H2O mojo包装器-允许来自Python的预测，而不需要Web服务器开销。
pyH2oMojo的Python项目详细描述
PyH2Omojo
H2O Mojo的非官方Python包装器
围绕一个H2O EasyPredictModelWrapper实例的轻量级Python包装器
实例化对象将启动绑定的jar文件，并在本地主机上建立一个与之通信的端口。
字典或json字符串通过套接字传递给predictor实例，输出通过子进程的stdout管道读回。
用法
from pyH2oMojo import H2oMojoPredictor
# pass at minimum, the filename of the MOJO, and the predictor type
predictor = H2oMojoPredictor("my_nn.zip", "multivariate", verbose=True)
print(H2oMojoPredictor.predict({"sepal_length":4.9, "sepal_width":3.0, "petal_length":1.4,"petal_width":0.2}))
>>> {"prediction":"Iris-setosa", "predictionIndex":1, "classProbabilities":[0.0, 0.944, 0.056]}
print(H2oMojoPredictor.supported_predictors())
>>> ["multivariate", "regression", "ordinal", "binomial", "autoencoder", "clustering", "dimreduction"]
# other constructor parameters include:
# x_cols=None # List of columns to be passed to predict() - by default everything is passed
# x_types=None # Dictionary of column name and types ('int', 'real', 'str') - these values will be converted before being sent to the model.
# connection_timeout=10.0 # Number of seconds to wait for the Java subprocess to start before raising a runtime error
# prediction_timeout=3.0 # Number of seconds to wait for a response from the Java subprocess before raising a runtime error
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