def func():
n_rows = [600000, 800000]
n_cols = [400, 600, 800, 1000, 1200]
for rows in n_rows:
# res = {}
# res['n_rows'] = []
# res['n_cols'] = []
# res['t_h2o'] = []
# res['t_sklearn'] = []
# res['r2_h2o'] = []
# res['r2_sklearn'] = []
for cols in n_cols:
X, y = generate_data(rows, cols)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
print(X_train.nbytes, 'bytes')
time_h2o, time_sklearn, r2_h2o, r2_sklearn = fit_model(
X_train, y_train, X_test, y_test, reg_type='lasso')
# res['n_rows'].append(rows)
# res['n_cols'].append(cols)
# res['t_h2o'].append(ret[0])
# res['t_sklearn'].append(ret[1])
# res['r2_h2o'].append(ret[2])
# res['r2_sklearn'].append(ret[3])
time.sleep(0.1)
# res = pd.DataFrame(res)
# res.to_csv("./benchmarks/results_%1.0f.csv" % rows, index=False)
time.sleep(0.1)
print('DONE!')
def fun(nGPUs=1,
nFolds=1,
nLambdas=100,
nAlphas=8,
validFraction=0.2,
whichdata=0,
double_precision=False):
name = str(sys._getframe().f_code.co_name)
t = time.time()
print("cwd: %s" % (os.getcwd()))
sys.stdout.flush()
name = sys._getframe(1).f_code.co_name
# pipes = startfunnel(os.path.join(os.getcwd(), "tmp/"), name)
print("Getting Data")
from h2o4gpu.datasets import fetch_20newsgroups, fetch_20newsgroups_vectorized, fetch_california_housing, \
fetch_covtype, fetch_kddcup99, fetch_lfw_pairs, fetch_lfw_people, fetch_mldata, fetch_olivetti_faces, \
fetch_rcv1, fetch_species_distributions
from h2o4gpu.model_selection import train_test_split
# Fetch dataset
if whichdata == 0:
data = fetch_20newsgroups() # runs
sizetokeep = 1000 # 1k rows for now
elif whichdata == 1:
data = fetch_20newsgroups_vectorized() # sparse
sizetokeep = 1000 # 1k rows for now
elif whichdata == 2:
data = fetch_california_housing() # runs
sizetokeep = 1000
elif whichdata == 3:
data = fetch_covtype()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 4:
data = fetch_kddcup99() # strings -> numeric
sizetokeep = 1000 # 1k rows for now
elif whichdata == 5:
data = fetch_lfw_pairs()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 6:
data = fetch_lfw_people()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 7:
data = fetch_mldata('iris')
sizetokeep = 1000 # 1k rows for now
elif whichdata == 8:
data = fetch_mldata('leukemia') # runs
sizetokeep = 1000 # 1k rows for now
elif whichdata == 9:
data = fetch_mldata('Whistler Daily Snowfall')
sizetokeep = 1000 # 1k rows for now
elif whichdata == 10:
data = fetch_olivetti_faces() # runs
sizetokeep = 100
elif whichdata == 11:
data = fetch_rcv1()
sizetokeep = 1000 # 1k rows for now
# data = data.todense() # FIXME: glm and kmeans h2o4gpu currently only supports dense matrices
elif whichdata == 12:
data = fetch_species_distributions()
sizetokeep = 1000 # 1k rows for now
else:
ValueError("No such whichdata")
try:
sizetokeep = min(sizetokeep, len(data.data[:, 0]))
X = data.data[0:sizetokeep, :]
except:
sizetokeep = min(sizetokeep, len(data.data[:]))
X = data.data[0:sizetokeep]
y = data.target[0:sizetokeep]
print("Got Data")
import numpy as np
# Create 0.8/0.2 train/test split
print("Split Data")
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
train_size=0.8,
random_state=42)
print("Encode Data")
# from h2o4gpu.preprocessing import Imputer
# imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
# imp.fit(X, y)
# Xencoded = imp.transform(X)
# yencoded = imp.transform(y)
import pandas as pd
X_test_pd = pd.DataFrame(X_test)
X_train_pd = pd.DataFrame(X_train)
# Importing LabelEncoder and initializing it
from h2o4gpu.preprocessing import LabelEncoder
le = LabelEncoder()
# Iterating over all the common columns in train and test
for col in X_test_pd.columns.values:
# Encoding only categorical variables
if X_test_pd[col].dtypes == 'object' or X_test_pd[
col].dtypes == 'bool':
# Using whole data to form an exhaustive list of levels
data = X_train_pd[col].append(X_test_pd[col])
le.fit(data.values)
X_train_pd[col] = le.transform(X_train_pd[col])
X_test_pd[col] = le.transform(X_test_pd[col])
X_train_pd = pd.get_dummies(X_train_pd).fillna(0.0)
X_test_pd = pd.get_dummies(X_test_pd).fillna(0.0)
y_train_pd = pd.Series(y_train).fillna(0.0)
y_test_pd = pd.Series(y_test).fillna(0.0)
# get back numpy
X_test = X_test_pd.values
X_train = X_train_pd.values
y_test = y_test_pd.values
y_train = y_train_pd.values
if double_precision:
mynptype = np.float64
else:
mynptype = np.float32
X_test = X_test.astype(mynptype)
X_train = X_train.astype(np.float64)
y_test = y_test.astype(np.float64)
y_train = y_train.astype(np.float64)
# TODO: Should write this to file and avoid doing encoding if already exists
t1 = time.time()
print("Start ElasticNetH2O")
rmse_train, rmse_test = run_glm(X_train,
y_train,
X_test,
y_test,
nGPUs=nGPUs,
nlambda=nLambdas,
nfolds=nFolds,
nalpha=nAlphas,
validFraction=validFraction,
verbose=0,
name=name,
tolerance=0.2,
tol=1E-2,
tol_seek_factor=1.0)
print("End ElasticNetH2O")
# check rmse
print(rmse_train[0, 0])
print(rmse_train[0, 1])
print(rmse_train[0, 2])
print(rmse_test[0, 2])
sys.stdout.flush()
print('/n Total execution time:%d' % (time.time() - t1))
print("TEST PASSED")
sys.stdout.flush()
print("Time taken: {}".format(time.time() - t))
# endfunnel(pipes)
print("DONE.")
sys.stdout.flush()
def fun(nGPUs=1, nFolds=1, nLambdas=100, nAlphas=8, validFraction=0.2, whichdata=0, double_precision=False):
name = str(sys._getframe().f_code.co_name)
t = time.time()
print("cwd: %s" % (os.getcwd()))
sys.stdout.flush()
name = sys._getframe(1).f_code.co_name
# pipes = startfunnel(os.path.join(os.getcwd(), "tmp/"), name)
print("Getting Data")
from h2o4gpu.datasets import fetch_20newsgroups, fetch_20newsgroups_vectorized, fetch_california_housing, \
fetch_covtype, fetch_kddcup99, fetch_lfw_pairs, fetch_lfw_people, fetch_mldata, fetch_olivetti_faces, \
fetch_rcv1, fetch_species_distributions
from h2o4gpu.model_selection import train_test_split
# Fetch dataset
if whichdata == 0:
data = fetch_20newsgroups() # runs
sizetokeep = 1000 # 1k rows for now
elif whichdata == 1:
data = fetch_20newsgroups_vectorized() # sparse
sizetokeep = 1000 # 1k rows for now
elif whichdata == 2:
data = fetch_california_housing() # runs
sizetokeep = 1000
elif whichdata == 3:
data = fetch_covtype()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 4:
data = fetch_kddcup99() # strings -> numeric
sizetokeep = 1000 # 1k rows for now
elif whichdata == 5:
data = fetch_lfw_pairs()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 6:
data = fetch_lfw_people()
sizetokeep = 1000 # 1k rows for now
elif whichdata == 7:
data = fetch_mldata('iris')
sizetokeep = 1000 # 1k rows for now
elif whichdata == 8:
data = fetch_mldata('leukemia') # runs
sizetokeep = 1000 # 1k rows for now
elif whichdata == 9:
data = fetch_mldata('Whistler Daily Snowfall')
sizetokeep = 1000 # 1k rows for now
elif whichdata == 10:
data = fetch_olivetti_faces() # runs
sizetokeep = 100
elif whichdata == 11:
data = fetch_rcv1()
sizetokeep = 1000 # 1k rows for now
#data = data.todense() # FIXME: glm and kmeans h2o4gpu currently only supports dense matrices
elif whichdata == 12:
data = fetch_species_distributions()
sizetokeep = 1000 # 1k rows for now
else:
ValueError("No such whichdata")
try:
sizetokeep = min(sizetokeep,len(data.data[:,0]))
X = data.data[0:sizetokeep, :]
except:
sizetokeep = min(sizetokeep, len(data.data[:]))
X = data.data[0:sizetokeep]
y = data.target[0:sizetokeep]
print("Got Data")
import numpy as np
# Create 0.8/0.2 train/test split
print("Split Data")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, train_size=0.8,
random_state=42)
print("Encode Data")
# from h2o4gpu.preprocessing import Imputer
# imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
# imp.fit(X, y)
# Xencoded = imp.transform(X)
# yencoded = imp.transform(y)
import pandas as pd
X_test_pd = pd.DataFrame(X_test)
X_train_pd = pd.DataFrame(X_train)
# Importing LabelEncoder and initializing it
from h2o4gpu.preprocessing import LabelEncoder
le = LabelEncoder()
# Iterating over all the common columns in train and test
for col in X_test_pd.columns.values:
# Encoding only categorical variables
if X_test_pd[col].dtypes == 'object' or X_test_pd[col].dtypes == 'bool':
# Using whole data to form an exhaustive list of levels
data = X_train_pd[col].append(X_test_pd[col])
le.fit(data.values)
X_train_pd[col] = le.transform(X_train_pd[col])
X_test_pd[col] = le.transform(X_test_pd[col])
X_train_pd = pd.get_dummies(X_train_pd).fillna(0.0)
X_test_pd = pd.get_dummies(X_test_pd).fillna(0.0)
y_train_pd = pd.Series(y_train).fillna(0.0)
y_test_pd = pd.Series(y_test).fillna(0.0)
# get back numpy
X_test = X_test_pd.values
X_train = X_train_pd.values
y_test = y_test_pd.values
y_train = y_train_pd.values
if double_precision:
mynptype = np.float64
else:
mynptype = np.float32
X_test = X_test.astype(mynptype)
X_train = X_train.astype(np.float64)
y_test = y_test.astype(np.float64)
y_train = y_train.astype(np.float64)
# TODO: Should write this to file and avoid doing encoding if already exists
t1 = time.time()
print("Start ElasticNetH2O")
rmse_train, rmse_test = run_glm(X_train, y_train, X_test, y_test, nGPUs=nGPUs, nlambda=nLambdas, nfolds=nFolds,
nalpha=nAlphas,
validFraction=validFraction, verbose=10, name=name, tolerance=0.2, tol=1E-2, tol_seek_factor=1.0)
print("End ElasticNetH2O")
# check rmse
print(rmse_train[0, 0])
print(rmse_train[0, 1])
print(rmse_train[0, 2])
print(rmse_test[0, 2])
sys.stdout.flush()
print('/n Total execution time:%d' % (time.time() - t1))
print("TEST PASSED")
sys.stdout.flush()
print("Time taken: {}".format(time.time() - t))
# endfunnel(pipes)
print("DONE.")
sys.stdout.flush()