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        "author": "Blake Anderson",
        "author_email": "blakebanders@gmail.com",
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        "description": "### pyH2oMojo\n\n#### Unofficial Python wrapper for H2o MOJO's\n\nA lightweight python wrapper around an H2o EasyPredictModelWrapper instance\n\nInstantiating the object will launch the bundled Jar file, and establish a port on localhost on which to communicate with.\n\nDictionaries or JSON strings are passed to the predictor instance over a socket, and the output is read back via the subprocess' stdout pipe.\n\n#### Usage\n\n```\nfrom pyH2oMojo import H2oMojoPredictor\n# pass at minimum, the filename of the MOJO, and the predictor type\npredictor = H2oMojoPredictor(\"my_nn.zip\", \"multivariate\", verbose=True)\n\nprint(H2oMojoPredictor.predict({\"sepal_length\":4.9, \"sepal_width\":3.0, \"petal_length\":1.4,\"petal_width\":0.2}))\n>>> {\"prediction\":\"Iris-setosa\", \"predictionIndex\":1, \"classProbabilities\":[0.0, 0.944, 0.056]}\n\nprint(H2oMojoPredictor.supported_predictors())\n>>> [\"multivariate\", \"regression\", \"ordinal\", \"binomial\", \"autoencoder\", \"clustering\", \"dimreduction\"]\n\n# other constructor parameters include:\n# x_cols=None # List of columns to be passed to predict() - by default everything is passed\n# x_types=None # Dictionary of column name and types ('int', 'real', 'str') - these values will be converted before being sent to the model. \n# connection_timeout=10.0 # Number of seconds to wait for the Java subprocess to start before raising a runtime error\n# prediction_timeout=3.0 # Number of seconds to wait for a response from the Java subprocess before raising a runtime error\n```\n\n",
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