def main():
bi.init("Docs Json", "../../../h2o-docs", clear_dir=False)
bi.vprint("Writing schemas.json...")
bi.write_to_file("schemas.json", json.dumps(bi.schemas(raw=True)))
bi.vprint("Writing routes.json...")
bi.write_to_file("routes.json", json.dumps(bi.endpoints(raw=True)))
def main():
bi.init("Thrift", "thrift")
schemas_map = bi.schemas_map()
ordered_schemas = OrderedDict()
for name, schema in schemas_map.items():
add_schema_to_dependency_array(schema, ordered_schemas, schemas_map)
bi.write_to_file("water/bindings/structs/H2O.thrift", generate_thrift(ordered_schemas))
type_adapter.vprint_translation_map()
def main():
bi.init("Python", "python")
for name, mb in bi.model_builders().items():
module = name
if name == "drf": module = "random_forest"
if name == "naivebayes": module = "naive_bayes"
bi.vprint("Generating model: " + name)
bi.write_to_file("%s.py" % module, gen_module(mb, name))
type_adapter.vprint_translation_map()
def main():
bi.init("C#", "CSharp")
for schema in bi.schemas():
name = schema["name"]
bi.vprint("Generating schema: " + name)
bi.write_to_file("h2o/%s.cs" % name, generate_schema(name, schema))
for name, values in bi.enums().items():
bi.vprint("Generating enum: " + name)
bi.write_to_file("h2o/%s.cs" % name, generate_enum(name, sorted(values)))
type_adapter.vprint_translation_map()
def main():
bi.init("Python", "../../../h2o-py/h2o/estimators", clear_dir=False)
modules = [("deeplearning", "H2OAutoEncoderEstimator")] # deeplearning module contains 2 classes in it...
for name, mb in bi.model_builders().items():
module = name
if name == "drf": module = "random_forest"
if name == "naivebayes": module = "naive_bayes"
bi.vprint("Generating model: " + name)
bi.write_to_file("%s.py" % module, gen_module(mb, name))
modules.append((module, algo_to_classname(name)))
bi.write_to_file("__init__.py", gen_init(modules))
type_adapter.vprint_translation_map()
def main():
bi.init("R", "../../../h2o-r/h2o-package/R", clear_dir=False)
for name, mb in bi.model_builders().items():
if name in ["aggregator"]:
continue
module = name
file_name = name
if name == "drf":
module = "randomForest"
file_name = "randomforest"
if name == "naivebayes": module = "naiveBayes"
if name == "stackedensemble": module = "stackedEnsemble"
if name == "pca": module = "prcomp"
bi.vprint("Generating model: " + name)
bi.write_to_file("%s.R" % file_name, gen_module(mb, name, module))
def main():
bi.init("Java", "java")
for schema in bi.schemas():
name = schema["name"]
bi.vprint("Generating schema: " + name)
bi.write_to_file("water/bindings/pojos/%s.java" % name, generate_schema(name, schema))
for name, values in bi.enums().items():
bi.vprint("Generating enum: " + name)
bi.write_to_file("water/bindings/pojos/%s.java" % name, generate_enum(name, sorted(values)))
for name, endpoints in bi.endpoint_groups().items():
bi.vprint("Generating proxy: " + name)
bi.write_to_file("water/bindings/proxies/retrofit/%s.java" % name, generate_proxy(name, endpoints))
bi.vprint("Generating H2oApi.java")
bi.write_to_file("water/bindings/H2oApi.java", generate_main_class(bi.endpoints()))
type_adapter.vprint_translation_map()
def main():
bi.init("Python", "../../../h2o-py/h2o/estimators", clear_dir=False)
modules = [("deeplearning", "H2OAutoEncoderEstimator", "Unsupervised"),
("estimator_base", "H2OEstimator", "Miscellaneous"),
("grid_search", "H2OGridSearch", "Miscellaneous"),
("automl", "H2OAutoML", "Miscellaneous")]
builders = filter(lambda b: b[0] != 'coxph', bi.model_builders().items()) # CoxPH is not supported in Python yet
for name, mb in builders:
module = name
if name == "drf": module = "random_forest"
if name == "naivebayes": module = "naive_bayes"
bi.vprint("Generating model: " + name)
bi.write_to_file("%s.py" % module, gen_module(mb, name))
category = "Supervised" if mb["supervised"] else "Unsupervised"
if name in {"svd", "word2vec"}:
category = "Miscellaneous"
modules.append((module, algo_to_classname(name), category))
bi.write_to_file("__init__.py", gen_init(modules))
bi.write_to_file("../../docs/modeling.rst", gen_models_docs(modules))
type_adapter1.vprint_translation_map()
def main():
bi.init("Python", "../../../h2o-py/h2o/estimators", clear_dir=False)
modules = [("deeplearning", "H2OAutoEncoderEstimator", "Unsupervised"),
("estimator_base", "H2OEstimator", "Miscellaneous"),
("grid_search", "H2OGridSearch", "Miscellaneous"),
("automl", "H2OAutoML", "Miscellaneous")]
builders = bi.model_builders().items()
for name, mb in builders:
module = name
if name == "drf": module = "random_forest"
if name == "naivebayes": module = "naive_bayes"
if name == "isolationforest": module = "isolation_forest"
bi.vprint("Generating model: " + name)
bi.write_to_file("%s.py" % module, gen_module(mb, name))
category = "Supervised" if mb["supervised"] else "Unsupervised"
if name in {"svd", "word2vec"}:
category = "Miscellaneous"
modules.append((module, algo_to_classname(name), category))
bi.write_to_file("__init__.py", gen_init(modules))
bi.write_to_file("../../docs/modeling.rst", gen_models_docs(modules))
type_adapter1.vprint_translation_map()
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import unicode_literals
import json
import bindings as bi
if __name__ == "__main__":
bi.init("Docs Json", "../../../h2o-docs", clear_dir=False)
bi.vprint("Writing schemas.json...")
bi.write_to_file("schemas.json", json.dumps(bi.schemas(raw=True)))
bi.vprint("Writing routes.json...")
bi.write_to_file("routes.json", json.dumps(bi.endpoints(raw=True)))
yield " * This file is auto-generated by h2o-3/h2o-bindings/bin/gen_csharp.py"
yield " * Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)"
yield " */"
yield "namespace ai.h2o"
yield "{"
yield " public enum " + name + " {"
for value in values:
yield " %s," % value
yield " }"
yield "}"
# ----------------------------------------------------------------------------------------------------------------------
# MAIN:
# ----------------------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
bi.init("C#", "CSharp")
type_adapter = CSharpTypeTranslator()
for schema in bi.schemas():
name = schema["name"]
bi.vprint("Generating schema: " + name)
bi.write_to_file("h2o/%s.cs" % name, generate_schema(name, schema))
for name, values in bi.enums().items():
bi.vprint("Generating enum: " + name)
bi.write_to_file("h2o/%s.cs" % name,
generate_enum(name, sorted(values)))
type_adapter.vprint_translation_map()
yield "/**"
yield " * This file is auto-generated by h2o-3/h2o-bindings/bin/gen_csharp.py"
yield " * Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)"
yield " */"
yield "namespace ai.h2o"
yield "{"
yield " public enum " + name + " {"
for value in values:
yield " %s," % value
yield " }"
yield "}"
# ----------------------------------------------------------------------------------------------------------------------
# MAIN:
# ----------------------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
bi.init("C#", "CSharp")
type_adapter = CSharpTypeTranslator()
for schema in bi.schemas():
name = schema["name"]
bi.vprint("Generating schema: " + name)
bi.write_to_file("h2o/%s.cs" % name, generate_schema(name, schema))
for name, values in bi.enums().items():
bi.vprint("Generating enum: " + name)
bi.write_to_file("h2o/%s.cs" % name, generate_enum(name, sorted(values)))
type_adapter.vprint_translation_map()
name = field["name"]
if name in thrift_reserved_words:
name += "_"
required = "required" if field["required"] else "optional"
yield bi.wrap(field["help"], indent=" # ")
yield " {num}: {req} {type} {name},".format(num=i,
req=required,
type=thrift_type,
name=name)
yield ""
yield "}"
yield ""
# ----------------------------------------------------------------------------------------------------------------------
# MAIN
# ----------------------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
bi.init("Thrift", "thrift")
type_adapter = ThriftTypeTranslator()
schemas_map = bi.schemas_map()
ordered_schemas = OrderedDict()
for name, schema in schemas_map.items():
add_schema_to_dependency_array(schema, ordered_schemas, schemas_map)
bi.write_to_file("water/bindings/structs/H2O.thrift",
generate_thrift(ordered_schemas))
type_adapter.vprint_translation_map()
for i, field in enumerate(schema["fields"]):
if field["name"] == "__meta":
continue
thrift_type = translate_type(field["type"], field["schema_name"])
name = field["name"]
if name in thrift_reserved_words:
name += "_"
required = "required" if field["required"] else "optional"
yield bi.wrap(field["help"], indent=" # ")
yield " {num}: {req} {type} {name},".format(num=i, req=required, type=thrift_type, name=name)
yield ""
yield "}"
yield ""
# ----------------------------------------------------------------------------------------------------------------------
# MAIN
# ----------------------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
bi.init("Thrift", "thrift")
type_adapter = ThriftTypeTranslator()
schemas_map = bi.schemas_map()
ordered_schemas = OrderedDict()
for name, schema in schemas_map.items():
add_schema_to_dependency_array(schema, ordered_schemas, schemas_map)
bi.write_to_file("water/bindings/structs/H2O.thrift", generate_thrift(ordered_schemas))
type_adapter.vprint_translation_map()