def hdfs_kmeans_airlines():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_file = "/datasets/airlines_all.csv"
print "Import airlines_all.csv from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_file)
airlines_h2o = h2o.import_file(url)
n = airlines_h2o.nrow
print "rows: {0}".format(n)
print "Run k-means++ with k = 7 and max_iterations = 10"
myX = range(8) + range(11, 16) + range(18, 21) + range(24, 29) + [9]
airlines_km = h2o.kmeans(training_frame=airlines_h2o,
x=airlines_h2o[myX],
k=7,
init="Furthest",
max_iterations=10,
standardize=True)
print airlines_km
else:
print "Not running on H2O internal network. No access to HDFS."
def directory_import():
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
url1 = "hdfs://{0}{1}".format(hdfs_name_node, "/datasets/iris/identical_iris_files/iris1.csv")
url2 = "hdfs://{0}{1}".format(hdfs_name_node, "/datasets/iris/identical_iris_files/")
print "Importing HDFS file {0} and directory {1}".format(url1, url2)
frm_one = h2o.import_file(url1)
frm_all = h2o.import_file(url2)
r1, c1 = frm_one.dim
ra, ca = frm_all.dim
assert r1*3 == ra, "Expected 3 times the rows, but got {0} and {1}".format(r1,ra)
assert c1 == ca, "Expected same number of cols, but got {0} and {1}".format(c1,ca)
small1 = h2o.locate("smalldata/jira/identical_files/iris1.csv")
small2 = small1.split("iris1.csv")[0]
print "Importing smalldata file {0} and directory {1}".format(small1, small2)
frm_one = h2o.import_file(small1)
frm_all = h2o.import_file(small2)
r1, c1 = frm_one.dim
ra, ca = frm_all.dim
assert r1*3 == ra, "Expected 3 times the rows, but got {0} and {1}".format(r1,ra)
assert c1 == ca, "Expected same number of cols, but got {0} and {1}".format(c1,ca)
def pubdev_1431(ip, port):
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
airlines_billion_file_1 = "/datasets/airlinesbillion.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, airlines_billion_file_1)
airlines_billion_1 = h2o.import_file(url)
airlines_billion_1[30] = airlines_billion_1[30].asfactor()
gbm = h2o.gbm(x=airlines_billion_1[0:30], y=airlines_billion_1[30], ntrees=1, distribution="bernoulli", max_depth=1)
predictions = gbm.predict(airlines_billion_1)
csv = os.path.join(os.getcwd(),"delete.csv")
h2o.download_csv(predictions,csv)
airlines_billion_2 = h2o.import_file(csv)
os.remove(csv)
r1, c1 = airlines_billion_1.dim
r2, c2 = airlines_billion_2.dim
assert r1 == r2 and c1 == c2, "Expect rows to be equal. r1: {0} and r2: {1}. Expect cols to be equal c1: {0} " \
"c2: {1}".format(r1,r2,c1,c2)
else:
print "Not running on H2O internal network. No access to HDFS."
def pubdev_1431(ip, port):
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
airlines_billion_file_1 = "/datasets/airlinesbillion.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, airlines_billion_file_1)
airlines_billion_1 = h2o.import_file(url)
airlines_billion_1[30] = airlines_billion_1[30].asfactor()
gbm = h2o.gbm(x=airlines_billion_1[0:30],
y=airlines_billion_1[30],
ntrees=1,
distribution="bernoulli",
max_depth=1)
predictions = gbm.predict(airlines_billion_1)
csv = os.path.join(os.getcwd(), "delete.csv")
h2o.download_csv(predictions, csv)
airlines_billion_2 = h2o.import_file(csv)
os.remove(csv)
r1, c1 = airlines_billion_1.dim
r2, c2 = airlines_billion_2.dim
assert r1 == r2 and c1 == c2, "Expect rows to be equal. r1: {0} and r2: {1}. Expect cols to be equal c1: {0} " \
"c2: {1}".format(r1,r2,c1,c2)
else:
print "Not running on H2O internal network. No access to HDFS."
def hdfs_basic():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_iris_file = "/datasets/runit/iris_wheader.csv"
hdfs_iris_dir = "/datasets/runit/iris_test_train"
#----------------------------------------------------------------------
# Single file cases.
#----------------------------------------------------------------------
print "Testing single file importHDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_iris_file)
iris_h2o = h2o.import_file(url)
iris_h2o.head()
iris_h2o.tail()
n = iris_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(
n, 150)
assert isinstance(
iris_h2o,
h2o.H2OFrame), "Wrong type. Expected H2OFrame, but got {0}".format(
type(iris_h2o))
print "Import worked"
#----------------------------------------------------------------------
# Directory file cases.
#----------------------------------------------------------------------
print "Testing directory importHDFS"
urls = [
"hdfs://{0}{1}/iris_test.csv".format(hdfs_name_node,
hdfs_iris_dir),
"hdfs://{0}{1}/iris_train.csv".format(hdfs_name_node,
hdfs_iris_dir)
]
iris_dir_h2o = h2o.import_file(urls)
iris_dir_h2o.head()
iris_dir_h2o.tail()
n = iris_dir_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(
n, 150)
assert isinstance(iris_dir_h2o, h2o.H2OFrame), "Wrong type. Expected H2OFrame, but got {0}".\
format(type(iris_dir_h2o))
print "Import worked"
else:
print "Not running on H2O internal network. No access to HDFS."
def pubdev_1421():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_airlines_test_file = "/datasets/airlines.test.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_airlines_test_file)
air_test = h2o.import_file(url)
def kmeans_mllib(ip, port):
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_cross_file = "/datasets/runit/BigCross.data"
print "Import BigCross.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_cross_file)
cross_h2o = h2o.import_file(url)
n = cross_h2o.nrow
err_mllib = np.genfromtxt(
h2o.locate("smalldata/mllib_bench/bigcross_wcsse.csv"),
delimiter=",",
skip_header=1)
ncent = [int(err_mllib[r][0]) for r in range(len(err_mllib))]
for k in ncent:
print "Run k-means++ with k = {0} and max_iterations = 10".format(
k)
cross_km = h2o.kmeans(training_frame=cross_h2o,
x=cross_h2o,
k=k,
init="PlusPlus",
max_iterations=10,
standardize=False)
clust_mllib = np.genfromtxt(
h2o.locate("smalldata/mllib_bench/bigcross_centers_" + str(k) +
".csv"),
delimiter=",").tolist()
clust_h2o = cross_km.centers()
# Sort in ascending order by first dimension for comparison purposes
clust_mllib.sort(key=lambda x: x[0])
clust_h2o.sort(key=lambda x: x[0])
print "\nMLlib Cluster Centers:\n"
print clust_mllib
print "\nH2O Cluster Centers:\n"
print clust_h2o
wcsse_mllib = err_mllib[err_mllib[0:4, 0].tolist().index(k)][1]
wcsse_h2o = cross_km.tot_withinss() / n
print "\nMLlib Average Within-Cluster SSE: \n".format(wcsse_mllib)
print "H2O Average Within-Cluster SSE: \n".format(wcsse_h2o)
assert wcsse_h2o == wcsse_mllib, "Expected mllib and h2o to get the same wcsse. Mllib got {0}, and H2O " \
"got {1}".format(wcsse_mllib, wcsse_h2o)
def pubdev_1421():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_airlines_test_file = "/datasets/airlines.test.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_airlines_test_file)
air_test = h2o.import_file(url)
def hdfs_kmeans_converge():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_cross_file = "/datasets/runit/BigCross.data"
print "Import BigCross.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_cross_file)
cross_h2o = h2o.import_file(url)
n = cross_h2o.nrow
print "rows: {0}".format(n)
ncent = 3
miters = 10
print "Run k-means with k = {0} and max_iterations = {1}".format(
ncent, miters)
cross1_km = h2o.kmeans(training_frame=cross_h2o,
x=cross_h2o[0:57],
k=ncent,
max_iterations=miters)
print cross1_km
print "Run k-means with init = final cluster centers and max_iterations = 1"
init_centers = h2o.H2OFrame(cross1_km.centers())
init_centers_key = init_centers.send_frame()
cross2_km = h2o.kmeans(training_frame=cross_h2o,
x=cross_h2o[0:57],
k=ncent,
user_points=init_centers_key,
max_iterations=1)
print cross2_km
print "Check k-means converged or maximum iterations reached"
c1 = h2o.H2OFrame(cross1_km.centers())
c2 = h2o.H2OFrame(cross2_km.centers())
avg_change = ((c1 - c2)**2).sum() / ncent
iters = cross1_km._model_json['output']['model_summary'].cell_values[
0][3]
assert avg_change < 1e-6 or iters > miters, "Expected k-means to converge or reach max iterations. avg_change = " \
"{0} and iterations = {1}".format(avg_change, iters)
else:
print "Not running on H2O internal network. No access to HDFS."
def pubdev_1431(ip, port):
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
airlines_billion_file = "/datasets/airlinesbillion.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, airlines_billion_file)
airlines_billion = h2o.import_file(url)
airlines_billion[30] = airlines_billion[30].asfactor()
gbm = h2o.gbm(x=airlines_billion[0:30], y=airlines_billion[30], ntrees=1, distribution="bernoulli", max_depth=1)
predictions = gbm.predict(airlines_billion)
csv = os.path.join(os.getcwd(),"delete.csv")
h2o.download_csv(predictions,csv)
os.remove(csv)
else:
print "Not running on H2O internal network. No access to HDFS."
def pubdev_1431():
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
airlines_billion_file = "/datasets/airlinesbillion.csv"
url = "hdfs://{0}{1}".format(hdfs_name_node, airlines_billion_file)
airlines_billion = h2o.import_file(url)
airlines_billion[30] = airlines_billion[30].asfactor()
gbm = h2o.gbm(x=airlines_billion[0:30], y=airlines_billion[30], ntrees=1, distribution="bernoulli", max_depth=1)
predictions = gbm.predict(airlines_billion)
csv = os.path.join(os.getcwd(),"delete.csv")
h2o.download_csv(predictions,csv)
os.remove(csv)
else:
print "Not running on H2O internal network. No access to HDFS."
def hdfs_basic():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_iris_file = "/datasets/runit/iris_wheader.csv"
hdfs_iris_dir = "/datasets/runit/iris_test_train"
#----------------------------------------------------------------------
# Single file cases.
#----------------------------------------------------------------------
print "Testing single file importHDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_iris_file)
iris_h2o = h2o.import_file(url)
iris_h2o.head()
iris_h2o.tail()
n = iris_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(n, 150)
assert isinstance(iris_h2o, h2o.H2OFrame), "Wrong type. Expected H2OFrame, but got {0}".format(type(iris_h2o))
print "Import worked"
#----------------------------------------------------------------------
# Directory file cases.
#----------------------------------------------------------------------
print "Testing directory importHDFS"
urls = ["hdfs://{0}{1}/iris_test.csv".format(hdfs_name_node, hdfs_iris_dir),
"hdfs://{0}{1}/iris_train.csv".format(hdfs_name_node, hdfs_iris_dir)]
iris_dir_h2o = h2o.import_file(urls)
iris_dir_h2o.head()
iris_dir_h2o.tail()
n = iris_dir_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(n, 150)
assert isinstance(iris_dir_h2o, h2o.H2OFrame), "Wrong type. Expected H2OFrame, but got {0}".\
format(type(iris_dir_h2o))
print "Import worked"
else:
print "Not running on H2O internal network. No access to HDFS."
def hdfs_kmeans(ip, port):
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_iris_file = "/datasets/runit/iris_wheader.csv"
hdfs_covtype_file = "/datasets/runit/covtype.data"
print "Import iris_wheader.csv from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_iris_file)
iris_h2o = h2o.import_file(url)
n = iris_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(
n, 150)
print "Running KMeans on iris"
iris_km = h2o.kmeans(training_frame=iris_h2o,
k=3,
x=iris_h2o[0:4],
max_iterations=10)
print iris_km
print "Importing covtype.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_covtype_file)
covtype_h2o = h2o.import_file(url)
n = covtype_h2o.nrow
print "rows: {0}".format(n)
assert n == 581012, "Wrong number of rows. Got {0}. Should have got {1}".format(
n, 581012)
print "Running KMeans on covtype"
covtype_km = h2o.kmeans(training_frame=covtype_h2o,
x=covtype_h2o[0:55],
k=8,
max_iterations=10)
print covtype_km
else:
print "Not running on H2O internal network. No access to HDFS."
def kmeans_mllib():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_cross_file = "/datasets/runit/BigCross.data"
print "Import BigCross.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_cross_file)
cross_h2o = h2o.import_file(url)
n = cross_h2o.nrow
err_mllib = np.genfromtxt(h2o.locate("smalldata/mllib_bench/bigcross_wcsse.csv"), delimiter=",", skip_header=1)
ncent = [int(err_mllib[r][0]) for r in range(len(err_mllib))]
for k in ncent:
print "Run k-means++ with k = {0} and max_iterations = 10".format(k)
cross_km = h2o.kmeans(training_frame = cross_h2o, x = cross_h2o, k = k, init = "PlusPlus",
max_iterations = 10, standardize = False)
clust_mllib = np.genfromtxt(h2o.locate("smalldata/mllib_bench/bigcross_centers_" + str(k) + ".csv"),
delimiter=",").tolist()
clust_h2o = cross_km.centers()
# Sort in ascending order by first dimension for comparison purposes
clust_mllib.sort(key=lambda x: x[0])
clust_h2o.sort(key=lambda x: x[0])
print "\nMLlib Cluster Centers:\n"
print clust_mllib
print "\nH2O Cluster Centers:\n"
print clust_h2o
wcsse_mllib = err_mllib[err_mllib[0:4,0].tolist().index(k)][1]
wcsse_h2o = cross_km.tot_withinss() / n
print "\nMLlib Average Within-Cluster SSE: \n".format(wcsse_mllib)
print "H2O Average Within-Cluster SSE: \n".format(wcsse_h2o)
assert wcsse_h2o == wcsse_mllib, "Expected mllib and h2o to get the same wcsse. Mllib got {0}, and H2O " \
"got {1}".format(wcsse_mllib, wcsse_h2o)
def hdfs_kmeans_converge(ip, port):
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_cross_file = "/datasets/runit/BigCross.data"
print "Import BigCross.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_cross_file)
cross_h2o = h2o.import_file(url)
n = cross_h2o.nrow
print "rows: {0}".format(n)
ncent = 3
miters = 10
print "Run k-means with k = {0} and max_iterations = {1}".format(ncent,miters)
cross1_km = h2o.kmeans(training_frame = cross_h2o, x=cross_h2o[0:57], k = ncent, max_iterations = miters)
print cross1_km
print "Run k-means with init = final cluster centers and max_iterations = 1"
init_centers = h2o.H2OFrame(cross1_km.centers())
init_centers_key = init_centers.send_frame()
cross2_km = h2o.kmeans(training_frame = cross_h2o, x=cross_h2o[0:57], k = ncent, user_points=init_centers_key,
max_iterations = 1)
print cross2_km
print "Check k-means converged or maximum iterations reached"
c1 = h2o.H2OFrame(cross1_km.centers())
c2 = h2o.H2OFrame(cross2_km.centers())
avg_change = ((c1-c2)**2).sum() / ncent
iters = cross1_km._model_json['output']['model_summary'].cell_values[0][3]
assert avg_change < 1e-6 or iters > miters, "Expected k-means to converge or reach max iterations. avg_change = " \
"{0} and iterations = {1}".format(avg_change, iters)
else:
print "Not running on H2O internal network. No access to HDFS."
def hdfs_kmeans_airlines():
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_file = "/datasets/airlines_all.csv"
print "Import airlines_all.csv from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_file)
airlines_h2o = h2o.import_file(url)
n = airlines_h2o.nrow
print "rows: {0}".format(n)
print "Run k-means++ with k = 7 and max_iterations = 10"
myX = range(8) + range(11,16) + range(18,21) + range(24,29) + [9]
airlines_km = h2o.kmeans(training_frame = airlines_h2o, x = airlines_h2o[myX], k = 7, init = "Furthest",
max_iterations = 10, standardize = True)
print airlines_km
else:
print "Not running on H2O internal network. No access to HDFS."
def directory_import():
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
url1 = "hdfs://{0}{1}".format(
hdfs_name_node, "/datasets/iris/identical_iris_files/iris1.csv")
url2 = "hdfs://{0}{1}".format(hdfs_name_node,
"/datasets/iris/identical_iris_files/")
print "Importing HDFS file {0} and directory {1}".format(url1, url2)
frm_one = h2o.import_file(url1)
frm_all = h2o.import_file(url2)
r1, c1 = frm_one.dim
ra, ca = frm_all.dim
assert r1 * 3 == ra, "Expected 3 times the rows, but got {0} and {1}".format(
r1, ra)
assert c1 == ca, "Expected same number of cols, but got {0} and {1}".format(
c1, ca)
small1 = tests.locate("smalldata/jira/identical_files/iris1.csv")
small2 = small1.split("iris1.csv")[0]
print "Importing smalldata file {0} and directory {1}".format(
small1, small2)
frm_one = h2o.import_file(small1)
frm_all = h2o.import_file(small2)
r1, c1 = frm_one.dim
ra, ca = frm_all.dim
assert r1 * 3 == ra, "Expected 3 times the rows, but got {0} and {1}".format(
r1, ra)
assert c1 == ca, "Expected same number of cols, but got {0} and {1}".format(
c1, ca)
def hdfs_kmeans(ip, port):
# Check if we are running inside the H2O network by seeing if we can touch
# the namenode.
running_inside_h2o = tests.is_running_internal_to_h2o()
if running_inside_h2o:
hdfs_name_node = tests.get_h2o_internal_hdfs_name_node()
hdfs_iris_file = "/datasets/runit/iris_wheader.csv"
hdfs_covtype_file = "/datasets/runit/covtype.data"
print "Import iris_wheader.csv from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_iris_file)
iris_h2o = h2o.import_file(url)
n = iris_h2o.nrow
print "rows: {0}".format(n)
assert n == 150, "Wrong number of rows. Got {0}. Should have got {1}".format(n, 150)
print "Running KMeans on iris"
iris_km = h2o.kmeans(training_frame = iris_h2o, k = 3, x = iris_h2o[0:4], max_iterations = 10)
print iris_km
print "Importing covtype.data from HDFS"
url = "hdfs://{0}{1}".format(hdfs_name_node, hdfs_covtype_file)
covtype_h2o = h2o.import_file(url)
n = covtype_h2o.nrow
print "rows: {0}".format(n)
assert n == 581012, "Wrong number of rows. Got {0}. Should have got {1}".format(n, 581012)
print "Running KMeans on covtype"
covtype_km = h2o.kmeans(training_frame = covtype_h2o, x = covtype_h2o[0:55], k = 8, max_iterations = 10)
print covtype_km
else:
print "Not running on H2O internal network. No access to HDFS."
