Task 9.3 - Weight recalibration via gradient descent:
- Recalibrator module fits logistic regression weights using Nx
- Trains on QSO positives vs random baseline negatives
- Cross-validates by month, normalizes weights to sum to 1.0
- Mix task: mix recalibrate_scorer --sample 5000 --epochs 2000
Task 9.4 - Side-by-side scorer comparison:
- ScorerDiff.compare/3 re-scores grid with old vs new weights
- Reports mean diff, regressions, improvements, per-band breakdown
- Mix task: mix scorer_diff --new-weights '{...}'
Phase 3 - NEXRAD ingestion pipeline:
- NexradClient fetches IEM n0q composite PNGs, extracts per-point
box statistics (mean/max dBZ, texture variance)
- NexradObservation schema with unique (lat, lon, observed_at)
- NexradWorker on :nexrad queue for background processing
- nexrad_texture backtest feature in Features module
- mix nexrad_backfill --limit 200
All tasks added to AdminTaskWorker and Release for production use.
1116 tests, 0 failures.
241 lines
8.3 KiB
Elixir
241 lines
8.3 KiB
Elixir
defmodule Microwaveprop.Workers.AdminTaskWorker do
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@moduledoc """
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Oban worker for long-running admin tasks (backtests, climatology, etc).
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Enqueued via `Microwaveprop.Release` so that `eval` returns immediately
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and the work runs in the normal Oban pipeline without blocking.
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Each task type is dispatched by the `"task"` arg:
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Microwaveprop.Workers.AdminTaskWorker.new(%{task: "backtest_all"})
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Microwaveprop.Workers.AdminTaskWorker.new(%{task: "backtest", feature: "naive_gradient"})
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Microwaveprop.Workers.AdminTaskWorker.new(%{task: "climatology", min_samples: 3})
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Microwaveprop.Workers.AdminTaskWorker.new(%{task: "native_derive", limit: 10000})
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"""
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use Oban.Worker, queue: :admin, max_attempts: 1, unique: [period: 60]
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import Ecto.Query
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alias Microwaveprop.Backtest
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alias Microwaveprop.Backtest.Features
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alias Microwaveprop.Propagation.Duct
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alias Microwaveprop.Propagation.Inversion
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alias Microwaveprop.Repo
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alias Microwaveprop.Weather.HrrrNativeProfile
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alias Microwaveprop.Weather.ThetaE
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require Logger
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@impl Oban.Worker
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def perform(%Oban.Job{args: %{"task" => "backtest_all"} = args}) do
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sample_size = Map.get(args, "sample_size", 5000)
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features = Features.all_features()
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Logger.info("AdminTask: running consolidated backtest for #{map_size(features)} features")
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results = Backtest.consolidated_report(features, sample_size: sample_size)
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markdown = Backtest.to_consolidated_markdown(results)
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path = "priv/backtest_reports/consolidated.md"
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File.mkdir_p!(Path.dirname(path))
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File.write!(path, markdown)
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Logger.info("AdminTask: consolidated backtest complete, wrote #{path}")
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => "backtest", "feature" => feature_name} = args}) do
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sample_size = Map.get(args, "sample_size", 5000)
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fun_atom = String.to_atom(feature_name)
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if !function_exported?(Features, fun_atom, 3) do
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Logger.error("AdminTask: unknown feature #{feature_name}")
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{:error, "unknown feature: #{feature_name}"}
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end
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feature_fun = &apply(Features, fun_atom, [&1, &2, &3])
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name = "Microwaveprop.Backtest.Features.#{feature_name}"
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Logger.info("AdminTask: running backtest for #{feature_name}")
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report = Backtest.evaluate(feature_fun, sample_size: sample_size, feature_name: name)
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distance_bins = Backtest.lift_by_distance(feature_fun, sample_size: sample_size)
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band_stats = Backtest.lift_by_band(feature_fun, sample_size: sample_size)
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markdown = Backtest.to_markdown(report, distance_bins: distance_bins, band_stats: band_stats)
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path = "priv/backtest_reports/#{feature_name}.md"
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File.mkdir_p!(Path.dirname(path))
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File.write!(path, markdown)
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Logger.info("AdminTask: backtest for #{feature_name} complete, wrote #{path}")
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => "climatology"} = args}) do
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min_samples = Map.get(args, "min_samples", 3)
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%{rows: combos} =
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Repo.query!(
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"""
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SELECT EXTRACT(MONTH FROM valid_time)::int AS month,
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EXTRACT(HOUR FROM valid_time)::int AS hour
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FROM hrrr_profiles
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WHERE surface_temp_c IS NOT NULL AND is_grid_point = true
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GROUP BY 1, 2
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ORDER BY 1, 2
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""",
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[],
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timeout: 120_000
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)
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Logger.info("AdminTask: building climatology (min_samples=#{min_samples}, #{length(combos)} batches)")
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total =
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combos
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|> Enum.with_index(1)
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|> Enum.reduce(0, fn {[month, hour], idx}, acc ->
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%{num_rows: count} =
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Repo.query!(
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"""
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INSERT INTO hrrr_climatology (id, lat, lon, month, hour,
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mean_surface_temp_c, stddev_surface_temp_c, sample_count)
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SELECT gen_random_uuid(), lat, lon,
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$2 AS month,
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$3 AS hour,
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AVG(surface_temp_c),
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STDDEV_SAMP(surface_temp_c),
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COUNT(*)
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FROM hrrr_profiles
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WHERE surface_temp_c IS NOT NULL
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AND is_grid_point = true
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AND EXTRACT(MONTH FROM valid_time)::int = $2
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AND EXTRACT(HOUR FROM valid_time)::int = $3
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GROUP BY lat, lon
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HAVING COUNT(*) >= $1
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ON CONFLICT (lat, lon, month, hour)
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DO UPDATE SET
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mean_surface_temp_c = EXCLUDED.mean_surface_temp_c,
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stddev_surface_temp_c = EXCLUDED.stddev_surface_temp_c,
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sample_count = EXCLUDED.sample_count
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""",
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[min_samples, month, hour],
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timeout: 300_000
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)
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if rem(idx, 10) == 0, do: Logger.info("AdminTask: climatology [#{idx}/#{length(combos)}]")
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acc + count
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end)
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Logger.info("AdminTask: climatology complete, upserted #{total} records")
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => "native_derive"} = args}) do
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limit = Map.get(args, "limit", 10_000)
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profiles =
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HrrrNativeProfile
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|> where([p], is_nil(p.bulk_richardson) and p.level_count > 2)
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|> limit(^limit)
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|> Repo.all()
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Logger.info("AdminTask: deriving fields for #{length(profiles)} native profiles")
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count =
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Enum.count(profiles, fn profile ->
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p = %{
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heights_m: profile.heights_m,
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temp_k: profile.temp_k,
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spfh: profile.spfh,
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pressure_pa: profile.pressure_pa,
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u_wind_ms: profile.u_wind_ms,
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v_wind_ms: profile.v_wind_ms,
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tke_m2s2: profile.tke_m2s2,
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level_count: profile.level_count
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}
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inversion_fields =
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case Inversion.find_inversion_top(p) do
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{:ok, %{height_m: top_h, level_idx: top_idx, base_idx: base_idx}} ->
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[
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inversion_top_m: top_h,
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bulk_richardson: Inversion.bulk_richardson(p, base_idx, top_idx),
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shear_at_top_ms: Inversion.shear_magnitude(p, base_idx, top_idx),
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theta_e_jump_k: ThetaE.theta_e_jump(p, base_idx, top_idx)
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]
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:none ->
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[inversion_top_m: nil, bulk_richardson: nil, shear_at_top_ms: nil, theta_e_jump_k: nil]
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end
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duct_result = Duct.analyze(p)
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fields = inversion_fields ++ [ducts: duct_result.ducts, best_duct_band_ghz: duct_result.best_duct_band_ghz]
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{1, _} =
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HrrrNativeProfile
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|> where([pr], pr.id == ^profile.id)
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|> Repo.update_all(set: fields)
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true
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end)
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Logger.info("AdminTask: derived fields for #{count} profiles")
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => "recalibrate"} = args}) do
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sample_size = Map.get(args, "sample_size", 5000)
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epochs = Map.get(args, "epochs", 2000)
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learning_rate = Map.get(args, "learning_rate", 0.01)
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Logger.info("AdminTask: running weight recalibration (sample=#{sample_size}, epochs=#{epochs}, lr=#{learning_rate})")
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result =
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Microwaveprop.Propagation.Recalibrator.fit(
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sample_size: sample_size,
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epochs: epochs,
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learning_rate: learning_rate
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)
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Logger.info("AdminTask: recalibration complete")
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Logger.info(" train_loss=#{result.train_loss}, val_loss=#{result.val_loss}, initial_loss=#{result.initial_loss}")
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for {factor, weight} <- Enum.sort(result.weights) do
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Logger.info(" #{factor}: #{weight}")
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end
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => "scorer_diff", "new_weights" => new_weights_json}}) do
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alias Microwaveprop.Propagation.BandConfig
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alias Microwaveprop.Propagation.ScorerDiff
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new_weights = Jason.decode!(new_weights_json)
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old_weights = Map.new(BandConfig.weights(), fn {k, v} -> {to_string(k), v} end)
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Logger.info("AdminTask: running scorer diff comparison")
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result = ScorerDiff.compare(old_weights, new_weights)
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markdown = ScorerDiff.to_markdown(result)
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path = "priv/scorer_diff_reports/latest.md"
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File.mkdir_p!(Path.dirname(path))
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File.write!(path, markdown)
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Logger.info(
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"AdminTask: scorer diff complete — #{result.summary.total} points, " <>
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"#{result.summary.regressions} regressions, #{result.summary.improvements} improvements"
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)
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Logger.info("AdminTask: wrote report to #{path}")
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:ok
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end
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def perform(%Oban.Job{args: %{"task" => task}}) do
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Logger.error("AdminTask: unknown task #{task}")
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{:error, "unknown task: #{task}"}
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end
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end
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