"))
h2oframe.show()
h2oframe['cylinders'].show()
foo = h2oframe["cylinders"]
foo.show()
h2oframe['cylinders'].asfactor().show()
meow = h2oframe['cylinders'].asfactor()
meow.show()
foo = h2oframe["cylinders"].isfactor()
assert not foo, "expected the foo H2OVec to be a not factor"
h2oframe["cylinders"] = h2oframe['cylinders'].asfactor()
h2oframe.show()
bar = h2oframe["cylinders"].isfactor()
assert bar, "expected the bar H2OVec to be a factor"