def setup_data(self):
"""
This function performs all initializations necessary:
load the data sets and set the training set indices and response column index
"""
# create and clean out the sandbox directory first
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(self.current_dir, self.test_name, True)
# randomly choose which family of GBM algo to use
self.family = self.families[random.randint(0, len(self.families)-1)]
# preload datasets, set x_indices, y_index and change response to factor for classification
if 'multinomial' in self.family:
self.training_metric = 'logloss'
self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filenames[1]))
self.y_index = self.training1_data.ncol-1
self.x_indices = list(range(self.y_index))
self.training1_data[self.y_index] = self.training1_data[self.y_index].round().asfactor()
self.scale_model = 1
else:
self.training1_data = h2o.import_file(path=pyunit_utils.locate(self.training1_filenames[0]))
self.y_index = self.training1_data.ncol-1
self.x_indices = list(range(self.y_index))
self.scale_model = 0.75
# save the training data files just in case the code crashed.
pyunit_utils.remove_csv_files(self.current_dir, ".csv", action='copy', new_dir_path=self.sandbox_dir)
def fiftycatRF():
# Training set has only 45 categories cat1 through cat45
#Log.info("Importing 50_cattest_train.csv data...\n")
train = h2o.import_file(path=pyunit_utils.locate("smalldata/gbm_test/50_cattest_train.csv"))
train["y"] = train["y"].asfactor()
#Log.info("Summary of 50_cattest_train.csv from H2O:\n")
#train.summary()
# Train H2O DRF Model:
#Log.info(paste("H2O DRF with parameters:\nclassification = TRUE, ntree = 50, depth = 20, nbins = 500\n", sep = ""))
model = h2o.random_forest(x=train[["x1", "x2"]], y=train["y"], ntrees=50, max_depth=20, nbins=500)
# Test dataset has all 50 categories cat1 through cat50
#Log.info("Importing 50_cattest_test.csv data...\n")
test = h2o.import_file(path=pyunit_utils.locate("smalldata/gbm_test/50_cattest_test.csv"))
#Log.info("Summary of 50_cattest_test.csv from H2O:\n")
#test.summary()
# Predict on test dataset with DRF model:
#Log.info("Performing predictions on test dataset...\n")
preds = model.predict(test)
preds.head()
# Get the confusion matrix and AUC
#Log.info("Confusion matrix of predictions (max accuracy):\n")
perf = model.model_performance(test)
perf.show()
cm = perf.confusion_matrix()
print(cm)
def wide_dataset_large():
print("Reading in Arcene training data for binomial modeling.")
trainDataResponse = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_train_labels.labels"), delimiter=' ')
trainDataResponse = np.where(trainDataResponse == -1, 0, 1)
trainDataFeatures = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_train.data"), delimiter=' ')
xtrain = np.transpose(trainDataFeatures).tolist()
ytrain = trainDataResponse.tolist()
trainData = h2o.H2OFrame.fromPython([ytrain]+xtrain)
trainData[0] = trainData[0].asfactor()
print("Run model on 3250 columns of Arcene with strong rules off.")
model = H2OGeneralizedLinearEstimator(family="binomial", lambda_search=False, alpha=1)
model.train(x=range(1,3250), y=0, training_frame=trainData)
print("Test model on validation set.")
validDataResponse = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_valid_labels.labels"), delimiter=' ')
validDataResponse = np.where(validDataResponse == -1, 0, 1)
validDataFeatures = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_valid.data"), delimiter=' ')
xvalid = np.transpose(validDataFeatures).tolist()
yvalid = validDataResponse.tolist()
validData = h2o.H2OFrame.fromPython([yvalid]+xvalid)
prediction = model.predict(validData)
print("Check performance of predictions.")
performance = model.model_performance(validData)
print("Check that prediction AUC better than guessing (0.5).")
assert performance.auc() > 0.5, "predictions should be better then pure chance"
def consistency_check():
try:
small = pyunit_utils.locate("h2o-py/demos/citi_bike_small.ipynb")
except ValueError:
small = pyunit_utils.locate("h2o-py/demos/citi_bike_small_NOPASS.ipynb")
try:
large = pyunit_utils.locate("h2o-py/demos/citi_bike_large.ipynb")
except ValueError:
large = pyunit_utils.locate("h2o-py/demos/citi_bike_large_NOPASS.ipynb")
results_dir = pyunit_utils.locate("results")
s = os.path.join(results_dir, os.path.basename(small).split('.')[0]+".py")
l = os.path.join(results_dir, os.path.basename(large).split('.')[0]+".py")
from tests import pydemo_utils
pydemo_utils.ipy_notebook_exec(small, save_and_norun = s)
pydemo_utils.ipy_notebook_exec(large, save_and_norun = l)
small_list = list(open(s, 'r'))
large_list = list(open(l, 'r'))
for s, l in zip(small_list, large_list):
if s != l:
assert s == "data = h2o.import_file(path=small_test)\n" and \
l != "data = h2o.import_file(path=large_test)\n", \
"This difference is not allowed between the small and large citibike demos.\nCitibike small: {0}" \
"Citibike large: {1}".format(s,l)
def test_relevel():
#First, compare againts itself
print("Importing prostate_cat.csv data...\n")
d = h2o.import_file(path = pyunit_utils.locate("smalldata/prostate/prostate_cat.csv"), na_strings=["NA","NA","NA","NA","NA","NA","NA","NA"])
mh2o1 = H2OGeneralizedLinearEstimator(family = "binomial", Lambda=0, missing_values_handling = "Skip")
mh2o1.train(x=list(range(1, d.ncol)), y=0, training_frame=d)
ns = mh2o1.coef().keys()
print(ns)
assert("DPROS.None" in ns, "None level IS NOT expected to be skipped by default")
assert(("DPROS.Both" not in ns), "Both level IS expected to be skipped by default")
x = d["DPROS"].relevel("None")
print(x)
d["DPROS"] = x[0]
mh2o2 = H2OGeneralizedLinearEstimator(family = "binomial", Lambda=0, missing_values_handling = "Skip")
mh2o2.train(x=list(range(1, d.ncol)), y=0, training_frame=d)
ns2 = mh2o2.coef().keys()
print(ns2)
assert("DPROS.None" in ns2, "None level IS NOT expected to be skipped by default")
assert(("DPROS.Both" not in ns2), "Both level IS expected to be skipped by default")
#Second, compare against R input (taken from runit_relevel.R)
dr = h2o.import_file(path = pyunit_utils.locate("smalldata/prostate/prostate_cat.csv"))
dr["DPROS"] = d["DPROS"].relevel("None")
#Results are from R but manualy reordered and renamed to match h2o naming and order
exp_coefs = {"Intercept": -7.63245 , "DPROS.Both": 1.39185, "DPROS.Left": 0.73482, "DPROS.Right": 1.51437, "RACE.White": 0.65160, "DCAPS.Yes": 0.49233,
"AGE":-0.01189 , "PSA": 0.02990, "VOL": -0.01141, "GLEASON": 0.96466927}
coeff_diff = {key: abs(exp_coefs[key] - mh2o2.coef().get(key, 0)) for key in exp_coefs.keys()}
assert (max(coeff_diff.values()) < 1e-4)
def xgboost_milsongs_gaussian_medium():
assert H2OXGBoostEstimator.available()
# Import big dataset to ensure run across multiple nodes
training_frame = h2o.import_file(pyunit_utils.locate("bigdata/laptop/milsongs/milsongs-train.csv.gz"))
test_frame = h2o.import_file(pyunit_utils.locate("bigdata/laptop/milsongs/milsongs-test.csv.gz"))
x = list(range(1,training_frame.ncol))
y = 0
# Model with maximum of 2 trees
model_2_trees = H2OXGBoostEstimator(training_frame=training_frame, learn_rate=0.3,
booster='gbtree', seed=1, ntrees=2, distribution='gaussian')
model_2_trees.train(x=x, y=y, training_frame=training_frame)
prediction_2_trees = model_2_trees.predict(test_frame)
assert prediction_2_trees.nrows == test_frame.nrows
# Model with 10 trees
model_10_trees = H2OXGBoostEstimator(training_frame=training_frame, learn_rate=0.3,
booster='gbtree', seed=1, ntrees=10, distribution='gaussian')
model_10_trees.train(x=x, y=y, training_frame=training_frame)
prediction_10_trees = model_10_trees.predict(test_frame)
assert prediction_10_trees.nrows == test_frame.nrows
## Mean square error on model with lower number of decision trees should be higher
assert model_2_trees.mse() > model_10_trees.mse()
def get_modelKmeans():
# Connect to a pre-existing cluster
# connect to localhost:54321
# Log.info("Importing benign.csv data...\n")
benign_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/benign.csv"))
# benign_h2o.summary()
benign_sci = np.genfromtxt(pyunit_utils.locate("smalldata/logreg/benign.csv"), delimiter=",")
# Impute missing values with column mean
imp = Imputer(missing_values="NaN", strategy="mean", axis=0)
benign_sci = imp.fit_transform(benign_sci)
for i in range(2, 7):
# Log.info("H2O K-Means")
km_h2o = H2OKMeansEstimator(k=i)
km_h2o.train(x=range(benign_h2o.ncol), training_frame=benign_h2o)
km_h2o.show()
model = h2o.get_model(km_h2o._id)
model.show()
km_sci = KMeans(n_clusters=i, init="k-means++", n_init=1)
km_sci.fit(benign_sci)
print "sckit centers"
print km_sci.cluster_centers_
def benignKmeans():
# Connect to a pre-existing cluster
# connect to localhost:54321
# Log.info("Importing benign.csv data...\n")
benign_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/benign.csv"))
#benign_h2o.summary()
benign_sci = np.genfromtxt(pyunit_utils.locate("smalldata/logreg/benign.csv"), delimiter=",")
# Impute missing values with column mean
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
benign_sci = imp.fit_transform(benign_sci)
# Log.info(paste("H2O K-Means with ", i, " clusters:\n", sep = ""))
from h2o.estimators.kmeans import H2OKMeansEstimator
for i in range(1,7):
benign_h2o_km = H2OKMeansEstimator(k=i)
benign_h2o_km.train(x = range(benign_h2o.ncol), training_frame=benign_h2o)
print "H2O centers"
print benign_h2o_km.centers()
benign_sci_km = KMeans(n_clusters=i, init='k-means++', n_init=1)
benign_sci_km.fit(benign_sci)
print "sckit centers"
print benign_sci_km.cluster_centers_
def iris_h2o_vs_sciKmeans():
# Connect to a pre-existing cluster
# connect to localhost:54321
iris_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris.csv"))
iris_sci = np.genfromtxt(pyunit_utils.locate("smalldata/iris/iris.csv"), delimiter=',')
iris_sci = iris_sci[:,0:4]
s =[[4.9,3.0,1.4,0.2],
[5.6,2.5,3.9,1.1],
[6.5,3.0,5.2,2.0]]
start = h2o.H2OFrame(s)
h2o_km = h2o.kmeans(x=iris_h2o[0:4], k=3, user_points=start, standardize=False)
sci_km = KMeans(n_clusters=3, init=np.asarray(s), n_init=1)
sci_km.fit(iris_sci)
# Log.info("Cluster centers from H2O:")
print("Cluster centers from H2O:")
h2o_centers = h2o_km.centers()
print(h2o_centers)
# Log.info("Cluster centers from scikit:")
print("Cluster centers from scikit:")
sci_centers = sci_km.cluster_centers_.tolist()
print(sci_centers)
for hcenter, scenter in zip(h2o_centers, sci_centers):
for hpoint, spoint in zip(hcenter,scenter):
assert (hpoint- spoint) < 1e-10, "expected centers to be the same"
def table_check():
df = h2o.import_file(path=pyunit_utils.locate("smalldata/prostate/prostate.csv"))
print(df[['AGE','RACE']].table(dense=True).head().as_data_frame(True))
print(df[['AGE','RACE']].table(dense=False).head().as_data_frame(True))
print(df[['RACE','AGE']].table(dense=True).head().as_data_frame(True))
print(df[['RACE','AGE']].table(dense=False).head().as_data_frame(True))
iris = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris.csv"))
# single column (frame)
table1 = iris["C5"].table()
assert table1[0,1] == 50, "Expected 50 of {0}, but got {1}".format(table1[0,0], table1[0,1])
assert table1[1,1] == 50, "Expected 50 of {0}, but got {1}".format(table1[1,0], table1[1,1])
assert table1[2,1] == 50, "Expected 50 of {0}, but got {1}".format(table1[2,0], table1[2,1])
# two-column (one argument)
#dense
table2 = iris["C1"].table(iris["C5"])
#not dense
table3 = iris["C1"].table(iris["C5"],dense=False)
#check same value
assert (table3[table3['C1'] == 5,'Iris-setosa'] == table2[(table2['C1'] == 5) & (table2['C5'] == 'Iris-setosa'),'Counts']).all()
assert (table2 == iris[["C1","C5"]].table()).all()
assert (table3 == iris[["C1","C5"]].table(dense=False)).all()
cars = h2o.import_file(path=pyunit_utils.locate("smalldata/junit/cars_20mpg.csv"))
table = cars[2].table().as_data_frame()
table = dict(table[1:])
table = {k:int(v) for k,v in list(table.items())}
expected = Counter(itertools.chain(*cars[2].as_data_frame()[1:]))
assert table == expected, "Expected {} for table counts but got {}".format(expected, table)
def offsets_and_distributions():
# cars
cars = h2o.upload_file(pyunit_utils.locate("smalldata/junit/cars_20mpg.csv"))
cars = cars[cars["economy_20mpg"].isna() == 0]
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
offset = h2o.H2OFrame.fromPython([[.5] for x in range(398)])
offset.set_name(0,"x1")
cars = cars.cbind(offset)
# insurance
insurance = h2o.import_file(pyunit_utils.locate("smalldata/glm_test/insurance.csv"))
insurance["offset"] = insurance["Holders"].log()
# bernoulli - offset not supported
#dl = h2o.deeplearning(x=cars[2:8], y=cars["economy_20mpg"], distribution="bernoulli", offset_column="x1",
# training_frame=cars)
#predictions = dl.predict(cars)
# gamma
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="gamma", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# gaussian
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="gaussian", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# poisson
dl = h2o.deeplearning(x=insurance[0:3], y=insurance["Claims"], distribution="poisson", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
# tweedie
dl = h2o.deeplearning(x=insurance.names[0:3], y="Claims", distribution="tweedie", offset_column="offset", training_frame=insurance)
predictions = dl.predict(insurance)
def col_names_check():
iris_wheader = h2o.import_file(pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))
assert iris_wheader.col_names == ["sepal_len","sepal_wid","petal_len","petal_wid","class"], \
"Expected {0} for column names but got {1}".format(["sepal_len","sepal_wid","petal_len","petal_wid","class"],
iris_wheader.col_names)
iris = h2o.import_file(pyunit_utils.locate("smalldata/iris/iris.csv"))
assert iris.col_names == ["C1","C2","C3","C4","C5"], "Expected {0} for column names but got " \
"{1}".format(["C1","C2","C3","C4","C5"], iris.col_names)
df = h2o.H2OFrame.from_python(list(zip(*np.random.randn(100,4).tolist())), column_names=list("ABCD"), column_types=["enum"]*4)
df.head()
assert df.col_names == list("ABCD"), "Expected {} for column names but got {}".format(list("ABCD"), df.col_names)
assert list(df.types.values()) == ["enum"]*4, "Expected {} for column types but got {}".format(["enum"]*4, df.types)
df = h2o.H2OFrame(list(zip(*np.random.randn(100,4).tolist())))
df.head()
assert df.col_names == ["C1","C2","C3","C4"], "Expected {} for column names but got {}".format(["C1","C2","C3","C4"]
, df.col_names)
assert list(df.types.values()) == ["real"]*4, "Expected {} for column types but got {}".format(["real"]*4, df.types)
df = h2o.H2OFrame({'B': ['a', 'a', 'b', 'NA', 'NA']})
df.head()
assert df.col_names == ["B"], "Expected {} for column names but got {}".format(["B"], df.col_names)
df = h2o.H2OFrame.from_python({'B': ['a', 'a', 'b', 'NA', 'NA']}, column_names=["X"])
df.head()
assert df.col_names == ["X"], "Expected {} for column names but got {}".format(["X"], df.col_names)
def user():
a = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))[0:4]
a.head()
print(a[0].names) # Column header
print(a[2,0]) # column 0, row 2 value
print(a[2,"sepal_len"]) # Column 0, row 2 value
(a[0] + 2).show() # Add 2 to every element; broadcast a constant
(a[0] + a[1]).show() # Add 2 columns; broadcast parallel add
sum(a).show()
print(a["sepal_len"].mean())
print()
print("Rows 50 through 77 in the `sepal_len` column")
a[50:78, "sepal_len"].show() # print out rows 50 thru 77 inclusive
print()
a["sepal_len"].show()
print(a[50:78, ["sepal_len", "sepal_wid"]].show())
a.show()
print("The column means: ")
print(a.mean())
print()
try:
print(a["Sepal_len"].dim) # Error, misspelt column name
except Exception:
pass # Expected error
b = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))[0:4]
c = a + b
d = c + c + sum(a)
e = c + a + 1
e.show()
# Note that "d=c+..." keeps the internal C expressions alive, until "d" goes
# out of scope even as we nuke "c"
c.show()
c = None
# Internal "ExprNode(c=a+b)" not dead!
print(1 + (a[0] + b[1]).mean())
import collections
c = h2o.H2OFrame(collections.OrderedDict({"A": [1, 2, 3], "B": [4, 5, 6]}))
c.show()
c.describe()
c.head()
c[0].show()
print(c[1,0])
c[0:2,0].show()
sliced = a[0:51,0]
sliced.show()
def checkpoint_new_category_in_predictor():
sv1 = h2o.upload_file(pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv"))
sv2 = h2o.upload_file(pyunit_utils.locate("smalldata/iris/setosa_versicolor.csv"))
vir = h2o.upload_file(pyunit_utils.locate("smalldata/iris/virginica.csv"))
print("checkpoint_new_category_in_predictor-1")
m1 = H2ODeepLearningEstimator(epochs=100)
m1.train(x=[0,1,2,4], y=3, training_frame=sv1)
m2 = H2ODeepLearningEstimator(epochs=200, checkpoint=m1.model_id)
m2.train(x=[0,1,2,4], y=3, training_frame=sv2)
print("checkpoint_new_category_in_predictor-2")
# attempt to continue building model, but with an expanded categorical predictor domain.
# this should fail
try:
m3 = H2ODeepLearningEstimator(epochs=200, checkpoint=m1.model_id)
m3.train(x=[0,1,2,4], y=3, training_frame=vir)
assert False, "Expected continued model-building to fail with new categories introduced in predictor"
except EnvironmentError:
pass
print("checkpoint_new_category_in_predictor-3")
# attempt to predict on new model, but with observations that have expanded categorical predictor domain.
predictions = m2.predict(vir)
print("checkpoint_new_category_in_predictor-4")
def stackedensemble_metalearner_seed_test():
# Import training set
train = h2o.import_file(path=pyunit_utils.locate("smalldata/testng/higgs_train_5k.csv"),
destination_frame="higgs_train_5k")
test = h2o.import_file(path=pyunit_utils.locate("smalldata/testng/higgs_test_5k.csv"),
destination_frame="higgs_test_5k")
# Identify predictors and response
x = train.columns
y = "response"
x.remove(y)
# Convert response to a factor
train[y] = train[y].asfactor()
test[y] = test[y].asfactor()
# Set number of folds for base learners
nfolds = 3
#Metalearner params for gbm, drf, glm, and deep deeplearning
gbm_params = {"sample_rate" : 0.3, "col_sample_rate" : 0.3}
# Train and cross-validate a GBM
my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=10,
nfolds=nfolds,
keep_cross_validation_predictions=True,
seed=1)
my_gbm.train(x=x, y=y, training_frame=train)
# Train and cross-validate a RF
my_rf = H2ORandomForestEstimator(ntrees=10,
nfolds=nfolds,
keep_cross_validation_predictions=True,
seed=1)
my_rf.train(x=x, y=y, training_frame=train)
#Train two SE models with same metalearner seeds
stack_gbm1 = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], metalearner_algorithm="gbm",
metalearner_params = gbm_params, seed = 55555)
stack_gbm2 = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], metalearner_algorithm="gbm",
metalearner_params = gbm_params, seed = 55555)
stack_gbm1.train(x=x, y=y, training_frame=train)
stack_gbm2.train(x=x, y=y, training_frame=train)
meta_gbm1 = h2o.get_model(stack_gbm1.metalearner()['name'])
meta_gbm2 = h2o.get_model(stack_gbm2.metalearner()['name'])
assert meta_gbm1.rmse(train=True) == meta_gbm2.rmse(train=True), "RMSE should match if same seed"
#Train two SE models with diff metalearner seeds
stack_gbm3 = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], metalearner_algorithm="gbm",
metalearner_params = gbm_params, seed = 55555)
stack_gbm4 = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf], metalearner_algorithm="gbm",
metalearner_params = gbm_params, seed = 98765)
stack_gbm3.train(x=x, y=y, training_frame=train)
stack_gbm4.train(x=x, y=y, training_frame=train)
meta_gbm3 = h2o.get_model(stack_gbm3.metalearner()['name'])
meta_gbm4 = h2o.get_model(stack_gbm4.metalearner()['name'])
assert meta_gbm3.rmse(train=True) != meta_gbm4.rmse(train=True), "RMSE should NOT match if diff seed"
def smallcat_gbm():
# Training set has 26 categories from A to Z
# Categories A, C, E, G, ... are perfect predictors of y = 1
# Categories B, D, F, H, ... are perfect predictors of y = 0
alphabet = h2o.import_file(path=pyunit_utils.locate("smalldata/gbm_test/alphabet_cattest.csv"))
alphabet["y"] = alphabet["y"].asfactor()
#Log.info("Summary of alphabet_cattest.csv from H2O:\n")
#alphabet.summary()
# Prepare data for scikit use
trainData = np.loadtxt(pyunit_utils.locate("smalldata/gbm_test/alphabet_cattest.csv"), delimiter=',', skiprows=1, converters={0:lambda s: ord(s.decode().split("\"")[1])})
trainDataResponse = trainData[:,1]
trainDataFeatures = trainData[:,0]
# Train H2O GBM Model:
gbm_h2o = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=1,
max_depth=1,
nbins=100)
gbm_h2o.train(x="X",y="y", training_frame=alphabet)
gbm_h2o.show()
# Train scikit GBM Model:
# Log.info("scikit GBM with same parameters:")
gbm_sci = ensemble.GradientBoostingClassifier(n_estimators=1, max_depth=1, max_features=None)
gbm_sci.fit(trainDataFeatures[:,np.newaxis],trainDataResponse)
def glrm_catagorical_bug_fix():
trainData = h2o.import_file(pyunit_utils.locate("smalldata/airlines/AirlinesTest.csv.zip"))
testData = h2o.import_file(pyunit_utils.locate("smalldata/airlines/AirlinesTrain.csv.zip"))
glrmModel = H2OGeneralizedLowRankEstimator(k=4)
glrmModel.train(x=trainData.names, training_frame=trainData)
predV = glrmModel.predict(testData)
print(predV)
def dim_checks():
# Log.info("Uploading logreg/princeton/cuse.dat")
h2o_data = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/prostate.csv"))
np_data = np.loadtxt(pyunit_utils.locate("smalldata/logreg/prostate.csv"), delimiter=',', skiprows=1)
h2o_rows, h2o_cols = h2o_data.dim
np_rows, np_cols = list(np_data.shape)
print('The dimensions of h2o frame is: {0} x {1}'.format(h2o_rows, h2o_cols))
print('The dimensions of numpy array is: {0} x {1}'.format(np_rows, np_cols))
assert [h2o_rows, h2o_cols] == [np_rows, np_cols], "expected equal number of columns and rows"
# Log.info("Slice out a column and data frame it, try dim on it...")
h2o_slice = h2o_data[4]
np_slice = np_data[:,4]
h2o_rows, h2o_cols = h2o_slice.dim
np_rows = np_slice.shape[0]
print('The dimensions of h2o column slice is: {0} x {1}'.format(h2o_rows, h2o_cols))
print('The dimensions of numpy array column slice is: {0} x 1'.format(np_rows))
assert [h2o_rows, h2o_cols] == [np_rows, 1], "expected equal number of columns and rows"
# Log.info("OK, now try an operator, e.g. '&', and then check dimensions agao...")
h2oColAmpFive = h2o_slice & 5
assert h2oColAmpFive.nrow == h2o_rows, "expected the number of rows to remain unchanged"
def link_functions_gaussian():
print("Read in prostate data.")
h2o_data = h2o.import_file(path=pyunit_utils.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.read_csv(zipfile.ZipFile(pyunit_utils.locate("smalldata/prostate/prostate_complete.csv.zip")).
open("prostate_complete.csv")).as_matrix()
sm_data_response = sm_data[:,9]
sm_data_features = sm_data[:,1:9]
print("Testing for family: GAUSSIAN")
print("Set variables for h2o.")
myY = "GLEASON"
myX = ["ID","AGE","RACE","CAPSULE","DCAPS","PSA","VOL","DPROS"]
print("Create models with canonical link: IDENTITY")
h2o_model = H2OGeneralizedLinearEstimator(family="gaussian", link="identity",alpha=0.5, Lambda=0)
h2o_model.train(x=myX, y=myY, training_frame=h2o_data)
sm_model = sm.GLM(endog=sm_data_response, exog=sm_data_features,
family=sm.families.Gaussian(sm.families.links.identity)).fit()
print("Compare model deviances for link function identity")
h2o_deviance = old_div(h2o_model.residual_deviance(), h2o_model.null_deviance())
sm_deviance = old_div(sm_model.deviance, sm_model.null_deviance)
assert h2o_deviance - sm_deviance < 0.01, "expected h2o to have an equivalent or better deviance measures"
def xgboost_insurance_gaussian_small():
assert H2OXGBoostEstimator.available()
# Import big dataset to ensure run across multiple nodes
training_frame = h2o.import_file(pyunit_utils.locate("smalldata/testng/insurance_train1.csv"))
test_frame = h2o.import_file(pyunit_utils.locate("smalldata/testng/insurance_validation1.csv"))
x = ['Age', 'District']
y = 'Claims'
# Model with maximum of 2 trees
model_2_trees = H2OXGBoostEstimator(training_frame=training_frame, learn_rate=0.7,
booster='gbtree', seed=1, ntrees=2, distribution='gaussian')
model_2_trees.train(x=x, y=y, training_frame=training_frame)
prediction_2_trees = model_2_trees.predict(test_frame)
assert prediction_2_trees.nrows == test_frame.nrows
# Model with 10 trees
model_10_trees = H2OXGBoostEstimator(training_frame=training_frame, learn_rate=0.7,
booster='gbtree', seed=1, ntrees=10, distribution='gaussian')
model_10_trees.train(x=x, y=y, training_frame=training_frame)
prediction_10_trees = model_10_trees.predict(test_frame)
assert prediction_10_trees.nrows == test_frame.nrows
## Mean square error on model with lower number of decision trees should be higher
assert model_2_trees.mse() > model_10_trees.mse()
def plot_test():
kwargs = {}
kwargs['server'] = True
air = h2o.import_file(pyunit_utils.locate("smalldata/airlines/AirlinesTrain.csv.zip"))
# Constructing test and train sets by sampling (20/80)
s = air[0].runif()
air_train = air[s <= 0.8]
air_valid = air[s > 0.8]
myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "fMonth", "fDayofMonth", "fDayOfWeek"]
myY = "IsDepDelayed"
air_gbm = h2o.gbm(x=air_train[myX], y=air_train[myY], validation_x=air_valid[myX], validation_y=air_valid[myY],
distribution="bernoulli", ntrees=100, max_depth=3, learn_rate=0.01)
# Plot ROC for training and validation sets
air_gbm.plot(type="roc", train=True, **kwargs)
air_gbm.plot(type="roc", valid=True, **kwargs)
air_test = h2o.import_file(pyunit_utils.locate("smalldata/airlines/AirlinesTest.csv.zip"))
perf = air_gbm.model_performance(air_test)
#Plot ROC for test set
perf.plot(type="roc", **kwargs)
def anomaly():
print("Deep Learning Anomaly Detection MNIST")
train = h2o.import_file(pyunit_utils.locate("bigdata/laptop/mnist/train.csv.gz"))
test = h2o.import_file(pyunit_utils.locate("bigdata/laptop/mnist/test.csv.gz"))
predictors = list(range(0,784))
resp = 784
# unsupervised -> drop the response column (digit: 0-9)
train = train[predictors]
test = test[predictors]
# 1) LEARN WHAT'S NORMAL
# train unsupervised Deep Learning autoencoder model on train_hex
ae_model = H2OAutoEncoderEstimator(activation="Tanh", hidden=[2], l1=1e-5, ignore_const_cols=False, epochs=1)
ae_model.train(x=predictors,training_frame=train)
# 2) DETECT OUTLIERS
# anomaly app computes the per-row reconstruction error for the test data set
# (passing it through the autoencoder model and computing mean square error (MSE) for each row)
test_rec_error = ae_model.anomaly(test)
# 3) VISUALIZE OUTLIERS
# Let's look at the test set points with low/median/high reconstruction errors.
# We will now visualize the original test set points and their reconstructions obtained
# by propagating them through the narrow neural net.
# Convert the test data into its autoencoded representation (pass through narrow neural net)
test_recon = ae_model.predict(test)
def fiftycatGBM():
# Training set has only 45 categories cat1 through cat45
#Log.info("Importing 50_cattest_train.csv data...\n")
train = h2o.import_file(path=pyunit_utils.locate("smalldata/gbm_test/50_cattest_train.csv"))
train["y"] = train["y"].asfactor()
#Log.info("Summary of 50_cattest_train.csv from H2O:\n")
#train.summary()
# Train H2O GBM Model:
#Log.info(paste("H2O GBM with parameters:\nntrees = 10, max_depth = 20, nbins = 20\n", sep = ""))
model = h2o.gbm(x=train[["x1","x2"]], y=train["y"], distribution="bernoulli", ntrees=10, max_depth=5, nbins=20)
model.show()
# Test dataset has all 50 categories cat1 through cat50
#Log.info("Importing 50_cattest_test.csv data...\n")
test = h2o.import_file(path=pyunit_utils.locate("smalldata/gbm_test/50_cattest_test.csv"))
#Log.info("Summary of 50_cattest_test.csv from H2O:\n")
#test.summary()
# Predict on test dataset with GBM model:
#Log.info("Performing predictions on test dataset...\n")
predictions = model.predict(test)
predictions.show()
# Get the confusion matrix and AUC
#Log.info("Confusion matrix of predictions (max accuracy):\n")
performance = model.model_performance(test)
test_cm = performance.confusion_matrix()
test_auc = performance.auc()
def shuffling_large():
print("Reading in Arcene training data for binomial modeling.")
train_data = h2o.upload_file(path=pyunit_utils.locate("smalldata/arcene/shuffle_test_version/arcene.csv"))
train_data_shuffled = h2o.upload_file(path=pyunit_utils.locate("smalldata/arcene/shuffle_test_version/arcene_shuffled.csv"))
print("Create model on original Arcene dataset.")
h2o_model = H2OGeneralizedLinearEstimator(family="binomial", lambda_search=True, alpha=0.5)
h2o_model.train(x=list(range(1000)), y=1000, training_frame=train_data)
print("Create second model on original Arcene dataset.")
h2o_model_2 = H2OGeneralizedLinearEstimator(family="binomial", lambda_search=True, alpha=0.5)
h2o_model_2.train(x=list(range(1000)), y=1000, training_frame=train_data)
print("Create model on shuffled Arcene dataset.")
h2o_model_s = H2OGeneralizedLinearEstimator(family="binomial", lambda_search=True, alpha=0.5)
h2o_model_s.train(x=list(range(1000)), y=1000, training_frame=train_data_shuffled)
print("Assert that number of predictors remaining and their respective coefficients are equal.")
for x, y in zip(h2o_model._model_json['output']['coefficients_table'].cell_values,h2o_model_2.
_model_json['output']['coefficients_table'].cell_values):
assert (type(x[1]) == type(y[1])) and (type(x[2]) == type(y[2])), "coefficients should be the same type"
if isinstance(x[1],float):
assert abs(x[1] - y[1]) < 5e-10, "coefficients should be equal"
if isinstance(x[2],float):
assert abs(x[2] - y[2]) < 5e-10, "coefficients should be equal"
for x, y in zip(h2o_model._model_json['output']['coefficients_table'].cell_values,h2o_model_s.
_model_json['output']['coefficients_table'].cell_values):
assert (type(x[1]) == type(y[1])) and (type(x[2]) == type(y[2])), "coefficients should be the same type"
if isinstance(x[1],float):
assert abs(x[1] - y[1]) < 5e-10, "coefficients should be equal"
if isinstance(x[2],float):
assert abs(x[2] - y[2]) < 5e-10, "coefficients should be equal"
def minmax_basic():
print "Uploading iris data..."
iris_h2o = h2o.import_file(pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))
iris_np = np.genfromtxt(pyunit_utils.locate("smalldata/iris/iris_wheader.csv"), delimiter=",", skip_header=1)
print "Computing min & max of the first column of iris..."
iris1_min = iris_h2o[0].min()
print "Minimum: {0}".format(iris1_min)
iris1_max = iris_h2o[0].max()
print "Maximum: {0}".format(iris1_max)
np_min = iris_np[:,0].min()
np_max = iris_np[:,0].max()
assert iris1_min == np_min, "Expected the same min value. H2O got {0}, but numpy got {1}".format(iris1_min, np_min)
assert iris1_max == np_max, "Expected the same max value. H2O got {0}, but numpy got {1}".format(iris1_max, np_max)
print "Computing min & max of all numeric columns of iris..."
irisall_min = iris_h2o[0:4].min()
print "Minimum: {0}".format(irisall_min)
irisall_max = iris_h2o[0:4].max()
print "Maximum: {0}".format(irisall_max)
np_min = iris_np[:,0:4].min()
np_max = iris_np[:,0:4].max()
assert irisall_min == np_min, "Expected the same min value. H2O got {0}, but numpy got {1}".format(irisall_min, np_min)
assert irisall_max == np_max, "Expected the same max value. H2O got {0}, but numpy got {1}".format(irisall_max, np_max)
print "min and max correctness"
data = [1,-0.1,0]
mn = min(data)
mx = max(data)
h2o_min = h2o.H2OFrame(data).min()
h2o_max = h2o.H2OFrame(data).max()
assert h2o_min == mn, "Expected the same min value. H2O got {0}, but python got {1}".format(h2o_min, mn)
assert h2o_max == mx, "Expected the same max value. H2O got {0}, but python got {1}".format(h2o_max, mx)
def export_file():
pros_hex = h2o.upload_file(pyunit_utils.locate("smalldata/prostate/prostate.csv"))
pros_hex[1] = pros_hex[1].asfactor()
pros_hex[3] = pros_hex[3].asfactor()
pros_hex[4] = pros_hex[4].asfactor()
pros_hex[5] = pros_hex[5].asfactor()
pros_hex[8] = pros_hex[8].asfactor()
p_sid = pros_hex.runif()
pros_train = pros_hex[p_sid > 0.2, :]
pros_test = pros_hex[p_sid <= 0.2, :]
glm = H2OGeneralizedLinearEstimator(family="binomial")
myglm = glm.train(x=list(range(2, pros_hex.ncol)), y=1, training_frame=pros_train)
mypred = glm.predict(pros_test)
def id_generator(size=6, chars=string.ascii_uppercase + string.digits):
return "".join(random.choice(chars) for _ in range(size))
fname = id_generator() + "_prediction.csv"
path = pyunit_utils.locate("results")
dname = path + "/" + fname
h2o.export_file(mypred, dname)
py_pred = pd.read_csv(dname)
print(py_pred.head())
h_pred = mypred.as_data_frame(True)
print(h_pred.head())
# Test to check if py_pred & h_pred are identical
assert_frame_equal(py_pred, h_pred)
def wide_dataset_large():
print("Reading in Arcene training data for binomial modeling.")
trainDataResponse = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_train_labels.labels"), delimiter=' ')
trainDataResponse = np.where(trainDataResponse == -1, 0, 1)
trainDataFeatures = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_train.data"), delimiter=' ')
trainData = h2o.H2OFrame(np.column_stack((trainDataResponse, trainDataFeatures)).tolist())
print("Run model on 3250 columns of Arcene with strong rules off.")
model = h2o.glm(x=trainData[1:3250], y=trainData[0].asfactor(), family="binomial", lambda_search=False, alpha=[1])
print("Test model on validation set.")
validDataResponse = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_valid_labels.labels"), delimiter=' ')
validDataResponse = np.where(validDataResponse == -1, 0, 1)
validDataFeatures = np.genfromtxt(pyunit_utils.locate("smalldata/arcene/arcene_valid.data"), delimiter=' ')
validData = h2o.H2OFrame(np.column_stack((validDataResponse, validDataFeatures)).tolist())
prediction = model.predict(validData)
print("Check performance of predictions.")
performance = model.model_performance(validData)
print("Check that prediction AUC better than guessing (0.5).")
assert performance.auc() > 0.5, "predictions should be better then pure chance"
def bigcat_gbm():
covtype = h2o.import_file(path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data"))
covtype[54] = covtype[54].asfactor()
covtypeTest = h2o.import_file(path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data"))
covtypeTest[54] = covtype[54].asfactor()
regular = H2OGradientBoostingEstimator(ntrees=10, seed=1234)
regular.train(x=list(range(54)), y=54, training_frame=covtype)
# do prediction on original dataset, no warnings
check_warnings(regular, 0, covtypeTest)
# drop response, no warnings
covtypeTest = covtypeTest.drop(54)
check_warnings(regular, 0, covtypeTest)
covtypeTest = covtypeTest.drop(1)
covtypeTest=covtypeTest.drop(1)
check_warnings(regular, 2, covtypeTest)
covtypeTest = h2o.import_file(path=pyunit_utils.locate("smalldata/covtype/covtype.20k.data"))
covtypeTest[54] = covtype[54].asfactor()
covtypeTest=covtypeTest.drop(3)
covtypeTest=covtypeTest.drop(5)
covtypeTest=covtypeTest.drop(7)
check_warnings(regular, 3, covtypeTest)
def import_multi():
airlines = h2o.import_file(path=[
pyunit_utils.locate("smalldata/testng/airlines_train.csv"),
pyunit_utils.locate("smalldata/testng/airlines_test.csv")
])
assert airlines.nrows == 24421 + 2691
def link_functions_binomial():
print("Read in prostate data.")
h2o_data = h2o.import_file(path=pyunit_utils.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.read_csv(zipfile.ZipFile(pyunit_utils.locate("smalldata/prostate/prostate_complete.csv.zip")).open("prostate_complete.csv")).as_matrix()
sm_data_response = sm_data[:,2]
sm_data_features = sm_data[:,[1,3,4,5,6,7,8,9]]
print("Testing for family: BINOMIAL")
print("Set variables for h2o.")
myY = "CAPSULE"
myX = ["ID","AGE","RACE","GLEASON","DCAPS","PSA","VOL","DPROS"]
print("Create models with canonical link: LOGIT")
h2o_model = h2o.glm(x=h2o_data[myX], y=h2o_data[myY].asfactor(), family="binomial", link="logit",alpha=[0.5], Lambda=[0])
sm_model = sm.GLM(endog=sm_data_response, exog=sm_data_features, family=sm.families.Binomial(sm.families.links.logit)).fit()
print("Compare model deviances for link function logit")
h2o_deviance = h2o_model.residual_deviance() / h2o_model.null_deviance()
sm_deviance = sm_model.deviance / sm_model.null_deviance
assert h2o_deviance - sm_deviance < 0.01, "expected h2o to have an equivalent or better deviance measures"
def glm_alpha_arrays_null_lambda_cv():
print(
"Testing glm cross-validation with alpha array, default lambda values for binomial models."
)
h2o_data = h2o.import_file(
pyunit_utils.locate("smalldata/glm_test/binomial_20_cols_10KRows.csv"))
enum_columns = [
"C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9", "C10"
]
for cname in enum_columns:
h2o_data[cname] = h2o_data[cname]
myY = "C21"
h2o_data["C21"] = h2o_data["C21"].asfactor()
myX = h2o_data.names.remove(myY)
data_frames = h2o_data.split_frame(ratios=[0.8])
training_data = data_frames[0]
test_data = data_frames[1]
# build model with CV but no validation dataset
cv_model = glm(family='binomial', alpha=[0.1, 0.5, 0.9], nfolds=3)
cv_model.train(training_frame=training_data, x=myX, y=myY)
cv_r = glm.getGLMRegularizationPath(cv_model)
# build model with CV and with validation dataset
cv_model_valid = glm(family='binomial', alpha=[0.1, 0.5, 0.9], nfolds=3)
cv_model_valid.train(training_frame=training_data,
validation_frame=test_data,
x=myX,
y=myY)
cv_r_valid = glm.getGLMRegularizationPath(cv_model_valid)
for l in range(0, len(cv_r['lambdas'])):
print("comparing coefficients for submodel {0}".format(l))
pyunit_utils.assertEqualCoeffDicts(cv_r['coefficients'][l],
cv_r_valid['coefficients'][l],
tol=1e-6)
pyunit_utils.assertEqualCoeffDicts(cv_r['coefficients_std'][l],
cv_r_valid['coefficients_std'][l],
tol=1e-6)
def hexdev_394():
path = pyunit_utils.locate("smalldata/covtype/covtype.20k.data")
c_types = [None] * 55
c_types[10] = "enum"
c_types[11] = "enum"
c_types[12] = "enum"
train = h2o.import_file(path, col_types=c_types)
cols = train.col_names # This returned space for first column name
x_cols = [colname for colname in cols if colname != "C55"]
splits = train.split_frame()
newtrain = splits[0]
newvalid = splits[1]
newtrain[54] = newtrain[54].asfactor()
newvalid[54] = newvalid[54].asfactor()
my_gbm = H2OGradientBoostingEstimator(distribution="multinomial",
ntrees=100,
learn_rate=0.1,
max_depth=6)
my_gbm.train(x=x_cols,
y=54,
training_frame=newtrain,
validation_frame=newvalid)
split1, split2 = train.split_frame()
split1[54] = split1[54].asfactor()
split2[54] = split2[54].asfactor()
my_gbm = H2OGradientBoostingEstimator(distribution="multinomial",
ntrees=100,
learn_rate=0.1,
max_depth=6)
my_gbm.train(x=x_cols,
y=54,
training_frame=split1,
validation_frame=split2)
def test_interaction_constraints():
prostate = h2o.import_file(path=pyunit_utils.locate("smalldata/logreg/prostate.csv"))
prostate.describe()
prostate[1] = prostate[1].asfactor()
constraints = [["AGE", "PSA"], ["GLEASON"]]
ntrees = 5
prostate_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=ntrees,
interaction_constraints=constraints,
seed=42)
prostate_gbm.train(x=list(range(2, 9)), y=1, training_frame=prostate)
prostate_gbm.predict(prostate)
importance = prostate_gbm.varimp(use_pandas=True)
print(importance)
# variables RACE, DPROS, DCAPS, VOL should have zero importance
assert importance["variable"][3] == "RACE"
assert importance["relative_importance"][3] == 0
assert importance["variable"][4] == "DPROS"
assert importance["relative_importance"][4] == 0
assert importance["variable"][5] == "DCAPS"
assert importance["relative_importance"][5] == 0
assert importance["variable"][6] == "VOL"
assert importance["relative_importance"][6] == 0
# check trees features
for i in range(ntrees):
tree = H2OTree(model=prostate_gbm, tree_number=i)
tree_features = set(filter(None, tree.features))
print("iteration: "+str(i))
print(set(constraints[0]))
print(set(constraints[1]))
print(tree_features)
assert tree_features.issubset(set(constraints[0])) or tree_features.issubset(set(constraints[1]))
def test_gridsearch():
h2o_data = h2o.import_file(path = pyunit_utils.locate("smalldata/gam_test/synthetic_20Cols_binomial_20KRows.csv"))
h2o_data['response'] = h2o_data['response'].asfactor()
h2o_data['C3'] = h2o_data['C3'].asfactor()
h2o_data['C7'] = h2o_data['C7'].asfactor()
h2o_data['C8'] = h2o_data['C8'].asfactor()
h2o_data['C10'] = h2o_data['C10'].asfactor()
names = h2o_data.names
myY = "response"
myX = names.remove(myY)
search_criteria = {'strategy': 'Cartesian'}
hyper_parameters = {'lambda': [1, 2],
'subspaces': [{'scale': [[0.001], [0.0002]], 'num_knots': [[5], [10]], 'bs':[[1], [0]],
'gam_columns': [[["c_0"]], [["c_1"]]]},
{'scale': [[0.001, 0.001, 0.001], [0.0002, 0.0002, 0.0002]],
'bs':[[1, 1, 1], [0, 1, 1]],
'num_knots': [[5, 10, 12], [6, 11, 13]],
'gam_columns': [[["c_0"], ["c_1", "c_2"], ["c_3", "c_4", "c_5"]],
[["c_1"], ["c_2", "c_3"], ["c_4", "c_5", "c_6"]]]}]}
hyper_parameters2 = {'lambda': [1, 2],
'subspaces': [{'scale': [[0.001], [0.0002]], 'num_knots': [[5], [10]], 'bs':[[1], [0]],
'gam_columns': [[["c_0"]], [["c_1"]]]},
{'scale': [[0.001, 0.001, 0.001], [0.0002, 0.0002, 0.0002]],
'bs':[[1, 1, 1], [0, 1, 1]],
'num_knots': [[5, 10, 12], [6, 11, 13]],
'gam_columns': [["c_0", ["c_1", "c_2"], ["c_3", "c_4", "c_5"]],
["c_1", ["c_2", "c_3"], ["c_4", "c_5", "c_6"]]]}]}
h2o_model = H2OGridSearch(H2OGeneralizedAdditiveEstimator(family="binomial", keep_gam_cols=True, seed=1),
hyper_params=hyper_parameters, search_criteria=search_criteria)
h2o_model.train(x = myX, y = myY, training_frame = h2o_data)
h2o_model2 = H2OGridSearch(H2OGeneralizedAdditiveEstimator(family="binomial", keep_gam_cols=True, seed=1),
hyper_params=hyper_parameters2, search_criteria=search_criteria)
h2o_model2.train(x = myX, y = myY, training_frame = h2o_data)
# compare two models by checking their coefficients. They should be the same
for index in range(0, len(h2o_model)):
model1 = h2o_model[index]
model2 = h2o_model2[index]
pyunit_utils.assertEqualCoeffDicts(model1.coef(), model2.coef(), tol=1e-6)
def cv_nfolds_gbm():
loan_data = h2o.import_file(
path=pyunit_utils.locate("bigdata/laptop/lending-club/loan.csv"))
loan_data["bad_loan"] = loan_data["bad_loan"].asfactor()
try:
# parallel main model building cannot be used when we use best CV iterations right now
set_best_cv(False)
model_default = H2OGradientBoostingEstimator(nfolds=5,
distribution="bernoulli",
ntrees=500,
score_tree_interval=3,
stopping_rounds=2,
seed=42)
try:
set_parallel(True)
model_default.train(y="bad_loan", training_frame=loan_data)
finally:
set_parallel(False)
preds_default = model_default.predict(loan_data)
model_sequential = H2OGradientBoostingEstimator(
nfolds=5,
distribution="bernoulli",
ntrees=500,
score_tree_interval=3,
stopping_rounds=2,
seed=42)
model_sequential.train(y="bad_loan", training_frame=loan_data)
preds_sequential = model_sequential.predict(loan_data)
assert model_default.actual_params[
"ntrees"] == model_sequential.actual_params["ntrees"]
pyunit_utils.compare_frames_local(preds_default,
preds_sequential,
prob=1.0)
finally:
set_best_cv(True)
def h2ogrid_checkpoints():
"""
Python API test: H2OGridSearch with export_checkpoints_dir
Copy from pyunit_gbm_random_grid.py
"""
air_hex = h2o.import_file(path=pyunit_utils.locate("smalldata/airlines/allyears2k_headers.zip"), destination_frame="air.hex")
myX = ["DayofMonth", "DayOfWeek"]
hyper_parameters = {
'ntrees': [5, 10]
}
search_crit = {'strategy': "RandomDiscrete",
'max_models': 5,
'seed': 1234,
'stopping_rounds' : 2,
'stopping_metric' : "AUTO",
'stopping_tolerance': 1e-2
}
checkpoints_dir = tempfile.mkdtemp()
air_grid = H2OGridSearch(H2OGradientBoostingEstimator, hyper_params=hyper_parameters, search_criteria=search_crit)
air_grid.train(x=myX, y="IsDepDelayed", training_frame=air_hex, distribution="bernoulli",
learn_rate=0.1,
max_depth=3,
nfolds=3,
export_checkpoints_dir=checkpoints_dir)
checkpoint_files = listdir(checkpoints_dir)
print(checkpoint_files)
num_files = len(checkpoint_files)
shutil.rmtree(checkpoints_dir)
assert_is_type(air_grid, H2OGridSearch)
assert num_files == 1 + (2 * (1 + 3)), "Unexpected number of checkpoint files" # 1 grid + 1 main model + 3 CV models for each model built
assert all(model in checkpoint_files for model in air_grid.get_grid().model_ids), \
"Some models do not have corresponding checkpoints"
def iris_dl_grid():
train = h2o.import_file(
path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))
# Run DL
hidden_opts = [[20, 20], [50, 50, 50]]
loss_opts = ["Quadratic", "CrossEntropy"]
size_of_hyper_space = len(hidden_opts) * len(loss_opts)
hyper_parameters = {"hidden": hidden_opts, "loss": loss_opts}
print "DL grid with the following hyper_parameters:", hyper_parameters
gs = H2OGridSearch(H2ODeepLearningEstimator, hyper_params=hyper_parameters)
gs.train(x=range(4), y=4, training_frame=train)
print gs.sort_by("mse")
assert len(gs) == size_of_hyper_space
total_grid_space = map(list, itertools.product(*hyper_parameters.values()))
for model in gs.models:
combo = [model.parms['loss']['actual_value']
] + [model.parms['hidden']['actual_value']]
assert combo in total_grid_space
total_grid_space.remove(combo)
def pubdev_random_cv():
cars = h2o.import_file(
path=pyunit_utils.locate("smalldata/junit/cars_20mpg.csv"))
response_col = "economy"
distribution = "gaussian"
predictors = ["displacement", "power", "weight", "acceleration", "year"]
gbm1 = h2o.gbm(y=cars[response_col],
x=cars[predictors],
nfolds=3,
distribution=distribution,
fold_assignment="Random")
gbm2 = h2o.gbm(y=cars[response_col],
x=cars[predictors],
nfolds=3,
distribution=distribution,
fold_assignment="Random")
mse1 = gbm1.mse(xval=True)
mse2 = gbm2.mse(xval=True)
assert mse1 != mse2, "The first model has an MSE of {0} and the second model has an MSE of {1}. Expected the " \
"first to be different from the second.".format(mse1, mse2)
def h2oimport_file():
"""
Python API test: h2o.import_file(path=None, destination_frame=None, parse=True, header=0, sep=None,
col_names=None, col_types=None, na_strings=None)
"""
try:
col_types=['enum','numeric','enum','enum','enum','numeric','numeric','numeric']
col_headers = ["CAPSULE","AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
hex_key = "training_data.hex"
training_data = h2o.import_file(pyunit_utils.locate("smalldata/prostate/prostate_cat.csv"),
destination_frame=hex_key, header=1, sep = ',',
col_names=col_headers, col_types=col_types, na_strings=["NA"])
assert_is_type(training_data, H2OFrame)
assert training_data.frame_id == hex_key, "frame_id was not assigned correctly. h2o.import_file() is not" \
" working."
assert len(set(training_data.col_names) & set(col_headers))==len(col_headers), "column names are incorrect. " \
"h2o.import_file() not working."
assert training_data.nrow==380, "number of rows is incorrect. h2o.import_file() is not working."
assert training_data.ncol==8, "number of columns is incorrect. h2o.import_file() is not working."
assert sum(training_data.nacnt())==3, "NA count is incorrect. h2o.import_file() is not working."
except Exception as e:
assert False, "h2o.import_file() command is not working."
def test_saved_binary_model_produces_same_predictions_as_original():
ds = prepare_data(blending)
base_models = train_base_models(ds)
se_model = train_stacked_ensemble(ds, base_models)
#Predict in ensemble in Py client
preds_py = se_model.predict(ds.test)
tmp_dir = tempfile.mkdtemp()
try:
bin_file = h2o.save_model(se_model, tmp_dir)
#Load binary model and predict
bin_model = h2o.load_model(pu.locate(bin_file))
preds_bin = bin_model.predict(ds.test)
finally:
shutil.rmtree(tmp_dir)
#Predictions from model in Py and binary model should be the same
pred_diff = preds_bin - preds_py
assert pred_diff["p0"].max() < 1e-11
assert pred_diff["p1"].max() < 1e-11
assert pred_diff["p0"].min() > -1e-11
assert pred_diff["p1"].min() > -1e-11
def cv_nfolds_gbm():
prostate = h2o.import_file(
path=pyunit_utils.locate("smalldata/logreg/prostate.csv"))
prostate[1] = prostate[1].asfactor()
prostate.summary()
from h2o.estimators.gbm import H2OGradientBoostingEstimator
prostate_gbm = H2OGradientBoostingEstimator(nfolds=5,
distribution="bernoulli")
prostate_gbm.train(x=range(2, 9), y=1, training_frame=prostate)
prostate_gbm.show()
# Can specify both nfolds >= 2 and validation data at once
try:
H2OGradientBoostingEstimator(nfolds=5, distribution="bernoulli").train(
x=range(2, 9),
y=1,
training_frame=prostate,
validation_frame=prostate)
assert True
except EnvironmentError:
assert False, "expected an error"
def pubdev_5167():
training_data = h2o.import_file(pyunit_utils.locate("bigdata/laptop/airlines_all.05p.csv"))
if 'IsDepDelayed' in training_data.names:
training_data['IsDepDelayed'] = training_data['IsDepDelayed'].asfactor()
else:
raise AttributeError("label {0} not found".format('IsDepDelayed'))
estimator = h2o.estimators.deeplearning.H2ODeepLearningEstimator(hidden=[50, 50, 50, 50, 50],
activation='rectifier',
adaptive_rate=True,
balance_classes=True,
epochs=50,
shuffle_training_data=True,
score_each_iteration=True,
stopping_metric='auc',
stopping_rounds=5,
stopping_tolerance=.01,
use_all_factor_levels=False,
variable_importances=False,
export_weights_and_biases=True,
seed=200)
estimator.train(x=training_data.names[:-1], y=training_data.names[-1], training_frame=training_data)
def mojo_model_glm_test():
# GLM
airlines = h2o.import_file(
path=pyunit_utils.locate("smalldata/testng/airlines_train.csv"))
glm = H2OGeneralizedLinearEstimator()
glm.train(x=["Origin", "Dest"], y="Distance", training_frame=airlines)
original_model_filename = tempfile.mkdtemp()
original_model_filename = glm.download_mojo(original_model_filename)
model = H2OGenericEstimator.from_file(original_model_filename)
assert model is not None
predictions = model.predict(airlines)
assert predictions is not None
assert predictions.nrows == 24421
assert model._model_json["output"]["model_summary"] is not None
assert len(model._model_json["output"]["model_summary"]._cell_values) > 0
generic_mojo_filename = tempfile.mkdtemp("zip", "genericMojo")
generic_mojo_filename = model.download_mojo(path=generic_mojo_filename)
assert os.path.getsize(generic_mojo_filename) == os.path.getsize(
original_model_filename)
def test_target_encoding_fit_method():
print("Check fit method of the TargetEncoder class")
targetColumnName = "survived"
foldColumnName = "kfold_column" # it is strange that we can't set name for generated kfold
teColumns = ["home.dest", "cabin", "embarked"]
targetEncoder = TargetEncoder(x=teColumns,
y=targetColumnName,
fold_column=foldColumnName,
blended_avg=True,
inflection_point=3,
smoothing=1)
trainingFrame = h2o.import_file(
pyunit_utils.locate("smalldata/gbm_test/titanic.csv"), header=1)
trainingFrame[targetColumnName] = trainingFrame[targetColumnName].asfactor(
)
trainingFrame[foldColumnName] = trainingFrame.kfold_column(n_folds=5,
seed=1234)
encodingMap = targetEncoder.fit(frame=trainingFrame)
assert encodingMap.map_keys['string'] == teColumns
assert encodingMap.frames[0]['num_rows'] == 583
def pubdev_5174():
x = h2o.import_file(pyunit_utils.locate('smalldata/jira/PUBDEV-5174.csv'),
header=1)
tt = x['rr'].unique()
gg = tt[:10000, 0]
ww = x[~x['rr'].isin(gg['C1'].as_data_frame()['C1'].tolist())]
print(x.nrow)
print(tt.nrow)
print(ww.nrow)
assert x.nrow == 1000000, "Original data has 1000000 rows"
assert tt.nrow == 499851, "Column rr has 499851 unique values"
assert ww.nrow == 979992, "Original data reduced has 979992 rows"
# What do we do with Tuples?
# there are 2 instances of 'cTeYX' and 2 of 'Todxf'
tup = ('cTeYX', 'Todxf')
ww_tuple = x[~x['rr'].isin(tup)]
assert ww_tuple.nrow == 999996, "Original data reduced has 999996 rows"
def test_infogram_personal_loan_cv_valid():
"""
Make sure safe infogram plot works with cv and validation dataset.
"""
fr = h2o.import_file(path=pyunit_utils.locate("smalldata/admissibleml_test/Bank_Personal_Loan_Modelling.csv"))
target = "Personal Loan"
fr[target] = fr[target].asfactor()
x = ["Experience","Income","Family","CCAvg","Education","Mortgage",
"Securities Account","CD Account","Online","CreditCard"]
splits = fr.split_frame(ratios=[0.80])
train = splits[0]
test = splits[1]
infogram_model_cv_v = H2OInfogram(seed = 12345, protected_columns=["Age","ZIP Code"], nfolds=5)
infogram_model_cv_v.train(x=x, y=target, training_frame=train, validation_frame=test) # cross-validation, validation
infogram_model_cv_v.plot(title="Infogram calcuated from training dataset", server=True) # plot infogram from training dataset
infogram_model_cv_v.plot(train=True, valid=True, title="Infogram calculated from training/validation dataset",
server=True) # plot infogram from validation dataset
infogram_model_cv_v.plot(train=True, valid=True, xval=True, title="Infogram calculated from "
"training/validation/xval holdout dataset",
server=True) # plot infogram from cv hold out dataset
relcmi_train = infogram_model_cv_v.get_admissible_score_frame()
relcmi_valid = infogram_model_cv_v.get_admissible_score_frame(valid=True)
assert relcmi_train.nrow==relcmi_valid.nrow
def setup_data(self):
"""
This function performs all initializations necessary:
load the data sets and set the training set indices and response column index
"""
# create and clean out the sandbox directory first
self.sandbox_dir = pyunit_utils.make_Rsandbox_dir(
self.current_dir, self.test_name, True)
# preload datasets
self.training1_data = h2o.import_file(
path=pyunit_utils.locate(self.training1_filename))
# set data set indices for predictors and response
self.y_index = self.training1_data.ncol - 1
self.x_indices = list(range(self.y_index))
# save the training data files just in case the code crashed.
pyunit_utils.remove_csv_files(self.current_dir,
".csv",
action='copy',
new_dir_path=self.sandbox_dir)
def pca_prostate():
print("Importing prostate.csv data...\n")
prostate = h2o.upload_file(
pyunit_utils.locate("smalldata/logreg/prostate.csv"))
print("Converting CAPSULE, RACE, DPROS and DCAPS columns to factors")
prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
prostate["RACE"] = prostate["RACE"].asfactor()
prostate["DPROS"] = prostate["DPROS"].asfactor()
prostate["DCAPS"] = prostate["DCAPS"].asfactor()
prostate.describe()
print(
"PCA on columns 3 to 9 with k = 3, retx = FALSE, transform = 'STANDARDIZE'"
)
fitPCA = H2OPCA(k=3, transform="NONE", pca_method="Power")
fitPCA.train(x=list(range(2, 9)), training_frame=prostate)
pred = fitPCA.predict(prostate)
print("Projection matrix:\n")
pred.head()
def perfect_separation_balanced():
print("Read in synthetic balanced dataset")
data = h2o.import_file(path=pyunit_utils.locate(
"smalldata/synthetic_perfect_separation/balanced.csv"))
print("Fit model on dataset")
model = H2OGeneralizedLinearEstimator(family="binomial",
lambda_search=True,
alpha=0.5,
Lambda=1e-8)
model.train(x=["x1", "x2"], y="y", training_frame=data)
print(
"Extract models' coefficients and assert reasonable values (ie. no greater than 50)"
)
print("Balanced dataset")
coef = [
c[1]
for c in model._model_json['output']['coefficients_table'].cell_values
if c[0] != "Intercept"
]
for c in coef:
assert c < 50, "coefficient is too large"
def group_by():
'''
This test checks that if a groupby operation is specified for frames with string columns, a warning is
generated about the string columns being skipped.
In addition, it checks that operations on numeric/enum columns are performed and generated the correct
expected outputs.
'''
# Connect to a pre-existing cluster
buffer = StringIO() # redirect output
sys.stderr = buffer
h2o_f1 = h2o.import_file(path=pyunit_utils.locate(
"smalldata/jira/test_groupby_with_strings.csv"),
col_types=['real', 'string', 'string', 'real'])
grouped = h2o_f1.group_by("C1")
grouped.mean(na="all").median(na="all").max(na="all").min(na="all").sum(
na="all")
print(grouped.get_frame())
print("Checking number of warning messages...")
check_warnings(
2, buffer) # make sure we receieved two warning, one per string row
def glrm_arrests():
print("Importing USArrests.csv data...")
arrestsH2O = h2o.upload_file(
pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
arrestsH2O.describe()
print("H2O initial Y matrix:\n")
initial_y = [[5.412, 65.24, -7.54, -0.032], [2.212, 92.24, -17.54, 23.268],
[0.312, 123.24, 14.46, 9.768], [1.012, 19.24, -15.54, -1.732]]
initial_y_h2o = h2o.H2OFrame(list(zip(*initial_y)))
initial_y_h2o.show()
print("H2O GLRM on de-meaned data with quadratic loss:\n")
glrm_h2o = H2OGeneralizedLowRankEstimator(k=4,
transform="DEMEAN",
loss="Quadratic",
gamma_x=0,
gamma_y=0,
init="User",
user_y=initial_y_h2o,
recover_svd=True)
glrm_h2o.train(x=arrestsH2O.names, training_frame=arrestsH2O)
glrm_h2o.show()
def glrm_arrests():
print "Importing USArrests.csv data..."
arrestsH2O = h2o.upload_file(
pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
arrestsH2O.describe()
print "H2O initial Y matrix:\n"
initial_y = [[5.412, 65.24, -7.54, -0.032], [2.212, 92.24, -17.54, 23.268],
[0.312, 123.24, 14.46, 9.768], [1.012, 19.24, -15.54, -1.732]]
initial_y_h2o = h2o.H2OFrame(initial_y)
initial_y_h2o.show()
print "H2O GLRM on de-meaned data with quadratic loss:\n"
glrm_h2o = h2o.glrm(x=arrestsH2O,
k=4,
transform="DEMEAN",
loss="Quadratic",
gamma_x=0,
gamma_y=0,
init="User",
user_y=initial_y_h2o,
recover_svd=True)
glrm_h2o.show()
def bigcatRF():
# Training set has 100 categories from cat001 to cat100
# Categories cat001, cat003, ... are perfect predictors of y = 1
# Categories cat002, cat004, ... are perfect predictors of y = 0
#Log.info("Importing bigcat_5000x2.csv data...\n")
bigcat = h2o.import_file(
path=pyunit_utils.locate("smalldata/gbm_test/bigcat_5000x2.csv"))
bigcat["y"] = bigcat["y"].asfactor()
#Log.info("Summary of bigcat_5000x2.csv from H2O:\n")
#bigcat.summary()
# Train H2O DRF Model:
#Log.info("H2O DRF (Naive Split) with parameters:\nclassification = TRUE, ntree = 1, depth = 1, nbins = 100, nbins_cats=10\n")
model = h2o.random_forest(x=bigcat[["X"]],
y=bigcat["y"],
ntrees=1,
max_depth=1,
nbins=100,
nbins_cats=10)
model.show()
def link_functions_tweedie_basic():
print "Read in prostate data."
hdf = h2o.upload_file(
pyunit_utils.locate("smalldata/prostate/prostate_complete.csv.zip"))
print "Testing for family: TWEEDIE"
print "Set variables for h2o."
y = "CAPSULE"
x = ["AGE", "RACE", "DCAPS", "PSA", "VOL", "DPROS", "GLEASON"]
print "Create models with canonical link: TWEEDIE"
model_h2o_tweedie = H2OGeneralizedLinearEstimator(family="tweedie",
link="tweedie",
alpha=0.5,
Lambda=0)
model_h2o_tweedie.train(x=x, y=y, training_frame=hdf)
print "Compare model deviances for link function tweedie (using precomputed values from R)"
deviance_h2o_tweedie = model_h2o_tweedie.residual_deviance(
) / model_h2o_tweedie.null_deviance()
assert 0.721452 - deviance_h2o_tweedie <= 0.01, "h2o's residual/null deviance is more than 0.01 lower than R's. h2o: " \
"{0}, r: {1}".format(deviance_h2o_tweedie, 0.721452)
def parametersKmeans():
print "Getting data..."
iris = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris.csv"))
print "Create and and duplicate..."
iris_km = h2o.kmeans(x=iris[0:4], k=3, seed=1234)
parameters = iris_km._model_json['parameters']
param_dict = {}
for p in range(len(parameters)):
param_dict[parameters[p]['label']] = parameters[p]['actual_value']
iris_km_again = h2o.kmeans(x=iris[0:4], **param_dict)
print "wss"
wss = iris_km.withinss().sort()
wss_again = iris_km_again.withinss().sort()
assert wss == wss_again, "expected wss to be equal"
print "centers"
centers = iris_km.centers()
centers_again = iris_km_again.centers()
assert centers == centers_again, "expected centers to be the same"
def setup_data(self):
"""
This function performs all initializations necessary:
load the data sets and set the training set indices and response column index
"""
self.h2o_data = h2o.import_file(path=pyunit_utils.locate(
"smalldata/glm_test/gaussian_20cols_10000Rows.csv"))
self.h2o_data["C1"] = self.h2o_data["C1"].asfactor()
self.h2o_data["C2"] = self.h2o_data["C2"].asfactor()
self.myX = ["C1", "C2"]
self.myY = "C21"
for lambda_param in self.hyper_parameters['lambda']:
for alpha_param in self.hyper_parameters['alpha']:
self.manual_gam_models.append(
H2OGeneralizedAdditiveEstimator(
family="gaussian",
gam_columns=["C11", "C12", "C13"],
keep_gam_cols=True,
scale=[1, 1, 1],
num_knots=[5, 5, 5],
alpha=alpha_param,
lambda_=lambda_param,
bs=[2, 0, 2]))
def vi_toy_test():
toy_data = h2o.import_file(
path=pyunit_utils.locate("smalldata/gbm_test/toy_data_RF.csv"))
#toy_data.summary()
toy_data[6] = toy_data[6].asfactor()
toy_data.show()
rf = h2o.random_forest(x=toy_data[[0, 1, 2, 3, 4, 5]],
y=toy_data[6],
ntrees=500,
max_depth=20,
nbins=100,
seed=0)
ranking = [
rf._model_json['output']['variable_importances'].cell_values[v][0]
for v in range(toy_data.ncol - 1)
]
print(ranking)
assert tuple(ranking) == tuple(
["V3", "V2", "V6", "V5", "V1",
"V4"]), "expected specific variable importance ranking"
def weights_gamma():
htable = h2o.upload_file(
pyunit_utils.locate("smalldata/gbm_test/moppe.csv"))
htable["premiekl"] = htable["premiekl"].asfactor()
htable["moptva"] = htable["moptva"].asfactor()
htable["zon"] = htable["zon"]
hh = H2OGradientBoostingEstimator(distribution="gamma",
ntrees=20,
max_depth=1,
min_rows=1,
learn_rate=1)
hh.train(x=range(3),
y="medskad",
training_frame=htable,
weights_column="antskad")
ph = hh.predict(htable)
assert abs(8.804447 - hh._model_json['output']['init_f']) < 1e-6 * 8.804447
assert abs(3751.01 - ph[0].min()) < 1e-4 * 3751.01
assert abs(15298.87 - ph[0].max()) < 1e-4 * 15298.87
assert abs(8121.98 - ph[0].mean()[0]) < 1e-4 * 8121.98
def test_teColumns_parameter_as_single_element():
print("Check fit method can accept non-array single column to encode")
targetColumnName = "survived"
foldColumnName = "kfold_column" # it is strange that we can't set name for generated kfold
teColumns = "home.dest"
targetEncoder = TargetEncoder(x=teColumns,
y=targetColumnName,
fold_column=foldColumnName,
blending_avg=True,
inflection_point=3,
smoothing=1)
trainingFrame = h2o.import_file(
pyunit_utils.locate("smalldata/gbm_test/titanic.csv"), header=1)
trainingFrame[targetColumnName] = trainingFrame[targetColumnName].asfactor(
)
trainingFrame[foldColumnName] = trainingFrame.kfold_column(n_folds=5,
seed=1234)
encodingMap = targetEncoder.fit(frame=trainingFrame)
assert encodingMap.map_keys['string'] == [teColumns]
assert encodingMap.frames[0]['num_rows'] == 583
def group_by():
# Connect to a pre-existing cluster
h2o_iris = h2o.import_file(
path=pyunit_utils.locate("smalldata/iris/iris_wheader.csv"))
na_handling = ["rm", "ignore", "all"]
print("Running smoke test")
# smoke test
for na in na_handling:
grouped = h2o_iris.group_by("class")
grouped \
.count(na=na) \
.min( na=na) \
.max( na=na) \
.mean( na=na) \
.var( na=na) \
.sd( na=na) \
.ss( na=na) \
.sum( na=na)
print(grouped.get_frame())
print(grouped.get_frame()
) # call get_frame() again to ensure that Pasha bug fix works.
