def create_fold_column_if_not_exist(h2o_base_table: h2o.H2OFrame,
fold_column: str,
nfolds: int = None) -> h2o.H2OFrame:
if fold_column and fold_column not in h2o_base_table.col_names:
h2o_fold_col = h2o_base_table.kfold_column(n_folds=nfolds)
h2o_fold_col.set_names([fold_column])
h2o_base_table = h2o_base_table.cbind(h2o_fold_col)
return h2o_base_table
def _train_test_split_as_frames(x, y, is_str=False, is_classifier=False):
y = y.astype(np.str) if is_str else y.astype(np.int64)
x_train, x_test, y_train, _ = train_test_split(x, y, test_size=0.3, random_state=42)
f_train_x = H2OFrame(x_train)
f_train_y = H2OFrame(y_train)
f_train = f_train_x.cbind(f_train_y)
if is_classifier:
f_train[f_train.ncol - 1] = f_train[f_train.ncol - 1].asfactor()
return f_train, x_test.astype(np.float32)
def bernoulli_synthetic_data_gbm_medium():
# Generate training dataset (adaptation of http://www.stat.missouri.edu/~speckman/stat461/boost.R)
train_rows = 10000
train_cols = 10
# Generate variables V1, ... V10
X_train = np.random.randn(train_rows, train_cols)
# y = +1 if sum_i x_{ij}^2 > chisq median on 10 df
y_train = np.asarray([1 if rs > scipy.stats.chi2.ppf(0.5, 10) else -1 for rs in [sum(r) for r in
np.multiply(X_train,X_train).tolist()]])
# Train scikit gbm
# TODO: grid-search
distribution = "bernoulli"
ntrees = 150
min_rows = 1
max_depth = 2
learn_rate = .01
nbins = 20
gbm_sci = ensemble.GradientBoostingClassifier(learning_rate=learn_rate, n_estimators=ntrees, max_depth=max_depth,
min_samples_leaf=min_rows, max_features=None)
gbm_sci.fit(X_train,y_train)
# Generate testing dataset
test_rows = 2000
test_cols = 10
# Generate variables V1, ... V10
X_test = np.random.randn(test_rows, test_cols)
# y = +1 if sum_i x_{ij}^2 > chisq median on 10 df
y_test = np.asarray([1 if rs > scipy.stats.chi2.ppf(0.5, 10) else -1 for rs in [sum(r) for r in
np.multiply(X_test,X_test).tolist()]])
# Score (AUC) the scikit gbm model on the test data
auc_sci = roc_auc_score(y_test, gbm_sci.predict_proba(X_test)[:,1])
# Compare this result to H2O
train_h2o = H2OFrame.fromPython(zip(*np.column_stack((y_train, X_train)).tolist()))
test_h2o = H2OFrame.fromPython(zip(*np.column_stack((y_test, X_test)).tolist()))
gbm_h2o = h2o.gbm(x=train_h2o[1:], y=train_h2o["C1"].asfactor(), distribution=distribution, ntrees=ntrees,
min_rows=min_rows, max_depth=max_depth, learn_rate=learn_rate, nbins=nbins)
gbm_perf = gbm_h2o.model_performance(test_h2o)
auc_h2o = gbm_perf.auc()
#Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o))
assert abs(auc_h2o - auc_sci) < 1e-2, "h2o (auc) performance degradation, with respect to scikit. h2o auc: {0} " \
"scickit auc: {1}".format(auc_h2o, auc_sci)
def fit_h2o(x_train, y_train, estimator):
parameters = estimator._parms
estimator_type = estimator.__class__
current_estimator = estimator_type()
current_estimator._parms = parameters
column_types_x = get_h2o_column_types(x_train.columns)
x_train = H2OFrame(x_train, column_types=column_types_x)
y_train = H2OFrame(list(y_train), column_types=['enum'])
training_frame = x_train.cbind(y_train) if y_train is not None else x_train
x_train = x_train.names
y_train = y_train.names[0]
current_estimator.train(x_train, y_train, training_frame)
return current_estimator
def test1():
badFrame = H2OFrame({"one": [4, 6, 1], "two": ["a", "b", "cde"], "three": [0, 5.2, 14]})
badClone = H2OFrame({"one": [4, 6, 1], "two": ["a", "b", "cde"], "three": [0, 5.2, 14]})
compare_frames(badFrame, badClone)
try:
badFrame.asfactor()
assert False, "The frame contaied a real number, an error should be thrown"
except H2OValueError: # as designed
pass
compare_frames(badFrame, badClone)
originalAfterOp = H2OFrame.get_frame(badFrame.frame_id)
compare_frames(badFrame, originalAfterOp)
goodFrame = H2OFrame({"one": [4, 6, 1], "two": ["a", "b", "cde"]})
goodClone = H2OFrame({"one": [4, 6, 1], "two": ["a", "b", "cde"]})
compare_frames(goodFrame, goodClone)
factoredFrame = goodFrame.asfactor()
originalAfterOp = H2OFrame.get_frame(goodFrame.frame_id)
compare_frames(goodFrame, originalAfterOp)
expectedFactoredFrame = H2OFrame({"one": [4, 6, 1], "two": ["a", "b", "cde"]}, column_types={"one":"categorical", "two": "enum"})
compare_frames(expectedFactoredFrame, factoredFrame)
refactoredFrame = expectedFactoredFrame.asfactor()
factoredAfterOp = H2OFrame.get_frame(refactoredFrame.frame_id)
compare_frames(expectedFactoredFrame, factoredAfterOp)
def predict_dataframe(data, node):
if node.classifier:
if isinstance(node.classifier, h2o.estimators.H2OEstimator):
if not isinstance(data, H2OFrame):
column_types = get_h2o_column_types(data.columns)
data_h2o = H2OFrame(data, column_types=column_types)
prediction = node.classifier.predict(data_h2o)
if len(prediction['predict'].as_data_frame().values) == 0:
prediction = np.array([])
else:
prediction = np.concatenate(
prediction['predict'].as_data_frame().values)
else:
prediction = node.classifier.predict(data)
data_right = data.iloc[[
i for i in range(len(prediction)) if prediction[i] == 1
]]
data_left = data.iloc[[
i for i in range(len(prediction)) if prediction[i] == 0
]]
prediction_left = predict_dataframe(data_left, node.left_node)
prediction_right = predict_dataframe(data_right, node.right_node)
return sorted(prediction_left + prediction_right)
else:
return [(i, list(node.classes)[0]) for i in list(data.index)]
def retrieve_h2o_base_table_predictors(self, h2o_base_table: h2o.H2OFrame):
cols_to_drop = [
'row_id',
self.auto_ml_config[AutoMLConfig.DATA][AutoMLConfig.LABEL_COL],
self.auto_ml_config[AutoMLConfig.DATA][AutoMLConfig.ORIGIN_COL]
] + self.auto_ml_config[AutoMLConfig.DATA][AutoMLConfig.CATEGORICAL_VARIABLES]
return h2o_base_table.drop(cols_to_drop).col_names
def build_auto_h2o(regressor, name):
transformer = ColumnTransformer(
[(column, CategoricalDomain(), [column])
for column in ["cylinders", "model_year", "origin"]] +
[(column, ContinuousDomain(), [column]) for column in
["displacement", "horsepower", "weight", "acceleration"]])
pipeline = PMMLPipeline([("transformer", transformer),
("uploader",
H2OFrameCreator(column_names=[
"cylinders", "model_year", "origin",
"displacement", "horsepower", "weight",
"acceleration"
],
column_types=[
"enum", "enum", "enum",
"numeric", "numeric",
"numeric", "numeric"
])), ("regressor", regressor)])
pipeline.fit(auto_X, H2OFrame(auto_y.to_frame()))
pipeline.verify(auto_X.sample(frac=0.05, random_state=13))
regressor = pipeline._final_estimator
store_mojo(regressor, name + ".zip")
store_pkl(pipeline, name + ".pkl")
mpg = pipeline.predict(auto_X)
mpg.set_names(["mpg"])
store_csv(mpg.as_data_frame(), name + ".csv")
def get_expected_output_frame(out_doc_ids, out_tokens, out_TFs, out_IDFs,
out_TFIDFs):
return H2OFrame(
OrderedDict([('DocID', out_doc_ids), ('Token', out_tokens),
('TF', out_TFs), ('IDF', out_IDFs),
('TF_IDF', out_TFIDFs)]),
column_types=['numeric', 'string', 'numeric', 'numeric', 'numeric'])
def optimum_threshold(self, hf: h2o.H2OFrame, model: H2OGenericEstimator) -> float:
""" Selects the best threshold for this model given the cost values of this instance
Args:
hf (DataFrame): Data used for evaluation. Must contain ground truth column named fraud
model (H2OModel): A model object to be evaluated
Returns: optimum_threshold (float): Indicates that if a model p1 value is less than this number
the prediction is 0 (not fraud). If the model p1 value is greater than
this number the prediction is 1 (fraud)
"""
# Extract the probability of the positive class from the predictions
df = hf.as_data_frame()
df['model_score'] = model.predict(test_data=hf).as_data_frame()['p1']
matrix = {str(model.model_id): {'x': [], 'y': []}}
# Calculate cost function for ever 1/100 ranging from 0 to 1
for t in range(1, 100):
t = t / 100
df['prediction'] = predict(df, t, 1, 'model_score')
df = reconcile(df, 'prediction', 'fraud', f"CM_{t}")
t_cost, df = outcome(df, self.inverse_costs, f"CM_{t}", f"costs_{t}")
matrix[str(model.model_id)]['x'].append(t)
matrix[str(model.model_id)]['y'].append(t_cost)
# Return threshold that produced the minimum cost
idx_min_cost = matrix[str(model.model_id)]['y'].index(min(matrix[str(model.model_id)]['y']))
optimum_threshold = matrix[str(model.model_id)]['x'][idx_min_cost]
print(f"optimum_threshold: {optimum_threshold}")
return optimum_threshold
def upload_file():
a = h2o.upload_file(pyunit_utils.locate("smalldata/logreg/prostate.csv"))
print a.describe()
from h2o import H2OFrame
# using lists []
py_list_to_h2o = H2OFrame.fromPython(zip(*[[0, 1, 2, 3, 4]]))
print py_list_to_h2o.describe()
py_list_to_h2o_2 = H2OFrame.fromPython(zip(*[[0, 1, 2, 3], [5, 6, "hi", "dog"]]))
print py_list_to_h2o_2.describe()
# using tuples ()
py_tuple_to_h2o = H2OFrame.fromPython(zip(*[(0, 1, 2, 3, 4)]))
print py_tuple_to_h2o.describe()
py_tuple_to_h2o_2 = H2OFrame.fromPython(zip(*((0, 1, 2, 3), (5, 6, "hi", "dog"))))
print py_tuple_to_h2o_2.describe()
# using dicts {}
py_dict_to_h2o = H2OFrame.fromPython({"column1": [5, 4, 3, 2, 1],
"column2": (1, 2, 3, 4, 5)})
py_dict_to_h2o.describe()
py_dict_to_h2o_2 = H2OFrame.fromPython({"colA": ["bilbo", "baggins"], "colB": ["meow"]})
print py_dict_to_h2o_2.describe()
# using collections.OrderedDict
import collections
d = {"colA": ["bilbo", "baggins"], "colB": ["meow"]} # still unordered!
py_ordered_dict_to_h2o = H2OFrame.fromPython(collections.OrderedDict(d))
py_ordered_dict_to_h2o.describe()
# make an ordered dictionary!
d2 = collections.OrderedDict()
d2["colA"] = ["bilbo", "baggins"]
d2["colB"] = ["meow"]
py_ordered_dict_to_h2o_2 = H2OFrame.fromPython(collections.OrderedDict(d2))
py_ordered_dict_to_h2o_2.describe()
def upload_file(ip, port):
h2o.init(ip, port)
a = h2o.upload_file("../../smalldata/logreg/prostate.csv")
print a.describe()
from h2o import H2OFrame
# using lists []
py_list_to_h2o = H2OFrame(python_obj=[0, 1, 2, 3, 4])
print py_list_to_h2o.describe()
py_list_to_h2o_2 = H2OFrame(python_obj=[[0, 1, 2, 3], [5, 6, "hi", "dog"]])
print py_list_to_h2o_2.describe()
# using tuples ()
py_tuple_to_h2o = H2OFrame(python_obj=(0, 1, 2, 3, 4))
print py_tuple_to_h2o.describe()
py_tuple_to_h2o_2 = H2OFrame(python_obj=((0, 1, 2, 3), (5, 6, "hi", "dog")))
print py_tuple_to_h2o_2.describe()
# using dicts {}
py_dict_to_h2o = H2OFrame(python_obj={"column1": [5, 4, 3, 2, 1],
"column2": (1, 2, 3, 4, 5)})
py_dict_to_h2o.describe()
py_dict_to_h2o_2 = H2OFrame(python_obj={"colA": ["bilbo", "baggins"], "colB": ["meow"]})
print py_dict_to_h2o_2.describe()
# using collections.OrderedDict
import collections
d = {"colA": ["bilbo", "baggins"], "colB": ["meow"]} # still unordered!
py_ordered_dict_to_h2o = H2OFrame(python_obj=collections.OrderedDict(d))
py_ordered_dict_to_h2o.describe()
# make an ordered dictionary!
d2 = collections.OrderedDict()
d2["colA"] = ["bilbo", "baggins"]
d2["colB"] = ["meow"]
py_ordered_dict_to_h2o_2 = H2OFrame(python_obj=collections.OrderedDict(d2))
py_ordered_dict_to_h2o_2.describe()
def upload_file():
a = h2o.upload_file(pyunit_utils.locate("smalldata/logreg/prostate.csv"))
print(a.describe())
from h2o import H2OFrame
# using lists []
py_list_to_h2o = H2OFrame([[0, 1, 2, 3, 4]])
print(py_list_to_h2o.describe())
py_list_to_h2o_2 = H2OFrame([[0, 1, 2, 3], [5, 6, "hi", "dog"]])
print(py_list_to_h2o_2.describe())
# using tuples ()
py_tuple_to_h2o = H2OFrame([(0, 1, 2, 3, 4)])
print(py_tuple_to_h2o.describe())
py_tuple_to_h2o_2 = H2OFrame(((0, 1, 2, 3), (5, 6, "hi", "dog")))
print(py_tuple_to_h2o_2.describe())
# using dicts {}
py_dict_to_h2o = H2OFrame({
"column1": [5, 4, 3, 2, 1],
"column2": (1, 2, 3, 4, 5)
})
py_dict_to_h2o.describe()
py_dict_to_h2o_2 = H2OFrame({
"colA": ["bilbo", "baggins"],
"colB": ["meow"]
})
print(py_dict_to_h2o_2.describe())
# using collections.OrderedDict
import collections
d = {"colA": ["bilbo", "baggins"], "colB": ["meow"]} # still unordered!
py_ordered_dict_to_h2o = H2OFrame(collections.OrderedDict(d))
py_ordered_dict_to_h2o.describe()
# make an ordered dictionary!
d2 = collections.OrderedDict()
d2["colA"] = ["bilbo", "baggins"]
d2["colB"] = ["meow"]
py_ordered_dict_to_h2o_2 = H2OFrame(collections.OrderedDict(d2))
py_ordered_dict_to_h2o_2.describe()
def _assert_expr_results_eq(expr_provider, skip_expr_assert=False):
flag = h2o.is_expr_optimizations_enabled()
try:
# Get result of optimized expression
h2o.enable_expr_optimizations(True)
opt_expr = expr_provider()
opt_result = H2OFrame._expr(opt_expr)
# Get result of full expression
h2o.enable_expr_optimizations(False)
noopt_expr = expr_provider()
noopt_result = H2OFrame._expr(noopt_expr)
if not skip_expr_assert:
assert opt_expr._debug_print() != noopt_expr._debug_print(), "The optimization should simplify expression!"
assert noopt_result.as_data_frame(use_pandas=False) == opt_result.as_data_frame(
use_pandas=False), "Results with/without expression optimization should match!"
return opt_expr, noopt_expr
finally:
h2o.enable_expr_optimizations(flag)
def add_unique_row_id(h2o_base_table: h2o.H2OFrame):
num_rows = h2o_base_table.shape[0]
ids = []
for id in range(0, num_rows):
ids.append(id)
h2o_id_frame = h2o.H2OFrame(ids)
return h2o_base_table.cbind(h2o_id_frame.set_names(['row_id']))
def predict_with_probabilities(self, data):
data_frame = H2OFrame(data, column_names=self._column_names)
preds = self._mojo_model.predict(data_frame).as_data_frame(use_pandas=True)
if len(preds.columns) == 1:
return [preds.to_numpy()]
else:
return [
preds.iloc[:, 0].to_numpy().astype(np.str),
preds.iloc[:, 1:].to_numpy()
]
def fold_prediction_result(x_train, y_train, x_test, y_test,
classification_types, basic_classifier):
"""
The training and prediction for one fold for all the types of classifiers indicated in classification_types
:param x_train: the training data
:param y_train: the training classes
:param x_test: the testing data
:param y_test: the testing classes
:param classification_types: the classification types to be considered
:param basic_classifier: the basic classifier to be used either independently or for the meta classifiers
:return: metrics_dict - dictionary containing a dictionary for every metric with data for every classifier
training_time - dictionary with training time in seconds for every classification type
test_time - dictionary with testing time in seconds for every classification type
"""
metrics_dict = {}
for metric in METRICS:
metrics_dict[metric] = {}
training_time = {}
test_time = {}
for classification in classification_types:
# logger.info("*****************************")
logger.info(classification)
if classification in ENCODING_TYPES:
classifier = EncodedClassifier(basic_classifier,
encoding_type=classification)
elif classification == "meta_binary_tree_classifier":
classifier = MetaBinaryTreeClassifier(basic_classifier)
elif classification == "standard-classifier":
classifier = basic_classifier
else:
raise Exception("The Classification Method is not a valid one")
start_time = time.time()
if isinstance(classifier, h2o.estimators.H2OEstimator):
classifier = fit_h2o(x_train, y_train, classifier)
else:
classifier.fit(x_train, y_train)
train_time = time.time() - start_time
if isinstance(classifier, h2o.estimators.H2OEstimator):
column_types = get_h2o_column_types(x_test.columns)
x_test = H2OFrame(x_test, column_types=column_types)
prediction = classifier.predict(x_test)
y_pred = np.concatenate(
prediction['predict'].as_data_frame().values)
else:
y_pred = classifier.predict(x_test)
prediction_time = time.time() - train_time - start_time
# Calculate metrics
for metric, f in METRICS.items():
metrics_dict[metric][classification] = f(y_test, y_pred)
training_time[classification] = train_time
test_time[classification] = prediction_time
return metrics_dict, training_time, test_time
def test_pav(y, X, w):
X = X.reshape(-1)
# run Isotonic Regression to extract thresholds
iso_reg = IsotonicRegression().fit(X, y, w)
thresholds_scikit = H2OFrame(np.column_stack(get_thresholds(iso_reg)))
print(thresholds_scikit.as_data_frame())
# now invoke H2O PAVA
thresholds_h2o = pav(y, X, w)
print(thresholds_h2o.as_data_frame())
assert_frame_equal(thresholds_scikit.as_data_frame(),
thresholds_h2o.as_data_frame())
def pubdev_6394():
# JUnit tests are to be found in RapidsTest class
data = [['location'], ['Ｘ県 Ａ市'], ['Ｘ県 Ｂ市'], ['Ｘ県 Ｂ市'], ['Ｙ県 Ｃ市'],
['Ｙ県 Ｃ市']]
original_frame = H2OFrame(data, header=True, column_types=['enum'])
assert original_frame.type('location') == 'enum'
assert original_frame.categories() == [u'Ｘ県 Ａ市', u'Ｘ県 Ｂ市', u'Ｙ県 Ｃ市']
# Reduce cardinality of 'location' column to 2 by reducing existing categorical values to ['Ｘ県','Y県']
expected_categories = [u'Ｘ県', u'Ｙ県']
transformed_frame = original_frame['location'].gsub(' .*', '')
print(transformed_frame)
assert transformed_frame.ncols == 1
assert transformed_frame.nrows == original_frame.nrows
assert transformed_frame.type('C1') == 'enum'
assert transformed_frame['C1'].categories() == expected_categories
# Test gsub without changing the cardinality
data = [['location'], ['ab'], ['ac'], ['ad'], ['ae'], ['af']]
original_frame = H2OFrame(data, header=True, column_types=['enum'])
assert original_frame.type('location') == 'enum'
assert original_frame.categories() == ['ab', 'ac', 'ad', 'ae', 'af']
expected_categories = ['b', 'c', 'd', 'e', 'f']
transformed_frame = original_frame['location'].gsub('a', '')
print(transformed_frame)
assert transformed_frame.ncols == 1
assert transformed_frame.nrows == original_frame.nrows
assert transformed_frame.type('C1') == 'enum'
assert transformed_frame['C1'].categories() == expected_categories
def process_w2v(df, w2v_model):
""" returns new df with text-features all replaced by word2vec features """
print("processind data with word2vec ...")
df = df.copy()
text_columns = w2v_model.text_columns
df_text = df[text_columns]
text_frame = H2OFrame(df_text)
for col in text_columns:
text_frame[col] = text_frame[col].ascharacter()
words = text_frame.tokenize(" ")
text_feats = w2v_model.transform(words, aggregate_method = "AVERAGE")
text_feats = text_feats.as_data_frame()
df.drop(columns=text_columns, inplace=True)
return pd.concat([df,text_feats], axis=1).reset_index()
def predict(self, x):
if isinstance(self.estimator, h2o.estimators.H2OEstimator):
column_types_x = get_h2o_column_types(x.columns)
x = H2OFrame(x, column_types=column_types_x)
results = pd.DataFrame(index=range(len(x)))
i = 0
for estimator in self.estimators_:
predictions = estimator.predict(x)
results[i] = predictions['predict'].as_data_frame().values
i += 1
else:
results = np.array(
[estimator.predict(x) for estimator in self.estimators_]).T
results = pd.DataFrame(results)
y_pred = decode_users(results, self.dict_code_user)
return np.array(y_pred)
def Predict(self, request: IrisRequest, context):
if not hasattr(request, 'SepalLength') or not hasattr(request, 'SepalWidth') \
or not hasattr(request, 'PetalLength') or not hasattr(request, 'PetalWidth'):
msg = 'wrong arguments for IrisRequest'
context.set_details(msg)
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
test_data = H2OFrame({
"SepalLength": request.SepalLength,
"SepalWidth": request.SepalWidth,
"PetalLength": request.PetalLength,
"PetalWidth": request.PetalWidth
})
prediction = self.model.predict(test_data).getrow()
species = SPECIES.get(np.argmax(prediction))
return IrisReply(species=species)
def merge_ages(frame, ages):
df = frame.merge(ages, all_x=True).sort('PassengerId').as_data_frame()
missing_rows = df['Age'].isna()
df.loc[missing_rows, 'Age'] = df.loc[missing_rows, 'predict']
# For odds turns of fate, need to convert the response var back to factor
# exactly here. (If the Pandas frame is converted to an H2O frame, the
# response column becomes a real number instead of an integer and H2O can
# convert integers to factors, but it cannot convert real numbers to factors.
df['Survived_factor'] = df['Survived_factor'].astype('category')
merged_frame = H2OFrame(df)
# Somehow, the columns, some columns get corrupted in by the merge
copy_df = h2o.deep_copy(merged_frame, 'copy_df')
copy_df['Age'] = merged_frame.pop('Age')
return copy_df.drop('predict')
def grouped_kfold(frame: H2OFrame, n_folds: int, src_col_name: str, dest_col_name='_kfold', seed=-1, remove_frame=True):
src_col_frame = frame[src_col_name]
group_col_uniq = src_col_frame.unique()
print(f"kfold group unique val count:: {src_col_name}, {group_col_uniq.nrows}")
kfold_col_frame = group_col_uniq.kfold_column(n_folds, seed)
group_col_kfold = group_col_uniq.cbind(kfold_col_frame)
group_col_kfold_named = group_col_kfold.set_names([src_col_name, dest_col_name])
kfold_frame = frame.merge(group_col_kfold_named)
# force eval...
print(f"merged frame id: {kfold_frame.frame_id}")
remove_frames([src_col_frame, group_col_uniq, group_col_kfold, kfold_col_frame, group_col_kfold_named])
if remove_frame:
h2o.remove(frame.frame_id)
return kfold_frame
def build_audit_h2o(classifier, name):
mapper = DataFrameMapper(
[([column], ContinuousDomain()) for column in ["Age", "Hours", "Income"]] +
[([column], CategoricalDomain()) for column in ["Employment", "Education", "Marital", "Occupation", "Gender", "Deductions"]]
)
pipeline = PMMLPipeline([
("mapper", mapper),
("uploader", H2OFrameCreator()),
("classifier", classifier)
])
pipeline.fit(audit_X, H2OFrame(audit_y.to_frame(), column_types = ["categorical"]))
pipeline.verify(audit_X.sample(frac = 0.05, random_state = 13))
classifier = pipeline._final_estimator
store_mojo(classifier, name)
store_pkl(pipeline, name)
adjusted = pipeline.predict(audit_X)
adjusted.set_names(["h2o(Adjusted)", "probability(0)", "probability(1)"])
store_csv(adjusted.as_data_frame(), name)
def predict_row(row, node):
if node.classifier:
if isinstance(node.classifier, h2o.estimators.H2OEstimator):
if not isinstance(row, H2OFrame):
column_types_row = get_h2o_column_types(row.columns)
row = H2OFrame(row, column_types=column_types_row)
prediction = node.classifier.predict(row)
prediction = np.concatenate(
prediction['predict'].as_data_frame().values)
else:
prediction = node.classifier.predict(row)
if prediction[0] == 0:
prediction = predict_row(row, node.left_node)
else:
prediction = predict_row(row, node.right_node)
else:
return list(node.classes)[0]
return prediction
def pubdev_6534():
df_data = [["D", "E", "NA", "NA"], ["1", "A", "NA", "NA"]]
df = H2OFrame.from_python(df_data,
column_types=['factor'] * 4,
na_strings=["NA"])
assert df.type("C1") == "enum"
assert df.type("C2") == "enum"
assert df.type("C3") == "int"
assert df.type("C4") == "int"
# convert empty col to enum
df['C3'] = df['C3'].asfactor()
# convert empty cols to char
df['C4'] = df['C4'].ascharacter()
print(df)
assert df.type("C3") == "enum"
assert df.type("C4") == "string"
def _prepare_one_hot(file, y, exclude_cols=None):
if exclude_cols is None:
exclude_cols = []
dir_path = os.path.dirname(os.path.realpath(__file__))
frame = h2o.import_file(dir_path + "/" + file)
train, test = frame.split_frame([0.95], seed=42)
cols_to_encode = []
other_cols = []
for name, ctype in test.types.items():
if name == y or name in exclude_cols:
pass
elif ctype == "enum":
cols_to_encode.append(name)
else:
other_cols.append(name)
train_frame = train.as_data_frame()
train_encode = train_frame.loc[:, cols_to_encode]
train_other = train_frame.loc[:, other_cols + [y]]
enc = OneHotEncoder(categories='auto', handle_unknown='ignore')
enc.fit(train_encode)
colnames = []
for cidx in range(len(cols_to_encode)):
for val in enc.categories_[cidx]:
colnames.append(cols_to_encode[cidx] + "." + val)
train_encoded = enc.transform(train_encode.values).toarray()
train_encoded = pd.DataFrame(train_encoded)
train_encoded.columns = colnames
train = train_other.join(train_encoded)
train = H2OFrame(train)
test_frame = test.as_data_frame()
test_encode = test_frame.loc[:, cols_to_encode]
test_other = test_frame.loc[:, other_cols]
test_encoded = enc.transform(test_encode.values).toarray()
test_encoded = pd.DataFrame(test_encoded)
test_encoded.columns = colnames
test = test_other.join(test_encoded)
return train, test
def model_performance(self, test_data=None):
"""
Compute the binary classifier model metrics on `test_data`
:param test_data: An H2OFrame
:return: A H2OBinomialMetrics object; prints model metrics summary
"""
if not test_data:
raise ValueError("Missing`test_data`.")
if not isinstance(test_data, H2OFrame):
raise ValueError("`test_data` must be of type H2OFrame. Got: " +
type(test_data))
fr_key = H2OFrame.send_frame(test_data)
url_suffix = "ModelMetrics/models/" + self._key + "/frames/" + fr_key
res = H2OConnection.post_json(url_suffix=url_suffix)
raw_metrics = res["model_metrics"][0]
return H2OBinomialModelMetrics(raw_metrics)
def test_fold_optimization_rbind_expr():
data0 = square_matrix(3, 0)
data1 = square_matrix(3, 1)
data2 = square_matrix(3, 2)
def get_expr():
return ExprNode("rbind", ExprNode("rbind", ExprNode("rbind", data0, data1), data0, data1),
data2)
(expr, _) = _assert_expr_results_eq(get_expr)
assert expr._op == "rbind", "Result operator is still cbind"
assert len(expr._children) == 5, "Results has 5 arguments"
fr = H2OFrame._expr(expr)
assert fr.dim == [15, 3]
assert fr.as_data_frame(use_pandas=False, header=False) == [['0'] * 3, ['0'] * 3, ['0'] * 3,
['1'] * 3, ['1'] * 3, ['1'] * 3,
['0'] * 3, ['0'] * 3, ['0'] * 3,
['1'] * 3, ['1'] * 3, ['1'] * 3,
['2'] * 3, ['2'] * 3, ['2'] * 3]
def classify(x_train, y_train, estimator, x_test):
"""
Make the classification and provide the result for the given estimator.
For the H2O library, the transformation in H2oFrame is integrated
:param x_train: the dataset for training
:param y_train: the classes for training
:param estimator: the estimator to be considered
:param x_test: the dataset for testing
:return: - the prediction for the x_test
- the trained estimator
"""
if isinstance(estimator, h2o.estimators.H2OEstimator):
current_estimator = fit_h2o(x_train, y_train, estimator)
column_types_x = get_h2o_column_types(x_test.columns)
x_test = H2OFrame(x_test, column_types=column_types_x)
prediction = current_estimator.predict(x_test)
return np.concatenate(
prediction['predict'].as_data_frame().values), current_estimator
else:
current_estimator = clone(estimator)
current_estimator.fit(x_train, y_train)
return current_estimator.predict(x_test), current_estimator
def pubdev_6393():
locations = [['location'],
['�Ｘ県 Ａ市 '], # First observation contains replacement character for unknown char
['Ｘ県 Ｂ市']]
frame = H2OFrame(locations, header=True, column_types=['enum'])
assert frame.ncols == 1
assert frame.nrows == len(locations) - 1
frame_categories= frame['location'].categories()
print(frame_categories)
frame_converted = frame['location'].ascharacter().asfactor()
assert frame_converted.ncols == 1
assert frame_converted.nrows == len(locations) - 1
frame_converted_categories = frame_converted.categories();
print(frame_converted_categories)
# Check for the representation of categoricals to be exactly the same
# No explicit check for any specific behavior, the behavior of Categorical and asFactor should be the same
for i in range(0,len(frame_converted_categories)):
assert frame_categories[i] == frame_converted_categories[i]
def pubdev_6439():
data = [
['C1'],
[
'Ｘ県 Ａ市 '
], # First observation contains replacement character for unknown char
['Ｘ県 Ｂ市']
]
frame = H2OFrame(data, header=True, column_types=['enum'])
frame_categories = frame['C1'].categories()
print(frame_categories)
# Two observations
assert len(frame_categories) == 2
assert len(
frame_categories[0]
) == 6 # First observation has six characters (space at the end)
assert len(
frame_categories[1]
) == 5 # Second observation has 5 characters (missing space at the end)
# Python 2 and 3 handle strings differently
if (sys.version_info[0] == 3):
assert ''.join(data[1]) == frame_categories[
0] # First categorical level equals to first observation
assert ''.join(data[2]) == frame_categories[
1] # Second categorical levels equals to second observation
elif (sys.version_info[0] == 2):
assert ''.join(data[1]).decode("utf-8") == frame_categories[
0] # First categorical level equals to first observation
assert ''.join(data[2]).decode("utf-8") == frame_categories[
1] # Second categorical levels equals to second observation
else:
assert False
def train_w2v(df, epochs=None, save_dir=None):
""" trains word2vec model on all text columns of df.
Returns w2v model object that can transform data.
"""
print("training word2vec model ...")
args = {}
if epochs is not None:
args['epochs'] = int(epochs)
if save_dir is not None:
args['export_checkpoints_dir'] = os.path.join(save_dir,"h2o_model/")
df = df.copy()
text_columns = get_text_cols(df)
print("Text columns are: ", text_columns)
df_text = df[text_columns]
text_frame = H2OFrame(df_text)
for col in text_columns:
text_frame[col] = text_frame[col].ascharacter()
words = text_frame.tokenize(" ")
w2v_model = H2OWord2vecEstimator(sent_sample_rate = 0.0, **args)
w2v_model.train(training_frame=words)
w2v_model.text_columns = text_columns
return w2v_model
#!/usr/bin/env python
from h2o import H2OFrame
import h2o as h2o
localH2O = h2o.init()
air = H2OFrame.from_csv(localH2O, "allyears_tiny.csv", index_col = False)
print(air.head())
air['RandNum'] = air.random.uniform()
print(air.head())
air_train = air.ix[air['RandNum'] <= 0.8]
air_valid = air.ix[(air['RandNum'] > 0.8) & (air['RandNum'] <= 0.9)]
air_test = air.ix[air['RandNum'] > 0.9]
myX = ["Origin", "Dest", "Distance", "UniqueCarrier", "Month", "DayofMonth", "DayOfWeek"]
myY = "IsDepDelayed"
air_gbm = h2o.gbm(x = myX, y = myY, data = air_train, validation = air_valid,
distribution = "multinomial",
n_trees = 10, interaction_depth = 3, shrinkage = 0.01,
importance = True)
print(air_gbm)
pred = h2o.predict(air_gbm, air_test)
print(pred.head())
#!/usr/bin/env python
from h2o import H2OFrame, H2OModel
import h2o as h2o
localH2O = h2o.init()
air = H2OFrame.from_csv(localH2O, "allyears_tiny.csv", index_col = False)
air.head().print()
X_air = air['Origin', 'Dest', 'Distance', 'UniqueCarrier', 'Month', 'DayofMonth', 'DayOfWeek']
y_air = air['IsDepDelayed']
X_air_train, X_air_valid, X_air_test, y_air_train, y_air_valid, y_air_test = \
H2OFrame.train_valid_test(X_air, y_air, valid_size = 0.1, test_size = 0.1)
my_gbm = H2OModel.GBM(distribution = "multinomial", n_trees = 10,
interaction_depth = 3, shrinkage = 0.01,
importance = True)
air_gbm = my_gbm.fit(x=X_air_train, y=y_air_train, x_valid=X_air_valid, y_valid=y_air_valid)
air_gbm.print()
pred = air_gbm.predict(X_air_test)
pred.head().print()
def temp_ctr(): return H2OFrame.temp_ctr() def rest_ctr(): return h2o.H2OConnection.rest_ctr()