def demo_body(go):
"""
Demo of H2O's Gradient Boosting estimator.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.describe()
go()
# Randomly split the dataset into ~70/30, training/test sets
train, test = prostate.split_frame(ratios=[0.70])
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2OGradientBoostingEstimator
prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ntrees=10, max_depth=8,
min_rows=10, learn_rate=0.2)
prostate_gbm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE", training_frame=train)
go()
# Show the model
prostate_gbm.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_gbm.predict(test)
predictions.show()
go()
# Fetch a tree, print number of tree nodes, show root node description
from h2o.tree import H2OTree, H2ONode
tree = H2OTree(prostate_gbm, 0, "0")
len(tree)
tree.left_children
tree.right_children
tree.root_node.show()
go()
# Show default performance metrics
performance = prostate_gbm.model_performance(test)
performance.show()
def demo_body(go):
"""
Demo of H2O's Gradient Boosting estimator.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.describe()
go()
# Randomly split the dataset into ~70/30, training/test sets
train, test = prostate.split_frame(ratios=[0.70])
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2OGradientBoostingEstimator
prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli", ntrees=10, max_depth=8,
min_rows=10, learn_rate=0.2)
prostate_gbm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE", training_frame=train)
go()
# Show the model
prostate_gbm.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_gbm.predict(test)
predictions.show()
go()
# Fetch a tree, print number of tree nodes, show root node description
from h2o.tree import H2OTree, H2ONode
tree = H2OTree(prostate_gbm, 0, "0")
len(tree)
tree.left_children
tree.right_children
tree.root_node.show()
go()
# Show default performance metrics
performance = prostate_gbm.model_performance(test)
performance.show()
def main():
args = parse_args()
h2o.init(ip=args.host, port=args.port)
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
# Print a description of the prostate data
prostate.describe()
# Randomly split the dataset into ~70/30, training/test sets
client.update_task_info({
'test_train': 0.7,
'learn_rate': 0.2,
})
train, test = prostate.split_frame(ratios=[0.70])
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
# Build a (classification) GLM
from h2o.estimators import H2OGradientBoostingEstimator
prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=10,
max_depth=8,
min_rows=10,
learn_rate=0.2)
prostate_gbm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE",
training_frame=train)
# Show the model
prostate_gbm.show()
# Predict on the test set and show the first ten predictions
predictions = prostate_gbm.predict(test)
predictions.show()
# Fetch a tree, print number of tree nodes, show root node description
from h2o.tree import H2OTree, H2ONode
tree = H2OTree(prostate_gbm, 0, "0")
tree.root_node.show()
# Show default performance metrics
performance = prostate_gbm.model_performance(test)
performance.show()
client.update_task_info({
'mse': performance.mse(),
'rmse': performance.rmse(),
'auc': performance.auc(),
'gini': performance.gini(),
'logloss': performance.logloss(),
})
def h2oload_dataset():
"""
Python API test: h2o.load_dataset(relative_path)
"""
try:
prostate = h2o.load_dataset("prostate")
assert_is_type(prostate, H2OFrame)
except Exception as e:
assert False, "h2o.load_dataset() command not is working."
def demo_body(go):
"""
Demo of H2O's Deep Learning model.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.summary()
go()
# Randomly split the dataset into ~70/30, training/test sets
r = prostate[0].runif()
train = prostate[r < 0.70]
test = prostate[r >= 0.70]
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2ODeepLearningEstimator
prostate_dl = H2ODeepLearningEstimator(activation="Tanh",
hidden=[10, 10, 10],
epochs=10000)
prostate_dl.train(x=list(set(prostate.col_names) - {"ID", "CAPSULE"}),
y="CAPSULE",
training_frame=train)
go()
# Show the model
prostate_dl.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_dl.predict(test)
predictions.show()
go()
# Show default performance metrics
performance = prostate_dl.model_performance(test)
performance.show()
def demo_body(go):
"""
Demo of H2O's Generalized Linear Estimator.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.summary()
go()
# Randomly split the dataset into ~70/30, training/test sets
r = prostate[0].runif()
train = prostate[r < 0.70]
test = prostate[r >= 0.70]
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2OGeneralizedLinearEstimator
prostate_glm = H2OGeneralizedLinearEstimator(family="binomial",
alpha=[0.5])
prostate_glm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE",
training_frame=train)
go()
# Show the model
prostate_glm.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_glm.predict(test)
predictions.show()
go()
# Show default performance metrics
performance = prostate_glm.model_performance(test)
performance.show()
def demo_body(go):
"""
Demo of H2O's Deep Learning model.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.describe()
go()
# Randomly split the dataset into ~70/30, training/test sets
train, test = prostate.split_frame(ratios=[0.70])
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2ODeepLearningEstimator
prostate_dl = H2ODeepLearningEstimator(activation="Tanh", hidden=[10, 10, 10], epochs=10000)
prostate_dl.train(x=list(set(prostate.col_names) - {"ID", "CAPSULE"}),
y="CAPSULE", training_frame=train)
go()
# Show the model
prostate_dl.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_dl.predict(test)
predictions.show()
go()
# Show default performance metrics
performance = prostate_dl.model_performance(test)
performance.show()
def demo_body(go):
"""
Demo of H2O's Generalized Linear Estimator.
This demo uploads a dataset to h2o, parses it, and shows a description.
Then it divides the dataset into training and test sets, builds a GLM
from the training set, and makes predictions for the test set.
Finally, default performance metrics are displayed.
"""
go()
# Connect to H2O
h2o.init()
go()
# Upload the prostate dataset that comes included in the h2o python package
prostate = h2o.load_dataset("prostate")
go()
# Print a description of the prostate data
prostate.describe()
go()
# Randomly split the dataset into ~70/30, training/test sets
train, test = prostate.split_frame(ratios=[0.70])
go()
# Convert the response columns to factors (for binary classification problems)
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
go()
# Build a (classification) GLM
from h2o.estimators import H2OGeneralizedLinearEstimator
prostate_glm = H2OGeneralizedLinearEstimator(family="binomial", alpha=[0.5])
prostate_glm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE", training_frame=train)
go()
# Show the model
prostate_glm.show()
go()
# Predict on the test set and show the first ten predictions
predictions = prostate_glm.predict(test)
predictions.show()
go()
# Show default performance metrics
performance = prostate_glm.model_performance(test)
performance.show()
def train_model():
# start h2o and load data
h2o.init()
h2o_df = h2o.load_dataset("iris.csv")
# train model
model = H2OGradientBoostingEstimator(ntrees=100,
max_depth=4,
learn_rate=0.1,
model_id='latest')
model.train(y="Species", training_frame=h2o_df)
# save model to MOJO
modelfile = model.download_mojo(path="models/", get_genmodel_jar=True)
print("Model saved to " + modelfile)
# save python model to disk
h2o.save_model(model=model,
path=os.getcwd() + '/models/h2o_model',
force=True)
def main():
h2o.init()
#df = h2o.import_file(path="smalldata/logreg/prostate.csv")
prostate = h2o.load_dataset("prostate")
prostate.describe()
train, test = prostate.split_frame(ratios=[0.70])
train["CAPSULE"] = train["CAPSULE"].asfactor()
test["CAPSULE"] = test["CAPSULE"].asfactor()
# Train model
from h2o.estimators import H2OGeneralizedLinearEstimator
prostate_glm = H2OGeneralizedLinearEstimator(family="binomial",
alpha=[0.5])
prostate_glm.train(x=["AGE", "RACE", "PSA", "VOL", "GLEASON"],
y="CAPSULE",
training_frame=train)
prostate_glm.show()
predictions = prostate_glm.predict(test)
predictions.show()
performance = prostate_glm.model_performance(test)
performance.show()
# Export model
model_path = h2o.save_model(prostate_glm, path="./h2o_model", force=True)
print(model_path)
model = prostate_glm
predictions = model.predict(test)
predictions.show()
performance = model.model_performance(test)
performance.show()
# Export test data
df = test.as_data_frame()
with open("data.json", "w") as f:
#json.dump(df.to_json(orient='records'), f)
#json.dump(df.to_json(orient='columns'), f)
json.dump(df.to_json(orient='index'), f)
def h2oload_dataset():
"""
Python API test: h2o.load_dataset(relative_path)
"""
prostate = h2o.load_dataset("prostate")
assert_is_type(prostate, H2OFrame)
def main():
# set random seed
seed(SEED)
####################################################################################################################
# logging config
# log file
logger.setLevel(logging.DEBUG)
# create console handler and file handler and set level to debug
sh = logging.StreamHandler()
sh.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter(
'%(asctime)s.%(msecs)06d: %(levelname)s %(name)s:%(lineno)d:\n%(message)s',
datefmt='%Y-%m-%d %I:%M:%S')
# add formatter
sh.setFormatter(formatter)
# add handler to logger
logger.addHandler(sh)
####################################################################################################################
# output directory
# output directory
out_dir = 'out-' + PREFIX + '-' + time_stamp
try:
if not os.path.exists(out_dir):
os.mkdir(out_dir)
log_name = out_dir + os.sep + PREFIX + '-' + time_stamp + '.log'
fh = logging.FileHandler(log_name)
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.info('Created output directory: %s' % out_dir)
except IOError:
print('Failed to create output directory: %s!' % out_dir)
sys.exit(-1)
####################################################################################################################
# training data
iterations = pd.DataFrame(
columns=['Main AUC', 'Main AIR', 'Adversary AUC'])
train_f_name = '/home/patrickh/Workspace/GWU_rml/tests/data/train_simulated_transformed.csv'
valid_f_name = '/home/patrickh/Workspace/GWU_rml/tests/data/test_simulated_transformed.csv'
x_names = [
'binary1', 'binary2', 'cat1_0', 'cat1_1', 'cat1_2', 'cat1_3', 'cat1_4',
'fried1_std', 'fried2_std', 'fried3_std', 'fried4_std', 'fried5_std'
]
y_name = 'outcome'
demo_name = 'ctrl_class1'
protected_level = 0
control_level = 1
####################################################################################################################
# training
iter_frame = pd.DataFrame(columns=['Adv. AUC', 'Main AUC', 'Main AIR'])
logger.info('Training ...')
# initialize main model
htrain = h2o.load_dataset(train_f_name)
htrain[y_name] = htrain[y_name].asfactor()
print(htrain.head())
hvalid = h2o.load_dataset(valid_f_name)
hvalid[y_name] = hvalid[y_name].asfactor()
print(hvalid.head())
main_ = model.gbm_grid(x_names, y_name, htrain, hvalid, SEED)
main_auc = main_.auc(valid=True)
acc = main_.accuracy(valid=True)
logger.info(
'Initial GBM grid search completed with accuracy %.4f at threshold %.4f.'
% (acc[0][1], acc[0][0]))
hvalid['pred'] = main_.predict(hvalid)['p1']
cm_protected = get_confusion_matrix(hvalid.as_data_frame(),
y_name,
'pred',
by=demo_name,
level=protected_level,
cutoff=acc[0][0])
logger.info(cm_protected)
cm_control = get_confusion_matrix(hvalid.as_data_frame(),
y_name,
'pred',
by=demo_name,
level=control_level,
cutoff=acc[0][0])
logger.info(cm_control)
cm_dict = {0: cm_protected, 1: cm_control}
main_air = get_air(cm_dict, control_level, protected_level)
logger.info('Initial GBM AIR %.4f.' % main_air)
iter_frame = iter_frame.append(
{
'Adv. AUC': np.nan,
'Main AUC': main_auc,
'Main AIR': main_air
},
ignore_index=True)
for i in range(0, EPOCHS):
# train adversary to create weights
adv_htrain = main_.predict_contributions(htrain)
adv_htrain['pred'] = main_.predict(htrain)[
'p1'] # probability of high priced
adv_htrain['demo'] = htrain[demo_name]
adv_htrain['demo'] = adv_htrain['demo'].asfactor()
print(adv_htrain.head())
adv_hvalid = main_.predict_contributions(hvalid)
adv_hvalid['pred'] = main_.predict(hvalid)[
'p1'] # probability of high priced
adv_hvalid['demo'] = hvalid[demo_name]
adv_hvalid['demo'] = adv_hvalid['demo'].asfactor()
print(adv_hvalid.head())
adversary_ = model.gbm_grid(x_names + ['pred'], 'demo', adv_htrain,
adv_hvalid, SEED)
adv_auc = adversary_.auc(valid=True)
logger.info('Epoch %d adversary AUC: %.4f.' % (int(i), adv_auc))
# re-train main model with weights
htrain['weight'] = adversary_.predict(adv_htrain)[
'p0'] # probability of control
print(htrain.head())
hvalid['weight'] = adversary_.predict(adv_hvalid)[
'p0'] # probability of control
print(hvalid.head())
main_ = model.gbm_grid(x_names,
y_name,
htrain,
hvalid,
SEED,
weight='weight')
main_auc = main_.auc(valid=True)
acc = main_.accuracy(valid=True)
logger.info(
'Epoch %d GBM grid search completed with accuracy %.4f at threshold %.4f.'
% (int(i), acc[0][1], acc[0][0]))
hvalid['pred'] = main_.predict(hvalid)['p1']
cm_protected = get_confusion_matrix(hvalid.as_data_frame(),
y_name,
'pred',
by=demo_name,
level=protected_level,
cutoff=acc[0][0])
logger.info(cm_protected)
cm_control = get_confusion_matrix(hvalid.as_data_frame(),
y_name,
'pred',
by=demo_name,
level=control_level,
cutoff=acc[0][0])
logger.info(cm_control)
cm_dict = {0: cm_protected, 1: cm_control}
main_air = get_air(cm_dict, control_level, protected_level)
logger.info('Epoch %d GBM AIR %.4f.' % (int(i), main_air))
iter_frame = iter_frame.append(
{
'Adv. AUC': adv_auc,
'Main AUC': main_auc,
'Main AIR': main_air
},
ignore_index=True)
logger.info(iter_frame)
iter_frame.to_csv(out_dir + os.sep + 'iter.csv')
####################################################################################################################
# end timer
toc = time.time() - tic
logger.info('All tasks completed in %.2f s' % toc)
enc.fit(data[f].values.reshape(-1, 1))
X_train = sparse.hstack(
(X_train, enc.transform(train[f].values.reshape(-1, 1))), 'csr')
X_test = sparse.hstack(
(X_test, enc.transform(test[f].values.reshape(-1, 1))), 'csr')
print(X_train.shape)
print(X_test.shape)
y_train = target.values
############# 特征工程 end
# train = h2o.upload_file(pre_root_path + '/jinnan_round1_train_20181227.csv')#"./cache_data/train_data_f{}.csv".format(1))
# test = h2o.upload_file(pre_root_path + '/jinnan_round1_testA_20181227.csv')#"./cache_data/test_data_f{}.csv".format(1))
print('load data to h2o')
train = h2o.load_dataset(X_train)
test = h2o.load_dataset(X_test)
# all_data = h2o.connect(train, test)
# all_data = X_train + X_test
# all_data = ' ' # 这是所有的特征，可以从参考baseline1
# feature_name = [i for i in all_data.columns if i not in ['样本id','收率']]
feature_name = mean_columns + numerical_columns
x = feature_name
y = '收率'
aml = H2OAutoML(max_models=320, seed=2019, max_runtime_secs=12800)
aml.train(x=feature_name, y=y, training_frame=train)
lb = aml.leaderboard
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o_df = h2o.load_dataset("prostate.csv")
h2o_df["CAPSULE"] = h2o_df["CAPSULE"].asfactor()
model = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=100,
max_depth=4,
learn_rate=0.1)
model.train(y="CAPSULE",
x=["AGE", "RACE", "PSA", "GLEASON"],
training_frame=h2o_df)
modefile = model.download_mojo(path="exp", get_genmodel_jar=True)
print("Model saved to " + modefile)
def h2oload_dataset():
"""
Python API test: h2o.load_dataset(relative_path)
"""
prostate = h2o.load_dataset("prostate")
assert_is_type(prostate, H2OFrame)
#!/usr/bin/env python
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()
h2o_df = h2o.load_dataset("prostate")
h2o_df["CAPSULE"] = h2o_df["CAPSULE"].asfactor()
model = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=100,
max_depth=4,
learn_rate=0.1)
model.train(y="CAPSULE",
x=["AGE", "RACE", "PSA", "GLEASON"],
training_frame=h2o_df)
modelfile = model.download_mojo(path="./model", get_genmodel_jar=True)
print("Model saved to " + modelfile)
