The Pipeline: Feature building, modeling, and evaluation¶
There are three major steps to the pipeline: features building, modeling,
and model evaluation. Each step is a submodule of the pipeline
and has its
own run
command-line interface, designed to be run from the repository root
as python -m pipeline.component.run
with command-line arguments. Or all
three steps may be run with a single invocation of python -m pipeline.run
.
The -h
flag will show the help for each command. A broad overview of each
component is provided here, with more specific inline documentation in the code
and exposed as module documentation below.
Preprocessing: Feature building¶
The command python -m pipeline.preprocessing.run yamls/default_sample.yaml
will use the sample experiment configuration to build the required feature
table. The feature tables are timestamped with the time at which the command
was run.
Modeling¶
The command python -m pipeline.modeling.run yamls/default_sample.yaml
will
use the sample experiment configuration and the most recently created feature
tables in order to train all the models specified in the files at the given
splits.
Evaluation¶
The command python -m pipeline.evaluation.run
will evaluate all unprocessed
models it finds in the database and compute the metrics found in the default
evaluation configuration file.
Module contents¶
- pipeline.preprocessing package
- Subpackages
- pipeline.preprocessing.features package
- Submodules
- pipeline.preprocessing.features.abstract module
- pipeline.preprocessing.features.class_map module
- pipeline.preprocessing.features.emsfeatures module
- pipeline.preprocessing.features.jimsfeatures module
- pipeline.preprocessing.features.mentalhealthfeatures module
- pipeline.preprocessing.features.miscfeatures module
- pipeline.preprocessing.features.person module
- pipeline.preprocessing.features.rsifeatures module
- pipeline.preprocessing.features.seqfeatures module
- Module contents
- pipeline.preprocessing.features package
- Submodules
- pipeline.preprocessing.feature_processor module
- pipeline.preprocessing.feature_table_builder module
- pipeline.preprocessing.run module
- Module contents
- Subpackages
- pipeline.evaluation package
- pipeline.modeling package