stypes # (stype.float64, stype.float64) # convert datatable results to common data types # res.to_pandas() # need pandas # res.to_numpy() # need numpy res. names # ('label.B', 'label.M') # retrieve the prediction column types res. stypes # (stype.float64, # stype.float64, # stype.float64, # stype.float64, # stype.float64, # stype.float64, # stype.float64, # stype.float64, # stype.float64) # make predictions on the example.csv file res = m. missing_values, header = True, separator = ',' ) pydt # clump_thickness uniformity_cell_size uniformity_cell_shape marginal_adhesion single_epithelial_cell_size bare_nuclei bland_chromatin normal_nucleoli mitoses # 0 8 1 3 10 6 6 9 1 1 # 1 2 1 2 2 5 3 4 8 8 # 2 1 1 1 9 4 10 3 5 4 # 3 2 6 9 10 4 8 1 1 3 # 4 10 10 8 1 8 3 6 3 4 # 5 1 8 4 5 10 1 2 5 3 # 6 2 10 2 9 1 2 9 3 8 # 7 2 8 9 2 10 10 3 5 4 # 8 6 3 8 5 2 3 5 3 4 # 9 4 2 2 8 1 2 8 9 1 # retrieve the column types pydt. fread ( "./mojo-pipeline/example.csv", na_strings = m. output_types # import the datatable module import datatable as dt # parse the example.csv file pydt = dt. output_names # retrieve the output types m. feature_types # retrieve the output names m. feature_names # retrieve the feature types m. missing_values # retrieve the feature names m.
uuid # retrieve a list of missing values m. created_time # 'Mon November 18 14:00:24 2019' # retrieve the UUID of the experiment m. model ( "./mojo-pipeline/pipeline.mojo" ) # retrieve the creation time of the MOJO m. # import the daimojo model package import daimojo.model # specify the location of the MOJO m = daimojo.
#JAVA RUNTIME ENVIRONMENT MAC 1.1.1 CODE#
Driverless AI k-LIME MOJO Reason Code Pipeline - Java Runtime.Downloading the Scoring Pipeline Runtimes.Driverless AI MOJO Scoring Pipeline - C++ Runtime with Python and R Wrappers.Driverless AI MOJO Scoring Pipeline - Java Runtime.Driverless AI MLI Standalone Python Scoring Package.Driverless AI Standalone Python Scoring Pipeline.
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