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102 Using brainome CLI

Brainome’s primary interface is the command line.

  1. brainome command line –help

  2. CLI documentation in depth


This notebook assumes brainome as installed per notebook brainome_101_Quick_Start

!python3 -m pip install brainome --quiet
!brainome --version
brainome v1.006-19-prod

1. brainome help

Ever forget a command parameter? Want to know what else we can do?

!brainome --help
usage: brainome [-h] [-version] [-headerless] [-target TARGET]
                [-ignorecolumns IGNORECOLUMNS] [-rank [ATTRIBUTERANK]]
                [-measureonly] [-f FORCEMODEL] [-nosplit] [-split FORCESPLIT]
                [-nsamples NSAMPLES] [-ignoreclasses IGNORELABELS]
                [-usecolumns IMPORTANTCOLUMNS] [-o OUTPUT] [-v] [-q] [-y]
                [-e EFFORT] [-biasmeter] [-novalidation] [-balance]
                [-O OPTIMIZE] [-nofun] [-modelonly]
                input [input ...]

Brainome Table Compiler (tm)  v1.006-19-prod

Required arguments:
  input                 Table as CSV files and/or URLs or Command above

Optional arguments:
  -h                    show this help message and exit
  -version, --version   show program's version number and exit

Basic options:
  -headerless           Headerless CSV input file.
  -target TARGET        Specify target column by name or number. Default: last column of table.
  -ignorecolumns IGNORECOLUMNS
                        Comma-separated list of columns to ignore by name or number.
                        Select the optimal subset of columns for accuracy on held out data
                        If optional parameter N is given, select the optimal N columns. Works best for DT.
  -measureonly          Only output measurements, no predictor is built.
  -f FORCEMODEL         Force model type: DT, NN, RF  Default: RF
  -nosplit              Use all of the data for training. Default: dataset is split between training and validation.
  -split FORCESPLIT     Pass it an integer between 50 and 90 telling our system to use that percent of the data for training, and the rest for validation

Intermediate options:
  -nsamples NSAMPLES    Train only on a subset of N random samples of the dataset. Default: entire dataset.
  -ignoreclasses IGNORELABELS
                        Comma-separated list of classes to ignore.
                        Comma-separated list of columns by name or number used to build the predictor.
  -o OUTPUT             Predictor filename. Default:
  -v                    Verbose output
  -q                    Quiet operation.
  -y                    Answers yes to all overwrite questions.

Advanced options:
  -e EFFORT             Increase compute time to improve accuracy. 1=<EFFORT<100. Default: 1
  -biasmeter            Measure model bias
  -novalidation         Do not measure validation scores for created predictor.
  -balance              Treat classes as if they were balanced (only active for NN).
  -O OPTIMIZE           Maximize true positives towards a single class.
  -nofun                Stop compilation if there are warnings.
  -modelonly            Perform only the measurements needed to build the model.

Measure and build a random forest predictor for titanic

Build a better predictor by ignoring columns:
	brainome titanic_train.csv -ignorecolumns "PassengerId,Name" -target Survived 

Automatically select the important columns by using ranking:
	brainome titanic_train.csv -rank -target Survived 

Build a neural network model with effort of 5:
	brainome titanic_train.csv -f NN -e 5 -target Survived

Measure headerless dataset:
	brainome -headerless -measureonly

Full documentation can be found at

2. CLI documentation in depth

Additional documentation can be found at