Refactor pipeline to support multiple advice-enabled models per field and disease
- Updated the job structure to include `anmod_id`, allowing for multiple mathematical models to be processed for each field and disease combination. - Enhanced the batch processing logic to discover and execute jobs for `(field, disease, anmod_id)` instead of just `(field, disease)`. - Modified the output file naming convention to include `anmod_id`, ensuring unique output for each model. - Adjusted database interactions to accommodate the new primary key structure based on `pathogen_model_id`. - Updated relevant documentation in `README.md` to reflect changes in job discovery, execution, and output generation. - Enhanced unit tests to validate the new functionality and ensure correct behavior across various scenarios.
This commit is contained in:
Arsham Mirehvandi
parent
aec2758714
commit
ebc8ad26b6
69
README.md
69
README.md
@ -8,13 +8,15 @@ a local Weaviate `ProductProfile` collection. It then enriches that JSON with
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full product label information and the previous advisory, asks a second LLM to
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write the farmer-facing advice, and stores the result in the `advisories` table.
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Each morning's `batch` run discovers `(field, disease)` pairs from the database.
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A disease can generate advice when `AI_agrosupport_an_pathogen_models.pat_mod_AI_model`
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is 1; its name is `pat_mod_desc`. A field is included when
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Each morning's `batch` run discovers `(field, disease, anmod_id)` jobs from the
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database. A disease can generate advice when
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`AI_agrosupport_an_pathogen_models.pat_mod_AI_model` is 1; its name is
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`pat_mod_desc`. A field model is included when
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`AI_agrosupport_agro_models.anmod_AI_advice` is 1 and `anmod_model` matches that
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name. The cultivated crop is read from the field layer. The run then executes
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the sequence above for each pair — in parallel, with one field's failure never
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blocking another's.
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name. Several mathematical models for the same field and disease stay separate
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jobs, one per `anmod_id`. The cultivated crop is read from the field layer. The
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run then executes the sequence above for each job — in parallel, with one
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field's failure never blocking another's.
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## Prerequisites
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@ -64,7 +66,11 @@ joined to `AI_agrosupport_an_colture` and mapped through
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[`vocab/crops.yaml`](vocab/crops.yaml) (`Vite` → `grapevine`). The English
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disease name is mapped through [`vocab/diseases.yaml`](vocab/diseases.yaml)
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(`PERONOSPORA` → `downy mildew`). Those two English names select the prompt
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and the product filter. One agro-model row is one job.
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and the product filter. One agro-model row is one job. Its `anmod_id` is
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written to `advisories.pathogen_model_id`. The same id is `model_model` in
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`AI_agrosupport_agro_model_tmp2`, and the forecast uses `model_description`
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from that model only. A field with three advice-enabled models for one disease
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produces three advisories.
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To enable another disease, set both flags in SQL. Add a `pat_mod_desc` mapping
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to `vocab/diseases.yaml` if it is not already there (`peronospora` already is),
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@ -120,7 +126,7 @@ pair still falling back to `_default` at startup.
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```powershell
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.\.venv\Scripts\Activate.ps1
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# Daily entry point: every field/disease pair enabled for advice in the database
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# Daily entry point: every advice-enabled field, disease, and model in the database
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python -m pipeline batch
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# Historical / test dates (useful when the live window has no FASE2/FASE5/INCUBAZPRIMARIA/observation)
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@ -135,8 +141,10 @@ python -m pipeline batch --skip-existing-advice
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# Build every job's JSON but skip LLM calls, vector search, and the DB write
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python -m pipeline batch --dry-run
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# Single-field debugging (keeps the pre-scaling CLI behaviour)
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# Single-field debugging. Without --anmod-id, every advice-enabled model for
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# that field and disease is run.
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python -m pipeline one --field-id 4085 --disease PERONOSPORA --dry-run
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python -m pipeline one --field-id 4085 --disease PERONOSPORA --anmod-id 11 --dry-run
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```
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`batch` accepts `--deadline HH:MM` and `--workers N` to override
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@ -179,18 +187,19 @@ jitter, `retry.attempts` tries), applied automatically inside
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Each run writes to `output/<YYYY-MM-DD>/`:
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- `field<field_id>__<crop>__<disease slug>.json` — one file per job (see fields below)
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- `field<field_id>__<crop>__<disease slug>__model<anmod_id>.json` — one file per job (see fields below)
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- `_run_report.json` — the operational artifact for the morning: totals by
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status, `jobs_started_after_deadline` (jobs that began after the 09:00
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target; they are still written), plus a per-job breakdown (`status`,
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`duration_s`, `recommended_count`, `allowed_products_empty`, `message`,
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and, on failure, `error_type` / `error_message`)
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target; they are still written), plus a per-job breakdown (`field_id`,
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`disease`, `anmod_id`, `status`, `duration_s`, `recommended_count`,
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`allowed_products_empty`, `message`, and, on failure, `error_type` /
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`error_message`)
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Per-job payload fields:
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| Field | Description |
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|---|---|
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| `run` | Metadata (as_of, field, crop, disease, organic, station, …) |
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| `run` | Metadata (as_of, field, crop, disease, anmod_id, organic, station, …) |
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| `json_for_advice_generation` | Full 11-day meteorological + forecast payload, plus `date_of_today`, `allowed_products`, `applied_treatment`, `last_advice`, and (when present) `last_applied_treatment`; this is what the advice LLM receives |
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| `json_for_query_synthesis` | Subset of the above, sent to the LLM for search query generation |
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| `generated_queries` | One or two natural-language search queries |
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@ -247,16 +256,18 @@ The process exit code is `0` only if every job in the report is `ok`, `no_risk`,
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## Database writes
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Each non-dry-run job inserts one row into `advisories` for `(issuance_date, field_id,
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disease)`. That triple is the table's primary key, so any existing row for
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the same triple is deleted first — re-running a day replaces its advisory
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instead of failing on a duplicate key. The delete-then-insert pair runs
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inside one transaction (`pipeline/db.py::transaction`) so a crash between
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the two statements cannot leave a field's advisory missing for the day, and
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deadlocks are retried per `retry:` in config.yaml.
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Each non-dry-run job inserts one row into `advisories` for `(issuance_date,
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pathogen_model_id)`. `pathogen_model_id` stores that job's `anmod_id`. That pair
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is the table's primary key, so any existing row for the same pair is deleted
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first — re-running a day replaces that model's advisory and leaves the other
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models for the same day in place. `field_id` and `disease` stay on the row:
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`disease` is the English name that selects the prompt and the product filter.
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The delete-then-insert pair runs inside one transaction (`pipeline/db.py::transaction`)
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so a crash between the two statements cannot leave that model's advisory
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missing for the day, and deadlocks are retried per `retry:` in config.yaml.
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The primary key's clustered index on `(issuance_date, field_id, disease)` also
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lets that DELETE seek straight to the rows it needs instead of scanning the
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The primary key's clustered index on `(issuance_date, pathogen_model_id)` also
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lets that DELETE seek straight to the row it needs instead of scanning the
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table, so concurrent workers don't block each other on unrelated rows.
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## Daily scheduling
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@ -275,7 +286,7 @@ with working directory `d:\Uni\Thesis\AI-Agro-Support`. Jobs still waiting at
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late in `jobs_started_after_deadline`. `--skip-existing-advice`
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makes a re-trigger (e.g. a scheduled retry later that morning) resumable at no
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extra cost: it does one bulk check against `advisories` up front and only runs jobs
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that don't have a row yet.
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that don't have a row yet for that pathogen model.
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## Observability (optional)
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@ -329,7 +340,7 @@ collector cannot block a worker or push the run past `schedule.deadline`.
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### What you get per job
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Every `(field, crop, disease, as_of)` job produces one trace, rooted at a
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Every `(field, crop, disease, anmod_id, as_of)` job produces one trace, rooted at a
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`run_job` span, with:
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- a `query_synthesis` span (gemini-3.8-flash by default) and an
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@ -381,10 +392,10 @@ is not worth a custom cost pipeline at this scale.
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## Pipeline stages
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Run once per `(field, disease)` job by `pipeline/job.py::run_job`:
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Run once per `(field, disease, anmod_id)` job by `pipeline/job.py::run_job`:
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1. **Worklist** — supported diseases (`AI_agrosupport_an_pathogen_models.pat_mod_AI_model = 1`) → advice-enabled field models (`AI_agrosupport_agro_models.anmod_AI_advice = 1`) → crop from `AI_agrosupport_cmp_layers` + `AI_agrosupport_an_colture`; organic flag is `cmplay_imp == 3` (`pipeline/worklist.py`, run once per batch, not per job)
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2. **Disease model** — per day over as_of±5, from the worklist's known `anmod_id`. `disease_forecast` is a list or null. Each `INCUBAZPRIMARIA` index is kept (`INCUBAZPRIMARIA2_80.7`, or `INCUBAZPRIMARIA_40.3` when the description has no trailing index), using that index's highest `model_value`. `FASE5` is appended when present; `FASE2` is appended only when `FASE5` is absent. `predicted_incubations_management` (loaded from the previous `advisories.advice_input_json`, then written back with this run) records each index's `start_date` and `infection_covered`. Today counts as provisional: a start locks only once the index appears on a day before `as_of`. A covered incubation alone is not risk.
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2. **Disease model** — per day over as_of±5, from the worklist's known `anmod_id`. Rows come from `AI_agrosupport_agro_model_tmp2` where `model_model` equals that id. Only those rows' `model_description` values and `model_value` are used. `disease_forecast` is a list or null. Each `INCUBAZPRIMARIA` index is kept (`INCUBAZPRIMARIA2_80.7`, or `INCUBAZPRIMARIA_40.3` when the description has no trailing index), using that index's highest `model_value`. `FASE5` is appended when present; `FASE2` is appended only when `FASE5` is absent. `predicted_incubations_management` (loaded from the previous `advisories.advice_input_json` for the same `pathogen_model_id`, then written back with this run) records each index's `start_date` and `infection_covered`. Today counts as provisional: a start locks only once the index appears on a day before `as_of`. A covered incubation alone is not risk.
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3. **Observation** — `AI_agrosupport_agro_ril_pathogen` (`rilpato_layer`/`rilpato_date`/`rilpato_diffusion`); `observation` is true for `as_of` or any of the past 5 days if a row exists there with `rilpato_diffusion` other than `32` (including `NULL`)
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4. **Weather** — `TDatiMeteo_D` for past days, `TDatiMeteo_D_FRC` for today/future
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5. **Phenology** — carry-forward of latest observation ≤ day; future days null; if today's phase is missing, use yesterday's
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@ -406,6 +417,6 @@ Run once per `(field, disease)` job by `pipeline/job.py::run_job`:
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9. **Vector search** — Weaviate `near_text` on `ProductProfile`, always filtered with `contains_any` on the SQL-allowlisted `product_id`s (`product_id` must use `tokenization: field`), then reranked locally with `reranker-transformers` (`ms-marco-MiniLM-L-6-v2`) on `situation_fit_summary`. One synthesized query retrieves 10 and keeps the top 4 in rerank order; two queries retrieve 6 each and keep the top 2 unique each (a product already kept for the first query is skipped so the second can fill its two slots from the rest of its reranked six)
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10. **Product JSON** — label details per product from `products`, `product_uses`, `label_chunks`, cached per `(crop, disease)`
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11. **Context enrichment** — `allowed_products`, `applied_treatment` expansion, `last_advice` (with issuance `date`) from the `advisories` table, and optional `last_applied_treatment` (spray date + products) when the recent window is empty but a spray exists in `as_of-25 .. as_of-6`, including when `allowed_products` is empty. Enrichment reuses the job's 25-day treatment history; it does not query operations again.
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11. **Context enrichment** — `allowed_products`, `applied_treatment` expansion, `last_advice` (with issuance `date`) from the `advisories` row for the same `pathogen_model_id`, and optional `last_applied_treatment` (spray date + products) when the recent window is empty but a spray exists in `as_of-25 .. as_of-6`, including when `allowed_products` is empty. Enrichment reuses the job's 25-day treatment history; it does not query operations again.
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12. **Advice generation** — second LLM profile returns the structured advisory
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13. **Persistence** — the advisory plus its collapsed input are written to `advisories` inside one transaction
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13. **Persistence** — the advisory plus its collapsed input are written to `advisories` with `pathogen_model_id` = `anmod_id`, inside one transaction
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@ -4,13 +4,14 @@
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[--field-id N ...] [--deadline HH:MM]
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[--skip-existing-advice] [--dry-run] [--workers N]
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python -m pipeline one --field-id N --disease NAME [--as-of DATE] [--dry-run]
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python -m pipeline one --field-id N --disease NAME [--anmod-id N]
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[--as-of DATE] [--dry-run]
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`batch` is the daily entry point: it discovers every (field, disease) job
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from AI_agrosupport_an_pathogen_models (pat_mod_AI_model = 1) and
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`batch` is the daily entry point: it discovers every (field, disease, anmod_id)
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job from AI_agrosupport_an_pathogen_models (pat_mod_AI_model = 1) and
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AI_agrosupport_agro_models (anmod_AI_advice = 1), and runs them through a
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bounded thread pool (see pipeline/batch.py). `one` keeps the original
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single-field behaviour for ad-hoc debugging.
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bounded thread pool (see pipeline/batch.py). `one` runs every advice-enabled
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model for one field and disease, or a single `--anmod-id`.
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"""
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from __future__ import annotations
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@ -28,7 +29,7 @@ from pipeline.observability import setup_tracing, shutdown_tracing
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from pipeline.output import write_job_output
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from pipeline.prompts import PromptRegistry
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from pipeline.resources import Resources
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from pipeline.stages.disease import find_anmod_id
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from pipeline.stages.disease import find_advice_anmod_ids
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from pipeline.stages.field import resolve_field
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from pipeline.vocab import load_crop_vocab, load_disease_vocab
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from pipeline.worklist import Job
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@ -77,39 +78,66 @@ def _run_one(args: argparse.Namespace) -> int:
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prompts = PromptRegistry(settings.prompts_dir)
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resources = Resources(settings, prompts)
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exit_code = 0
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try:
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conn = resources.sql_connection()
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field = resolve_field(conn, settings.field_id, crop_vocab)
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anmod_id = find_anmod_id(conn, settings.field_id, settings.disease_name)
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anmod_ids = find_advice_anmod_ids(conn, settings.field_id, settings.disease_name)
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if args.anmod_id is not None:
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if args.anmod_id not in anmod_ids:
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raise NoModelConfiguredError(
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f"anmod_id {args.anmod_id} is not an advice-enabled model "
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f"for {settings.disease_name} on field {settings.field_id}."
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)
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anmod_ids = [args.anmod_id]
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logger.info(
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"Field %s disease %s (%s): %d advice-enabled model(s) %s",
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settings.field_id,
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settings.disease_name,
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disease_english,
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len(anmod_ids),
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anmod_ids,
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)
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resources.load_product_index()
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job = Job(
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field_id=settings.field_id,
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crop_english=field.crop_english,
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crop_italian=field.crop_italian,
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model_name=settings.disease_name,
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disease_english=disease_english,
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organic=field.organic,
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station=field.cmplay_station,
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anmod_id=anmod_id,
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)
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result = run_job(resources, job, args.as_of, dry_run=args.dry_run)
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for anmod_id in anmod_ids:
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job = Job(
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field_id=settings.field_id,
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crop_english=field.crop_english,
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crop_italian=field.crop_italian,
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model_name=settings.disease_name,
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disease_english=disease_english,
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organic=field.organic,
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station=field.cmplay_station,
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anmod_id=anmod_id,
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)
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result = run_job(resources, job, args.as_of, dry_run=args.dry_run)
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if result.payload is not None:
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path = write_job_output(settings.output_dir, args.as_of, result.payload)
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logger.info("Wrote output -> %s", path)
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if result.status == "failed":
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logger.error(
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"anmod_id=%s %s: %s",
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anmod_id,
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result.error_type,
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result.error_message,
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)
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exit_code = 1
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else:
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logger.info(
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"Done: anmod_id=%s status=%s message=%s",
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anmod_id,
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result.status,
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result.message,
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)
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finally:
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resources.close()
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if result.payload is not None:
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path = write_job_output(settings.output_dir, args.as_of, result.payload)
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logger.info("Wrote output -> %s", path)
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if result.status == "failed":
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logger.error("%s: %s", result.error_type, result.error_message)
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return 1
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logger.info("Done: status=%s message=%s", result.status, result.message)
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return 0
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return exit_code
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def _run_batch(args: argparse.Namespace) -> int:
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"""`python -m pipeline batch`: every database-enabled field/disease pair."""
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"""`python -m pipeline batch`: every advice-enabled field, disease, and model."""
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settings = load_settings()
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setup_tracing(settings)
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crop_vocab = load_crop_vocab(settings.crops_vocab)
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@ -173,7 +201,9 @@ def main(argv: list[str] | None = None) -> int:
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)
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batch_parser.add_argument(
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"--skip-existing-advice", action="store_true",
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help="Skip (field, disease) pairs that already have a row in advisories for --as-of.",
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help=(
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"Skip jobs that already have an advisories row for this pathogen model and --as-of."
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),
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)
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batch_parser.add_argument(
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"--dry-run", action="store_true",
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@ -186,7 +216,10 @@ def main(argv: list[str] | None = None) -> int:
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one_parser = subparsers.add_parser(
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"one",
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help="Run a single field/disease pair (debugging; the original single-field CLI behaviour).",
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help=(
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"Run one field and disease. Without --anmod-id, every advice-enabled "
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"mathematical model for that pair is run."
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),
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)
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one_parser.add_argument(
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"--as-of", type=_parse_as_of, default=None,
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@ -199,6 +232,13 @@ def main(argv: list[str] | None = None) -> int:
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"--disease", default=None,
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help="Disease model name, e.g. PERONOSPORA (defaults to config.yaml's disease_name).",
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)
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one_parser.add_argument(
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"--anmod-id", type=int, default=None,
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help=(
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"Run only this agro-model id. Default: every advice-enabled model "
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"for the field and disease."
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),
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)
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one_parser.add_argument(
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"--dry-run", action="store_true",
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help="Build the JSON but skip LLM calls, vector search, and the advisories DB write.",
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@ -1,7 +1,7 @@
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"""Batch orchestrator: discover jobs, run them across a bounded thread pool,
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and write one run report per day.
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One `ThreadPoolExecutor` runs every discovered (field, disease) job so a slow
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One `ThreadPoolExecutor` runs every discovered (field, disease, anmod_id) job so a slow
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crop cannot serialise the others; `pipeline.job.run_job` isolates each job's
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failures from the rest. This module owns everything above that: worklist
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discovery + CLI filters, the `--skip-existing-advice` resume check, the
|
||||
@ -27,7 +27,9 @@ from pipeline.worklist import Job, build_jobs
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_EXISTING_ADVICE_SQL = "SELECT field_id, disease FROM {advisories} WHERE issuance_date = ?"
|
||||
_EXISTING_ADVICE_SQL = (
|
||||
"SELECT pathogen_model_id FROM {advisories} WHERE issuance_date = ?"
|
||||
)
|
||||
|
||||
|
||||
def _parse_deadline(deadline_str: str) -> datetime:
|
||||
@ -90,7 +92,7 @@ def filter_jobs(
|
||||
return filtered
|
||||
|
||||
|
||||
def _existing_advice_keys(resources: Resources, as_of: date) -> set[tuple[int, str]]:
|
||||
def _existing_advice_keys(resources: Resources, as_of: date) -> set[int]:
|
||||
"""One bulk SELECT instead of one per job, for --skip-existing-advice."""
|
||||
conn = resources.sql_connection()
|
||||
rows = fetch_all(
|
||||
@ -98,7 +100,7 @@ def _existing_advice_keys(resources: Resources, as_of: date) -> set[tuple[int, s
|
||||
qualify_app_sql(resources.settings.sql, _EXISTING_ADVICE_SQL),
|
||||
(as_of,),
|
||||
)
|
||||
return {(int(row.field_id), str(row.disease)) for row in rows}
|
||||
return {int(row.pathogen_model_id) for row in rows}
|
||||
|
||||
|
||||
def run_batch(
|
||||
@ -151,11 +153,11 @@ def run_batch(
|
||||
|
||||
resources.load_product_index()
|
||||
|
||||
existing: set[tuple[int, str]] = set()
|
||||
existing: set[int] = set()
|
||||
if skip_existing_advice:
|
||||
existing = _existing_advice_keys(resources, as_of)
|
||||
logger.info(
|
||||
"%d field/disease pair(s) already have advice for %s and will be skipped",
|
||||
"%d model row(s) already have advice for %s and will be skipped",
|
||||
len(existing),
|
||||
as_of.isoformat(),
|
||||
)
|
||||
@ -195,7 +197,7 @@ def run_batch(
|
||||
deadline_str,
|
||||
)
|
||||
|
||||
if (job.field_id, job.disease_english) in existing:
|
||||
if job.anmod_id in existing:
|
||||
return JobResult(job=job, status="skipped_existing")
|
||||
|
||||
if started_late:
|
||||
@ -206,7 +208,7 @@ def run_batch(
|
||||
# Worker threads start with an empty OpenTelemetry context
|
||||
# (`contextvars` do not cross `ThreadPoolExecutor.submit`). That is
|
||||
# intentional: `job_span` opens inside the worker and becomes a root
|
||||
# span, one independent trace per (field, crop, disease, as_of). Do
|
||||
# span, one independent trace per (field, crop, disease, anmod_id, as_of). Do
|
||||
# not wrap this submit in a tracing context manager from this thread
|
||||
# — it would not reach the workers. See pipeline/observability.py.
|
||||
workers = workers_override or settings.concurrency.workers
|
||||
@ -219,10 +221,11 @@ def run_batch(
|
||||
result = future.result()
|
||||
except BaseException as exc: # noqa: BLE001 - defense in depth; run_job already isolates failures
|
||||
logger.exception(
|
||||
"Unexpected error scheduling job field=%s crop=%s disease=%s",
|
||||
"Unexpected error scheduling job field=%s crop=%s disease=%s anmod_id=%s",
|
||||
job.field_id,
|
||||
job.crop_english,
|
||||
job.disease_english,
|
||||
job.anmod_id,
|
||||
)
|
||||
result = JobResult(
|
||||
job=job,
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
"""Run the full single-field pipeline for one (field, crop, disease) job.
|
||||
"""Run the full pipeline for one (field, crop, disease, anmod_id) job.
|
||||
|
||||
This is the per-job unit of work the batch orchestrator (`pipeline.batch`)
|
||||
dispatches to its thread pool. It is the same sequence the original
|
||||
@ -88,8 +88,11 @@ def json_default(obj: Any) -> Any:
|
||||
|
||||
|
||||
def output_stem(job: Job, disease_english: str) -> str:
|
||||
"""File name stem (no extension) for this job's per-field output JSON."""
|
||||
return f"field{job.field_id}__{pair_slug(job.crop_english, disease_english)}"
|
||||
"""File name stem (no extension). Includes anmod_id so each mathematical model has its own file."""
|
||||
return (
|
||||
f"field{job.field_id}__{pair_slug(job.crop_english, disease_english)}"
|
||||
f"__model{job.anmod_id}"
|
||||
)
|
||||
|
||||
|
||||
def run_job(resources: Resources, job: Job, as_of: date, dry_run: bool = False) -> JobResult:
|
||||
@ -97,7 +100,7 @@ def run_job(resources: Resources, job: Job, as_of: date, dry_run: bool = False)
|
||||
Run the full pipeline for one job.
|
||||
|
||||
Wrapped in `job_span`, the one root span per `(field, crop, disease,
|
||||
as_of)` job (a no-op when observability is disabled/unreachable -- see
|
||||
anmod_id, as_of)` job (a no-op when observability is disabled/unreachable -- see
|
||||
`pipeline/observability.py`). The span records the final `status` (and,
|
||||
on failure, the error type) but never changes the fault-isolation
|
||||
behaviour below: a failure always comes back as a `failed` JobResult,
|
||||
@ -109,10 +112,11 @@ def run_job(resources: Resources, job: Job, as_of: date, dry_run: bool = False)
|
||||
payload = _run_job_inner(resources, job, as_of, dry_run)
|
||||
except BaseException as exc: # noqa: BLE001 - fault isolation is the whole point of this wrapper
|
||||
logger.exception(
|
||||
"Job failed: field=%s crop=%s disease=%s",
|
||||
"Job failed: field=%s crop=%s disease=%s anmod_id=%s",
|
||||
job.field_id,
|
||||
job.crop_english,
|
||||
job.disease_english,
|
||||
job.anmod_id,
|
||||
)
|
||||
span.set_attribute("job.status", "failed")
|
||||
span.set_attribute("job.error_type", type(exc).__name__)
|
||||
@ -163,8 +167,8 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
|
||||
if disease.station is None:
|
||||
raise PipelineError(
|
||||
f"Field {job.field_id}: unable to determine weather station "
|
||||
"(model_station / cmplay_station both missing)."
|
||||
f"Field {job.field_id} anmod_id={job.anmod_id}: unable to determine "
|
||||
"weather station (model_station / cmplay_station both missing)."
|
||||
)
|
||||
|
||||
weather = load_weather(conn, disease.station, as_of)
|
||||
@ -176,7 +180,12 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
raise RuntimeError("load_product_index() must run before run_job().")
|
||||
previous_register = parse_register(
|
||||
load_previous_incubation_register(
|
||||
conn, job.field_id, disease.disease_english, as_of, settings.sql
|
||||
conn,
|
||||
job.field_id,
|
||||
disease.disease_english,
|
||||
job.anmod_id,
|
||||
as_of,
|
||||
settings.sql,
|
||||
)
|
||||
)
|
||||
interval_open = last_treatment_interval_open(
|
||||
@ -217,10 +226,11 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"Field %s (%s/%s): station=%s has_risk=%s first_future=%s",
|
||||
"Field %s (%s/%s) anmod_id=%s: station=%s has_risk=%s first_future=%s",
|
||||
job.field_id,
|
||||
job.crop_english,
|
||||
job.disease_english,
|
||||
job.anmod_id,
|
||||
disease.station,
|
||||
has_risk,
|
||||
future_disease,
|
||||
@ -286,9 +296,10 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
recommended: list[RecommendedProduct] = []
|
||||
if has_risk and interval_open:
|
||||
logger.info(
|
||||
"Field %s: last treatment interval still open; product search "
|
||||
"Field %s anmod_id=%s: last treatment interval still open; product search "
|
||||
"skipped, advice generated without allowed products.",
|
||||
job.field_id,
|
||||
job.anmod_id,
|
||||
)
|
||||
elif has_risk:
|
||||
query_system_prompt = resources.prompts.query_prompt(job.crop_english, disease.disease_english)
|
||||
@ -328,24 +339,31 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
for item in recommended
|
||||
]
|
||||
logger.info(
|
||||
"Field %s: recommended products: %s",
|
||||
"Field %s anmod_id=%s: recommended products: %s",
|
||||
job.field_id,
|
||||
job.anmod_id,
|
||||
[p["product_name"] for p in payload["recommended_products"]],
|
||||
)
|
||||
else:
|
||||
payload["status"] = "no_risk"
|
||||
logger.warning(
|
||||
"Field %s: no uncovered incubation, FASE2, or FASE5 in the 11-day "
|
||||
"window, and no true observation in the past 5 days or today; "
|
||||
"Field %s anmod_id=%s: no uncovered incubation, FASE2, or FASE5 in the "
|
||||
"11-day window, and no true observation in the past 5 days or today; "
|
||||
"product search skipped, advice generated without allowed "
|
||||
"products.",
|
||||
job.field_id,
|
||||
job.anmod_id,
|
||||
)
|
||||
|
||||
builder = resources.product_json_builder(job.crop_english, disease.disease_english)
|
||||
with resources.sql_sem:
|
||||
last_summary, last_treatments, last_date = load_last_advice(
|
||||
conn, job.field_id, disease.disease_english, as_of, settings.sql
|
||||
conn,
|
||||
job.field_id,
|
||||
disease.disease_english,
|
||||
job.anmod_id,
|
||||
as_of,
|
||||
settings.sql,
|
||||
)
|
||||
enrich_advice_json(
|
||||
json_for_advice_generation,
|
||||
@ -379,6 +397,7 @@ def _run_job_inner(resources: Resources, job: Job, as_of: date, dry_run: bool) -
|
||||
issuance_date=as_of,
|
||||
field_id=job.field_id,
|
||||
disease_english=disease.disease_english,
|
||||
pathogen_model_id=job.anmod_id,
|
||||
advice_input_json=advice_input_json,
|
||||
result=result,
|
||||
sql=settings.sql,
|
||||
|
||||
@ -266,7 +266,7 @@ def _span(name: str, kind: str, metadata: dict[str, Any] | None = None) -> Itera
|
||||
|
||||
def job_span(job: "Job", as_of: date, dry_run: bool) -> contextlib.AbstractContextManager:
|
||||
"""
|
||||
One root CHAIN span per `(field, crop, disease, as_of)` job.
|
||||
One root CHAIN span per `(field, crop, disease, anmod_id, as_of)` job.
|
||||
|
||||
`run_job` sets the final `job.status` (and, on failure, `job.error_type`)
|
||||
on the yielded span itself before the `with` block exits; this function
|
||||
@ -276,6 +276,7 @@ def job_span(job: "Job", as_of: date, dry_run: bool) -> contextlib.AbstractConte
|
||||
"field_id": job.field_id,
|
||||
"crop": job.crop_english,
|
||||
"disease": job.disease_english,
|
||||
"anmod_id": job.anmod_id,
|
||||
"as_of": as_of.isoformat(),
|
||||
"organic": job.organic,
|
||||
"station": job.station,
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
The single-field CLI wrote `output/<date>_field<id>.json`, which collides as
|
||||
soon as one field has two diseases. The batch instead writes one file per
|
||||
(field, disease) pair under a per-day directory, plus a `_run_report.json`
|
||||
(field, disease, anmod_id) job under a per-day directory, plus a `_run_report.json`
|
||||
that is the actual operational artifact: it says whether the morning's run
|
||||
succeeded, and if not, exactly which jobs failed and why.
|
||||
"""
|
||||
@ -50,6 +50,7 @@ def _job_report_entry(result: JobResult) -> dict[str, Any]:
|
||||
"field_id": job.field_id,
|
||||
"crop": job.crop_english,
|
||||
"disease": job.disease_english,
|
||||
"anmod_id": job.anmod_id,
|
||||
"organic": job.organic,
|
||||
"station": job.station,
|
||||
"status": result.status,
|
||||
|
||||
@ -28,8 +28,7 @@ _RECOMMENDED_DOSES_MAX_CHARS = 100
|
||||
_DELETE_ADVICE_SQL = """
|
||||
DELETE FROM {advisories}
|
||||
WHERE issuance_date = ?
|
||||
AND field_id = ?
|
||||
AND disease = ?
|
||||
AND pathogen_model_id = ?
|
||||
"""
|
||||
|
||||
_INSERT_ADVICE_SQL = """
|
||||
@ -37,6 +36,7 @@ INSERT INTO {advisories} (
|
||||
issuance_date,
|
||||
field_id,
|
||||
disease,
|
||||
pathogen_model_id,
|
||||
advice_input_json,
|
||||
full_advice,
|
||||
advice_summary,
|
||||
@ -44,7 +44,7 @@ INSERT INTO {advisories} (
|
||||
recommended_product_names,
|
||||
recommended_doses,
|
||||
recommended_application_date
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
"""
|
||||
|
||||
|
||||
@ -174,21 +174,21 @@ def insert_advice(
|
||||
issuance_date: date,
|
||||
field_id: int,
|
||||
disease_english: str,
|
||||
pathogen_model_id: int,
|
||||
advice_input_json: dict[str, Any],
|
||||
result: AdviceResult,
|
||||
sql: SqlSettings,
|
||||
) -> None:
|
||||
"""
|
||||
Store the advisory, replacing any earlier row for the same day/field/disease.
|
||||
Store the advisory, replacing any earlier row for the same day and model.
|
||||
|
||||
The table's primary key is (issuance_date, field_id, disease), so an explicit
|
||||
delete keeps re-runs of the same day from violating it instead of
|
||||
accumulating duplicates. The
|
||||
delete and insert run inside one transaction: with a single sequential
|
||||
runner, autocommitting each statement separately was harmless, but the
|
||||
batch pipeline writes from several worker threads at once, and a crash
|
||||
between the two statements would otherwise leave that field's advisory
|
||||
silently missing for the day.
|
||||
The table's primary key is (issuance_date, pathogen_model_id).
|
||||
pathogen_model_id is the agro-model anmod_id. An explicit delete keeps a
|
||||
re-run of that model from violating the key, and leaves other models for
|
||||
the same day in place. The delete and insert run inside one transaction:
|
||||
the batch pipeline writes from several worker threads at once, and a crash
|
||||
between the two statements would otherwise leave that model's advisory
|
||||
missing for the day.
|
||||
"""
|
||||
recommended_product_names = _json_column(
|
||||
result.recommended_product_names,
|
||||
@ -206,7 +206,7 @@ def insert_advice(
|
||||
execute(
|
||||
conn,
|
||||
qualify_app_sql(sql, _DELETE_ADVICE_SQL),
|
||||
(issuance_date, field_id, disease_english),
|
||||
(issuance_date, pathogen_model_id),
|
||||
)
|
||||
execute(
|
||||
conn,
|
||||
@ -215,6 +215,7 @@ def insert_advice(
|
||||
issuance_date,
|
||||
field_id,
|
||||
disease_english,
|
||||
pathogen_model_id,
|
||||
sent,
|
||||
result.full_advice,
|
||||
result.advice_summary,
|
||||
@ -225,9 +226,10 @@ def insert_advice(
|
||||
),
|
||||
)
|
||||
logger.info(
|
||||
"Stored advice for field %s / %s on %s (is_treatment_recommended=%s).",
|
||||
"Stored advice for field %s / %s / model %s on %s (is_treatment_recommended=%s).",
|
||||
field_id,
|
||||
disease_english,
|
||||
pathogen_model_id,
|
||||
issuance_date.isoformat(),
|
||||
int(result.is_treatment_recommended),
|
||||
)
|
||||
|
||||
@ -28,8 +28,7 @@ SELECT TOP 1
|
||||
advice_summary,
|
||||
recommended_product_names
|
||||
FROM {advisories}
|
||||
WHERE field_id = ?
|
||||
AND disease = ?
|
||||
WHERE pathogen_model_id = ?
|
||||
AND issuance_date < ?
|
||||
ORDER BY issuance_date DESC
|
||||
"""
|
||||
@ -48,8 +47,7 @@ def _row_date(value: Any) -> date | None:
|
||||
_LAST_SENT_SQL = """
|
||||
SELECT TOP 1 advice_input_json
|
||||
FROM {advisories}
|
||||
WHERE field_id = ?
|
||||
AND disease = ?
|
||||
WHERE pathogen_model_id = ?
|
||||
AND issuance_date < ?
|
||||
ORDER BY issuance_date DESC
|
||||
"""
|
||||
@ -59,14 +57,15 @@ def load_previous_incubation_register(
|
||||
conn: pyodbc.Connection,
|
||||
field_id: int,
|
||||
disease_english: str,
|
||||
pathogen_model_id: int,
|
||||
as_of: date,
|
||||
sql: SqlSettings,
|
||||
) -> dict[str, Any]:
|
||||
"""Return predicted_incubations_management from the latest advice before as_of."""
|
||||
"""Return predicted_incubations_management from this model's latest advice before as_of."""
|
||||
row = fetch_one(
|
||||
conn,
|
||||
qualify_app_sql(sql, _LAST_SENT_SQL),
|
||||
(field_id, disease_english, as_of),
|
||||
(pathogen_model_id, as_of),
|
||||
)
|
||||
if row is None or not row.advice_input_json:
|
||||
return {}
|
||||
@ -74,9 +73,11 @@ def load_previous_incubation_register(
|
||||
payload = json.loads(row.advice_input_json)
|
||||
except (TypeError, json.JSONDecodeError):
|
||||
logger.warning(
|
||||
"Previous advice_input_json for field %s / %s is not JSON; incubation register starts empty.",
|
||||
"Previous advice_input_json for field %s / %s / model %s is not JSON; "
|
||||
"incubation register starts empty.",
|
||||
field_id,
|
||||
disease_english,
|
||||
pathogen_model_id,
|
||||
)
|
||||
return {}
|
||||
if not isinstance(payload, dict):
|
||||
@ -91,20 +92,22 @@ def load_last_advice(
|
||||
conn: pyodbc.Connection,
|
||||
field_id: int,
|
||||
disease_english: str,
|
||||
pathogen_model_id: int,
|
||||
as_of: date,
|
||||
sql: SqlSettings,
|
||||
) -> tuple[str, list[str], date | None]:
|
||||
"""Return (advice_summary, treatment names, issuance date) of the most recent advice before as_of."""
|
||||
"""Return (advice_summary, treatment names, issuance date) of this model's latest advice before as_of."""
|
||||
row = fetch_one(
|
||||
conn,
|
||||
qualify_app_sql(sql, _LAST_ADVICE_SQL),
|
||||
(field_id, disease_english, as_of),
|
||||
(pathogen_model_id, as_of),
|
||||
)
|
||||
if row is None:
|
||||
logger.info(
|
||||
"No previous advice for field %s / %s before %s.",
|
||||
"No previous advice for field %s / %s / model %s before %s.",
|
||||
field_id,
|
||||
disease_english,
|
||||
pathogen_model_id,
|
||||
as_of.isoformat(),
|
||||
)
|
||||
return "", [], None
|
||||
|
||||
@ -11,7 +11,7 @@ from typing import Any
|
||||
import pyodbc
|
||||
|
||||
from pipeline.db import fetch_all
|
||||
from pipeline.errors import NoModelConfiguredError
|
||||
from pipeline.errors import NoModelConfiguredError, PipelineError
|
||||
from pipeline.window import build_window
|
||||
|
||||
|
||||
@ -25,11 +25,12 @@ class DiseaseForecast:
|
||||
|
||||
|
||||
_MODEL_SQL = """
|
||||
SELECT anmod_id, anmod_model, anmod_enabled
|
||||
SELECT anmod_id, anmod_model, anmod_AI_advice
|
||||
FROM AI_agrosupport_agro_models
|
||||
WHERE anmod_cmplay = ?
|
||||
"""
|
||||
|
||||
# model_model is anmod_id. model_description belongs to that mathematical model.
|
||||
_TMP2_SQL = """
|
||||
SELECT
|
||||
CONVERT(date, model_date) AS model_day,
|
||||
@ -153,25 +154,41 @@ def _resolve_day_fase(records: _DayRecords) -> list[str] | None:
|
||||
return labels or None
|
||||
|
||||
|
||||
def find_anmod_id(conn: pyodbc.Connection, field_id: int, disease_name: str) -> int:
|
||||
"""
|
||||
Look up the anmod_id for a field's disease model by name.
|
||||
def _advice_enabled(value: Any) -> bool:
|
||||
if value is None or isinstance(value, bool):
|
||||
return bool(value)
|
||||
try:
|
||||
return int(value) == 1
|
||||
except (TypeError, ValueError):
|
||||
return str(value).strip() == "1"
|
||||
|
||||
Used by the single-field `one` CLI path, where `anmod_id` is not known
|
||||
ahead of time. The batch worklist resolves it in one database join
|
||||
(see `pipeline.worklist.build_jobs`), so it never calls this per field.
|
||||
|
||||
def find_advice_anmod_ids(
|
||||
conn: pyodbc.Connection,
|
||||
field_id: int,
|
||||
disease_name: str,
|
||||
) -> list[int]:
|
||||
"""
|
||||
Every advice-enabled anmod_id for this field and disease name.
|
||||
|
||||
A field can store several mathematical models for one disease. Each row
|
||||
with anmod_AI_advice = 1 is its own id. The batch worklist resolves the
|
||||
same rows in one join (see `pipeline.worklist.build_jobs`); `one` calls
|
||||
this when the id is not already known.
|
||||
"""
|
||||
models = fetch_all(conn, _MODEL_SQL, (field_id,))
|
||||
target = disease_name.strip().upper()
|
||||
match = next(
|
||||
(row for row in models if (row.anmod_model or "").strip().upper() == target),
|
||||
None,
|
||||
)
|
||||
if match is None:
|
||||
ids = {
|
||||
int(row.anmod_id)
|
||||
for row in models
|
||||
if (row.anmod_model or "").strip().upper() == target
|
||||
and _advice_enabled(row.anmod_AI_advice)
|
||||
}
|
||||
if not ids:
|
||||
raise NoModelConfiguredError(
|
||||
f"No disease forecast model is configured for {disease_name}."
|
||||
f"No advice-enabled disease forecast model is configured for {disease_name}."
|
||||
)
|
||||
return int(match.anmod_id)
|
||||
return sorted(ids)
|
||||
|
||||
|
||||
def resolve_disease_forecast(
|
||||
@ -182,11 +199,16 @@ def resolve_disease_forecast(
|
||||
as_of: date,
|
||||
fallback_station: int | None,
|
||||
) -> DiseaseForecast:
|
||||
"""Find the disease model for the field and build the 11-day forecast list."""
|
||||
anmod_id = find_anmod_id(conn, field_id, disease_name)
|
||||
"""Build the 11-day forecast when the field has one advice-enabled model."""
|
||||
ids = find_advice_anmod_ids(conn, field_id, disease_name)
|
||||
if len(ids) != 1:
|
||||
raise PipelineError(
|
||||
f"Field {field_id} has {len(ids)} advice-enabled models for {disease_name}. "
|
||||
"Call resolve_disease_forecast_by_anmod with the anmod_id to use."
|
||||
)
|
||||
return resolve_disease_forecast_by_anmod(
|
||||
conn,
|
||||
anmod_id=anmod_id,
|
||||
anmod_id=ids[0],
|
||||
disease_name=disease_name,
|
||||
disease_english=disease_english,
|
||||
as_of=as_of,
|
||||
@ -202,7 +224,12 @@ def resolve_disease_forecast_by_anmod(
|
||||
as_of: date,
|
||||
fallback_station: int | None,
|
||||
) -> DiseaseForecast:
|
||||
"""Build the 11-day forecast list for an already-known anmod_id."""
|
||||
"""
|
||||
Build the 11-day forecast list for an already-known anmod_id.
|
||||
|
||||
tmp2 rows are limited to model_model = anmod_id. The forecast is built
|
||||
from those rows' model_description and model_value.
|
||||
"""
|
||||
window = build_window(as_of)
|
||||
start, end = window[0], window[-1]
|
||||
rows = fetch_all(conn, _TMP2_SQL, (anmod_id, start, end))
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
"""Database-driven job discovery: find every (field, disease) pair to run today.
|
||||
"""Database-driven job discovery: find every advice-enabled agro-model row to run today.
|
||||
|
||||
The batch does not start from a field ID or from a crop list in config.yaml.
|
||||
It reads the diseases that can generate advice
|
||||
@ -6,7 +6,8 @@ It reads the diseases that can generate advice
|
||||
`pat_mod_desc`) and the fields that have advice enabled for one of those
|
||||
diseases (`AI_agrosupport_agro_models.anmod_AI_advice = 1`). The cultivated
|
||||
crop comes from the field layer. Each matching agro-model row becomes one
|
||||
`Job`.
|
||||
`Job`, including several mathematical models (`anmod_id`) for the same field
|
||||
and disease.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@ -27,7 +28,7 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Job:
|
||||
"""One field/disease pair to run the pipeline for."""
|
||||
"""One field, disease, and mathematical model (`anmod_id`) to run."""
|
||||
|
||||
field_id: int
|
||||
crop_english: str
|
||||
|
||||
@ -21,7 +21,7 @@ from pipeline.config import (
|
||||
WeaviateSettings,
|
||||
WorklistSettings,
|
||||
)
|
||||
from pipeline.job import JobResult
|
||||
from pipeline.job import JobResult, output_stem
|
||||
from pipeline.output import write_run_report
|
||||
from pipeline.worklist import Job
|
||||
|
||||
@ -30,7 +30,7 @@ def _at(hour: int, minute: int = 0, second: int = 0) -> datetime:
|
||||
return datetime.combine(date.today(), dtime(hour, minute, second))
|
||||
|
||||
|
||||
def _job(field_id: int = 4012) -> Job:
|
||||
def _job(field_id: int = 4012, anmod_id: int = 10) -> Job:
|
||||
return Job(
|
||||
field_id=field_id,
|
||||
crop_english="grapevine",
|
||||
@ -39,7 +39,7 @@ def _job(field_id: int = 4012) -> Job:
|
||||
disease_english="downy mildew",
|
||||
organic=False,
|
||||
station=7,
|
||||
anmod_id=10,
|
||||
anmod_id=anmod_id,
|
||||
)
|
||||
|
||||
|
||||
@ -133,7 +133,8 @@ class RunBatchDeadlineTests(unittest.TestCase):
|
||||
*,
|
||||
now,
|
||||
skip_existing: bool = False,
|
||||
existing: set[tuple[int, str]] | None = None,
|
||||
existing: set[int] | None = None,
|
||||
log_level: str = "WARNING",
|
||||
) -> dict:
|
||||
captured: dict = {}
|
||||
prompts = MagicMock()
|
||||
@ -157,7 +158,7 @@ class RunBatchDeadlineTests(unittest.TestCase):
|
||||
patch("pipeline.batch.write_run_report", side_effect=_write_report),
|
||||
patch("pipeline.batch._existing_advice_keys", return_value=existing or set()),
|
||||
patch("pipeline.batch.datetime") as frozen,
|
||||
self.assertLogs("pipeline.batch", level="WARNING") as logs,
|
||||
self.assertLogs("pipeline.batch", level=log_level) as logs,
|
||||
):
|
||||
frozen.now.side_effect = now_fn
|
||||
frozen.combine = datetime.combine
|
||||
@ -196,7 +197,7 @@ class RunBatchDeadlineTests(unittest.TestCase):
|
||||
[job],
|
||||
now=_at(10, 0),
|
||||
skip_existing=True,
|
||||
existing={(job.field_id, job.disease_english)},
|
||||
existing={job.anmod_id},
|
||||
)
|
||||
|
||||
self.assertEqual(captured["run_job"].call_count, 0)
|
||||
@ -215,6 +216,39 @@ class RunBatchDeadlineTests(unittest.TestCase):
|
||||
self.assertEqual(sum("80%" in message for message in captured["logs"]), 1)
|
||||
self.assertFalse(any("has passed" in message for message in captured["logs"]))
|
||||
|
||||
def test_skip_existing_is_per_pathogen_model(self) -> None:
|
||||
stored = _job(anmod_id=20)
|
||||
other = _job(anmod_id=11)
|
||||
captured = self._run(
|
||||
[stored, other],
|
||||
now=_at(8, 0),
|
||||
skip_existing=True,
|
||||
existing={stored.anmod_id},
|
||||
log_level="INFO",
|
||||
)
|
||||
|
||||
self.assertEqual(captured["run_job"].call_count, 1)
|
||||
self.assertEqual(captured["run_job"].call_args.args[1].anmod_id, 11)
|
||||
self.assertEqual(
|
||||
sorted(result.status for result in captured["results"]),
|
||||
["ok", "skipped_existing"],
|
||||
)
|
||||
|
||||
|
||||
class OutputStemTests(unittest.TestCase):
|
||||
def test_each_model_has_its_own_file_stem(self) -> None:
|
||||
stems = {
|
||||
output_stem(_job(anmod_id=11), "downy mildew"),
|
||||
output_stem(_job(anmod_id=20), "downy mildew"),
|
||||
}
|
||||
self.assertEqual(
|
||||
stems,
|
||||
{
|
||||
"field4012__grapevine__downy_mildew__model11",
|
||||
"field4012__grapevine__downy_mildew__model20",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
class RunReportTests(unittest.TestCase):
|
||||
def test_report_records_late_starts_without_skipped_deadline(self) -> None:
|
||||
@ -229,6 +263,7 @@ class RunReportTests(unittest.TestCase):
|
||||
empty = write_run_report(Path(tmp), date(2026, 10, 14), [])
|
||||
|
||||
self.assertEqual(report["jobs_started_after_deadline"], 2)
|
||||
self.assertEqual(report["jobs"][0]["anmod_id"], 10)
|
||||
self.assertNotIn("skipped_deadline", report["totals"])
|
||||
self.assertIn("skipped_existing", report["totals"])
|
||||
self.assertEqual(empty["jobs_started_after_deadline"], 0)
|
||||
|
||||
@ -138,10 +138,14 @@ class QualifiedSqlTests(unittest.TestCase):
|
||||
def test_advice_reads_qualify_advisories(self) -> None:
|
||||
as_of = date(2026, 4, 16)
|
||||
with patch("pipeline.stages.advice_context.fetch_one", return_value=None) as fetch:
|
||||
load_last_advice(MagicMock(), 4012, "downy mildew", as_of, self.split)
|
||||
load_previous_incubation_register(MagicMock(), 4012, "downy mildew", as_of, self.split)
|
||||
load_last_advice(MagicMock(), 4012, "downy mildew", 11, as_of, self.split)
|
||||
load_previous_incubation_register(
|
||||
MagicMock(), 4012, "downy mildew", 11, as_of, self.split
|
||||
)
|
||||
for call in fetch.call_args_list:
|
||||
self.assertIn("[Agrosupport].dbo.[advisories]", call.args[1])
|
||||
self.assertIn("pathogen_model_id = ?", call.args[1])
|
||||
self.assertEqual(call.args[2], (11, as_of))
|
||||
|
||||
def test_advice_write_qualifies_advisories(self) -> None:
|
||||
result = AdviceResult(
|
||||
@ -161,13 +165,23 @@ class QualifiedSqlTests(unittest.TestCase):
|
||||
issuance_date=date(2026, 4, 16),
|
||||
field_id=4012,
|
||||
disease_english="downy mildew",
|
||||
pathogen_model_id=11,
|
||||
advice_input_json={},
|
||||
result=result,
|
||||
sql=self.split,
|
||||
)
|
||||
delete_sql, insert_sql = (call.args[1] for call in execute.call_args_list)
|
||||
delete_call, insert_call = execute.call_args_list
|
||||
delete_sql, insert_sql = delete_call.args[1], insert_call.args[1]
|
||||
self.assertIn("DELETE FROM [Agrosupport].dbo.[advisories]", delete_sql)
|
||||
self.assertIn("AND pathogen_model_id = ?", delete_sql)
|
||||
self.assertNotIn("field_id", delete_sql)
|
||||
self.assertNotIn("disease", delete_sql)
|
||||
self.assertEqual(delete_call.args[2], (date(2026, 4, 16), 11))
|
||||
self.assertIn("INSERT INTO [Agrosupport].dbo.[advisories]", insert_sql)
|
||||
self.assertIn("field_id", insert_sql)
|
||||
self.assertIn("disease", insert_sql)
|
||||
self.assertIn("pathogen_model_id", insert_sql)
|
||||
self.assertEqual(insert_call.args[2][3], 11)
|
||||
|
||||
|
||||
def _called_name(node: ast.Call) -> str | None:
|
||||
|
||||
@ -3,13 +3,20 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import unittest
|
||||
from datetime import date
|
||||
from decimal import Decimal
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import patch
|
||||
|
||||
from pipeline.errors import NoModelConfiguredError, PipelineError
|
||||
from pipeline.stages.disease import (
|
||||
_DayRecords,
|
||||
_format_model_value,
|
||||
_incub_label,
|
||||
_resolve_day_fase,
|
||||
find_advice_anmod_ids,
|
||||
resolve_disease_forecast,
|
||||
resolve_disease_forecast_by_anmod,
|
||||
)
|
||||
|
||||
|
||||
@ -121,5 +128,92 @@ class IncubLabelTests(unittest.TestCase):
|
||||
self.assertIsNone(_resolve_day_fase(_DayRecords()))
|
||||
|
||||
|
||||
def _model(anmod_id: int, name: str = "PERONOSPORA", advice: int = 1) -> SimpleNamespace:
|
||||
return SimpleNamespace(anmod_id=anmod_id, anmod_model=name, anmod_AI_advice=advice)
|
||||
|
||||
|
||||
def _tmp_row(day: date, description: str, value: object, station: int = 7) -> SimpleNamespace:
|
||||
return SimpleNamespace(
|
||||
model_day=day,
|
||||
model_description=description,
|
||||
model_value=value,
|
||||
model_station=station,
|
||||
)
|
||||
|
||||
|
||||
class AdviceModelLookupTests(unittest.TestCase):
|
||||
def test_keeps_every_advice_enabled_id_for_the_disease(self) -> None:
|
||||
rows = [
|
||||
_model(20),
|
||||
_model(11, name="Peronospora"),
|
||||
_model(12, advice=0),
|
||||
_model(13, name="OIDIO"),
|
||||
]
|
||||
with patch("pipeline.stages.disease.fetch_all", return_value=rows) as fetch:
|
||||
ids = find_advice_anmod_ids(object(), 4012, "PERONOSPORA")
|
||||
|
||||
self.assertEqual(ids, [11, 20])
|
||||
self.assertIn("anmod_AI_advice", fetch.call_args.args[1])
|
||||
|
||||
def test_disabled_only_match_is_not_configured(self) -> None:
|
||||
with patch("pipeline.stages.disease.fetch_all", return_value=[_model(12, advice=0)]):
|
||||
with self.assertRaises(NoModelConfiguredError):
|
||||
find_advice_anmod_ids(object(), 4012, "PERONOSPORA")
|
||||
|
||||
def test_forecast_reads_model_description_for_that_anmod_id(self) -> None:
|
||||
as_of = date(2026, 6, 15)
|
||||
with patch(
|
||||
"pipeline.stages.disease.fetch_all",
|
||||
return_value=[_tmp_row(as_of, "INCUBAZPRIMARIA2", 80.7)],
|
||||
) as fetch:
|
||||
forecast = resolve_disease_forecast_by_anmod(
|
||||
object(),
|
||||
anmod_id=11,
|
||||
disease_name="PERONOSPORA",
|
||||
disease_english="downy mildew",
|
||||
as_of=as_of,
|
||||
fallback_station=None,
|
||||
)
|
||||
|
||||
sql, params = fetch.call_args.args[1], fetch.call_args.args[2]
|
||||
self.assertIn("WHERE model_model = ?", sql)
|
||||
self.assertIn("model_description", sql)
|
||||
self.assertEqual(params[0], 11)
|
||||
self.assertEqual(forecast.anmod_id, 11)
|
||||
self.assertEqual(forecast.forecasts_by_day[as_of], ["INCUBAZPRIMARIA2_80.7"])
|
||||
|
||||
def test_single_enabled_model_is_used_for_the_forecast(self) -> None:
|
||||
as_of = date(2026, 6, 15)
|
||||
with patch(
|
||||
"pipeline.stages.disease.fetch_all",
|
||||
side_effect=[[_model(11)], [_tmp_row(as_of, "FASE5", None, station=4)]],
|
||||
) as fetch:
|
||||
forecast = resolve_disease_forecast(
|
||||
object(),
|
||||
4012,
|
||||
"PERONOSPORA",
|
||||
"downy mildew",
|
||||
as_of,
|
||||
None,
|
||||
)
|
||||
|
||||
self.assertEqual(forecast.anmod_id, 11)
|
||||
self.assertEqual(forecast.forecasts_by_day[as_of], ["FASE5"])
|
||||
self.assertEqual(fetch.call_args_list[1].args[2][0], 11)
|
||||
|
||||
def test_several_enabled_models_are_not_collapsed_to_the_first(self) -> None:
|
||||
rows = [_model(11), _model(20)]
|
||||
with patch("pipeline.stages.disease.fetch_all", return_value=rows):
|
||||
with self.assertRaises(PipelineError):
|
||||
resolve_disease_forecast(
|
||||
object(),
|
||||
4012,
|
||||
"PERONOSPORA",
|
||||
"downy mildew",
|
||||
date(2026, 6, 15),
|
||||
None,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user