def cv_carsGLM(ip,port):
# Connect to h2o
h2o.init(ip,port)
# read in the dataset and construct training set (and validation set)
cars = h2o.import_frame(path=h2o.locate("smalldata/junit/cars_20mpg.csv"))
# choose the type model-building exercise (multinomial classification or regression). 0:regression, 1:binomial,
# 2:multinomial
problem = random.sample(range(3),1)[0]
# pick the predictors and the correct response column
# TODO: add more families
predictors = ["displacement","power","weight","acceleration","year"]
if problem == 1 :
family = "binomial"
response_col = "economy_20mpg"
cars[response_col] = cars[response_col].asfactor()
elif problem == 2 :
family = "poisson"
response_col = "cylinders"
else :
family = "gaussian"
response_col = "economy"
print "Family: {0}".format(family)
print "Response column: {0}".format(response_col)
# TODO: add 'seed' to GLM. PUBDEV-1705
## cross-validation
## check that cv metrics are the same over (seeded) repeated runs
nfolds = random.randint(3,10)
glm1 = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, family=family, seed=1234)
glm2 = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, family=family, seed=1234)
h2o.check_models(glm1, glm2)
## boundary cases
# 1. nfolds = number of observations (leave-one-out cross-validation)
glm = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow(), family=family, seed=1234)
# TODO: manually construct the cross-validation metrics and compare
# TODO: PUBDEV-1697
# 2. nfolds = 0
glm1 = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=0, family=family)
# check that this is equivalent to no nfolds
glm2 = h2o.glm(y=cars[response_col], x=cars[predictors], family=family)
h2o.check_models(glm1, glm2)
# 3. more folds than observations equivalent to (seeded) leave-one-out
glm3 = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow()+1, family=family, seed=1234)
h2o.check_models(glm, glm3)
## error cases
# 1. nfolds == 1 or < 0
# TODO: PUBDEV-1696
try:
glm = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=random.randint(-10000,-1),
family=family)
glm = h2o.glm(y=cars[response_col], x=cars[predictors], nfolds=1, family=family)
assert False, "Expected model-build to fail when nfolds is 1 or < 0"
except EnvironmentError:
assert True
# 2. cross-validation and regular validation attempted
r = cars[0].runif()
train = cars[r > .2]
valid = cars[r <= .2]
try:
glm = h2o.glm(y=train[response_col], x=train[predictors], nfolds=random.randint(3,10), validation_y=valid[1],
validation_x=valid[predictors], family=family)
assert False, "Expected model-build to fail when both cross-validation and regular validation is attempted"
except EnvironmentError:
assert True
def cv_carsGBM(ip,port):
# read in the dataset and construct training set (and validation set)
cars = h2o.import_file(path=h2o.locate("smalldata/junit/cars_20mpg.csv"))
# choose the type model-building exercise (multinomial classification or regression). 0:regression, 1:binomial,
# 2:multinomial
problem = random.sample(range(3),1)[0]
# pick the predictors and response column, along with the correct distribution
predictors = ["displacement","power","weight","acceleration","year"]
if problem == 1 :
response_col = "economy_20mpg"
distribution = "bernoulli"
cars[response_col] = cars[response_col].asfactor()
elif problem == 2 :
response_col = "cylinders"
distribution = "multinomial"
cars[response_col] = cars[response_col].asfactor()
else :
response_col = "economy"
distribution = "gaussian"
print "Distribution: {0}".format(distribution)
print "Response column: {0}".format(response_col)
## cross-validation
# 1. check that cv metrics are the same over repeated "Modulo" runs
nfolds = random.randint(3,10)
gbm1 = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, distribution=distribution, ntrees=5,
fold_assignment="Modulo")
gbm2 = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, distribution=distribution, ntrees=5,
fold_assignment="Modulo")
h2o.check_models(gbm1, gbm2, True)
# 2. check that cv metrics are different over repeated "Random" runs
nfolds = random.randint(3,10)
gbm1 = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, distribution=distribution, ntrees=5,
fold_assignment="Random")
gbm2 = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=nfolds, distribution=distribution, ntrees=5,
fold_assignment="Random")
try:
h2o.check_models(gbm1, gbm2, True)
assert False, "Expected models to be different over repeated Random runs"
except AssertionError:
assert True
# 3. folds_column
num_folds = random.randint(2,5)
fold_assignments = h2o.H2OFrame(python_obj=[[random.randint(0,num_folds-1)] for f in range(cars.nrow())])
fold_assignments.setNames(["fold_assignments"])
cars = cars.cbind(fold_assignments)
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], training_frame=cars, distribution=distribution, ntrees=5,
fold_column="fold_assignments", keep_cross_validation_predictions=True)
num_cv_models = len(gbm._model_json['output']['cross_validation_models'])
assert num_cv_models==num_folds, "Expected {0} cross-validation models, but got " \
"{1}".format(num_folds, num_cv_models)
cv_model1 = h2o.get_model(gbm._model_json['output']['cross_validation_models'][0]['name'])
cv_model2 = h2o.get_model(gbm._model_json['output']['cross_validation_models'][1]['name'])
assert isinstance(cv_model1, type(gbm)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model1),type(gbm))
assert isinstance(cv_model2, type(gbm)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model2),type(gbm))
# 4. keep_cross_validation_predictions
cv_predictions = gbm1._model_json['output']['cross_validation_predictions']
assert cv_predictions is None, "Expected cross-validation predictions to be None, but got {0}".format(cv_predictions)
cv_predictions = gbm._model_json['output']['cross_validation_predictions']
assert len(cv_predictions)==num_folds, "Expected the same number of cross-validation predictions " \
"as folds, but got {0}".format(len(cv_predictions))
# # 5. manually construct models
# fold1 = cars[cars["fold_assignments"]==0]
# fold2 = cars[cars["fold_assignments"]==1]
# manual_model1 = h2o.gbm(y=fold2[response_col],
# x=fold2[predictors],
# validation_y=fold1[response_col],
# validation_x=fold1[predictors], ntrees=5,
# distribution=distribution)
# manual_model2 = h2o.gbm(y=fold1[response_col],
# x=fold1[predictors],
# validation_y=fold2[response_col],
# validation_x=fold2[predictors], ntrees=5,
# distribution=distribution)
## boundary cases
# 1. nfolds = number of observations (leave-one-out cross-validation)
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow(), distribution=distribution, ntrees=5,
fold_assignment="Modulo")
# 2. nfolds = 0
gbm1 = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=0, distribution=distribution, ntrees=5)
# check that this is equivalent to no nfolds
gbm2 = h2o.gbm(y=cars[response_col], x=cars[predictors], distribution=distribution, ntrees=5)
h2o.check_models(gbm1, gbm2)
# 3. cross-validation and regular validation attempted
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=random.randint(3,10), validation_y=cars[response_col], ntrees=5,
validation_x=cars[predictors], distribution=distribution)
## error cases
# 1. nfolds == 1 or < 0
try:
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=random.sample([-1,1], 1)[0], ntrees=5,
distribution=distribution)
assert False, "Expected model-build to fail when nfolds is 1 or < 0"
except EnvironmentError:
assert True
# 2. more folds than observations
try:
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow()+1, distribution=distribution, ntrees=5,
fold_assignment="Modulo")
assert False, "Expected model-build to fail when nfolds > nobs"
except EnvironmentError:
assert True
# 3. fold_column and nfolds both specified
try:
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], nfolds=3, fold_column="fold_assignments", ntrees=5,
distribution=distribution, training_frame=cars)
assert False, "Expected model-build to fail when fold_column and nfolds both specified"
except EnvironmentError:
assert True
# 4. fold_column and fold_assignment both specified
try:
gbm = h2o.gbm(y=cars[response_col], x=cars[predictors], fold_assignment="Random", fold_column="fold_assignments", ntrees=5,
distribution=distribution, training_frame=cars)
assert False, "Expected model-build to fail when fold_column and fold_assignment both specified"
except EnvironmentError:
assert True
def cv_carsDL(ip,port):
# read in the dataset and construct training set (and validation set)
cars = h2o.import_frame(path=h2o.locate("smalldata/junit/cars_20mpg.csv"))
# choose the type model-building exercise (multinomial classification or regression). 0:regression, 1:binomial,
# 2:multinomial
problem = random.sample(range(3),1)[0]
# pick the predictors and the correct response column
predictors = ["displacement","power","weight","acceleration","year"]
if problem == 1 :
response_col = "economy_20mpg"
cars[response_col] = cars[response_col].asfactor()
elif problem == 2 :
response_col = "cylinders"
cars[response_col] = cars[response_col].asfactor()
else :
response_col = "economy"
print "Response column: {0}".format(response_col)
## cross-validation
## basic
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=random.randint(3,10), fold_assignment="Modulo")
## check that cv metrics are different over repeated "Random" runs
nfolds = random.randint(3,10)
dl1 = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Random")
dl2 = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Random")
try:
h2o.check_models(dl1, dl2, True)
assert False, "Expected models to be different over repeated Random runs"
except AssertionError:
assert True
## boundary cases
# 1. nfolds = number of observations (leave-one-out cross-validation)
# TODO: manually construct the cross-validation metrics and compare
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow(), fold_assignment="Modulo")
# 2. nfolds = 0
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=0)
# 3. cross-validation and regular validation attempted
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=random.randint(3,10),
validation_y=cars[response_col], validation_x=cars[predictors])
## error cases
# 1. nfolds == 1 or < 0
try:
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=random.sample([-1,1], 1)[0])
assert False, "Expected model-build to fail when nfolds is 1 or < 0"
except EnvironmentError:
assert True
# 2. more folds than observations
try:
dl = h2o.deeplearning(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow()+1, fold_assignment="Modulo")
assert False, "Expected model-build to fail when nfolds > nobs"
except EnvironmentError:
assert True
def cv_carsRF(ip,port):
# read in the dataset and construct training set (and validation set)
cars = h2o.import_frame(path=h2o.locate("smalldata/junit/cars_20mpg.csv"))
# choose the type model-building exercise (multinomial classification or regression). 0:regression, 1:binomial,
# 2:multinomial
problem = random.sample(range(3),1)[0]
# pick the predictors and the correct response column
predictors = ["displacement","power","weight","acceleration","year"]
if problem == 1 :
response_col = "economy_20mpg"
cars[response_col] = cars[response_col].asfactor()
elif problem == 2 :
response_col = "cylinders"
cars[response_col] = cars[response_col].asfactor()
else :
response_col = "economy"
print "Response column: {0}".format(response_col)
## cross-validation
# 1. check that cv metrics are the same over repeated seeded "Modulo" runs
nfolds = random.randint(3,10)
rf1 = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Modulo", seed=1234)
rf2 = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Modulo", seed=1234)
h2o.check_models(rf1, rf2, True)
# 2. check that cv metrics are different over repeated "Random" runs
nfolds = random.randint(3,10)
rf1 = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Random")
rf2 = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=nfolds, fold_assignment="Random")
try:
h2o.check_models(rf1, rf2, True)
assert False, "Expected models to be different over repeated Random runs"
except AssertionError:
assert True
# 3. folds_column
num_folds = random.randint(2,5)
fold_assignments = h2o.H2OFrame(python_obj=[[random.randint(0,num_folds-1)] for f in range(cars.nrow())])
fold_assignments.setNames(["fold_assignments"])
cars = cars.cbind(fold_assignments)
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], training_frame=cars,
fold_column="fold_assignments", keep_cross_validation_predictions=True)
num_cv_models = len(rf._model_json['output']['cross_validation_models'])
assert num_cv_models==num_folds, "Expected {0} cross-validation models, but got " \
"{1}".format(num_folds, num_cv_models)
cv_model1 = h2o.get_model(rf._model_json['output']['cross_validation_models'][0]['name'])
cv_model2 = h2o.get_model(rf._model_json['output']['cross_validation_models'][1]['name'])
assert isinstance(cv_model1, type(rf)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model1),type(rf))
assert isinstance(cv_model2, type(rf)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model2),type(rf))
# 4. keep_cross_validation_predictions
cv_predictions = rf1._model_json['output']['cross_validation_predictions']
assert cv_predictions is None, "Expected cross-validation predictions to be None, but got {0}".format(cv_predictions)
cv_predictions = rf._model_json['output']['cross_validation_predictions']
assert len(cv_predictions)==num_folds, "Expected the same number of cross-validation predictions " \
"as folds, but got {0}".format(len(cv_predictions))
## boundary cases
# 1. nfolds = number of observations (leave-one-out cross-validation)
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow(), fold_assignment="Modulo")
# 2. nfolds = 0
rf1 = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=0, seed=1234)
# check that this is equivalent to no nfolds
rf2 = h2o.random_forest(y=cars[response_col], x=cars[predictors], seed=1234)
h2o.check_models(rf1, rf2)
# 3. cross-validation and regular validation attempted
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=random.randint(3,10),
validation_y=cars[response_col], validation_x=cars[predictors])
## error cases
# 1. nfolds == 1 or < 0
try:
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=random.sample([-1,1], 1)[0])
assert False, "Expected model-build to fail when nfolds is 1 or < 0"
except EnvironmentError:
assert True
# 2. more folds than observations
try:
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=cars.nrow()+1, fold_assignment="Modulo")
assert False, "Expected model-build to fail when nfolds > nobs"
except EnvironmentError:
assert True
# 3. fold_column and nfolds both specified
try:
rf = h2o.random_forest(y=cars[response_col], x=cars[predictors], nfolds=3, fold_column="fold_assignments",
training_frame=cars)
assert False, "Expected model-build to fail when fold_column and nfolds both specified"
except EnvironmentError:
assert True
def cv_carsRF(ip, port):
# read in the dataset and construct training set (and validation set)
cars = h2o.import_frame(path=h2o.locate("smalldata/junit/cars_20mpg.csv"))
# choose the type model-building exercise (multinomial classification or regression). 0:regression, 1:binomial,
# 2:multinomial
problem = random.sample(range(3), 1)[0]
# pick the predictors and the correct response column
predictors = ["displacement", "power", "weight", "acceleration", "year"]
if problem == 1:
response_col = "economy_20mpg"
cars[response_col] = cars[response_col].asfactor()
elif problem == 2:
response_col = "cylinders"
cars[response_col] = cars[response_col].asfactor()
else:
response_col = "economy"
print "Response column: {0}".format(response_col)
## cross-validation
# 1. check that cv metrics are the same over repeated seeded "Modulo" runs
nfolds = random.randint(3, 10)
rf1 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=nfolds,
fold_assignment="Modulo",
seed=1234)
rf2 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=nfolds,
fold_assignment="Modulo",
seed=1234)
h2o.check_models(rf1, rf2, True)
# 2. check that cv metrics are different over repeated "Random" runs
nfolds = random.randint(3, 10)
rf1 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=nfolds,
fold_assignment="Random")
rf2 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=nfolds,
fold_assignment="Random")
try:
h2o.check_models(rf1, rf2, True)
assert False, "Expected models to be different over repeated Random runs"
except AssertionError:
assert True
# 3. folds_column
num_folds = random.randint(2, 5)
fold_assignments = h2o.H2OFrame(
python_obj=[[random.randint(0, num_folds - 1)]
for f in range(cars.nrow())])
fold_assignments.setNames(["fold_assignments"])
cars = cars.cbind(fold_assignments)
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
training_frame=cars,
fold_column="fold_assignments",
keep_cross_validation_predictions=True)
num_cv_models = len(rf._model_json['output']['cross_validation_models'])
assert num_cv_models==num_folds, "Expected {0} cross-validation models, but got " \
"{1}".format(num_folds, num_cv_models)
cv_model1 = h2o.get_model(
rf._model_json['output']['cross_validation_models'][0]['name'])
cv_model2 = h2o.get_model(
rf._model_json['output']['cross_validation_models'][1]['name'])
assert isinstance(cv_model1, type(rf)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model1),type(rf))
assert isinstance(cv_model2, type(rf)), "Expected cross-validation model to be the same model type as the " \
"constructed model, but got {0} and {1}".format(type(cv_model2),type(rf))
# 4. keep_cross_validation_predictions
cv_predictions = rf1._model_json['output']['cross_validation_predictions']
assert cv_predictions is None, "Expected cross-validation predictions to be None, but got {0}".format(
cv_predictions)
cv_predictions = rf._model_json['output']['cross_validation_predictions']
assert len(cv_predictions)==num_folds, "Expected the same number of cross-validation predictions " \
"as folds, but got {0}".format(len(cv_predictions))
## boundary cases
# 1. nfolds = number of observations (leave-one-out cross-validation)
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=cars.nrow(),
fold_assignment="Modulo")
# 2. nfolds = 0
rf1 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=0,
seed=1234)
# check that this is equivalent to no nfolds
rf2 = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
seed=1234)
h2o.check_models(rf1, rf2)
# 3. cross-validation and regular validation attempted
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=random.randint(3, 10),
validation_y=cars[response_col],
validation_x=cars[predictors])
## error cases
# 1. nfolds == 1 or < 0
try:
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=random.sample([-1, 1], 1)[0])
assert False, "Expected model-build to fail when nfolds is 1 or < 0"
except EnvironmentError:
assert True
# 2. more folds than observations
try:
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=cars.nrow() + 1,
fold_assignment="Modulo")
assert False, "Expected model-build to fail when nfolds > nobs"
except EnvironmentError:
assert True
# 3. fold_column and nfolds both specified
try:
rf = h2o.random_forest(y=cars[response_col],
x=cars[predictors],
nfolds=3,
fold_column="fold_assignments",
training_frame=cars)
assert False, "Expected model-build to fail when fold_column and nfolds both specified"
except EnvironmentError:
assert True
