102 Using brainome CLI
Contents
102 Using brainome CLI¶
Brainome’s primary interface is the command line.
brainome command line –help
CLI documentation in depth
Prerequisites¶
This notebook assumes brainome as installed per notebook brainome_101_Quick_Start
!python3 -m pip install brainome --quiet
!brainome --version
WARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.10/x64/bin/python3 -m pip install --upgrade pip' command.
/opt/hostedtoolcache/Python/3.9.10/x64/lib/python3.9/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
from pandas import MultiIndex, Int64Index
brainome v1.8-120-prod
1. brainome help¶
Ever forget a command parameter? Want to know what else we can do?
!brainome --help
/opt/hostedtoolcache/Python/3.9.10/x64/lib/python3.9/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
from pandas import MultiIndex, Int64Index
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] [-nofun] [-modelonly]
[-json JSON] [-C REGULARIZATION_STRENGTH]
input [input ...]
Brainome Table Compiler (tm) v1.8-120-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.
-rank [ATTRIBUTERANK]
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.
-usecolumns IMPORTANTCOLUMNS
Comma-separated list of columns by name or number used to build the predictor.
-o OUTPUT Predictor filename. Default: a.py
-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.
-nofun Stop compilation if there are warnings.
-modelonly Perform only the measurements needed to build the model.
-json JSON Document the session using json formatting.
-C REGULARIZATION_STRENGTH
Examples:
Measure and build a random forest predictor for titanic
brainome https://download.brainome.ai/data/public/titanic_train.csv
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 https://download.brainome.ai/data/public/bank.csv -headerless -measureonly
Full documentation can be found at https://www.brainome.ai/documentation
2. CLI documentation in depth¶
Additional documentation can be found at
Next steps¶
Check out 103 Model Selection