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Revise path logic and move models and data to top-level #30
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@@ -36,7 +36,7 @@ def recsys_metric(recommendations, row_index, news_struuid_ID): | |||
# use the url of Article | |||
impressions_truth = ( | |||
pd.read_table( | |||
src_dir() / "data" / "test_mind_large" / "behaviors.tsv", | |||
f"{project_root()}/data/test_mind_large/behaviors.tsv", |
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Let's keep the pathlib style here
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This updates the path-location logic and rearranges the repository structure to be more logical and reduce wasted space during Docker image build:
src
,data
,models
directories for source code, input data, and model checkpoints, respectively.model_file_path
function to locate a model file, checking in aPOPROX_MODELS
environment variable, the project root, and the Conda environment prefix, in that order. This will enable POPROX model data to be stored in a data location during deployment.project_root
and makes it search based on the current working directory, looking forpyproject.toml
, so that it can work seamlessly regardless of whether and howpoprox_recommenders
has been installed.It also adds tests for the
paths
module, and depends on #31 and the docker tests to make sure none of this breaks our code in a way that can be detected at test time.