Edit DVC deps to include cmd run files

dvc.yaml

@@ -3,6 +3,8 @@ stages:
     cmd: Rscript pipeline/00-ingest.R
     desc: >
       Ingest training and assessment data from Athena + generate condo strata
+    deps:
+    - pipeline/00-ingest.R
     params:
       - assessment
       - input
@@ -20,6 +22,8 @@ stages:
       Train a LightGBM model with cross-validation. Generate model objects,
       data recipes, and predictions on the test set (most recent 10% of sales)
     deps:
+    - pipeline/01-train.R
+    - input/training_data.parquet
       - input/training_data.parquet
     params:
       - cv
@@ -55,6 +59,13 @@ stages:
       County. Also generate flags, calculate land values, and make any
       post-modeling changes
     deps:
+    - pipeline/02-assess.R
+    - input/assessment_data.parquet
+    - input/condo_strata_data.parquet
+    - input/land_nbhd_rate_data.parquet
+    - input/training_data.parquet
+    - output/workflow/fit/model_workflow_fit.zip
+    - output/workflow/recipe/model_workflow_recipe.rds
       - input/assessment_data.parquet
       - input/condo_strata_data.parquet
       - input/land_nbhd_rate_data.parquet
@@ -82,6 +93,9 @@ stages:
         2. An assessor-specific ratio study comparing estimated assessments to
            the previous year's sales
     deps:
+    - pipeline/03-evaluate.R
+    - output/assessment_pin/model_assessment_pin.parquet
+    - output/test_card/model_test_card.parquet
       - output/assessment_pin/model_assessment_pin.parquet
       - output/test_card/model_test_card.parquet
     params:
@@ -105,6 +119,10 @@ stages:
       Generate SHAP values for each card and feature as well as feature
       importance metrics for each feature
     deps:
+    - pipeline/04-interpret.R
+    - input/assessment_data.parquet
+    - output/workflow/fit/model_workflow_fit.zip
+    - output/workflow/recipe/model_workflow_recipe.rds
       - input/assessment_data.parquet
       - output/workflow/fit/model_workflow_fit.zip
       - output/workflow/recipe/model_workflow_recipe.rds
@@ -125,6 +143,11 @@ stages:
       Save run timings and run metadata to disk and render a performance report
       using Quarto.
     deps:
+    - pipeline/05-finalize.R
+    - output/intermediate/timing/model_timing_train.parquet
+    - output/intermediate/timing/model_timing_assess.parquet
+    - output/intermediate/timing/model_timing_evaluate.parquet
+    - output/intermediate/timing/model_timing_interpret.parquet
       - output/intermediate/timing/model_timing_train.parquet
       - output/intermediate/timing/model_timing_assess.parquet
       - output/intermediate/timing/model_timing_evaluate.parquet
@@ -155,6 +178,24 @@ stages:
       outputs prior to upload and attach a unique run ID. This step requires
       access to the CCAO Data AWS account, and so is assumed to be internal-only
     deps:
+    - pipeline/06-upload.R
+    - output/parameter_final/model_parameter_final.parquet
+    - output/parameter_range/model_parameter_range.parquet
+    - output/parameter_search/model_parameter_search.parquet
+    - output/workflow/fit/model_workflow_fit.zip
+    - output/workflow/recipe/model_workflow_recipe.rds
+    - output/test_card/model_test_card.parquet
+    - output/assessment_card/model_assessment_card.parquet
+    - output/assessment_pin/model_assessment_pin.parquet
+    - output/performance/model_performance_test.parquet
+    - output/performance_quantile/model_performance_quantile_test.parquet
+    - output/performance/model_performance_assessment.parquet
+    - output/performance_quantile/model_performance_quantile_assessment.parquet
+    - output/shap/model_shap.parquet
+    - output/feature_importance/model_feature_importance.parquet
+    - output/metadata/model_metadata.parquet
+    - output/timing/model_timing.parquet
+    - reports/performance/performance.html
       - output/parameter_final/model_parameter_final.parquet
       - output/parameter_range/model_parameter_range.parquet
       - output/parameter_search/model_parameter_search.parquet
@@ -179,6 +220,8 @@ stages:
       Generate Desk Review spreadsheets and iasWorld upload CSVs from a finished
       run. NOT automatically run since it is typically only run once. Manually
       run once a model is selected
+    deps:
+    - pipeline/07-export.R
     params:
       - assessment.year
       - input.min_sale_year