def getLambdaModel(ip,port):
print("Read data")
prostate = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate.csv"))
myX = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
myY = "CAPSULE"
family = random.choice(["gaussian","binomial"])
print(family)
print("Do lambda search and build models")
if family == "gaussian":
model = h2o.glm(x=prostate[myX], y=prostate[myY], family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
else:
model = h2o.glm(x=prostate[myX], y=prostate[myY].asfactor(), family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
print("the models were built over the following lambda values: ")
all_lambdas = model.models(1).lambda_all()
print(all_lambdas)
for i in range(10):
Lambda = random.sample(all_lambdas,1)
print("For Lambda we get this model:")
m1 = h2o.getGLMLambdaModel(model.models(random.randint(0,len(model.models()-1)),Lambda=Lambda))
m1.show()
print("this model should be same as the one above:")
m2 = h2o.getGLMLambdaModel(model.models(random.randint(0,len(model.models()-1)),Lambda=Lambda))
m2.show()
assert m1==m2, "expected models to be equal"
def getLambdaModel():
print("Read data")
prostate = h2o.import_file(path=tests.locate("smalldata/logreg/prostate.csv"))
myX = ["AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
myY = "CAPSULE"
family = random.choice(["gaussian","binomial"])
print(family)
print("Do lambda search and build models")
if family == "gaussian":
model = h2o.glm(x=prostate[myX], y=prostate[myY], family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
else:
model = h2o.glm(x=prostate[myX], y=prostate[myY].asfactor(), family=family, standardize=True, use_all_factor_levels=True, lambda_search=True)
print("the models were built over the following lambda values: ")
all_lambdas = model.models(1).lambda_all()
print(all_lambdas)
for i in range(10):
Lambda = random.sample(all_lambdas,1)
print("For Lambda we get this model:")
m1 = h2o.getGLMLambdaModel(model.models(random.randint(0,len(model.models()-1)),Lambda=Lambda))
m1.show()
print("this model should be same as the one above:")
m2 = h2o.getGLMLambdaModel(model.models(random.randint(0,len(model.models()-1)),Lambda=Lambda))
m2.show()
assert m1==m2, "expected models to be equal"
def grid_lambda_search():
# Log.info("Importing prostate.csv data...\n")
prostate = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/prostate.csv"))
#prostate.summary()
# Log.info("H2O GLM (binomial) with parameters: alpha = c(0.25, 0.5), nlambda = 20, lambda_search = TRUE, nfolds: 2\n")
model = H2OGeneralizedLinearEstimator(family="binomial", nlambdas=5, lambda_search=True, n_folds=2)
model.train(x=list(range(2,9)), y=1, training_frame=prostate)
# model = h2o.glm(x=prostate[2:9], y=prostate[1], family="binomial", nlambdas=5, lambda_search=True, n_folds=2)
if random.random() < 0.5:
model_idx = 0
else:
model_idx = 1
model_bestlambda = model.models(model_idx)
params_bestlambda = model.params()
# Log.info(cat("All lambda values returned:\n", params_bestlambda.lambdas()))
assert len(params_bestlambda.lambdas()) <= 5, "expected 5 or less lambdas"
random_lambda = random.choice(params_bestlambda.lambdas())
print("RANDOM LAMBDA")
print(random_lambda)
# Log.info(cat("Retrieving model corresponding to alpha =", params_bestlambda.alpha(), "and randomly chosen lambda", random_lambda, "\n"))
random_model = model.getGLMLambdaModel(model_bestlambda, random_lambda)
# Log.info("EXPECTING THESE TO BE EQUAL")
print(random_model.Lambda())
print(random_lambda)
assert random_model.Lambda() == random_lambda, "expected lambdas to be equal"
# Log.info(cat("Retrieving model corresponding to alpha =", params_bestlambda.alpha(), "and best lambda", params_bestlambda.lambdaBest(), "\n"))
best_model = h2o.getGLMLambdaModel(model_bestlambda, params_bestlambda.lambda_best())
assert best_model.model() == model_bestlambda.model(), "expected models to be equal"
# Log.info("H2O GLM (binomial) with parameters: alpha = [0.25, 0.5], nlambda = 20, lambda_search = TRUE, nfolds: 2\n")
prostate_search = H2OGeneralizedLinearEstimator(family="binomial", alpha=[0.25, 0.5], nlambdas=5, lambda_search=True, n_folds=2)
prostate_search.train(x=list(range(2,9)),y=1,training_frame=prostate)
# prostate_search = h2o.glm(x=prostate[2:9], y=prostate[1], family="binomial", alpha=[0.25, 0.5], nlambdas=5, lambda_search=True, n_folds=2)
model_search = prostate_search.models(model_idx)
models_best = model_search.models(model_search.best_model())
params_best = models_best.params()
assert params_bestlambda.lambda_best() == params_best.lambda_best(), "expected lambdas to be equal"
assert len(params_best.lambda_all()) <= 20, "expected 20 or fewer lambdas"
def grid_lambda_search(ip, port):
# Connect to h2o
h2o.init(ip, port)
# Log.info("Importing prostate.csv data...\n")
prostate = h2o.import_frame(
path=h2o.locate("smalldata/logreg/prostate.csv"))
#prostate.summary()
# Log.info("H2O GLM (binomial) with parameters: alpha = c(0.25, 0.5), nlambda = 20, lambda_search = TRUE, nfolds: 2\n")
model = h2o.glm(x=prostate[2:9],
y=prostate[1],
family="binomial",
nlambdas=5,
lambda_search=True,
n_folds=2)
if random.random() < 0.5:
model_idx = 0
else:
model_idx = 1
model_bestlambda = model.models(model_idx)
params_bestlambda = model.params()
# Log.info(cat("All lambda values returned:\n", params_bestlambda.lambdas()))
assert len(params_bestlambda.lambdas()) <= 5, "expected 5 or less lambdas"
random_lambda = random.choice(params_bestlambda.lambdas())
print("RANDOM LAMBDA")
print(random_lambda)
# Log.info(cat("Retrieving model corresponding to alpha =", params_bestlambda.alpha(), "and randomly chosen lambda", random_lambda, "\n"))
random_model = model.getGLMLambdaModel(model_bestlambda, random_lambda)
# Log.info("EXPECTING THESE TO BE EQUAL")
print(random_model.Lambda())
print(random_lambda)
assert random_model.Lambda(
) == random_lambda, "expected lambdas to be equal"
# Log.info(cat("Retrieving model corresponding to alpha =", params_bestlambda.alpha(), "and best lambda", params_bestlambda.lambdaBest(), "\n"))
best_model = h2o.getGLMLambdaModel(model_bestlambda,
params_bestlambda.lambda_best())
assert best_model.model() == model_bestlambda.model(
), "expected models to be equal"
# Log.info("H2O GLM (binomial) with parameters: alpha = [0.25, 0.5], nlambda = 20, lambda_search = TRUE, nfolds: 2\n")
prostate_search = h2o.glm(x=prostate[2:9],
y=prostate[1],
family="binomial",
alpha=[0.25, 0.5],
nlambdas=5,
lambda_search=True,
n_folds=2)
model_search = prostate_search.models(model_idx)
models_best = model_search.models(model_search.best_model())
params_best = models_best.params()
assert params_bestlambda.lambda_best() == params_best.lambda_best(
), "expected lambdas to be equal"
assert len(params_best.lambda_all()) <= 20, "expected 20 or fewer lambdas"
